diff --git a/.env.example b/.env.example index 26f6e3a29..a63a9b4b1 100644 --- a/.env.example +++ b/.env.example @@ -71,6 +71,10 @@ LANGFLOW_SUPERUSER= # Example: LANGFLOW_SUPERUSER_PASSWORD=123456 LANGFLOW_SUPERUSER_PASSWORD= +# Should store environment variables in the database +# Values: true, false +LANGFLOW_STORE_ENVIRONMENT_VARIABLES= + # STORE_URL # Example: LANGFLOW_STORE_URL=https://api.langflow.store # LANGFLOW_STORE_URL= diff --git a/.github/actions/poetry_caching/action.yml b/.github/actions/poetry_caching/action.yml new file mode 100644 index 000000000..fb76b1723 --- /dev/null +++ b/.github/actions/poetry_caching/action.yml @@ -0,0 +1,94 @@ +# An action for setting up poetry install with caching. +# Using a custom action since the default action does not +# take poetry install groups into account. +# Action code from: +# https://github.com/actions/setup-python/issues/505#issuecomment-1273013236 +# Copy of https://github.com/langchain-ai/langchain/blob/2f8dd1a1619f25daa4737df4d378b1acd6ff83c4/.github/actions/poetry_setup/action.yml +name: poetry-install-with-caching +description: Poetry install with support for caching of dependency groups. + +inputs: + python-version: + description: Python version, supporting MAJOR.MINOR only + required: true + + poetry-version: + description: Poetry version + required: true + + cache-key: + description: Cache key to use for manual handling of caching + required: true + + working-directory: + description: Directory whose poetry.lock file should be cached + required: true + +runs: + using: composite + steps: + - uses: actions/setup-python@v5 + name: Setup python ${{ inputs.python-version }} + id: setup-python + with: + python-version: ${{ inputs.python-version }} + + - uses: actions/cache@v4 + id: cache-bin-poetry + name: Cache Poetry binary - Python ${{ inputs.python-version }} + env: + SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1" + with: + path: | + /opt/pipx/venvs/poetry + # This step caches the poetry installation, so make sure it's keyed on the poetry version as well. + key: bin-poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-${{ inputs.poetry-version }} + + - name: Refresh shell hashtable and fixup softlinks + if: steps.cache-bin-poetry.outputs.cache-hit == 'true' + shell: bash + env: + POETRY_VERSION: ${{ inputs.poetry-version }} + PYTHON_VERSION: ${{ inputs.python-version }} + run: | + set -eux + + # Refresh the shell hashtable, to ensure correct `which` output. + hash -r + + # `actions/cache@v3` doesn't always seem able to correctly unpack softlinks. + # Delete and recreate the softlinks pipx expects to have. + rm /opt/pipx/venvs/poetry/bin/python + cd /opt/pipx/venvs/poetry/bin + ln -s "$(which "python$PYTHON_VERSION")" python + chmod +x python + cd /opt/pipx_bin/ + ln -s /opt/pipx/venvs/poetry/bin/poetry poetry + chmod +x poetry + + # Ensure everything got set up correctly. + /opt/pipx/venvs/poetry/bin/python --version + /opt/pipx_bin/poetry --version + + - name: Install poetry + if: steps.cache-bin-poetry.outputs.cache-hit != 'true' + shell: bash + env: + POETRY_VERSION: ${{ inputs.poetry-version }} + PYTHON_VERSION: ${{ inputs.python-version }} + # Install poetry using the python version installed by setup-python step. + run: pipx install "poetry==$POETRY_VERSION" --python '${{ steps.setup-python.outputs.python-path }}' --verbose + + - name: Restore pip and poetry cached dependencies + uses: actions/cache@v4 + env: + SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4" + WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }} + with: + path: | + ~/.cache/pip + ~/.cache/pypoetry/virtualenvs + ~/.cache/pypoetry/cache + ~/.cache/pypoetry/artifacts + ${{ env.WORKDIR }}/.venv + key: py-deps-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles(format('{0}/**/poetry.lock', env.WORKDIR)) }} diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml deleted file mode 100644 index d4a67f8f4..000000000 --- a/.github/workflows/ci.yml +++ /dev/null @@ -1,44 +0,0 @@ -name: "Async API tests" - -on: - push: - branches: - - dev - pull_request: - branches: - - dev - - main - -jobs: - build-and-test: - runs-on: ubuntu-latest - env: - OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} - steps: - - name: Checkout code - uses: actions/checkout@v4 - - - name: Cache Docker layers - uses: actions/cache@v4 - with: - path: /tmp/.buildx-cache - key: ${{ runner.os }}-buildx-${{ github.sha }} - restore-keys: | - ${{ runner.os }}-buildx- - - - name: Set up Docker - run: docker --version && docker-compose --version - - - name: "Create env file" - working-directory: ./deploy - run: | - echo "${{ secrets.ENV_FILE }}" > .env - - - name: Build and start services - - working-directory: ./deploy - run: docker compose up --exit-code-from tests tests result_backend broker celeryworker db --build - continue-on-error: true - - # - name: Stop services - # run: docker compose down diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml index c78ba4c71..e1b427322 100644 --- a/.github/workflows/lint.yml +++ b/.github/workflows/lint.yml @@ -26,20 +26,24 @@ jobs: - "3.11" steps: - uses: actions/checkout@v4 - - name: Install poetry - run: | - pipx install poetry==$POETRY_VERSION - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 - id: setup-python + - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_caching" with: python-version: ${{ matrix.python-version }} - cache: poetry + poetry-version: ${{ env.POETRY_VERSION }} + cache-key: ${{ runner.os }}-poetry-${{ env.POETRY_VERSION }}-${{ hashFiles('**/poetry.lock') }} - name: Install Python dependencies run: | poetry env use ${{ matrix.python-version }} poetry install - if: ${{ steps.setup-python.outputs.cache-hit != 'true' }} - - name: Analysing the code with our lint + - name: Get .mypy_cache to speed up mypy + uses: actions/cache@v4 + env: + SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2" + with: + path: | + ./.mypy_cache + key: ${{ runner.os }}-mypy-${{ hashFiles('**/pyproject.toml') }} + - name: Lint check run: | make lint diff --git a/.github/workflows/pre-release-base.yml b/.github/workflows/pre-release-base.yml index ac2a29a94..44ffc92ee 100644 --- a/.github/workflows/pre-release-base.yml +++ b/.github/workflows/pre-release-base.yml @@ -1,26 +1,20 @@ name: Langflow Base Pre-release - +run-name: Langflow Base Pre-release by @${{ github.actor }} on: - pull_request: - types: - - closed - branches: - - dev - paths: - - "src/backend/base/pyproject.toml" workflow_dispatch: inputs: - force_release: - description: "Force a release" - required: false - default: "false" + release_package: + description: "Release package" + required: true + type: boolean + default: false env: POETRY_VERSION: "1.8.2" jobs: if_release: - if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'pre-release') }} || ${{ github.event_name == 'workflow_dispatch' && inputs.force_release == 'true' }} + if: inputs.release_package == true runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -50,22 +44,11 @@ jobs: fi - name: Build project for distribution run: make build base=true - - name: Publish to PyPI env: POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }} run: | make publish base=true - - name: Create Release - uses: ncipollo/release-action@v1 - with: - artifacts: "dist/*" - token: ${{ secrets.GITHUB_TOKEN }} - draft: false - generateReleaseNotes: true - prerelease: true - tag: v${{ steps.check-version.outputs.version }} - commit: dev - name: Set up QEMU uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx diff --git a/.github/workflows/pre-release-langflow.yml b/.github/workflows/pre-release-langflow.yml index 11f894e1a..4c0be75c9 100644 --- a/.github/workflows/pre-release-langflow.yml +++ b/.github/workflows/pre-release-langflow.yml @@ -1,19 +1,13 @@ name: Langflow Pre-release - +run-name: Langflow Pre-release by @${{ github.actor }} on: - pull_request: - types: - - closed - branches: - - dev - paths: - - "pyproject.toml" workflow_dispatch: inputs: - force_release: - description: "Force a release" - required: false - default: "false" + release_package: + description: "Release package" + required: true + type: boolean + default: false workflow_run: workflows: ["pre-release-base"] types: [completed] @@ -24,7 +18,7 @@ env: jobs: if_release: - if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'pre-release') }} || ${{ github.event_name == 'workflow_dispatch' && inputs.force_release == 'true' }} + if: inputs.release_package == true runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 @@ -38,7 +32,7 @@ jobs: - name: Check Version id: check-version run: | - version=$(cd src/backend/base && poetry version --short) + version=$(poetry version --short) last_released_version=$(curl -s "https://pypi.org/pypi/langflow/json" | jq -r '.releases | keys | .[]' | sort -V | tail -n 1) if [ "$version" = "$last_released_version" ]; then echo "Version $version is already released. Skipping release." @@ -55,16 +49,6 @@ jobs: POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }} run: | make publish main=true - - name: Create Release - uses: ncipollo/release-action@v1 - with: - artifacts: "dist/*" - token: ${{ secrets.GITHUB_TOKEN }} - draft: false - generateReleaseNotes: true - prerelease: true - tag: v${{ steps.check-version.outputs.version }} - commit: dev - name: Set up QEMU uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx @@ -83,3 +67,14 @@ jobs: tags: | logspace/langflow:${{ steps.check-version.outputs.version }} logspace/langflow:1.0-alpha + + - name: Create Release + uses: ncipollo/release-action@v1 + with: + artifacts: "dist/*" + token: ${{ secrets.GITHUB_TOKEN }} + draft: false + generateReleaseNotes: true + prerelease: true + tag: v${{ steps.check-version.outputs.version }} + commit: dev diff --git a/.github/workflows/python_test.yml b/.github/workflows/python_test.yml index a04a208f3..b0cfee04e 100644 --- a/.github/workflows/python_test.yml +++ b/.github/workflows/python_test.yml @@ -29,19 +29,16 @@ jobs: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} steps: - uses: actions/checkout@v4 - - name: Install poetry - run: pipx install poetry==$POETRY_VERSION - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v5 - id: setup-python + - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_caching" with: python-version: ${{ matrix.python-version }} - cache: "poetry" + poetry-version: ${{ env.POETRY_VERSION }} + cache-key: ${{ runner.os }}-poetry-${{ env.POETRY_VERSION }}-${{ hashFiles('**/poetry.lock') }} - name: Install Python dependencies run: | poetry env use ${{ matrix.python-version }} poetry install - if: ${{ steps.setup-python.outputs.cache-hit != 'true' }} - name: Run unit tests run: | - make tests + make tests args="-n auto" diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 8004618f6..5d522c230 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -31,15 +31,6 @@ jobs: id: check-version run: | echo version=$(poetry version --short) >> $GITHUB_OUTPUT - - name: Create Release - uses: ncipollo/release-action@v1 - with: - artifacts: "dist/*" - token: ${{ secrets.GITHUB_TOKEN }} - draft: false - generateReleaseNotes: true - tag: v${{ steps.check-version.outputs.version }} - commit: main - name: Publish to PyPI env: POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }} @@ -63,3 +54,12 @@ jobs: tags: | logspace/langflow:${{ steps.check-version.outputs.version }} logspace/langflow:latest + - name: Create Release + uses: ncipollo/release-action@v1 + with: + artifacts: "dist/*" + token: ${{ secrets.GITHUB_TOKEN }} + draft: false + generateReleaseNotes: true + tag: v${{ steps.check-version.outputs.version }} + commit: main diff --git a/.gitignore b/.gitignore index 0d74c2208..03eed1556 100644 --- a/.gitignore +++ b/.gitignore @@ -264,4 +264,5 @@ scratchpad* chroma*/* stuff/* src/frontend/playwright-report/index.html -*.bak \ No newline at end of file +*.bak +prof/* \ No newline at end of file diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index c58bb92f1..383d21344 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -16,12 +16,12 @@ The branch structure is as follows: ## 🚩GitHub Issues -Our [issues](https://github.com/logspace-ai/langflow/issues) page is kept up to date +Our [issues](https://github.com/langflow-ai/langflow/issues) page is kept up to date with bugs, improvements, and feature requests. There is a taxonomy of labels to help with sorting and discovery of issues of interest. If you're looking for help with your code, consider posting a question on the -[GitHub Discussions board](https://github.com/logspace-ai/langflow/discussions). Please +[GitHub Discussions board](https://github.com/langflow-ai/langflow/discussions). Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's **shared publicly**, so that more people can benefit from it. @@ -40,7 +40,7 @@ so that more people can benefit from it. ## Issue labels -[See this page](https://github.com/logspace-ai/langflow/labels) for an overview of +[See this page](https://github.com/langflow-ai/langflow/labels) for an overview of the system we use to tag our issues and pull requests. ## Local development diff --git a/GCP_DEPLOYMENT.md b/GCP_DEPLOYMENT.md index e00e9b1f8..12b6b0239 100644 --- a/GCP_DEPLOYMENT.md +++ b/GCP_DEPLOYMENT.md @@ -4,25 +4,27 @@ This guide will help you set up a Langflow development VM in a Google Cloud Plat > **Note**: When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment. +## Standard VM -## Standard VM -[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md) +[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md) This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment. +
## Spot/Preemptible Instance -[![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md) +[![Open in Cloud Shell - Spot Instance](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md) When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts. ## Pricing (approximate) -> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator) -
-| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes | -| -------------- | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | ----- | -| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs | -| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance | -| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month | +> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator) +>
+ +| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes | +| ------------------ | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | -------------------------------------------------------------------------- | +| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs | +| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance | +| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month | diff --git a/Makefile b/Makefile index 965086f99..6d279e62f 100644 --- a/Makefile +++ b/Makefile @@ -8,6 +8,14 @@ env ?= .env open_browser ?= true path = src/backend/base/langflow/frontend +codespell: + @poetry install --with spelling + poetry run codespell --toml pyproject.toml + +fix_codespell: + @poetry install --with spelling + poetry run codespell --toml pyproject.toml --write + setup_poetry: pipx install poetry @@ -39,20 +47,16 @@ coverage: # allow passing arguments to pytest tests: - @make install_backend - poetry run pytest tests --instafail $(args) # Use like: format: - poetry run ruff . --fix + poetry run ruff check . --fix poetry run ruff format . cd src/frontend && npm run format lint: - make install_backend poetry run mypy --namespace-packages -p "langflow" - poetry run ruff . --fix install_frontend: cd src/frontend && npm install @@ -129,12 +133,12 @@ frontendc: make run_frontend install_backend: - @echo 'Setting up the environment' - @make setup_env @echo 'Installing backend dependencies' - @poetry install --extras deploy + @poetry install backend: + @echo 'Setting up the environment' + @make setup_env make install_backend @-kill -9 `lsof -t -i:7860` ifdef login diff --git a/README.md b/README.md index 2d1b0f14a..d7c7014aa 100644 --- a/README.md +++ b/README.md @@ -1,9 +1,11 @@ -# [![Langflow](https://github.com/logspace-ai/langflow/blob/dev/docs/static/img/hero.png)](https://www.langflow.org) +# [![Langflow](https://github.com/langflow-ai/langflow/blob/dev/docs/static/img/hero.png)](https://www.langflow.org) + ### [Langflow](https://www.langflow.org) is a new, visual way to build, iterate and deploy AI apps. # ⚡️ Documentation and Community + - [Documentation](https://docs.langflow.org) - [Discord](https://discord.com/invite/EqksyE2EX9) @@ -26,7 +28,7 @@ Then, run Langflow with: python -m langflow run ``` -You can also preview Langflow in [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow-Preview). [Clone the space using this link](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true), to create your own Langflow workspace in minutes. +You can also preview Langflow in [HuggingFace Spaces](https://huggingface.co/spaces/Langflow/Langflow-Preview). [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true), to create your own Langflow workspace in minutes. # 🎨 Creating Flows @@ -94,7 +96,7 @@ Follow our step-by-step guide to deploy Langflow on Google Cloud Platform (GCP) Alternatively, click the **"Open in Cloud Shell"** button below to launch Google Cloud Shell, clone the Langflow repository, and start an **interactive tutorial** that will guide you through the process of setting up the necessary resources and deploying Langflow on your GCP project. -[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md) +[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md) ## Deploy on Railway @@ -102,7 +104,7 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google ## Deploy on Render - + Deploy to Render @@ -112,12 +114,12 @@ We welcome contributions from developers of all levels to our open-source projec --- -[![Star History Chart](https://api.star-history.com/svg?repos=logspace-ai/langflow&type=Timeline)](https://star-history.com/#logspace-ai/langflow&Date) +[![Star History Chart](https://api.star-history.com/svg?repos=langflow-ai/langflow&type=Timeline)](https://star-history.com/#langflow-ai/langflow&Date) # 🌟 Contributors -[![langflow contributors](https://contrib.rocks/image?repo=logspace-ai/langflow)](https://github.com/logspace-ai/langflow/graphs/contributors) +[![langflow contributors](https://contrib.rocks/image?repo=langflow-ai/langflow)](https://github.com/langflow-ai/langflow/graphs/contributors) # 📄 License -Langflow is released under the MIT License. See the LICENSE file for details. +Langflow is released under the MIT License. See the [LICENSE](LICENSE) file for details. diff --git a/base.Dockerfile b/base.Dockerfile index 936943cd1..3946af99d 100644 --- a/base.Dockerfile +++ b/base.Dockerfile @@ -57,7 +57,7 @@ RUN apt-get update \ # install poetry - respects $POETRY_VERSION & $POETRY_HOME # The --mount will mount the buildx cache directory to where -# Poetry and Pip store their cache so that they can re-use it +# Poetry and Pip store their cache so that they can reuse it RUN --mount=type=cache,target=/root/.cache \ curl -sSL https://install.python-poetry.org | python3 - diff --git a/deploy/base.Dockerfile b/deploy/base.Dockerfile index 58fae3dab..84b448e2d 100644 --- a/deploy/base.Dockerfile +++ b/deploy/base.Dockerfile @@ -56,7 +56,7 @@ RUN apt-get update \ # install poetry - respects $POETRY_VERSION & $POETRY_HOME # The --mount will mount the buildx cache directory to where -# Poetry and Pip store their cache so that they can re-use it +# Poetry and Pip store their cache so that they can reuse it RUN --mount=type=cache,target=/root/.cache \ curl -sSL https://install.python-poetry.org | python3 - diff --git a/docker_example/README.md b/docker_example/README.md index 9e72dc645..9eadce1f3 100644 --- a/docker_example/README.md +++ b/docker_example/README.md @@ -1,9 +1,65 @@ -# LangFlow Docker Running +# Running LangFlow with Docker -```sh -git clone git@github.com:logspace-ai/langflow.git -cd langflow/docker_example -docker compose up -``` +This guide will help you get LangFlow up and running using Docker and Docker Compose. -The web UI will be accessible on port [7860](http://localhost:7860/) \ No newline at end of file +## Prerequisites + +- Docker +- Docker Compose + +## Steps + +1. Clone the LangFlow repository: + + ```sh + git clone https://github.com/langflow-ai/langflow.git + ``` + +2. Navigate to the `docker_example` directory: + + ```sh + cd langflow/docker_example + ``` + +3. Run the Docker Compose file: + + ```sh + docker compose up + ``` + +LangFlow will now be accessible at [http://localhost:7860/](http://localhost:7860/). + +## Docker Compose Configuration + +The Docker Compose configuration spins up two services: `langflow` and `postgres`. + +### LangFlow Service + +The `langflow` service uses the `logspace/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service. + +Environment variables: + +- `LANGFLOW_DATABASE_URL`: The connection string for the PostgreSQL database. +- `LANGFLOW_CONFIG_DIR`: The directory where LangFlow stores logs, file storage, monitor data, and secret keys. + +Volumes: + +- `langflow-data`: This volume is mapped to `/var/lib/langflow` in the container. + +### PostgreSQL Service + +The `postgres` service uses the `postgres:16` Docker image and exposes port 5432. + +Environment variables: + +- `POSTGRES_USER`: The username for the PostgreSQL database. +- `POSTGRES_PASSWORD`: The password for the PostgreSQL database. +- `POSTGRES_DB`: The name of the PostgreSQL database. + +Volumes: + +- `langflow-postgres`: This volume is mapped to `/var/lib/postgresql/data` in the container. + +## Switching to a Specific LangFlow Version + +If you want to use a specific version of LangFlow, you can modify the `image` field under the `langflow` service in the Docker Compose file. For example, to use version 1.0-alpha, change `logspace/langflow:latest` to `logspace/langflow:1.0-alpha`. diff --git a/docker_example/pre.Dockerfile b/docker_example/pre.Dockerfile new file mode 100644 index 000000000..40fd4ce6d --- /dev/null +++ b/docker_example/pre.Dockerfile @@ -0,0 +1,3 @@ +FROM logspace/langflow:1.0-alpha + +CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"] diff --git a/docker_example/pre.docker-compose.yml b/docker_example/pre.docker-compose.yml new file mode 100644 index 000000000..577f7bb3a --- /dev/null +++ b/docker_example/pre.docker-compose.yml @@ -0,0 +1,30 @@ +version: "3.8" + +services: + langflow: + image: logspace/langflow:1.0-alpha + ports: + - "7860:7860" + depends_on: + - postgres + environment: + - LANGFLOW_DATABASE_URL=postgresql://langflow:langflow@postgres:5432/langflow + # This variable defines where the logs, file storage, monitor data and secret keys are stored. + - LANGFLOW_CONFIG_DIR=/var/lib/langflow + volumes: + - langflow-data:/var/lib/langflow + + postgres: + image: postgres:16 + environment: + POSTGRES_USER: langflow + POSTGRES_PASSWORD: langflow + POSTGRES_DB: langflow + ports: + - "5432:5432" + volumes: + - langflow-postgres:/var/lib/postgresql/data + +volumes: + langflow-postgres: + langflow-data: diff --git a/docs/docs/components/custom.mdx b/docs/docs/components/custom.mdx index b07f953fa..bdf631314 100644 --- a/docs/docs/components/custom.mdx +++ b/docs/docs/components/custom.mdx @@ -37,7 +37,7 @@ The CustomComponent class serves as the foundation for creating custom component | _`langflow.field_typing.Prompt`_ | | _`langchain.chains.base.Chain`_ | | _`langchain.PromptTemplate`_ | - | _`langchain.llms.base.BaseLLM`_ | + | _`from langchain.schema.language_model import BaseLanguageModel`_ | | _`langchain.Tool`_ | | _`langchain.document_loaders.base.BaseLoader`_ | | _`langchain.schema.Document`_ | diff --git a/docs/docs/components/embeddings.mdx b/docs/docs/components/embeddings.mdx index 9a20e5821..2d401bbf6 100644 --- a/docs/docs/components/embeddings.mdx +++ b/docs/docs/components/embeddings.mdx @@ -123,7 +123,12 @@ Used to load [OpenAI’s](https://openai.com/) embedding models. Wrapper around [Google Vertex AI](https://cloud.google.com/vertex-ai) [Embeddings API](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings). -Vertex AI is a cloud computing platform offered by Google Cloud Platform (GCP). It provides access, management, and development of applications and services through global data centers. To use Vertex AI PaLM, you need to have the [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) Python package installed and credentials configured for your environment. + Vertex AI is a cloud computing platform offered by Google Cloud Platform + (GCP). It provides access, management, and development of applications and + services through global data centers. To use Vertex AI PaLM, you need to have + the + [google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/) + Python package installed and credentials configured for your environment. - **credentials:** The default custom credentials (google.auth.credentials.Credentials) to use. diff --git a/docs/docs/components/models.mdx b/docs/docs/components/models.mdx index 1c3b404b5..fc6d7924e 100644 --- a/docs/docs/components/models.mdx +++ b/docs/docs/components/models.mdx @@ -1,4 +1,4 @@ -import Admonition from '@theme/Admonition'; +import Admonition from "@theme/Admonition"; # Models @@ -13,16 +13,17 @@ This component facilitates the generation of text using the LLM (Large Language - **System Message (Optional):** A system message to pass to the model. - **Model ID (Optional):** Specifies the model ID to be used for text generation. Defaults to _`"anthropic.claude-instant-v1"`_. Available options include: - - _`"ai21.j2-grande-instruct"`_ - - _`"ai21.j2-jumbo-instruct"`_ - - _`"ai21.j2-mid"`_ - - _`"ai21.j2-mid-v1"`_ - - _`"ai21.j2-ultra"`_ - - _`"ai21.j2-ultra-v1"`_ - - _`"anthropic.claude-instant-v1"`_ - - _`"anthropic.claude-v1"`_ - - _`"anthropic.claude-v2"`_ - - _`"cohere.command-text-v14"`_ + + - _`"ai21.j2-grande-instruct"`_ + - _`"ai21.j2-jumbo-instruct"`_ + - _`"ai21.j2-mid"`_ + - _`"ai21.j2-mid-v1"`_ + - _`"ai21.j2-ultra"`_ + - _`"ai21.j2-ultra-v1"`_ + - _`"anthropic.claude-instant-v1"`_ + - _`"anthropic.claude-v1"`_ + - _`"anthropic.claude-v2"`_ + - _`"cohere.command-text-v14"`_ - **Credentials Profile Name (Optional):** Specifies the name of the credentials profile. @@ -39,12 +40,12 @@ This component facilitates the generation of text using the LLM (Large Language - **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. -

- Ensure that necessary credentials are provided to connect to the Amazon Bedrock API. If connection fails, a ValueError will be raised. -

+

+ Ensure that necessary credentials are provided to connect to the Amazon + Bedrock API. If connection fails, a ValueError will be raised. +

- --- ### Anthropic @@ -54,10 +55,11 @@ This component allows the generation of text using Anthropic Chat&Completion lar **Params** - **Model Name:** Specifies the name of the Anthropic model to be used for text generation. Available options include: - - _`"claude-2.1"`_ - - _`"claude-2.0"`_ - - _`"claude-instant-1.2"`_ - - _`"claude-instant-1"`_ + + - _`"claude-2.1"`_ + - _`"claude-2.0"`_ + - _`"claude-instant-1.2"`_ + - _`"claude-instant-1"`_ - **Anthropic API Key:** Your Anthropic API key. @@ -84,25 +86,27 @@ This component allows the generation of text using the LLM (Large Language Model **Params** - **Model Name:** Specifies the name of the Azure OpenAI model to be used for text generation. Available options include: - - _`"gpt-35-turbo"`_ - - _`"gpt-35-turbo-16k"`_ - - _`"gpt-35-turbo-instruct"`_ - - _`"gpt-4"`_ - - _`"gpt-4-32k"`_ - - _`"gpt-4-vision"`_ + + - _`"gpt-35-turbo"`_ + - _`"gpt-35-turbo-16k"`_ + - _`"gpt-35-turbo-instruct"`_ + - _`"gpt-4"`_ + - _`"gpt-4-32k"`_ + - _`"gpt-4-vision"`_ - **Azure Endpoint:** Your Azure endpoint, including the resource. Example: `https://example-resource.azure.openai.com/`. - **Deployment Name:** Specifies the name of the deployment. - **API Version:** Specifies the version of the Azure OpenAI API to be used. Available options include: - - _`"2023-03-15-preview"`_ - - _`"2023-05-15"`_ - - _`"2023-06-01-preview"`_ - - _`"2023-07-01-preview"`_ - - _`"2023-08-01-preview"`_ - - _`"2023-09-01-preview"`_ - - _`"2023-12-01-preview"`_ + + - _`"2023-03-15-preview"`_ + - _`"2023-05-15"`_ + - _`"2023-06-01-preview"`_ + - _`"2023-07-01-preview"`_ + - _`"2023-08-01-preview"`_ + - _`"2023-09-01-preview"`_ + - _`"2023-12-01-preview"`_ - **API Key:** Your Azure OpenAI API key. @@ -118,7 +122,6 @@ This component allows the generation of text using the LLM (Large Language Model For detailed documentation and integration guides, please refer to the [Azure OpenAI Component Documentation](https://python.langchain.com/docs/integrations/llms/azure_openai). - --- ### Cohere @@ -265,7 +268,7 @@ This component facilitates text generation using OpenAI's models. - **OpenAI API Base (Optional):** The base URL of the OpenAI API. Defaults to _`https://api.openai.com/v1`_. -- **OpenAI API Key (Optional):** The API key for accessing the OpenAI API. +- **OpenAI API Key (Optional):** The API key for accessing the OpenAI API. - **Temperature:** Controls the creativity of model responses. Defaults to _`0.7`_. @@ -282,16 +285,17 @@ This component facilitates the generation of text using Baidu Qianfan chat model **Params** - **Model Name:** Specifies the name of the Qianfan chat model to be used for text generation. Available options include: - - _`"ERNIE-Bot"`_ - - _`"ERNIE-Bot-turbo"`_ - - _`"BLOOMZ-7B"`_ - - _`"Llama-2-7b-chat"`_ - - _`"Llama-2-13b-chat"`_ - - _`"Llama-2-70b-chat"`_ - - _`"Qianfan-BLOOMZ-7B-compressed"`_ - - _`"Qianfan-Chinese-Llama-2-7B"`_ - - _`"ChatGLM2-6B-32K"`_ - - _`"AquilaChat-7B"`_ + + - _`"ERNIE-Bot"`_ + - _`"ERNIE-Bot-turbo"`_ + - _`"BLOOMZ-7B"`_ + - _`"Llama-2-7b-chat"`_ + - _`"Llama-2-13b-chat"`_ + - _`"Llama-2-70b-chat"`_ + - _`"Qianfan-BLOOMZ-7B-compressed"`_ + - _`"Qianfan-Chinese-Llama-2-7B"`_ + - _`"ChatGLM2-6B-32K"`_ + - _`"AquilaChat-7B"`_ - **Qianfan Ak:** Your Baidu Qianfan access key, obtainable from [here](https://cloud.baidu.com/product/wenxinworkshop). @@ -343,4 +347,4 @@ The `ChatVertexAI` is a component for generating text using Vertex AI Chat large - **Stream (Optional):** Specifies whether to stream the response from the model. Defaults to _`False`_. -- **System Message (Optional):** System message to pass to the model. \ No newline at end of file +- **System Message (Optional):** System message to pass to the model. diff --git a/docs/docs/contributing/community.md b/docs/docs/contributing/community.md index 6bb62641d..604487133 100644 --- a/docs/docs/contributing/community.md +++ b/docs/docs/contributing/community.md @@ -2,11 +2,11 @@ ## 🤖 Join **Langflow** Discord server - Join us to ask questions and showcase your projects. +Join us to ask questions and showcase your projects. - Let's bring together the building blocks of AI integration! +Let's bring together the building blocks of AI integration! - Langflow [Discord](https://discord.gg/EqksyE2EX9) server. +Langflow [Discord](https://discord.gg/EqksyE2EX9) server. --- @@ -15,9 +15,10 @@ Follow [@langflow_ai](https://twitter.com/langflow_ai) on **Twitter** to get the latest news about **Langflow**. --- + ## ⭐️ Star **Langflow** on GitHub -You can "star" **Langflow** in [GitHub](https://github.com/logspace-ai/langflow). +You can "star" **Langflow** in [GitHub](https://github.com/langflow-ai/langflow). By adding a star, other users will be able to find it more easily and see that it has been already useful for others. @@ -25,14 +26,12 @@ By adding a star, other users will be able to find it more easily and see that i ## 👀 Watch the GitHub repository for releases -You can "watch" **Langflow** in [GitHub](https://github.com/logspace-ai/langflow). - +You can "watch" **Langflow** in [GitHub](https://github.com/langflow-ai/langflow). If you select "Watching" instead of "Releases only" you will receive notifications when someone creates a new issue or question. You can also specify that you only want to be notified about new issues, discussions, PRs, etc. - Then you can try and help them solve those questions. --- -Thanks! 🚀 \ No newline at end of file +Thanks! 🚀 diff --git a/docs/docs/contributing/github-issues.md b/docs/docs/contributing/github-issues.md index 41cc674e1..269c976cd 100644 --- a/docs/docs/contributing/github-issues.md +++ b/docs/docs/contributing/github-issues.md @@ -1,11 +1,11 @@ # GitHub Issues -Our [issues](https://github.com/logspace-ai/langflow/issues) page is kept up to date +Our [issues](https://github.com/langflow-ai/langflow/issues) page is kept up to date with bugs, improvements, and feature requests. There is a taxonomy of labels to help with sorting and discovery of issues of interest. If you're looking for help with your code, consider posting a question on the -[GitHub Discussions board](https://github.com/logspace-ai/langflow/discussions). Please +[GitHub Discussions board](https://github.com/langflow-ai/langflow/discussions). Please understand that we won't be able to provide individual support via email. We also believe that help is much more valuable if it's **shared publicly**, so that more people can benefit from it. @@ -21,7 +21,6 @@ so that more people can benefit from it. logs or tracebacks, you can wrap them in `
` and `
`. This [collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) so it only becomes visible on click, making the issue easier to read and follow. - ## Issue labels -[See this page](https://github.com/logspace-ai/langflow/labels) for an overview of the system we use to tag our issues and pull requests. \ No newline at end of file +[See this page](https://github.com/langflow-ai/langflow/labels) for an overview of the system we use to tag our issues and pull requests. diff --git a/docs/docs/contributing/how-contribute.md b/docs/docs/contributing/how-contribute.md index 53b430496..4939edaee 100644 --- a/docs/docs/contributing/how-contribute.md +++ b/docs/docs/contributing/how-contribute.md @@ -1,6 +1,6 @@ # How to contribute? -👋 Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/logspace-ai/langflow). If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible. +👋 Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/langflow-ai/langflow). If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible. As an open-source project in a rapidly developing field, we are extremely open to contributions, whether in the form of a new feature, improved infra, or better documentation. @@ -10,6 +10,7 @@ To contribute to this project, please follow a ["fork and pull request"](https:/ Please do not try to push directly to this repo unless you are a maintainer. --- + ## Local development You can develop Langflow using docker compose, or locally. @@ -17,6 +18,7 @@ You can develop Langflow using docker compose, or locally. We provide a .vscode/launch.json file for debugging the backend in VSCode, which is a lot faster than using docker compose. Setting up hooks: + ```bash make init ``` @@ -48,7 +50,6 @@ And the frontend: make frontend ``` - --- ## Docker compose diff --git a/docs/docs/deployment/gcp-deployment.md b/docs/docs/deployment/gcp-deployment.md index 032426d94..bd2b48968 100644 --- a/docs/docs/deployment/gcp-deployment.md +++ b/docs/docs/deployment/gcp-deployment.md @@ -6,10 +6,9 @@ This guide will help you set up a Langflow development VM in a Google Cloud Plat > Note: When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment. +## Standard VM - -## Standard VM -[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md) +[![Open in Cloud Shell](https://gstatic.com/cloudssh/images/open-btn.svg)](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md) This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment. @@ -24,11 +23,11 @@ When running as a [spot (preemptible) instance](https://cloud.google.com/compute --- ## Pricing (approximate) + > For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator) - -| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes | -| -------------- | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | ----- | -| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs | -| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance | -| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month | +| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes | +| ------------------ | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | -------------------------------------------------------------------------- | +| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs | +| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance | +| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month | diff --git a/docs/docs/getting-started/basic-prompting.mdx b/docs/docs/getting-started/basic-prompting.mdx new file mode 100644 index 000000000..e69de29bb diff --git a/docs/docs/getting-started/blog-writer.mdx b/docs/docs/getting-started/blog-writer.mdx new file mode 100644 index 000000000..e69de29bb diff --git a/docs/docs/getting-started/document-qa.mdx b/docs/docs/getting-started/document-qa.mdx new file mode 100644 index 000000000..e69de29bb diff --git a/docs/docs/getting-started/memory-chatbot.mdx b/docs/docs/getting-started/memory-chatbot.mdx new file mode 100644 index 000000000..e69de29bb diff --git a/docs/docs/getting-started/rag-with-astradb.mdx b/docs/docs/getting-started/rag-with-astradb.mdx new file mode 100644 index 000000000..01daa7b6f --- /dev/null +++ b/docs/docs/getting-started/rag-with-astradb.mdx @@ -0,0 +1,195 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import Admonition from "@theme/Admonition"; + +# 🌟 RAG with Astra DB + +This guide will walk you through how to build a RAG (Retrieval Augmented Generation) application using **Astra DB** and **Langflow**. + +[Astra DB](https://www.datastax.com/products/datastax-astra?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=astradb) is a cloud-native database built on Apache Cassandra that is optimized for the cloud. It is a fully managed database-as-a-service that simplifies operations and reduces costs. Astra DB is built on the same technology that powers the largest Cassandra deployments in the world. + +In this guide, we will use Astra DB as a vector store to store and retrieve the documents that will be used by the RAG application to generate responses. + + + This guide assumes that you have Langflow up and running. If you are new to + Langflow, you can check out the [Getting Started](/) guide. + + +TLDR; + +- [Create a free Astra DB account](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-a-free-astra-db-account) +- Duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) +- Create a new database, get a **Token** and the **API Endpoint** +- Click on the **New Project** button and look for Vector Store RAG. This will create a new project with the necessary components +- Import the project into Langflow by dropping it on the Canvas or My Collection page +- Update the **Token** and **API Endpoint** in the **Astra DB** components +- Update the OpenAI API key in the **OpenAI** components +- Run the ingestion flow which is the one that uses the **Astra DB** component +- Click on the ⚡ _Run_ button and start interacting with your RAG application + +# First things first + +## Create an Astra DB Database + +To get started, you will need to [create an Astra DB database](https://astra.datastax.com/signup?utm_source=langflow-pre-release&utm_medium=referral&utm_campaign=langflow-announcement&utm_content=create-an-astradb-database). + +Once you have created an account, you will be taken to the Astra DB dashboard. Click on the **Create Database** button. + + + +Now you will need to configure your database. Choose the **Serverless (Vector)** deployment type, and pick a Database name, provider and region. + +After you have configured your database, click on the **Create Database** button. + + + +Once your database is initialized, to the right of the page, you will see the _Database Details_ section which contains a button for you to copy the **API Endpoint** and another to generate a **Token**. + + + +Now we are all set to start building our RAG application using Astra DB and Langflow. + +## (Optional) Duplicate the Langflow 1.0 HuggingFace Space + +If you haven't already, now is the time to launch Langflow. To make things easier, you can duplicate our [Langflow 1.0 Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) which sets up a Langflow instance just for you. + +## Open the Vector Store RAG Project + +To get started, click on the **New Project** button and look for the **Vector Store RAG** project. This will open a starter project with the necessary components to run a RAG application using Astra DB. + + + +This project consists of two flows. The simpler one is the **Ingestion Flow** which is responsible for ingesting the documents into the Astra DB database. + +Your first step should be to understand what each flow does and how they interact with each other. + +The ingestion flow consists of: + +- **Files** component that uploads a text file to Langflow +- **Recursive Character Text Splitter** component that splits the text into smaller chunks +- **OpenAIEmbeddings** component that generates embeddings for the text chunks +- **Astra DB** component that stores the text chunks in the Astra DB database + + + +Now, let's update the **Astra DB** and **Astra DB Search** components with the **Token** and **API Endpoint** that we generated earlier, and the OpenAI Embeddings components with your OpenAI API key. + + + +And run it! This will ingest the Text data from your file into the Astra DB database. + + + +Now, on to the **RAG Flow**. This flow is responsible for generating responses to your queries. It will define all of the steps from getting the User's input to generating a response and displaying it in the Interaction Panel. + +The RAG flow is a bit more complex. It consists of: + +- **Chat Input** component that defines where to put the user input coming from the Interaction Panel +- **OpenAI Embeddings** component that generates embeddings from the user input +- **Astra DB Search** component that retrieves the most relevant Records from the Astra DB database +- **Text Output** component that turns the Records into Text by concatenating them and also displays it in the Interaction Panel + - One interesting point you'll see here is that this component is named `Extracted Chunks`, and that is how it will appear in the Interaction Panel +- **Prompt** component that takes in the user input and the retrieved Records as text and builds a prompt for the OpenAI model +- **OpenAI** component that generates a response to the prompt +- **Chat Output** component that displays the response in the Interaction Panel + + + +To run it all we have to do is click on the ⚡ _Run_ button and start interacting with your RAG application. + + + +This opens the Interaction Panel where you can chat your data. + +Because this flow has a **Chat Input** and a **Text Output** component, the Panel displays a chat input at the bottom and the Extracted Chunks section on the left. + + + +Once we interact with it we get a response and the Extracted Chunks section is updated with the retrieved records. + + + +And that's it! You have successfully ran a RAG application using Astra DB and Langflow. + +# Conclusion + +In this guide, we have learned how to run a RAG application using Astra DB and Langflow. +We have seen how to create an Astra DB database, import the Astra DB RAG Flows project into Langflow, and run the ingestion and RAG flows. diff --git a/docs/docs/guidelines/chat-widget.mdx b/docs/docs/guidelines/chat-widget.mdx index 46ed974a8..6d47e123b 100644 --- a/docs/docs/guidelines/chat-widget.mdx +++ b/docs/docs/guidelines/chat-widget.mdx @@ -78,7 +78,7 @@ The Chat Widget can be embedded into any HTML page, inside a _``_ tag, as To embed the Chat Widget using React, you'll need to insert this _` + ``` Then, declare your Web Component and encapsulate it in a React component. @@ -115,7 +115,7 @@ Finally, you can place the component anywhere in your code to display the Chat W To use it in Angular, first add this _` + ``` When you use a custom web component in an Angular template, the Angular compiler might show a warning when it doesn't recognize the custom elements by default. To suppress this warning, add _`CUSTOM_ELEMENTS_SCHEMA`_ to the module's _`@NgModule.schemas`_. @@ -185,7 +185,7 @@ Use the widget API to customize your Chat Widget: | Prop | Type | Required | Description | -| --------------------- | ------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| --------------------- | ------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------- | | bot_message_style | JSON | No | Applies custom formatting to bot messages. | | chat_input_field | String | Yes | Defines the type of the input field for chat messages. | | chat_inputs | JSON | Yes | Determines the chat input elements and their respective values. | @@ -208,4 +208,4 @@ Use the widget API to customize your Chat Widget: | tweaks | JSON | No | Applies additional custom adjustments for the associated flow. | | user_message_style | JSON | No | Determines the formatting for user messages in the chat window. | | width | Number | No | Sets the width of the chat window in pixels. | -| window_title | String | No | Sets the title displayed in the chat window's header or title bar. | +| window_title | String | No | Sets the title displayed in the chat window's header or title bar. | import ThemedImage from "@theme/ThemedImage"; | diff --git a/docs/docs/guidelines/custom-component.mdx b/docs/docs/guidelines/custom-component.mdx index a509214b8..6decb3833 100644 --- a/docs/docs/guidelines/custom-component.mdx +++ b/docs/docs/guidelines/custom-component.mdx @@ -131,7 +131,7 @@ class MyComponent(CustomComponent): --- -The [Return Type Annotation](https://docs.python.org/3/library/typing.html) of the _`build`_ method defines the component type (e.g., Chain, BaseLLM, or basic Python types). Check out all supported types in the [component reference](../components/custom). +The [Return Type Annotation](https://docs.python.org/3/library/typing.html) of the _`build`_ method defines the component type (e.g., Chain, BaseLanguageModel, or basic Python types). Check out all supported types in the [component reference](../components/custom). ```python from langflow.custom import CustomComponent @@ -366,7 +366,7 @@ For advanced customization, Langflow offers the option to create and load custom ### Folder Structure -Create a folder that follows the same structural conventions as the [config.yaml](https://github.com/logspace-ai/langflow/blob/dev/src/backend/base/langflow/config.yaml) file. Inside this main directory, use a `custom_components` subdirectory for your custom components. +Create a folder that follows the same structural conventions as the [config.yaml](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/config.yaml) file. Inside this main directory, use a `custom_components` subdirectory for your custom components. Inside `custom_components`, you can create a Python file for each component. Similarly, any custom agents should be housed in an `agents` subdirectory. @@ -406,4 +406,5 @@ Langflow will attempt to load all of the components found in the specified direc Once your custom components have been loaded successfully, they will appear in Langflow's sidebar. From there, you can add them to your Langflow canvas for use. However, please note that components with errors will not be available for addition to the canvas. Always ensure your code is error-free before attempting to load components. -Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding! +Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding!import ZoomableImage from "/src/theme/ZoomableImage.js"; +import Admonition from "@theme/Admonition"; diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx index 5bfceb2ef..7ee26d0e0 100644 --- a/docs/docs/index.mdx +++ b/docs/docs/index.mdx @@ -9,8 +9,6 @@ Langflow is an easy way to build from simple to complex AI applications. It is a {" "} -{" "} - - All Global Variables are encrypted and cannot be accessed by anyone but you. + All Credential Global Variables are encrypted and cannot be accessed by anyone + but you. + You can set _`LANGFLOW_STORE_ENVIRONMENT_VARIABLES`_ to _`false`_ in your + `.env` file to prevent this behavior. + + +You can also set _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ to a list of variables that you want to get from the environment. + +The default list at the moment is: + +- ANTHROPIC_API_KEY +- ASTRA_DB_API_ENDPOINT +- ASTRA_DB_APPLICATION_TOKEN +- AZURE_OPENAI_API_KEY +- AZURE_OPENAI_API_DEPLOYMENT_NAME +- AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME +- AZURE_OPENAI_API_INSTANCE_NAME +- AZURE_OPENAI_API_VERSION +- COHERE_API_KEY +- GOOGLE_API_KEY +- HUGGINGFACEHUB_API_TOKEN +- OPENAI_API_KEY +- SEARCHAPI_API_KEY +- SERPAPI_API_KEY +- VECTARA_CUSTOMER_ID +- VECTARA_CORPUS_ID +- VECTARA_API_KEY + + + Set _`LANGFLOW_VARIABLES_TO_GET_FROM_ENVIRONMENT`_ as a comma-separated list + of variables (e.g. _`"VARIABLE1, VARIABLE2"`_) or as a JSON-encoded string + (e.g. _`'["VARIABLE1", "VARIABLE2"]'`_). + diff --git a/docs/docs/whats-new/a-new-chapter-langflow.mdx b/docs/docs/whats-new/a-new-chapter-langflow.mdx index 3e5572145..bc3e498ba 100644 --- a/docs/docs/whats-new/a-new-chapter-langflow.mdx +++ b/docs/docs/whats-new/a-new-chapter-langflow.mdx @@ -61,7 +61,7 @@ We wanted to create start projects that would help you learn about new features For now, we have: -- **[Basic Prompting (Hello, world!)](/guides/basic-prompting)**: A simple flow that shows you how to use the Prompt Component and how to talk like a pirate. +- **[Basic Prompting (Hello, World)](/guides/basic-prompting)**: A simple flow that shows you how to use the Prompt Component and how to talk like a pirate. - **[Vector Store RAG](/guides/rag-with-astradb)**: A flow that shows you how to ingest data into a Vector Store and then use it to run a RAG application. - **[Memory Chatbot](/guides/memory-chatbot)**: This one shows you how to create a simple chatbot that can remember things about the user. - **[Document QA](/guides/document-qa)**: This flow shows you how to build a simple flow that helps you get answers about a document. diff --git a/docs/docs/whats-new/migrating-to-one-point-zero.mdx b/docs/docs/whats-new/migrating-to-one-point-zero.mdx index 987f00277..c140bdf94 100644 --- a/docs/docs/whats-new/migrating-to-one-point-zero.mdx +++ b/docs/docs/whats-new/migrating-to-one-point-zero.mdx @@ -14,7 +14,7 @@ import Admonition from "@theme/Admonition"; Langflow 1.0 is a significant update that brings many exciting changes and improvements to the platform. This guide will walk you through the key improvements and help you migrate your existing projects to the new version. -If you have any questions or need assistance during the migration process, please don't hesitate to reach out to in our [Discord](https://discord.gg/wZSWQaukgJ) or [GitHub](https://github.com/logspace-ai/langflow/issues) community. +If you have any questions or need assistance during the migration process, please don't hesitate to reach out to in our [Discord](https://discord.gg/wZSWQaukgJ) or [GitHub](https://github.com/langflow-ai/langflow/issues) community. We have a special channel in our Discord server dedicated to Langflow 1.0 migration, where you can ask questions, share your experiences, and get help from the community. diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index 979953918..f64b180c7 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -44,7 +44,7 @@ module.exports = { // sidebarPath: 'sidebars.js', }, gtag: { - trackingID: 'G-XHC7G628ZP', + trackingID: "G-XHC7G628ZP", anonymizeIP: true, }, theme: { @@ -87,7 +87,7 @@ module.exports = { // right { position: "right", - href: "https://github.com/logspace-ai/langflow", + href: "https://github.com/langflow-ai/langflow", position: "right", className: "header-github-link", target: "_blank", @@ -124,7 +124,7 @@ module.exports = { }, announcementBar: { content: - '⭐️ If you like ⛓️Langflow, star it on GitHub! ⭐️', + '⭐️ If you like ⛓️Langflow, star it on GitHub! ⭐️', backgroundColor: "#E8EBF1", //Mustard Yellow #D19900 #D4B20B - Salmon #E9967A textColor: "#1C1E21", isCloseable: false, diff --git a/docs/sidebars.js b/docs/sidebars.js index 18e309f13..74c3dc4c8 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -66,7 +66,7 @@ module.exports = { }, { type: "category", - label: "Step-by-Step Guides", + label: "Extended Components", collapsed: false, items: ["guides/langfuse_integration"], }, diff --git a/poetry.lock b/poetry.lock index d6a99f4c2..4b2d4c912 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,87 +2,87 @@ [[package]] name = "aiohttp" -version = "3.9.3" +version = "3.9.4" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.8" files = [ - {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:939677b61f9d72a4fa2a042a5eee2a99a24001a67c13da113b2e30396567db54"}, - {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1f5cd333fcf7590a18334c90f8c9147c837a6ec8a178e88d90a9b96ea03194cc"}, - {file = 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Based on Python type hints." optional = false python-versions = ">=3.7" files = [ - {file = "typer-0.12.1-py3-none-any.whl", hash = "sha256:43ebb23c8a358c3d623e31064359a65f50229d0bf73ae8dfd203f49d9126ae06"}, - {file = "typer-0.12.1.tar.gz", hash = "sha256:72d218ef3c686aed9c6ff3ca25b238aee0474a1628b29c559b18b634cfdeca88"}, + {file = "typer-0.12.3-py3-none-any.whl", hash = "sha256:070d7ca53f785acbccba8e7d28b08dcd88f79f1fbda035ade0aecec71ca5c914"}, + {file = "typer-0.12.3.tar.gz", hash = "sha256:49e73131481d804288ef62598d97a1ceef3058905aa536a1134f90891ba35482"}, ] [package.dependencies] @@ -9170,13 +9159,13 @@ files = [ [[package]] name = "types-redis" -version = "4.6.0.20240311" +version = "4.6.0.20240409" description = "Typing stubs for redis" optional = false python-versions = ">=3.8" files = [ - {file = "types-redis-4.6.0.20240311.tar.gz", hash = "sha256:e049bbdff0e0a1f8e701b64636811291d21bff79bf1e7850850a44055224a85f"}, - {file = "types_redis-4.6.0.20240311-py3-none-any.whl", hash = "sha256:6b9d68a29aba1ee400c823d8e5fe88675282eb69d7211e72fe65dbe54b33daca"}, + {file = "types-redis-4.6.0.20240409.tar.gz", hash = "sha256:ce217c279581d769df992c5b76d61c65425b0a679626048e633e643868eb881b"}, + {file = "types_redis-4.6.0.20240409-py3-none-any.whl", hash = "sha256:a3b92760c49a034827a0c3825206728df4e61e981c1324099d4414335af4f52f"}, ] [package.dependencies] @@ -10251,11 +10240,10 @@ test = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] [extras] -all = [] deploy = ["celery", "flower", "redis"] local = ["ctransformers", "llama-cpp-python", "sentence-transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.12" -content-hash = "0d658da1ee75640f74d7335685760091df2a452e84eef81dcda2590dd4044e92" +content-hash = "9dd152b30031767c522c77e2ad5fc4597a8d1590b13968af143bd382e056b2a1" diff --git a/pyproject.toml b/pyproject.toml index cc7ac04ae..85231375a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langflow" -version = "1.0.0a9" +version = "1.0.0a18" description = "A Python package with a built-in web application" authors = ["Logspace "] maintainers = [ @@ -12,7 +12,7 @@ maintainers = [ "Otávio Anovazzi ", "Rodrigo Nader ", ] -repository = "https://github.com/logspace-ai/langflow" +repository = "https://github.com/langflow-ai/langflow" license = "MIT" readme = "README.md" keywords = ["nlp", "langchain", "openai", "gpt", "gui"] @@ -85,10 +85,10 @@ langchain-openai = "^0.1.1" [tool.poetry.group.dev.dependencies] types-redis = "^4.6.0.5" ipykernel = "^6.29.0" -mypy = "^1.8.0" -ruff = "^0.2.1" +mypy = "^1.9.0" +ruff = "^0.3.5" httpx = "*" -pytest = "^8.0.0" +pytest = "^8.1.0" types-requests = "^2.31.0" requests = "^2.31.0" pytest-cov = "^4.1.0" @@ -106,11 +106,23 @@ pytest-sugar = "^1.0.0" respx = "^0.21.1" pytest-instafail = "^0.5.0" pytest-asyncio = "^0.23.0" +pytest-profiling = "^1.7.0" [tool.poetry.extras] deploy = ["celery", "redis", "flower"] local = ["llama-cpp-python", "sentence-transformers", "ctransformers"] -all = ["deploy", "local"] + + +[tool.poetry.group.spelling] +optional = true + +[tool.poetry.group.spelling.dependencies] +codespell = "^2.2.6" + +[tool.codespell] +skip = '.git,*.pdf,*.svg,*.pdf,*.yaml,*.ipynb,poetry.lock,*.min.js,*.css,package-lock.json,*.trig' +# Ignore latin etc +ignore-regex = '.*(Stati Uniti|Tense=Pres).*' [tool.pytest.ini_options] diff --git a/render.yaml b/render.yaml index 4c17923c6..583a3c324 100644 --- a/render.yaml +++ b/render.yaml @@ -4,7 +4,7 @@ services: name: langflow runtime: docker dockerfilePath: ./Dockerfile - repo: https://github.com/logspace-ai/langflow + repo: https://github.com/langflow-ai/langflow branch: main healthCheckPath: /health autoDeploy: false diff --git a/scripts/aws/README.ja.md b/scripts/aws/README.ja.md index b7ad49f21..86e8699bb 100644 --- a/scripts/aws/README.ja.md +++ b/scripts/aws/README.ja.md @@ -3,6 +3,7 @@ **想定時間**: 30 分 ## 説明 + Langflow on AWS では、 [AWS Cloud Development Kit](https://aws.amazon.com/cdk/?nc2=type_a) (CDK) を用いて Langflow を AWS 上にデプロイする方法を学べます。 このチュートリアルは、AWS アカウントと AWS に関する基本的な知識を有していることを前提としています。 @@ -10,44 +11,47 @@ Langflow on AWS では、 [AWS Cloud Development Kit](https://aws.amazon.com/cdk ![langflow-archi](./img/langflow-archi.png) AWS CDK によって Langflow のアプリケーションをデプロイします。アプリケーションは [Amazon CloudFront](https://aws.amazon.com/cloudfront/?nc1=h_ls) を介して配信されます。CloudFront は 2 つのオリジンを有しています。1 つ目は静的な Web サイトを配信するための [Amazon Simple Storage Service](https://aws.amazon.com/s3/?nc1=h_ls) (S3)、2 つ目は バックエンドと通信するための [Application Load Balancer](https://aws.amazon.com/elasticloadbalancing/application-load-balancer/?nc1=h_ls) (ALB) です。ALB の背後には FastAPI が動作する [AWS Fargate](https://aws.amazon.com/fargate/?nc2=type_a) 、データベースの [Amazon Aurora](https://aws.amazon.com/rds/aurora/?nc2=type_a) が作成されます。 Fargate は [Amazon Elastic Container Registry](https://aws.amazon.com/ecr/?nc1=h_ls) (ECR) に保存された Docker イメージを使用します。 -Auroraのシークレットは [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/?nc2=type_a) によって管理されます。 +Aurora のシークレットは [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/?nc2=type_a) によって管理されます。 # 環境構築とデプロイ方法 + 1. [AWS CloudShell](https://us-east-1.console.aws.amazon.com/cloudshell/home?region=us-east-1)を開きます。 1. 以下のコマンドを実行します。 - ```shell - git clone https://github.com/aws-samples/cloud9-setup-for-prototyping - cd cloud9-setup-for-prototyping - ./bin/bootstrap - ``` + + ```shell + git clone https://github.com/aws-samples/cloud9-setup-for-prototyping + cd cloud9-setup-for-prototyping + ./bin/bootstrap + ``` 1. `Done!` と表示されたら [AWS Cloud9](https://us-east-1.console.aws.amazon.com/cloud9control/home?region=us-east-1#/) から `cloud9-for-prototyping` を開きます。 - ![make-cloud9](./img/langflow-cloud9.png) + ![make-cloud9](./img/langflow-cloud9.png) 1. 以下のコマンドを実行します。 - ```shell - git clone https://github.com/logspace-ai/langflow.git - cd langflow/scripts/aws - cp .env.example .env # 環境設定を変える場合はこのファイル(.env)を編集してください。 - npm ci - cdk bootstrap - cdk deploy - ``` + ```shell + git clone https://github.com/langflow-ai/langflow.git + cd langflow/scripts/aws + cp .env.example .env # 環境設定を変える場合はこのファイル(.env)を編集してください。 + npm ci + cdk bootstrap + cdk deploy + ``` 1. 表示される URL にアクセスします。 - ```shell - Outputs: - LangflowAppStack.frontendURLXXXXXX = https://XXXXXXXXXXX.cloudfront.net - ``` + ```shell + Outputs: + LangflowAppStack.frontendURLXXXXXX = https://XXXXXXXXXXX.cloudfront.net + ``` 1. サインイン画面でユーザー名とパスワードを入力します。`.env`ファイルでユーザー名とパスワードを設定していない場合、ユーザー名は`admin`、パスワードは`123456`で設定されます。 - ![make-cloud9](./img/langflow-signin.png) + ![make-cloud9](./img/langflow-signin.png) # 環境の削除 -1. `Cloud9` で以下のコマンドを実行します。 - ```shell - bash delete-resources.sh - ``` +1. `Cloud9` で以下のコマンドを実行します。 + + ```shell + bash delete-resources.sh + ``` 1. [AWS CloudFormation](https://us-east-1.console.aws.amazon.com/cloudformation/home?region=us-east-1#/getting-started)を開き、`aws-cloud9-cloud9-for-prototyping-XXXX` を選択して削除します。 - ![delete-cfn](./img/langflow-cfn.png) \ No newline at end of file + ![delete-cfn](./img/langflow-cfn.png) diff --git a/scripts/aws/README.md b/scripts/aws/README.md index 031718f75..da561747f 100644 --- a/scripts/aws/README.md +++ b/scripts/aws/README.md @@ -10,7 +10,7 @@ This tutorial assumes you have an AWS account and basic knowledge of AWS. The architecture of the application to be created: ![langflow-archi](./img/langflow-archi.png) Langflow is deployed using AWS CDK. The application is distributed via [Amazon CloudFront](https://aws.amazon.com/cloudfront/?nc1=h_ls), which has two origins: the first is [Amazon Simple Storage Service](https://aws.amazon.com/s3/?nc1=h_ls) (S3) for serving a static website, and the second is an [Application Load Balancer](https://aws.amazon.com/elasticloadbalancing/application-load-balancer/?nc1=h_ls) (ALB) for communicating with the backend. [AWS Fargate](https://aws.amazon.com/fargate/?nc2=type_a), where FastAPI runs and [Amazon Aurora](https://aws.amazon.com/rds/aurora/?nc2=type_a), the database, are created behind the ALB. -Fargate uses a Docker image stored in [Amazon Elastic Container Registry](https://aws.amazon.com/ecr/?nc1=h_ls) (ECR). +Fargate uses a Docker image stored in [Amazon Elastic Container Registry](https://aws.amazon.com/ecr/?nc1=h_ls) (ECR). Aurora's secret is managed by [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/?nc2=type_a). # How to set up your environment and deploy langflow @@ -26,14 +26,14 @@ Aurora's secret is managed by [AWS Secrets Manager](https://aws.amazon.com/secre 1. When you see `Done!` in Cloudshell, open `c9-for-langflow` from [AWS Cloud9](https://us-east-1.console.aws.amazon.com/cloud9control/home?region=us-east-1#/). ![make-cloud9](./img/langflow-cloud9-en.png) 1. Run the following command in the Cloud9 terminal. - ```shell - git clone https://github.com/logspace-ai/langflow.git - cd langflow/scripts/aws - cp .env.example .env # Edit this file if you need environment settings - npm ci - cdk bootstrap - cdk deploy - ``` + ```shell + git clone https://github.com/langflow-ai/langflow.git + cd langflow/scripts/aws + cp .env.example .env # Edit this file if you need environment settings + npm ci + cdk bootstrap + cdk deploy + ``` 1. Access the URL displayed. ```shell Outputs: @@ -50,4 +50,4 @@ Aurora's secret is managed by [AWS Secrets Manager](https://aws.amazon.com/secre ``` 1. Open [AWS CloudFormation](https://us-east-1.console.aws.amazon.com/cloudformation/home?region=us-east-1#/getting-started), select `aws-cloud9-c9-for-langflow-XXXX` and delete it. ![delete-cfn](./img/langflow-cfn.png) -s \ No newline at end of file + s diff --git a/src/backend/base/langflow/__main__.py b/src/backend/base/langflow/__main__.py index bfb2a5311..925e6a7a1 100644 --- a/src/backend/base/langflow/__main__.py +++ b/src/backend/base/langflow/__main__.py @@ -9,11 +9,6 @@ import click import httpx import typer from dotenv import load_dotenv -from langflow.main import setup_app -from langflow.services.database.utils import session_getter -from langflow.services.deps import get_db_service, get_settings_service -from langflow.services.utils import initialize_services, initialize_settings_service -from langflow.utils.logger import configure, logger from multiprocess import Process, cpu_count # type: ignore from packaging import version as pkg_version from rich import box @@ -22,6 +17,13 @@ from rich.console import Console from rich.panel import Panel from rich.table import Table +from langflow.main import setup_app +from langflow.services.database.utils import session_getter +from langflow.services.deps import get_db_service +from langflow.services.utils import initialize_services +from langflow.utils.logger import configure, logger +from langflow.utils.util import update_settings + console = Console() app = typer.Typer(no_args_is_help=True) @@ -67,44 +69,10 @@ def set_var_for_macos_issue(): logger.debug("Set OBJC_DISABLE_INITIALIZE_FORK_SAFETY to YES to avoid error") -def update_settings( - config: str, - cache: Optional[str] = None, - dev: bool = False, - remove_api_keys: bool = False, - components_path: Optional[Path] = None, - store: bool = True, -): - """Update the settings from a config file.""" - - # Check for database_url in the environment variables - initialize_settings_service() - settings_service = get_settings_service() - if config: - logger.debug(f"Loading settings from {config}") - settings_service.settings.update_from_yaml(config, dev=dev) - if remove_api_keys: - logger.debug(f"Setting remove_api_keys to {remove_api_keys}") - settings_service.settings.update_settings(REMOVE_API_KEYS=remove_api_keys) - if cache: - logger.debug(f"Setting cache to {cache}") - settings_service.settings.update_settings(CACHE=cache) - if components_path: - logger.debug(f"Adding component path {components_path}") - settings_service.settings.update_settings(COMPONENTS_PATH=components_path) - if not store: - logger.debug("Setting store to False") - settings_service.settings.update_settings(STORE=False) - - @app.command() def run( - host: str = typer.Option( - "127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST" - ), - workers: int = typer.Option( - 1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS" - ), + host: str = typer.Option("127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"), + workers: int = typer.Option(1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"), timeout: int = typer.Option(300, help="Worker timeout in seconds."), port: int = typer.Option(7860, help="Port to listen on.", envvar="LANGFLOW_PORT"), components_path: Optional[Path] = typer.Option( @@ -112,19 +80,11 @@ def run( help="Path to the directory containing custom components.", envvar="LANGFLOW_COMPONENTS_PATH", ), - config: str = typer.Option( - Path(__file__).parent / "config.yaml", help="Path to the configuration file." - ), + config: str = typer.Option(Path(__file__).parent / "config.yaml", help="Path to the configuration file."), # .env file param - env_file: Path = typer.Option( - None, help="Path to the .env file containing environment variables." - ), - log_level: str = typer.Option( - "critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL" - ), - log_file: Path = typer.Option( - "logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE" - ), + env_file: Path = typer.Option(None, help="Path to the .env file containing environment variables."), + log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"), + log_file: Path = typer.Option("logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"), cache: Optional[str] = typer.Option( envvar="LANGFLOW_LANGCHAIN_CACHE", help="Type of cache to use. (InMemoryCache, SQLiteCache)", @@ -192,17 +152,21 @@ def run( # Define an env variable to know if we are just testing the server if "pytest" in sys.modules: return - - if platform.system() in ["Windows"]: - # Run using uvicorn on MacOS and Windows - # Windows doesn't support gunicorn - # MacOS requires an env variable to be set to use gunicorn - run_on_windows(host, port, log_level, options, app) - else: - # Run using gunicorn on Linux - run_on_mac_or_linux(host, port, log_level, options, app, open_browser) - if open_browser: - click.launch(f"http://{host}:{port}") + try: + if platform.system() in ["Windows"]: + # Run using uvicorn on MacOS and Windows + # Windows doesn't support gunicorn + # MacOS requires an env variable to be set to use gunicorn + process = run_on_windows(host, port, log_level, options, app) + else: + # Run using gunicorn on Linux + process = run_on_mac_or_linux(host, port, log_level, options, app) + if open_browser: + click.launch(f"http://{host}:{port}") + if process: + process.join() + except KeyboardInterrupt: + pass def wait_for_server_ready(host, port): @@ -218,13 +182,12 @@ def wait_for_server_ready(host, port): def run_on_mac_or_linux(host, port, log_level, options, app): - webapp_process = Process( - target=run_langflow, args=(host, port, log_level, options, app) - ) + webapp_process = Process(target=run_langflow, args=(host, port, log_level, options, app)) webapp_process.start() wait_for_server_ready(host, port) print_banner(host, port) + return webapp_process def run_on_windows(host, port, log_level, options, app): @@ -233,6 +196,7 @@ def run_on_windows(host, port, log_level, options, app): """ print_banner(host, port) run_langflow(host, port, log_level, options, app) + return None def is_port_in_use(port, host="localhost"): @@ -316,9 +280,7 @@ def build_new_version_notice(current_version: str, package_name: str): f"A new pre-release version of {package_name} is available: {latest_version}", ) else: - latest_version = httpx.get(f"https://pypi.org/pypi/{package_name}/json").json()[ - "info" - ]["version"] + latest_version = httpx.get(f"https://pypi.org/pypi/{package_name}/json").json()["info"]["version"] if not version_is_prerelease(latest_version): return ( False, @@ -331,7 +293,7 @@ def is_prerelease(version: str) -> bool: return "a" in version or "b" in version or "rc" in version -def fetch_latest_version(package_name: str, include_prerelease: bool) -> str: +def fetch_latest_version(package_name: str, include_prerelease: bool) -> Optional[str]: response = httpx.get(f"https://pypi.org/pypi/{package_name}/json") versions = response.json()["releases"].keys() valid_versions = [v for v in versions if include_prerelease or not is_prerelease(v)] @@ -342,9 +304,7 @@ def fetch_latest_version(package_name: str, include_prerelease: bool) -> str: def build_version_notice(current_version: str, package_name: str) -> str: latest_version = fetch_latest_version(package_name, is_prerelease(current_version)) - if latest_version and pkg_version.parse(current_version) < pkg_version.parse( - latest_version - ): + if latest_version and pkg_version.parse(current_version) < pkg_version.parse(latest_version): release_type = "pre-release" if is_prerelease(latest_version) else "version" return f"A new {release_type} of {package_name} is available: {latest_version}" return "" @@ -375,7 +335,7 @@ def print_banner(host: str, port: int): package_name = "" try: - from langflow.version import __version__ as langflow_version + from langflow.version import __version__ as langflow_version # type: ignore is_pre_release |= is_prerelease(langflow_version) # Update pre-release status notice = build_version_notice(langflow_version, "langflow") @@ -393,9 +353,7 @@ def print_banner(host: str, port: int): from importlib import metadata langflow_base_version = metadata.version("langflow-base") - is_pre_release |= is_prerelease( - langflow_base_version - ) # Update pre-release status + is_pre_release |= is_prerelease(langflow_base_version) # Update pre-release status notice = build_version_notice(langflow_base_version, "langflow-base") notice = stylize_text(notice, "langflow-base", is_pre_release) if notice: @@ -414,12 +372,10 @@ def print_banner(host: str, port: int): notices.append(f"Run '{pip_command}' to update.") styled_notices = [f"[bold]{notice}[/bold]" for notice in notices if notice] - styled_package_name = stylize_text( - package_name, package_name, any("pre-release" in notice for notice in notices) - ) + styled_package_name = stylize_text(package_name, package_name, any("pre-release" in notice for notice in notices)) title = f"[bold]Welcome to :chains: {styled_package_name}[/bold]\n" - info_text = "Collaborate, and contribute at our [bold][link=https://github.com/logspace-ai/langflow]GitHub Repo[/link][/bold] :rocket:" + info_text = "Collaborate, and contribute at our [bold][link=https://github.com/langflow-ai/langflow]GitHub Repo[/link][/bold] :rocket:" access_link = f"Access [link=http://{host}:{port}]http://{host}:{port}[/link]" panel_content = "\n\n".join([title, *styled_notices, info_text, access_link]) @@ -459,12 +415,8 @@ def run_langflow(host, port, log_level, options, app): @app.command() def superuser( username: str = typer.Option(..., prompt=True, help="Username for the superuser."), - password: str = typer.Option( - ..., prompt=True, hide_input=True, help="Password for the superuser." - ), - log_level: str = typer.Option( - "error", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL" - ), + password: str = typer.Option(..., prompt=True, hide_input=True, help="Password for the superuser."), + log_level: str = typer.Option("error", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"), ): """ Create a superuser. @@ -491,11 +443,23 @@ def superuser( @app.command() -def migration(test: bool = typer.Option(True, help="Run migrations in test mode.")): +def migration( + test: bool = typer.Option(True, help="Run migrations in test mode."), + fix: bool = typer.Option( + False, + help="Fix migrations. This is a destructive operation, and should only be used if you know what you are doing.", + ), +): """ Run or test migrations. """ - initialize_services() + if fix: + if not typer.confirm( + "This will delete all data necessary to fix migrations. Are you sure you want to continue?" + ): + raise typer.Abort() + + initialize_services(fix_migration=fix) db_service = get_db_service() if not test: db_service.run_migrations() diff --git a/src/backend/base/langflow/alembic/versions/79e675cb6752_change_datetime_type.py b/src/backend/base/langflow/alembic/versions/79e675cb6752_change_datetime_type.py new file mode 100644 index 000000000..5f9de6034 --- /dev/null +++ b/src/backend/base/langflow/alembic/versions/79e675cb6752_change_datetime_type.py @@ -0,0 +1,99 @@ +"""Change datetime type + +Revision ID: 79e675cb6752 +Revises: e3bc869fa272 +Create Date: 2024-04-11 19:23:10.697335 + +""" +from calendar import c +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op +from sqlalchemy.dialects import postgresql +from sqlalchemy.engine.reflection import Inspector + +# revision identifiers, used by Alembic. +revision: str = "79e675cb6752" +down_revision: Union[str, None] = "e3bc869fa272" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + conn = op.get_bind() + inspector = Inspector.from_engine(conn) # type: ignore + table_names = inspector.get_table_names() + # ### commands auto generated by Alembic - please adjust! ### + if "apikey" in table_names: + columns = inspector.get_columns("apikey") + created_at_column = next((column for column in columns if column["name"] == "created_at"), None) + if created_at_column is not None and created_at_column["type"] == postgresql.TIMESTAMP(): + with op.batch_alter_table("apikey", schema=None) as batch_op: + batch_op.alter_column( + "created_at", + existing_type=postgresql.TIMESTAMP(), + type_=sa.DateTime(timezone=True), + existing_nullable=False, + ) + if "variable" in table_names: + columns = inspector.get_columns("variable") + created_at_column = next((column for column in columns if column["name"] == "created_at"), None) + updated_at_column = next((column for column in columns if column["name"] == "updated_at"), None) + with op.batch_alter_table("variable", schema=None) as batch_op: + if created_at_column is not None and created_at_column["type"] == postgresql.TIMESTAMP(): + batch_op.alter_column( + "created_at", + existing_type=postgresql.TIMESTAMP(), + type_=sa.DateTime(timezone=True), + existing_nullable=True, + ) + if updated_at_column is not None and updated_at_column["type"] == postgresql.TIMESTAMP(): + batch_op.alter_column( + "updated_at", + existing_type=postgresql.TIMESTAMP(), + type_=sa.DateTime(timezone=True), + existing_nullable=True, + ) + + # ### end Alembic commands ### + + +def downgrade() -> None: + conn = op.get_bind() + inspector = Inspector.from_engine(conn) # type: ignore + table_names = inspector.get_table_names() + # ### commands auto generated by Alembic - please adjust! ### + if "variable" in table_names: + columns = inspector.get_columns("variable") + created_at_column = next((column for column in columns if column["name"] == "created_at"), None) + updated_at_column = next((column for column in columns if column["name"] == "updated_at"), None) + with op.batch_alter_table("variable", schema=None) as batch_op: + if updated_at_column is not None and updated_at_column["type"] == sa.DateTime(timezone=True): + batch_op.alter_column( + "updated_at", + existing_type=sa.DateTime(timezone=True), + type_=postgresql.TIMESTAMP(), + existing_nullable=True, + ) + if created_at_column is not None and created_at_column["type"] == sa.DateTime(timezone=True): + batch_op.alter_column( + "created_at", + existing_type=sa.DateTime(timezone=True), + type_=postgresql.TIMESTAMP(), + existing_nullable=True, + ) + + if "apikey" in table_names: + columns = inspector.get_columns("apikey") + created_at_column = next((column for column in columns if column["name"] == "created_at"), None) + if created_at_column is not None and created_at_column["type"] == sa.DateTime(timezone=True): + with op.batch_alter_table("apikey", schema=None) as batch_op: + batch_op.alter_column( + "created_at", + existing_type=sa.DateTime(timezone=True), + type_=postgresql.TIMESTAMP(), + existing_nullable=False, + ) + + # ### end Alembic commands ### diff --git a/src/backend/base/langflow/alembic/versions/e3bc869fa272_fix_nullable.py b/src/backend/base/langflow/alembic/versions/e3bc869fa272_fix_nullable.py new file mode 100644 index 000000000..dbcf7fd9f --- /dev/null +++ b/src/backend/base/langflow/alembic/versions/e3bc869fa272_fix_nullable.py @@ -0,0 +1,62 @@ +"""Fix nullable + +Revision ID: e3bc869fa272 +Revises: 1a110b568907 +Create Date: 2024-04-10 19:17:22.820455 + +""" + +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op +from sqlalchemy.engine.reflection import Inspector + +# revision identifiers, used by Alembic. +revision: str = "e3bc869fa272" +down_revision: Union[str, None] = "1a110b568907" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + + +def upgrade() -> None: + conn = op.get_bind() + inspector = Inspector.from_engine(conn) # type: ignore + table_names = inspector.get_table_names() + # ### commands auto generated by Alembic - please adjust! ### + if "variable" not in table_names: + return + + column_names = [column["name"] for column in inspector.get_columns("variable")] + + with op.batch_alter_table("variable", schema=None) as batch_op: + if "created_at" in column_names: + batch_op.alter_column( + "created_at", + existing_type=sa.TIMESTAMP(timezone=True), + nullable=True, + # existing_server_default expects str | bool | Identity | Computed | None + # sa.text("now()") is not a valid value for existing_server_default + existing_server_default=False, + ) + + # ### end Alembic commands ### + + +def downgrade() -> None: + conn = op.get_bind() + inspector = Inspector.from_engine(conn) # type: ignore + table_names = inspector.get_table_names() + # ### commands auto generated by Alembic - please adjust! ### + if "variable" not in table_names: + return + with op.batch_alter_table("variable", schema=None) as batch_op: + if "created_at" in inspector.get_columns("variable"): + batch_op.alter_column( + "created_at", + existing_type=sa.TIMESTAMP(timezone=True), + nullable=False, + existing_server_default=False, + ) + + # ### end Alembic commands ### diff --git a/src/backend/base/langflow/api/v1/endpoints.py b/src/backend/base/langflow/api/v1/endpoints.py index 4b032f6cb..1db888970 100644 --- a/src/backend/base/langflow/api/v1/endpoints.py +++ b/src/backend/base/langflow/api/v1/endpoints.py @@ -14,7 +14,6 @@ from langflow.api.v1.schemas import ( RunResponse, SimplifiedAPIRequest, TaskStatusResponse, - Tweaks, UpdateCustomComponentRequest, UploadFileResponse, ) @@ -24,6 +23,7 @@ from langflow.interface.custom.custom_component import CustomComponent from langflow.interface.custom.directory_reader import DirectoryReader from langflow.interface.custom.utils import build_custom_component_template from langflow.processing.process import process_tweaks, run_graph_internal +from langflow.schema.graph import Tweaks from langflow.services.auth.utils import api_key_security, get_current_active_user from langflow.services.cache.utils import save_uploaded_file from langflow.services.database.models.flow import Flow @@ -373,7 +373,7 @@ async def create_upload_file( @router.get("/version") def get_version(): try: - from langflow.version import __version__ + from langflow.version import __version__ # type: ignore version = __version__ package = "Langflow" diff --git a/src/backend/base/langflow/api/v1/login.py b/src/backend/base/langflow/api/v1/login.py index fba61d3b4..8c9fb5bd4 100644 --- a/src/backend/base/langflow/api/v1/login.py +++ b/src/backend/base/langflow/api/v1/login.py @@ -1,7 +1,5 @@ from fastapi import APIRouter, Depends, HTTPException, Request, Response, status from fastapi.security import OAuth2PasswordRequestForm -from sqlmodel import Session - from langflow.api.v1.schemas import Token from langflow.services.auth.utils import ( authenticate_user, @@ -9,8 +7,14 @@ from langflow.services.auth.utils import ( create_user_longterm_token, create_user_tokens, ) -from langflow.services.deps import get_session, get_settings_service +from langflow.services.deps import ( + get_session, + get_settings_service, + get_variable_service, +) from langflow.services.settings.manager import SettingsService +from langflow.services.variable.service import VariableService +from sqlmodel import Session router = APIRouter(tags=["Login"]) @@ -22,6 +26,7 @@ async def login_to_get_access_token( db: Session = Depends(get_session), # _: Session = Depends(get_current_active_user) settings_service=Depends(get_settings_service), + variable_service: VariableService = Depends(get_variable_service), ): auth_settings = settings_service.auth_settings try: @@ -52,6 +57,7 @@ async def login_to_get_access_token( secure=auth_settings.ACCESS_SECURE, expires=auth_settings.ACCESS_TOKEN_EXPIRE_SECONDS, ) + variable_service.initialize_user_variables(user.id, db) return tokens else: raise HTTPException( @@ -66,10 +72,11 @@ async def auto_login( response: Response, db: Session = Depends(get_session), settings_service=Depends(get_settings_service), + variable_service: VariableService = Depends(get_variable_service), ): auth_settings = settings_service.auth_settings if settings_service.auth_settings.AUTO_LOGIN: - tokens = create_user_longterm_token(db) + user_id, tokens = create_user_longterm_token(db) response.set_cookie( "access_token_lf", tokens["access_token"], @@ -78,6 +85,7 @@ async def auto_login( secure=auth_settings.ACCESS_SECURE, expires=None, # Set to None to make it a session cookie ) + variable_service.initialize_user_variables(user_id, db) return tokens raise HTTPException( @@ -91,7 +99,9 @@ async def auto_login( @router.post("/refresh") async def refresh_token( - request: Request, response: Response, settings_service: "SettingsService" = Depends(get_settings_service) + request: Request, + response: Response, + settings_service: "SettingsService" = Depends(get_settings_service), ): auth_settings = settings_service.auth_settings @@ -129,3 +139,4 @@ async def logout(response: Response): response.delete_cookie("refresh_token_lf") response.delete_cookie("access_token_lf") return {"message": "Logout successful"} + return {"message": "Logout successful"} diff --git a/src/backend/base/langflow/api/v1/schemas.py b/src/backend/base/langflow/api/v1/schemas.py index 631c37fbb..f5d0b97d1 100644 --- a/src/backend/base/langflow/api/v1/schemas.py +++ b/src/backend/base/langflow/api/v1/schemas.py @@ -4,10 +4,11 @@ from pathlib import Path from typing import Any, Dict, List, Optional, Union from uuid import UUID -from pydantic import BaseModel, ConfigDict, Field, RootModel, field_validator, model_serializer +from pydantic import BaseModel, ConfigDict, Field, field_validator, model_serializer from langflow.graph.schema import RunOutputs from langflow.schema import dotdict +from langflow.schema.graph import Tweaks from langflow.schema.schema import InputType, OutputType from langflow.services.database.models.api_key.model import ApiKeyRead from langflow.services.database.models.base import orjson_dumps @@ -283,36 +284,6 @@ class InputValueRequest(BaseModel): ) -class Tweaks(RootModel): - root: dict[str, Union[str, dict[str, str]]] = Field( - description="A dictionary of tweaks to adjust the flow's execution. Allows customizing flow behavior dynamically. All tweaks are overridden by the input values.", - ) - model_config = { - "json_schema_extra": { - "examples": [ - { - "parameter_name": "value", - "Component Name": {"parameter_name": "value"}, - "component_id": {"parameter_name": "value"}, - } - ] - } - } - - # This should behave like a dict - def __getitem__(self, key): - return self.root[key] - - def __setitem__(self, key, value): - self.root[key] = value - - def __delitem__(self, key): - del self.root[key] - - def items(self): - return self.root.items() - - class SimplifiedAPIRequest(BaseModel): input_value: Optional[str] = Field(default="", description="The input value") input_type: Optional[InputType] = Field(default="chat", description="The input type") diff --git a/src/backend/base/langflow/api/v1/validate.py b/src/backend/base/langflow/api/v1/validate.py index 97841bca6..2954fd13a 100644 --- a/src/backend/base/langflow/api/v1/validate.py +++ b/src/backend/base/langflow/api/v1/validate.py @@ -4,7 +4,7 @@ from fastapi import APIRouter, HTTPException from loguru import logger from langflow.api.v1.base import Code, CodeValidationResponse, PromptValidationResponse, ValidatePromptRequest -from langflow.base.prompts.utils import ( +from langflow.base.prompts.api_utils import ( add_new_variables_to_template, get_old_custom_fields, remove_old_variables_from_template, diff --git a/src/backend/base/langflow/base/constants.py b/src/backend/base/langflow/base/constants.py index a54d6eff5..c9a41a100 100644 --- a/src/backend/base/langflow/base/constants.py +++ b/src/backend/base/langflow/base/constants.py @@ -24,4 +24,5 @@ FIELD_FORMAT_ATTRIBUTES = [ "real_time_refresh", "refresh_button", "refresh_button_text", + "options", ] diff --git a/src/backend/base/langflow/base/data/utils.py b/src/backend/base/langflow/base/data/utils.py index c3fda5e34..09169393b 100644 --- a/src/backend/base/langflow/base/data/utils.py +++ b/src/backend/base/langflow/base/data/utils.py @@ -10,7 +10,26 @@ from langflow.schema.schema import Record # Types of files that can be read simply by file.read() # and have 100% to be completely readable -TEXT_FILE_TYPES = ["txt", "md", "mdx", "csv", "json", "yaml", "yml", "xml", "html", "htm", "pdf", "docx"] +TEXT_FILE_TYPES = [ + "txt", + "md", + "mdx", + "csv", + "json", + "yaml", + "yml", + "xml", + "html", + "htm", + "pdf", + "docx", + "py", + "sh", + "sql", + "js", + "ts", + "tsx", +] def is_hidden(path: Path) -> bool: diff --git a/src/backend/base/langflow/base/io/chat.py b/src/backend/base/langflow/base/io/chat.py index eb9787a79..ea24dc968 100644 --- a/src/backend/base/langflow/base/io/chat.py +++ b/src/backend/base/langflow/base/io/chat.py @@ -79,7 +79,7 @@ class ChatComponent(CustomComponent): records = add_messages([record]) return records[0] - def build( + def build_with_record( self, sender: Optional[str] = "User", sender_name: Optional[str] = "User", @@ -116,3 +116,41 @@ class ChatComponent(CustomComponent): if session_id and isinstance(result, (Record, str)): self.store_message(result, session_id, sender, sender_name) return result + + def build_no_record( + self, + sender: Optional[str] = "User", + sender_name: Optional[str] = "User", + input_value: Optional[str] = None, + session_id: Optional[str] = None, + return_record: Optional[bool] = False, + record_template: str = "Text: {text}\nData: {data}", + ) -> Union[Text, Record]: + input_value_record: Optional[Record] = None + if return_record: + if isinstance(input_value, Record): + # Update the data of the record + input_value.data["sender"] = sender + input_value.data["sender_name"] = sender_name + input_value.data["session_id"] = session_id + else: + input_value_record = Record( + text=input_value, + data={ + "sender": sender, + "sender_name": sender_name, + "session_id": session_id, + }, + ) + elif isinstance(input_value, Record): + input_value = records_to_text(template=record_template, records=input_value) + if not input_value: + input_value = "" + if return_record and input_value_record: + result: Union[Text, Record] = input_value_record + else: + result = input_value + self.status = result + if session_id and isinstance(result, (Record, str)): + self.store_message(result, session_id, sender, sender_name) + return result diff --git a/src/backend/base/langflow/base/prompts/api_utils.py b/src/backend/base/langflow/base/prompts/api_utils.py new file mode 100644 index 000000000..89a17399c --- /dev/null +++ b/src/backend/base/langflow/base/prompts/api_utils.py @@ -0,0 +1,83 @@ +from fastapi import HTTPException +from langchain.prompts import PromptTemplate +from loguru import logger + +from langflow.api.v1.base import INVALID_NAMES, check_input_variables +from langflow.interface.utils import extract_input_variables_from_prompt +from langflow.template.field.prompt import DefaultPromptField + + +def validate_prompt(prompt_template: str, silent_errors: bool = False) -> list[str]: + input_variables = extract_input_variables_from_prompt(prompt_template) + + # Check if there are invalid characters in the input_variables + input_variables = check_input_variables(input_variables) + if any(var in INVALID_NAMES for var in input_variables): + raise ValueError(f"Invalid input variables. None of the variables can be named {', '.join(input_variables)}. ") + + try: + PromptTemplate(template=prompt_template, input_variables=input_variables) + except Exception as exc: + logger.error(f"Invalid prompt: {exc}") + if not silent_errors: + raise ValueError(f"Invalid prompt: {exc}") from exc + + return input_variables + + +def get_old_custom_fields(custom_fields, name): + try: + if len(custom_fields) == 1 and name == "": + # If there is only one custom field and the name is empty string + # then we are dealing with the first prompt request after the node was created + name = list(custom_fields.keys())[0] + + old_custom_fields = custom_fields[name] + if not old_custom_fields: + old_custom_fields = [] + + old_custom_fields = old_custom_fields.copy() + except KeyError: + old_custom_fields = [] + custom_fields[name] = [] + return old_custom_fields + + +def add_new_variables_to_template(input_variables, custom_fields, template, name): + for variable in input_variables: + try: + template_field = DefaultPromptField(name=variable, display_name=variable) + if variable in template: + # Set the new field with the old value + template_field.value = template[variable]["value"] + + template[variable] = template_field.to_dict() + + # Check if variable is not already in the list before appending + if variable not in custom_fields[name]: + custom_fields[name].append(variable) + + except Exception as exc: + logger.exception(exc) + raise HTTPException(status_code=500, detail=str(exc)) from exc + + +def remove_old_variables_from_template(old_custom_fields, input_variables, custom_fields, template, name): + for variable in old_custom_fields: + if variable not in input_variables: + try: + # Remove the variable from custom_fields associated with the given name + if variable in custom_fields[name]: + custom_fields[name].remove(variable) + + # Remove the variable from the template + template.pop(variable, None) + + except Exception as exc: + logger.exception(exc) + raise HTTPException(status_code=500, detail=str(exc)) from exc + + +def update_input_variables_field(input_variables, template): + if "input_variables" in template: + template["input_variables"]["value"] = input_variables diff --git a/src/backend/base/langflow/base/prompts/utils.py b/src/backend/base/langflow/base/prompts/utils.py index ff4cf5079..fbf46dd71 100644 --- a/src/backend/base/langflow/base/prompts/utils.py +++ b/src/backend/base/langflow/base/prompts/utils.py @@ -1,12 +1,19 @@ -from fastapi import HTTPException -from langchain.prompts import PromptTemplate from langchain_core.documents import Document -from loguru import logger -from langflow.api.v1.base import INVALID_NAMES, check_input_variables -from langflow.interface.utils import extract_input_variables_from_prompt from langflow.schema import Record -from langflow.template.field.prompt import DefaultPromptField + + +def record_to_string(record: Record) -> str: + """ + Convert a record to a string. + + Args: + record (Record): The record to convert. + + Returns: + str: The record as a string. + """ + return record.get_text() def dict_values_to_string(d: dict) -> dict: @@ -35,19 +42,6 @@ def dict_values_to_string(d: dict) -> dict: return d -def record_to_string(record: Record) -> str: - """ - Convert a record to a string. - - Args: - record (Record): The record to convert. - - Returns: - str: The record as a string. - """ - return record.get_text() - - def document_to_string(document: Document) -> str: """ Convert a document to a string. @@ -59,79 +53,3 @@ def document_to_string(document: Document) -> str: str: The document as a string. """ return document.page_content - - -def validate_prompt(prompt_template: str, silent_errors: bool = False) -> list[str]: - input_variables = extract_input_variables_from_prompt(prompt_template) - - # Check if there are invalid characters in the input_variables - input_variables = check_input_variables(input_variables) - if any(var in INVALID_NAMES for var in input_variables): - raise ValueError(f"Invalid input variables. None of the variables can be named {', '.join(input_variables)}. ") - - try: - PromptTemplate(template=prompt_template, input_variables=input_variables) - except Exception as exc: - logger.error(f"Invalid prompt: {exc}") - if not silent_errors: - raise ValueError(f"Invalid prompt: {exc}") from exc - - return input_variables - - -def get_old_custom_fields(custom_fields, name): - try: - if len(custom_fields) == 1 and name == "": - # If there is only one custom field and the name is empty string - # then we are dealing with the first prompt request after the node was created - name = list(custom_fields.keys())[0] - - old_custom_fields = custom_fields[name] - if not old_custom_fields: - old_custom_fields = [] - - old_custom_fields = old_custom_fields.copy() - except KeyError: - old_custom_fields = [] - custom_fields[name] = [] - return old_custom_fields - - -def add_new_variables_to_template(input_variables, custom_fields, template, name): - for variable in input_variables: - try: - template_field = DefaultPromptField(name=variable, display_name=variable) - if variable in template: - # Set the new field with the old value - template_field.value = template[variable]["value"] - - template[variable] = template_field.to_dict() - - # Check if variable is not already in the list before appending - if variable not in custom_fields[name]: - custom_fields[name].append(variable) - - except Exception as exc: - logger.exception(exc) - raise HTTPException(status_code=500, detail=str(exc)) from exc - - -def remove_old_variables_from_template(old_custom_fields, input_variables, custom_fields, template, name): - for variable in old_custom_fields: - if variable not in input_variables: - try: - # Remove the variable from custom_fields associated with the given name - if variable in custom_fields[name]: - custom_fields[name].remove(variable) - - # Remove the variable from the template - template.pop(variable, None) - - except Exception as exc: - logger.exception(exc) - raise HTTPException(status_code=500, detail=str(exc)) from exc - - -def update_input_variables_field(input_variables, template): - if "input_variables" in template: - template["input_variables"]["value"] = input_variables diff --git a/src/backend/base/langflow/base/tools/__init__.py b/src/backend/base/langflow/base/tools/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/src/backend/base/langflow/base/tools/base.py b/src/backend/base/langflow/base/tools/base.py new file mode 100644 index 000000000..e1c4d5fdc --- /dev/null +++ b/src/backend/base/langflow/base/tools/base.py @@ -0,0 +1,23 @@ +from langflow.field_typing import Tool + + +def build_status_from_tool(tool: Tool): + """ + Builds a status string representation of a tool. + + Args: + tool (Tool): The tool object to build the status for. + + Returns: + str: The status string representation of the tool, including its name, description, and arguments (if any). + """ + description_repr = repr(tool.description).strip("'") + args_str = "\n".join( + [ + f"- {arg_name}: {arg_data['description']}" + for arg_name, arg_data in tool.args.items() + if "description" in arg_data + ] + ) + status = f"Name: {tool.name}\nDescription: {description_repr}" + return status + (f"\nArguments:\n{args_str}" if args_str else "") diff --git a/src/backend/base/langflow/components/agents/XMLAgent.py b/src/backend/base/langflow/components/agents/XMLAgent.py index 687bfff6f..117399af9 100644 --- a/src/backend/base/langflow/components/agents/XMLAgent.py +++ b/src/backend/base/langflow/components/agents/XMLAgent.py @@ -4,7 +4,7 @@ from langchain.agents import create_xml_agent from langchain_core.prompts import PromptTemplate from langflow.base.agents.agent import LCAgentComponent -from langflow.field_typing import BaseLLM, BaseMemory, Text, Tool +from langflow.field_typing import BaseLanguageModel, BaseMemory, Text, Tool class XMLAgentComponent(LCAgentComponent): @@ -66,7 +66,7 @@ class XMLAgentComponent(LCAgentComponent): async def build( self, input_value: str, - llm: BaseLLM, + llm: BaseLanguageModel, tools: List[Tool], prompt: str, memory: Optional[BaseMemory] = None, diff --git a/src/backend/base/langflow/components/chains/LLMChain.py b/src/backend/base/langflow/components/chains/LLMChain.py index 9c2b3eae2..9fba051db 100644 --- a/src/backend/base/langflow/components/chains/LLMChain.py +++ b/src/backend/base/langflow/components/chains/LLMChain.py @@ -1,9 +1,10 @@ from typing import Optional -from langchain.chains import LLMChain +from langchain.chains.llm import LLMChain -from langflow.field_typing import BaseLanguageModel, BaseMemory, BasePromptTemplate, Text +from langflow.field_typing import BaseLanguageModel, BaseMemory, Text from langflow.interface.custom.custom_component import CustomComponent +from langchain_core.prompts import PromptTemplate class LLMChainComponent(CustomComponent): @@ -19,10 +20,11 @@ class LLMChainComponent(CustomComponent): def build( self, - prompt: BasePromptTemplate, + template: Text, llm: BaseLanguageModel, memory: Optional[BaseMemory] = None, ) -> Text: + prompt = PromptTemplate.from_template(template) runnable = LLMChain(prompt=prompt, llm=llm, memory=memory) result_dict = runnable.invoke({}) output_key = runnable.output_key diff --git a/src/backend/base/langflow/components/data/APIRequest.py b/src/backend/base/langflow/components/data/APIRequest.py index 988fc0d4c..83a1edb9d 100644 --- a/src/backend/base/langflow/components/data/APIRequest.py +++ b/src/backend/base/langflow/components/data/APIRequest.py @@ -3,7 +3,6 @@ import json from typing import List, Optional import httpx - from langflow.interface.custom.custom_component import CustomComponent from langflow.schema import Record @@ -47,8 +46,8 @@ class APIRequest(CustomComponent): client: httpx.AsyncClient, method: str, url: str, - headers: Optional[Record] = None, - body: Optional[Record] = None, + headers: Optional[dict] = None, + body: Optional[dict] = None, timeout: int = 5, ) -> Record: method = method.upper() diff --git a/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py b/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py index f177fa1f1..0b8959101 100644 --- a/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py +++ b/src/backend/base/langflow/components/embeddings/OpenAIEmbeddings.py @@ -1,6 +1,7 @@ from typing import Any, Dict, List, Optional from langchain_openai.embeddings.base import OpenAIEmbeddings +from pydantic.v1 import SecretStr from langflow.field_typing import Embeddings, NestedDict from langflow.interface.custom.custom_component import CustomComponent @@ -113,6 +114,10 @@ class OpenAIEmbeddingsComponent(CustomComponent): # This is to avoid errors with Vector Stores (e.g Chroma) if disallowed_special == ["all"]: disallowed_special = "all" # type: ignore + if openai_api_key: + api_key = SecretStr(openai_api_key) + else: + api_key = None return OpenAIEmbeddings( tiktoken_enabled=tiktoken_enable, @@ -128,7 +133,7 @@ class OpenAIEmbeddingsComponent(CustomComponent): model=model, model_kwargs=model_kwargs, base_url=openai_api_base, - api_key=openai_api_key, + api_key=api_key, openai_api_type=openai_api_type, api_version=openai_api_version, organization=openai_organization, diff --git a/src/backend/base/langflow/components/experimental/FlowTool.py b/src/backend/base/langflow/components/experimental/FlowTool.py index ac48a0f03..07f3b0e38 100644 --- a/src/backend/base/langflow/components/experimental/FlowTool.py +++ b/src/backend/base/langflow/components/experimental/FlowTool.py @@ -1,14 +1,14 @@ from typing import Any, List, Optional +from asyncer import syncify from langchain_core.tools import StructuredTool -from loguru import logger - from langflow.custom import CustomComponent from langflow.field_typing import Tool from langflow.graph.graph.base import Graph from langflow.helpers.flow import build_function_and_schema from langflow.schema.dotdict import dotdict from langflow.schema.schema import Record +from loguru import logger class FlowToolComponent(CustomComponent): @@ -74,6 +74,7 @@ class FlowToolComponent(CustomComponent): graph = Graph.from_payload(flow_record.data["data"]) dynamic_flow_function, schema = build_function_and_schema(flow_record, graph) tool = StructuredTool.from_function( + func=syncify(dynamic_flow_function, raise_sync_error=False), # type: ignore coroutine=dynamic_flow_function, name=name, description=description, diff --git a/src/backend/base/langflow/components/helpers/MemoryComponent.py b/src/backend/base/langflow/components/helpers/MemoryComponent.py index 670954b25..9590fe8a3 100644 --- a/src/backend/base/langflow/components/helpers/MemoryComponent.py +++ b/src/backend/base/langflow/components/helpers/MemoryComponent.py @@ -61,6 +61,6 @@ class MemoryComponent(CustomComponent): limit=n_messages, order=order, ) - messages_str = records_to_text(template=record_template, records=messages) + messages_str = records_to_text(template=record_template or "", records=messages) self.status = messages_str return messages_str diff --git a/src/backend/base/langflow/components/helpers/SplitText.py b/src/backend/base/langflow/components/helpers/SplitText.py index a141bbddc..565ee1ffc 100644 --- a/src/backend/base/langflow/components/helpers/SplitText.py +++ b/src/backend/base/langflow/components/helpers/SplitText.py @@ -1,4 +1,4 @@ -from typing import Optional +from typing import Optional, Union from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter from langchain_core.documents import Document @@ -51,6 +51,8 @@ class SplitTextComponent(CustomComponent): chunk_overlap: Optional[int] = 200, recursive: bool = False, ) -> list[Record]: + if separators is None: + separators = [] separators = [unescape_string(x) for x in separators] # Make sure chunk_size and chunk_overlap are ints @@ -58,7 +60,7 @@ class SplitTextComponent(CustomComponent): chunk_size = int(chunk_size) if isinstance(chunk_overlap, str): chunk_overlap = int(chunk_overlap) - + splitter: Optional[Union[CharacterTextSplitter, RecursiveCharacterTextSplitter]] = None if recursive: splitter = RecursiveCharacterTextSplitter( separators=separators, diff --git a/src/backend/base/langflow/components/inputs/ChatInput.py b/src/backend/base/langflow/components/inputs/ChatInput.py index 00eaab98c..40c3267ab 100644 --- a/src/backend/base/langflow/components/inputs/ChatInput.py +++ b/src/backend/base/langflow/components/inputs/ChatInput.py @@ -28,7 +28,7 @@ class ChatInput(ChatComponent): session_id: Optional[str] = None, return_record: Optional[bool] = False, ) -> Union[Text, Record]: - return super().build( + return super().build_no_record( sender=sender, sender_name=sender_name, input_value=input_value, diff --git a/src/backend/base/langflow/components/inputs/TextInput.py b/src/backend/base/langflow/components/inputs/TextInput.py index 5408c03b2..c9cdee0b0 100644 --- a/src/backend/base/langflow/components/inputs/TextInput.py +++ b/src/backend/base/langflow/components/inputs/TextInput.py @@ -26,7 +26,7 @@ class TextInput(TextComponent): def build( self, - input_value: Optional[str] = "", + input_value: Optional[Text] = "", record_template: Optional[str] = "", ) -> Text: return super().build(input_value=input_value, record_template=record_template) diff --git a/src/backend/base/langflow/components/model_specs/AmazonBedrockSpecs.py b/src/backend/base/langflow/components/model_specs/AmazonBedrockSpecs.py index 1a18f5c08..ff36820f5 100644 --- a/src/backend/base/langflow/components/model_specs/AmazonBedrockSpecs.py +++ b/src/backend/base/langflow/components/model_specs/AmazonBedrockSpecs.py @@ -1,6 +1,5 @@ from typing import Optional - -from langchain.llms.base import BaseLLM +from langflow.field_typing import BaseLanguageModel from langchain_community.llms.bedrock import Bedrock from langflow.interface.custom.custom_component import CustomComponent @@ -46,7 +45,7 @@ class AmazonBedrockComponent(CustomComponent): endpoint_url: Optional[str] = None, streaming: bool = False, cache: Optional[bool] = None, - ) -> BaseLLM: + ) -> BaseLanguageModel: try: output = Bedrock( credentials_profile_name=credentials_profile_name, diff --git a/src/backend/base/langflow/components/model_specs/AnthropicLLMSpecs.py b/src/backend/base/langflow/components/model_specs/AnthropicLLMSpecs.py index 2ea78162b..016eaeb2d 100644 --- a/src/backend/base/langflow/components/model_specs/AnthropicLLMSpecs.py +++ b/src/backend/base/langflow/components/model_specs/AnthropicLLMSpecs.py @@ -1,14 +1,14 @@ from typing import Optional from langchain.llms.base import BaseLanguageModel -from langchain_community.chat_models.anthropic import ChatAnthropic +from langchain_anthropic import ChatAnthropic from pydantic.v1 import SecretStr from langflow.interface.custom.custom_component import CustomComponent -class AnthropicLLM(CustomComponent): - display_name: str = "AnthropicLLM" +class ChatAntropicSpecsComponent(CustomComponent): + display_name: str = "Anthropic" description: str = "Anthropic Chat&Completion large language models." icon = "Anthropic" diff --git a/src/backend/base/langflow/components/model_specs/AnthropicSpecs.py b/src/backend/base/langflow/components/model_specs/AnthropicSpecs.py deleted file mode 100644 index 23c284888..000000000 --- a/src/backend/base/langflow/components/model_specs/AnthropicSpecs.py +++ /dev/null @@ -1,49 +0,0 @@ -from typing import Optional - -from langchain_community.llms.anthropic import Anthropic -from pydantic.v1 import SecretStr - -from langflow.field_typing import BaseLanguageModel, NestedDict -from langflow.interface.custom.custom_component import CustomComponent - - -class AnthropicComponent(CustomComponent): - display_name = "Anthropic" - description = "Anthropic large language models." - icon = "Anthropic" - - def build_config(self): - return { - "anthropic_api_key": { - "display_name": "Anthropic API Key", - "type": str, - "password": True, - }, - "anthropic_api_url": { - "display_name": "Anthropic API URL", - "type": str, - }, - "model_kwargs": { - "display_name": "Model Kwargs", - "field_type": "NestedDict", - "advanced": True, - }, - "temperature": { - "display_name": "Temperature", - "field_type": "float", - }, - } - - def build( - self, - anthropic_api_key: str, - anthropic_api_url: str, - model_kwargs: NestedDict = {}, - temperature: Optional[float] = None, - ) -> BaseLanguageModel: - return Anthropic( - anthropic_api_key=SecretStr(anthropic_api_key), - anthropic_api_url=anthropic_api_url, - model_kwargs=model_kwargs, - temperature=temperature, - ) diff --git a/src/backend/base/langflow/components/model_specs/BaiduQianfanChatEndpointsSpecs.py b/src/backend/base/langflow/components/model_specs/BaiduQianfanChatEndpointsSpecs.py index 9c39acdf6..a60fb9a64 100644 --- a/src/backend/base/langflow/components/model_specs/BaiduQianfanChatEndpointsSpecs.py +++ b/src/backend/base/langflow/components/model_specs/BaiduQianfanChatEndpointsSpecs.py @@ -1,9 +1,9 @@ from typing import Optional -from langchain.llms.base import BaseLLM from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint from pydantic.v1 import SecretStr +from langflow.field_typing import BaseLanguageModel from langflow.interface.custom.custom_component import CustomComponent @@ -79,7 +79,7 @@ class QianfanChatEndpointComponent(CustomComponent): temperature: Optional[float] = None, penalty_score: Optional[float] = None, endpoint: Optional[str] = None, - ) -> BaseLLM: + ) -> BaseLanguageModel: try: output = QianfanChatEndpoint( # type: ignore model=model, diff --git a/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py b/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py index acbf8ba28..21ed13e70 100644 --- a/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py +++ b/src/backend/base/langflow/components/model_specs/BaiduQianfanLLMEndpointsSpecs.py @@ -1,9 +1,9 @@ from typing import Optional from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint -from langchain.llms.base import BaseLLM from langflow.interface.custom.custom_component import CustomComponent +from langflow.field_typing import BaseLanguageModel class QianfanLLMEndpointComponent(CustomComponent): @@ -78,7 +78,7 @@ class QianfanLLMEndpointComponent(CustomComponent): temperature: Optional[float] = None, penalty_score: Optional[float] = None, endpoint: Optional[str] = None, - ) -> BaseLLM: + ) -> BaseLanguageModel: try: output = QianfanLLMEndpoint( # type: ignore model=model, diff --git a/src/backend/base/langflow/components/model_specs/ChatAnthropicSpecs.py b/src/backend/base/langflow/components/model_specs/ChatAnthropicSpecs.py index a4a37b283..b03a9b737 100644 --- a/src/backend/base/langflow/components/model_specs/ChatAnthropicSpecs.py +++ b/src/backend/base/langflow/components/model_specs/ChatAnthropicSpecs.py @@ -1,67 +1,89 @@ -from typing import Callable, Optional, Union +from typing import Optional -from langchain_community.chat_models.anthropic import ChatAnthropic +from langchain_anthropic import ChatAnthropic from pydantic.v1.types import SecretStr + from langflow.custom import CustomComponent from langflow.field_typing import BaseLanguageModel -class ChatAnthropicComponent(CustomComponent): - display_name = "ChatAnthropic" - description = "`Anthropic` chat large language models." - documentation = "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic" +class AnthropicLLM(CustomComponent): + display_name: str = "Anthropic" + description: str = "Generate text using Anthropic Chat&Completion LLMs." icon = "Anthropic" + field_order = [ + "model", + "anthropic_api_key", + "max_tokens", + "temperature", + "anthropic_api_url", + ] + def build_config(self): return { + "model": { + "display_name": "Model Name", + "options": [ + "claude-3-opus-20240229", + "claude-3-sonnet-20240229", + "claude-3-haiku-20240307", + "claude-2.1", + "claude-2.0", + "claude-instant-1.2", + "claude-instant-1", + ], + "info": "Name of the model to use.", + "required": True, + "value": "claude-3-opus-20240229", + }, "anthropic_api_key": { "display_name": "Anthropic API Key", - "field_type": "str", + "required": True, "password": True, - }, - "model_kwargs": { - "display_name": "Model Kwargs", - "field_type": "dict", - "advanced": True, - }, - "model_name": { - "display_name": "Model Name", - "field_type": "str", - "advanced": False, - "required": False, - "options": ["claude-3-opus-20240229", "claude-3-sonnet-20240229", "claude-3-haiku-20240307"], - }, - "temperature": { - "display_name": "Temperature", - "field_type": "float", + "info": "Your Anthropic API key.", }, "max_tokens": { "display_name": "Max Tokens", "field_type": "int", - "advanced": False, - "required": False, + "advanced": True, + "value": 256, }, - "top_k": {"display_name": "Top K", "field_type": "int", "advanced": True}, - "top_p": {"display_name": "Top P", "field_type": "float", "advanced": True}, + "temperature": { + "display_name": "Temperature", + "field_type": "float", + "value": 0.1, + }, + "anthropic_api_url": { + "display_name": "Anthropic API URL", + "advanced": True, + "info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.", + }, + "code": {"show": False}, } def build( self, - anthropic_api_key: str, - model_kwargs: dict = {}, - model_name: str = "claude-3-opus-20240229", + model: str, + anthropic_api_key: Optional[str] = None, + max_tokens: Optional[int] = None, temperature: Optional[float] = None, - max_tokens: Optional[int] = 1024, - top_k: Optional[int] = None, - top_p: Optional[float] = None, - ) -> Union[BaseLanguageModel, Callable]: - return ChatAnthropic( - anthropic_api_key=SecretStr(anthropic_api_key), - model_kwargs=model_kwargs, - model_name=model_name, - temperature=temperature, - max_tokens=max_tokens, # type: ignore - top_k=top_k, - top_p=top_p, - ) + anthropic_api_url: Optional[str] = None, + ) -> BaseLanguageModel: + # Set default API endpoint if not provided + if not anthropic_api_url: + anthropic_api_url = "https://api.anthropic.com" + + try: + output = ChatAnthropic( + model_name=model, + anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None), + max_tokens_to_sample=max_tokens, # type: ignore + temperature=temperature, + anthropic_api_url=anthropic_api_url, + ) + except Exception as e: + raise ValueError("Could not connect to Anthropic API.") from e + + return output diff --git a/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py b/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py index 439266fbb..840682f4d 100644 --- a/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py +++ b/src/backend/base/langflow/components/model_specs/ChatLiteLLMSpecs.py @@ -1,4 +1,4 @@ -from typing import Any, Callable, Dict, Optional, Union +from typing import Any, Dict, Optional from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException from langflow.field_typing import BaseLanguageModel diff --git a/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py b/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py index fcaf80965..704090620 100644 --- a/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py +++ b/src/backend/base/langflow/components/model_specs/ChatOpenAISpecs.py @@ -1,9 +1,9 @@ -from typing import Optional, Union +from typing import Optional -from langchain.llms import BaseLLM +from langflow.field_typing import BaseLanguageModel from langchain_community.chat_models.openai import ChatOpenAI -from langflow.field_typing import BaseLanguageModel, NestedDict +from langflow.field_typing import NestedDict from langflow.interface.custom.custom_component import CustomComponent @@ -68,7 +68,7 @@ class ChatOpenAIComponent(CustomComponent): openai_api_base: Optional[str] = None, openai_api_key: Optional[str] = None, temperature: float = 0.7, - ) -> Union[BaseLanguageModel, BaseLLM]: + ) -> BaseLanguageModel: if not openai_api_base: openai_api_base = "https://api.openai.com/v1" return ChatOpenAI( diff --git a/src/backend/base/langflow/components/model_specs/ChatVertexAISpecs.py b/src/backend/base/langflow/components/model_specs/ChatVertexAISpecs.py index fd8b5b427..f2c377546 100644 --- a/src/backend/base/langflow/components/model_specs/ChatVertexAISpecs.py +++ b/src/backend/base/langflow/components/model_specs/ChatVertexAISpecs.py @@ -1,6 +1,5 @@ -from typing import List, Optional, Union +from typing import List, Optional -from langchain.llms import BaseLLM from langchain_community.chat_models.vertexai import ChatVertexAI from langchain_core.messages.base import BaseMessage @@ -74,7 +73,7 @@ class ChatVertexAIComponent(CustomComponent): top_k: int = 40, top_p: float = 0.95, verbose: bool = False, - ) -> Union[BaseLanguageModel, BaseLLM]: + ) -> BaseLanguageModel: return ChatVertexAI( credentials=credentials, examples=examples, diff --git a/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py b/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py index c3e74b1bd..c16fb2c2c 100644 --- a/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py +++ b/src/backend/base/langflow/components/model_specs/HuggingFaceEndpointsSpecs.py @@ -1,6 +1,6 @@ from typing import Optional -from langchain.llms.base import BaseLLM +from langflow.field_typing import BaseLanguageModel from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint from langflow.interface.custom.custom_component import CustomComponent @@ -32,7 +32,7 @@ class HuggingFaceEndpointsComponent(CustomComponent): task: str = "text2text-generation", huggingfacehub_api_token: Optional[str] = None, model_kwargs: Optional[dict] = None, - ) -> BaseLLM: + ) -> BaseLanguageModel: try: output = HuggingFaceEndpoint( # type: ignore endpoint_url=endpoint_url, diff --git a/src/backend/base/langflow/components/model_specs/OllamaLLMSpecs.py b/src/backend/base/langflow/components/model_specs/OllamaLLMSpecs.py index 8d94467db..4ba5502d3 100644 --- a/src/backend/base/langflow/components/model_specs/OllamaLLMSpecs.py +++ b/src/backend/base/langflow/components/model_specs/OllamaLLMSpecs.py @@ -1,6 +1,6 @@ from typing import List, Optional -from langchain.llms.base import BaseLLM +from langflow.field_typing import BaseLanguageModel from langchain_community.llms.ollama import Ollama from langflow.interface.custom.custom_component import CustomComponent @@ -118,7 +118,7 @@ class OllamaLLM(CustomComponent): tfs_z: Optional[float] = None, top_k: Optional[int] = None, top_p: Optional[int] = None, - ) -> BaseLLM: + ) -> BaseLanguageModel: if not base_url: base_url = "http://localhost:11434" diff --git a/src/backend/base/langflow/components/model_specs/VertexAISpecs.py b/src/backend/base/langflow/components/model_specs/VertexAISpecs.py index f186f2ed0..c9664408d 100644 --- a/src/backend/base/langflow/components/model_specs/VertexAISpecs.py +++ b/src/backend/base/langflow/components/model_specs/VertexAISpecs.py @@ -1,6 +1,6 @@ -from typing import Callable, Dict, Optional, Union +from typing import Dict, Optional -from langchain.llms import BaseLLM +from langflow.field_typing import BaseLanguageModel from langchain_community.llms.vertexai import VertexAI from langflow.interface.custom.custom_component import CustomComponent @@ -129,7 +129,7 @@ class VertexAIComponent(CustomComponent): top_p: float = 0.95, tuned_model_name: Optional[str] = None, verbose: bool = False, - ) -> Union[BaseLLM, Callable]: + ) -> BaseLanguageModel: return VertexAI( credentials=credentials, location=location, diff --git a/src/backend/base/langflow/components/model_specs/__init__.py b/src/backend/base/langflow/components/model_specs/__init__.py index 7bf6c2881..eb5c3d822 100644 --- a/src/backend/base/langflow/components/model_specs/__init__.py +++ b/src/backend/base/langflow/components/model_specs/__init__.py @@ -1,10 +1,7 @@ from .AmazonBedrockSpecs import AmazonBedrockComponent -from .AnthropicLLMSpecs import AnthropicLLM -from .AnthropicSpecs import AnthropicComponent -from .AzureChatOpenAISpecs import AzureChatOpenAISpecsComponent from .BaiduQianfanChatEndpointsSpecs import QianfanChatEndpointComponent from .BaiduQianfanLLMEndpointsSpecs import QianfanLLMEndpointComponent -from .ChatAnthropicSpecs import ChatAnthropicComponent +from .ChatAnthropicSpecs import AnthropicLLM from .ChatLiteLLMSpecs import ChatLiteLLMComponent from .ChatOllamaEndpointSpecs import ChatOllamaComponent from .ChatOpenAISpecs import ChatOpenAIComponent @@ -17,12 +14,11 @@ from .VertexAISpecs import VertexAIComponent __all__ = [ "AmazonBedrockComponent", - "AnthropicLLM", - "AnthropicComponent", + "ChatAntropicSpecsComponent", "AzureChatOpenAISpecsComponent", "QianfanChatEndpointComponent", "QianfanLLMEndpointComponent", - "ChatAnthropicComponent", + "AnthropicLLM", "ChatLiteLLMComponent", "ChatOllamaComponent", "ChatOpenAIComponent", diff --git a/src/backend/base/langflow/components/models/AzureOpenAIModel.py b/src/backend/base/langflow/components/models/AzureOpenAIModel.py index 9e1d32bb7..1440df4d0 100644 --- a/src/backend/base/langflow/components/models/AzureOpenAIModel.py +++ b/src/backend/base/langflow/components/models/AzureOpenAIModel.py @@ -2,6 +2,7 @@ from typing import Optional from langchain.llms.base import BaseLanguageModel from langchain_openai import AzureChatOpenAI +from pydantic.v1 import SecretStr from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent @@ -105,13 +106,17 @@ class AzureChatOpenAIComponent(LCModelComponent): max_tokens: Optional[int] = 1000, stream: bool = False, ) -> BaseLanguageModel: + if api_key: + secret_api_key = SecretStr(api_key) + else: + secret_api_key = None try: output = AzureChatOpenAI( model=model, azure_endpoint=azure_endpoint, azure_deployment=azure_deployment, api_version=api_version, - api_key=api_key, + api_key=secret_api_key, temperature=temperature, max_tokens=max_tokens, ) diff --git a/src/backend/base/langflow/components/models/OpenAIModel.py b/src/backend/base/langflow/components/models/OpenAIModel.py index 678151064..1997170f5 100644 --- a/src/backend/base/langflow/components/models/OpenAIModel.py +++ b/src/backend/base/langflow/components/models/OpenAIModel.py @@ -1,6 +1,7 @@ from typing import Optional from langchain_openai import ChatOpenAI +from pydantic.v1 import SecretStr from langflow.base.constants import STREAM_INFO_TEXT from langflow.base.models.model import LCModelComponent @@ -94,12 +95,17 @@ class OpenAIModelComponent(LCModelComponent): ) -> Text: if not openai_api_base: openai_api_base = "https://api.openai.com/v1" + if openai_api_key: + api_key = SecretStr(openai_api_key) + else: + api_key = None + output = ChatOpenAI( max_tokens=max_tokens, model_kwargs=model_kwargs, model=model_name, base_url=openai_api_base, - api_key=openai_api_key, + api_key=api_key, temperature=temperature, ) diff --git a/src/backend/base/langflow/components/outputs/ChatOutput.py b/src/backend/base/langflow/components/outputs/ChatOutput.py index 9ca162c4c..888b85025 100644 --- a/src/backend/base/langflow/components/outputs/ChatOutput.py +++ b/src/backend/base/langflow/components/outputs/ChatOutput.py @@ -19,11 +19,11 @@ class ChatOutput(ChatComponent): return_record: Optional[bool] = False, record_template: Optional[str] = "{text}", ) -> Union[Text, Record]: - return super().build( + return super().build_with_record( sender=sender, sender_name=sender_name, input_value=input_value, session_id=session_id, return_record=return_record, - record_template=record_template, + record_template=record_template or "", ) diff --git a/src/backend/base/langflow/components/outputs/TextOutput.py b/src/backend/base/langflow/components/outputs/TextOutput.py index fe2d8d145..8d14ee357 100644 --- a/src/backend/base/langflow/components/outputs/TextOutput.py +++ b/src/backend/base/langflow/components/outputs/TextOutput.py @@ -24,5 +24,5 @@ class TextOutput(TextComponent): }, } - def build(self, input_value: Optional[Text] = "", record_template: str = "") -> Text: + def build(self, input_value: Optional[Text] = "", record_template: Optional[str] = "") -> Text: return super().build(input_value=input_value, record_template=record_template) diff --git a/src/backend/base/langflow/components/retrievers/MultiQueryRetriever.py b/src/backend/base/langflow/components/retrievers/MultiQueryRetriever.py index 0dec1c028..8dd6d8579 100644 --- a/src/backend/base/langflow/components/retrievers/MultiQueryRetriever.py +++ b/src/backend/base/langflow/components/retrievers/MultiQueryRetriever.py @@ -1,8 +1,8 @@ -from typing import Callable, Optional, Union +from typing import Optional from langchain.retrievers import MultiQueryRetriever -from langflow.field_typing import BaseLLM, BaseRetriever, PromptTemplate +from langflow.field_typing import BaseRetriever, PromptTemplate, BaseLanguageModel from langflow.interface.custom.custom_component import CustomComponent @@ -39,11 +39,11 @@ class MultiQueryRetrieverComponent(CustomComponent): def build( self, - llm: BaseLLM, + llm: BaseLanguageModel, retriever: BaseRetriever, prompt: Optional[PromptTemplate] = None, parser_key: str = "lines", - ) -> Union[Callable, MultiQueryRetriever]: + ) -> MultiQueryRetriever: if not prompt: return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, parser_key=parser_key) else: diff --git a/src/backend/base/langflow/components/toolkits/VectorStoreInfo.py b/src/backend/base/langflow/components/toolkits/VectorStoreInfo.py index 1775d816a..626a14fd8 100644 --- a/src/backend/base/langflow/components/toolkits/VectorStoreInfo.py +++ b/src/backend/base/langflow/components/toolkits/VectorStoreInfo.py @@ -1,5 +1,3 @@ -from typing import Callable, Union - from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo from langchain_community.vectorstores import VectorStore @@ -22,5 +20,5 @@ class VectorStoreInfoComponent(CustomComponent): vectorstore: VectorStore, description: str, name: str, - ) -> Union[VectorStoreInfo, Callable]: + ) -> VectorStoreInfo: return VectorStoreInfo(vectorstore=vectorstore, description=description, name=name) diff --git a/src/backend/base/langflow/components/tools/PythonREPLTool.py b/src/backend/base/langflow/components/tools/PythonREPLTool.py new file mode 100644 index 000000000..6cc7d8649 --- /dev/null +++ b/src/backend/base/langflow/components/tools/PythonREPLTool.py @@ -0,0 +1,68 @@ +import importlib + +from langchain.agents import Tool +from langchain_experimental.utilities import PythonREPL + +from langflow.base.tools.base import build_status_from_tool +from langflow.custom import CustomComponent + + +class PythonREPLToolComponent(CustomComponent): + display_name = "Python REPL Tool" + description = "A tool for running Python code in a REPL environment." + + def build_config(self): + return { + "name": {"display_name": "Name", "info": "The name of the tool."}, + "description": {"display_name": "Description", "info": "A description of the tool."}, + "global_imports": { + "display_name": "Global Imports", + "info": "A list of modules to import globally, e.g. ['math', 'numpy'].", + }, + } + + def get_globals(self, globals: list[str]) -> dict: + """ + Retrieves the global variables from the specified modules. + + Args: + globals (list[str]): A list of module names. + + Returns: + dict: A dictionary containing the global variables from the specified modules. + """ + global_dict = {} + for module in globals: + try: + imported_module = importlib.import_module(module) + global_dict[imported_module.__name__] = imported_module + except ImportError: + print(f"Could not import module {module}") + return global_dict + + def build( + self, + name: str = "python_repl", + description: str = "A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.", + global_imports: list[str] = ["math"], + ) -> Tool: + """ + Builds a Python REPL tool. + + Args: + name (str, optional): The name of the tool. Defaults to "python_repl". + description (str, optional): The description of the tool. Defaults to "A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`. ". + global_imports (list[str], optional): A list of global imports to be available in the Python REPL. Defaults to ["math"]. + + Returns: + Tool: The built Python REPL tool. + """ + _globals = self.get_globals(global_imports) + python_repl = PythonREPL(_globals=_globals) + tool = Tool( + name=name, + description=description, + func=python_repl.run, + ) + self.status = build_status_from_tool(tool) + return tool diff --git a/src/backend/base/langflow/components/tools/__init__.py b/src/backend/base/langflow/components/tools/__init__.py index 27072c109..3d64a723c 100644 --- a/src/backend/base/langflow/components/tools/__init__.py +++ b/src/backend/base/langflow/components/tools/__init__.py @@ -1,5 +1,7 @@ +from .PythonREPLTool import PythonREPLToolComponent from .RetrieverTool import RetrieverToolComponent -from .SearchAPITool import SearchApiToolComponent from .SearchApi import SearchApi +from .SearchAPITool import SearchApiToolComponent -__all__ = ["RetrieverToolComponent", "SearchApiToolComponent", "SearchApi"] + +__all__ = ["RetrieverToolComponent", "SearchApiToolComponent", "SearchApi", "PythonREPLToolComponent"] diff --git a/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py b/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py index 96ba35743..3f318c748 100644 --- a/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/PineconeSearch.py @@ -3,6 +3,7 @@ from typing import List, Optional from langflow.components.vectorstores.base.model import LCVectorStoreComponent from langflow.components.vectorstores.Pinecone import PineconeComponent from langflow.field_typing import Embeddings, Text +from langflow.field_typing.constants import NestedDict from langflow.schema import Record @@ -32,7 +33,6 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent): "default": "", "required": True, }, - "search_kwargs": {"display_name": "Search Kwargs", "default": "{}"}, "pool_threads": { "display_name": "Pool Threads", "default": 1, @@ -57,6 +57,7 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent): pinecone_api_key: Optional[str] = None, namespace: Optional[str] = "default", search_type: str = "similarity", + search_kwargs: Optional[NestedDict] = None, ) -> List[Record]: # type: ignore[override] vector_store = super().build( embedding=embedding, @@ -70,7 +71,13 @@ class PineconeSearchComponent(PineconeComponent, LCVectorStoreComponent): ) if not vector_store: raise ValueError("Failed to load the Pinecone index.") + if search_kwargs is None: + search_kwargs = {} return self.search_with_vector_store( - vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results + vector_store=vector_store, + input_value=input_value, + search_type=search_type, + k=number_of_results, + **search_kwargs, ) diff --git a/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py b/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py index 8fcc5d761..a64343e17 100644 --- a/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/QdrantSearch.py @@ -86,13 +86,18 @@ class QdrantSearchComponent(QdrantComponent, LCVectorStoreComponent): port=port, prefer_grpc=prefer_grpc, prefix=prefix, - search_kwargs=search_kwargs, timeout=timeout, url=url, ) if not vector_store: raise ValueError("Failed to load the Qdrant index.") + if search_kwargs is None: + search_kwargs = {} return self.search_with_vector_store( - vector_store=vector_store, input_value=input_value, search_type=search_type, k=number_of_results + vector_store=vector_store, + input_value=input_value, + search_type=search_type, + k=number_of_results, + **search_kwargs, ) diff --git a/src/backend/base/langflow/components/vectorstores/AstraDB.py b/src/backend/base/langflow/components/vectorstores/AstraDB.py index 460d1f6db..47fa2a881 100644 --- a/src/backend/base/langflow/components/vectorstores/AstraDB.py +++ b/src/backend/base/langflow/components/vectorstores/AstraDB.py @@ -105,7 +105,7 @@ class AstraDBVectorStoreComponent(CustomComponent): bulk_insert_batch_concurrency: Optional[int] = None, bulk_insert_overwrite_concurrency: Optional[int] = None, bulk_delete_concurrency: Optional[int] = None, - setup_mode: str = "Async", + setup_mode: str = "Sync", pre_delete_collection: bool = False, metadata_indexing_include: Optional[List[str]] = None, metadata_indexing_exclude: Optional[List[str]] = None, diff --git a/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py b/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py index 5a12488e4..6c800957a 100644 --- a/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py +++ b/src/backend/base/langflow/components/vectorstores/MongoDBAtlasVector.py @@ -1,7 +1,7 @@ from typing import List, Optional from langchain_community.vectorstores.mongodb_atlas import MongoDBAtlasVectorSearch -from langflow.field_typing import Embeddings, NestedDict +from langflow.field_typing import Embeddings from langflow.interface.custom.custom_component import CustomComponent from langflow.schema.schema import Record @@ -19,7 +19,6 @@ class MongoDBAtlasComponent(CustomComponent): "db_name": {"display_name": "Database Name"}, "index_name": {"display_name": "Index Name"}, "mongodb_atlas_cluster_uri": {"display_name": "MongoDB Atlas Cluster URI"}, - "search_kwargs": {"display_name": "Search Kwargs", "advanced": True}, } def build( @@ -30,9 +29,7 @@ class MongoDBAtlasComponent(CustomComponent): db_name: str = "", index_name: str = "", mongodb_atlas_cluster_uri: str = "", - search_kwargs: Optional[NestedDict] = None, ) -> MongoDBAtlasVectorSearch: - search_kwargs = search_kwargs or {} try: from pymongo import MongoClient except ImportError: @@ -56,7 +53,6 @@ class MongoDBAtlasComponent(CustomComponent): db_name=db_name, index_name=index_name, mongodb_atlas_cluster_uri=mongodb_atlas_cluster_uri, - search_kwargs=search_kwargs, ) else: vector_store = MongoDBAtlasVectorSearch( diff --git a/src/backend/base/langflow/components/vectorstores/Pinecone.py b/src/backend/base/langflow/components/vectorstores/Pinecone.py index e4d6de3aa..f596320b7 100644 --- a/src/backend/base/langflow/components/vectorstores/Pinecone.py +++ b/src/backend/base/langflow/components/vectorstores/Pinecone.py @@ -33,7 +33,6 @@ class PineconeComponent(CustomComponent): "default": "", "required": True, }, - "search_kwargs": {"display_name": "Search Kwargs", "default": "{}"}, "pool_threads": { "display_name": "Pool Threads", "default": 1, diff --git a/src/backend/base/langflow/components/vectorstores/Qdrant.py b/src/backend/base/langflow/components/vectorstores/Qdrant.py index 61f8e14af..e6b3ddbc9 100644 --- a/src/backend/base/langflow/components/vectorstores/Qdrant.py +++ b/src/backend/base/langflow/components/vectorstores/Qdrant.py @@ -4,7 +4,7 @@ from langchain.schema import BaseRetriever from langchain_community.vectorstores import VectorStore from langchain_community.vectorstores.qdrant import Qdrant -from langflow.field_typing import Embeddings, NestedDict +from langflow.field_typing import Embeddings from langflow.interface.custom.custom_component import CustomComponent from langflow.schema.schema import Record @@ -37,7 +37,6 @@ class QdrantComponent(CustomComponent): "port": {"display_name": "Port", "advanced": True}, "prefer_grpc": {"display_name": "Prefer gRPC", "advanced": True}, "prefix": {"display_name": "Prefix", "advanced": True}, - "search_kwargs": {"display_name": "Search Kwargs", "advanced": True}, "timeout": {"display_name": "Timeout", "advanced": True}, "url": {"display_name": "URL", "advanced": True}, } @@ -59,7 +58,6 @@ class QdrantComponent(CustomComponent): port: Optional[int] = 6333, prefer_grpc: bool = False, prefix: Optional[str] = None, - search_kwargs: Optional[NestedDict] = None, timeout: Optional[int] = None, url: Optional[str] = None, ) -> Union[VectorStore, Qdrant, BaseRetriever]: @@ -111,7 +109,6 @@ class QdrantComponent(CustomComponent): port=port, prefer_grpc=prefer_grpc, prefix=prefix, - search_kwargs=search_kwargs, timeout=timeout, url=url, ) diff --git a/src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py b/src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py index 876742117..df80b3699 100644 --- a/src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py +++ b/src/backend/base/langflow/components/vectorstores/SupabaseVectorStore.py @@ -5,7 +5,7 @@ from langchain_community.vectorstores import VectorStore from langchain_community.vectorstores.supabase import SupabaseVectorStore from supabase.client import Client, create_client -from langflow.field_typing import Embeddings, NestedDict +from langflow.field_typing import Embeddings from langflow.interface.custom.custom_component import CustomComponent from langflow.schema.schema import Record @@ -19,7 +19,6 @@ class SupabaseComponent(CustomComponent): "inputs": {"display_name": "Input", "input_types": ["Document", "Record"]}, "embedding": {"display_name": "Embedding"}, "query_name": {"display_name": "Query Name"}, - "search_kwargs": {"display_name": "Search Kwargs", "advanced": True}, "supabase_service_key": {"display_name": "Supabase Service Key"}, "supabase_url": {"display_name": "Supabase URL"}, "table_name": {"display_name": "Table Name", "advanced": True}, @@ -30,7 +29,6 @@ class SupabaseComponent(CustomComponent): embedding: Embeddings, inputs: Optional[List[Record]] = None, query_name: str = "", - search_kwargs: NestedDict = {}, supabase_service_key: str = "", supabase_url: str = "", table_name: str = "", @@ -46,7 +44,6 @@ class SupabaseComponent(CustomComponent): documents=documents, embedding=embedding, query_name=query_name, - search_kwargs=search_kwargs, client=supabase, table_name=table_name, ) diff --git a/src/backend/base/langflow/graph/graph/base.py b/src/backend/base/langflow/graph/graph/base.py index 8261d0b8c..cccb1739e 100644 --- a/src/backend/base/langflow/graph/graph/base.py +++ b/src/backend/base/langflow/graph/graph/base.py @@ -249,7 +249,10 @@ class Graph: vertex.update_raw_params({"session_id": session_id}) # Process the graph try: - await self.process() + start_component_id = next( + (vertex_id for vertex_id in self._is_input_vertices if "chat" in vertex_id.lower()), None + ) + await self.process(start_component_id=start_component_id) self.increment_run_count() except Exception as exc: logger.exception(exc) @@ -293,18 +296,27 @@ class Graph: # run the async function in a sync way # this could be used in a FastAPI endpoint # so we should take care of the event loop - loop = asyncio.get_event_loop() - return loop.run_until_complete( - self.arun( - inputs=inputs, - inputs_components=input_components, - types=types, - outputs=outputs, - session_id=session_id, - stream=stream, - ) + coro = self.arun( + inputs=inputs, + inputs_components=input_components, + types=types, + outputs=outputs, + session_id=session_id, + stream=stream, ) + try: + # Attempt to get the running event loop; if none, an exception is raised + loop = asyncio.get_running_loop() + if loop.is_closed(): + raise RuntimeError("The running event loop is closed.") + except RuntimeError: + # If there's no running event loop or it's closed, use asyncio.run + return asyncio.run(coro) + + # If there's an existing, open event loop, use it to run the async function + return loop.run_until_complete(coro) + async def arun( self, inputs: list[Dict[str, str]], @@ -345,7 +357,7 @@ class Graph: if types is None: types = [] for _ in range(len(inputs) - len(types)): - types.append("any") + types.append("chat") # default to chat for run_inputs, components, input_type in zip(inputs, inputs_components, types): run_outputs = await self._run( inputs=run_inputs, @@ -733,8 +745,10 @@ class Graph: vertices.append(vertex) return vertices - async def process(self) -> "Graph": + async def process(self, start_component_id: Optional[str] = None) -> "Graph": """Processes the graph with vertices in each layer run in parallel.""" + + self.sort_vertices(start_component_id=start_component_id) vertices_layers = self.sorted_vertices_layers vertex_task_run_count: Dict[str, int] = {} for layer_index, layer in enumerate(vertices_layers): @@ -904,7 +918,7 @@ class Graph: return ChatVertex elif node_name in ["ShouldRunNext"]: return RoutingVertex - elif node_name in ["SharedState", "Notify", "GetNotified"]: + elif node_name in ["SharedState", "Notify", "Listen"]: return StateVertex elif node_base_type in lazy_load_vertex_dict.VERTEX_TYPE_MAP: return lazy_load_vertex_dict.VERTEX_TYPE_MAP[node_base_type] @@ -1116,6 +1130,34 @@ class Graph: return vertices_layers + def sort_layer_by_dependency(self, vertices_layers: List[List[str]]) -> List[List[str]]: + """Sorts the vertices in each layer by dependency, ensuring no vertex depends on a subsequent vertex.""" + sorted_layers = [] + + for layer in vertices_layers: + sorted_layer = self._sort_single_layer_by_dependency(layer) + sorted_layers.append(sorted_layer) + + return sorted_layers + + def _sort_single_layer_by_dependency(self, layer: List[str]) -> List[str]: + """Sorts a single layer by dependency using a stable sorting method.""" + # Build a map of each vertex to its index in the layer for quick lookup. + index_map = {vertex: index for index, vertex in enumerate(layer)} + # Create a sorted copy of the layer based on dependency order. + sorted_layer = sorted(layer, key=lambda vertex: self._max_dependency_index(vertex, index_map), reverse=True) + + return sorted_layer + + def _max_dependency_index(self, vertex_id: str, index_map: Dict[str, int]) -> int: + """Finds the highest index a given vertex's dependencies occupy in the same layer.""" + vertex = self.get_vertex(vertex_id) + max_index = -1 + for successor in vertex.successors: # Assuming vertex.successors is a list of successor vertex identifiers. + if successor.id in index_map: + max_index = max(max_index, index_map[successor.id]) + return max_index + def sort_vertices( self, stop_component_id: Optional[str] = None, @@ -1137,6 +1179,9 @@ class Graph: vertices_layers = self.layered_topological_sort(vertices) vertices_layers = self.sort_by_avg_build_time(vertices_layers) # vertices_layers = self.sort_chat_inputs_first(vertices_layers) + # Now we should sort each layer in a way that we make sure + # vertex V does not depend on vertex V+1 + vertices_layers = self.sort_layer_by_dependency(vertices_layers) self.increment_run_count() self._sorted_vertices_layers = vertices_layers first_layer = vertices_layers[0] diff --git a/src/backend/base/langflow/graph/graph/state_manager.py b/src/backend/base/langflow/graph/graph/state_manager.py index 880adca55..f04667a3d 100644 --- a/src/backend/base/langflow/graph/graph/state_manager.py +++ b/src/backend/base/langflow/graph/graph/state_manager.py @@ -1,15 +1,22 @@ from typing import TYPE_CHECKING, Callable -from langflow.services.deps import get_state_service from loguru import logger +from langflow.services.deps import get_settings_service, get_state_service + if TYPE_CHECKING: from langflow.services.state.service import StateService class GraphStateManager: def __init__(self): - self.state_service: "StateService" = get_state_service() + try: + self.state_service: "StateService" = get_state_service() + except Exception as e: + logger.debug(f"Error getting state service. Defaulting to InMemoryStateService: {e}") + from langflow.services.state.service import InMemoryStateService + + self.state_service = InMemoryStateService(get_settings_service()) def append_state(self, key, new_state, run_id: str): self.state_service.append_state(key, new_state, run_id) diff --git a/src/backend/base/langflow/graph/vertex/base.py b/src/backend/base/langflow/graph/vertex/base.py index 7bcdc1b8c..f300b2dda 100644 --- a/src/backend/base/langflow/graph/vertex/base.py +++ b/src/backend/base/langflow/graph/vertex/base.py @@ -1,10 +1,10 @@ import ast import asyncio import inspect +import os import types from enum import Enum from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Dict, Iterator, List, Optional -import os from loguru import logger @@ -315,7 +315,8 @@ class Vertex: raise e params[field_name] = full_path elif field.get("required"): - raise ValueError(f"File path not found for {self.display_name}") + field_display_name = field.get("display_name") + raise ValueError(f"File path not found for {field_display_name} in component {self.display_name}") elif field.get("type") in DIRECT_TYPES and params.get(field_name) is None: val = field.get("value") if field.get("type") == "code": diff --git a/src/backend/base/langflow/helpers/flow.py b/src/backend/base/langflow/helpers/flow.py index 654122dcd..36b852a99 100644 --- a/src/backend/base/langflow/helpers/flow.py +++ b/src/backend/base/langflow/helpers/flow.py @@ -1,11 +1,10 @@ -from typing import TYPE_CHECKING, Any, Callable, Coroutine, List, Optional, Tuple, Type, Union, cast - -from pydantic.v1 import BaseModel, Field, create_model -from sqlmodel import select +from typing import TYPE_CHECKING, Any, Awaitable, Callable, List, Optional, Tuple, Type, Union, cast from langflow.schema.schema import INPUT_FIELD_NAME, Record from langflow.services.database.models.flow.model import Flow from langflow.services.deps import session_scope +from pydantic.v1 import BaseModel, Field, create_model +from sqlmodel import select if TYPE_CHECKING: from langflow.graph.graph.base import Graph @@ -86,7 +85,7 @@ async def run_flow( return await graph.arun(inputs_list, inputs_components=inputs_components, types=types) -def generate_function_for_flow(inputs: List["Vertex"], flow_id: str) -> Coroutine: +def generate_function_for_flow(inputs: List["Vertex"], flow_id: str) -> Callable[..., Awaitable[Any]]: """ Generate a dynamic flow function based on the given inputs and flow ID. @@ -145,7 +144,9 @@ async def flow_function({func_args}): return local_scope["flow_function"] -def build_function_and_schema(flow_record: Record, graph: "Graph") -> Tuple[Callable, Type[BaseModel]]: +def build_function_and_schema( + flow_record: Record, graph: "Graph" +) -> Tuple[Callable[..., Awaitable[Any]], Type[BaseModel]]: """ Builds a dynamic function and schema for a given flow. diff --git a/src/backend/base/langflow/initial_setup/setup.py b/src/backend/base/langflow/initial_setup/setup.py index f2181f85e..ca2bc230a 100644 --- a/src/backend/base/langflow/initial_setup/setup.py +++ b/src/backend/base/langflow/initial_setup/setup.py @@ -84,7 +84,7 @@ def log_node_changes(node_changes_log): logger.debug("\n".join(formatted_messages)) -def load_starter_projects(): +def load_starter_projects() -> list[tuple[Path, dict]]: starter_projects = [] folder = Path(__file__).parent / "starter_projects" for file in folder.glob("*.json"): diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json index ca465fc23..fa049eec2 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Basic Prompting (Hello, world!).json @@ -1,888 +1,888 @@ { - "id": "c091a57f-43a7-4a5e-b352-035ae8d8379c", - "data": { - "nodes": [ - { - "id": "Prompt-uxBqP", - "type": "genericNode", - "position": { - "x": 53.588791333410654, - "y": -107.07318910019967 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: ", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "user_input": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "user_input", - "display_name": "user_input", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "str", - "Text" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "user_input" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-uxBqP", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": true, - "width": 384, - "height": 383, - "dragging": false, - "positionAbsolute": { - "x": 53.588791333410654, - "y": -107.07318910019967 - } + "id": "c091a57f-43a7-4a5e-b352-035ae8d8379c", + "data": { + "nodes": [ + { + "id": "Prompt-uxBqP", + "type": "genericNode", + "position": { + "x": 53.588791333410654, + "y": -107.07318910019967 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "user_input": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "user_input", + "display_name": "user_input", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "OpenAIModel-k39HS", - "type": "genericNode", - "position": { - "x": 634.8148772766217, - "y": 27.035057029045305 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-3.5-turbo", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-4-0125-preview", - "gpt-4-1106-preview", - "gpt-4-vision-preview", - "gpt-3.5-turbo-0125", - "gpt-3.5-turbo-1106" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": true, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-k39HS", - "description": "Generates text using OpenAI LLMs.", - "display_name": "OpenAI" - }, - "selected": false, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 634.8148772766217, - "y": 27.035057029045305 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "object", + "str", + "Text" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "user_input" + ] }, - { - "id": "ChatOutput-njtka", - "type": "genericNode", - "position": { - "x": 1193.250417197867, - "y": 71.88476890163852 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Interaction Panel.", - "icon": "ChatOutput", - "base_classes": [ - "Record", - "Text", - "str", - "object" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-njtka" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 1193.250417197867, - "y": 71.88476890163852 - }, - "dragging": false - }, - { - "id": "ChatInput-P3fgL", - "type": "genericNode", - "position": { - "x": -495.2223093083827, - "y": -232.56998443685862 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Interaction Panel.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "hi" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Interaction Panel.", - "icon": "ChatInput", - "base_classes": [ - "object", - "Record", - "str", - "Text" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-P3fgL" - }, - "selected": false, - "width": 384, - "height": 375, - "positionAbsolute": { - "x": -495.2223093083827, - "y": -232.56998443685862 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "OpenAIModel-k39HS", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}", - "target": "ChatOutput-njtka", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-njtka", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-k39HS" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-k39HS{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}-ChatOutput-njtka{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" - }, - { - "source": "Prompt-uxBqP", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}", - "target": "OpenAIModel-k39HS", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-k39HS", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-uxBqP" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-uxBqP{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}-OpenAIModel-k39HS{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" - }, - { - "source": "ChatInput-P3fgL", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}", - "target": "Prompt-uxBqP", - "targetHandle": "{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "user_input", - "id": "Prompt-uxBqP", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Record", - "str", - "Text" - ], - "dataType": "ChatInput", - "id": "ChatInput-P3fgL" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-P3fgL{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}-Prompt-uxBqP{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" - } - ], - "viewport": { - "x": 260.58251815500563, - "y": 318.2261172111936, - "zoom": 0.43514115784696294 + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-uxBqP", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": true, + "width": 384, + "height": 383, + "dragging": false, + "positionAbsolute": { + "x": 53.588791333410654, + "y": -107.07318910019967 } - }, - "description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ", - "name": "Basic Prompting (Hello, world!)", - "last_tested_version": "1.0.0a4", - "is_component": false + }, + { + "id": "OpenAIModel-k39HS", + "type": "genericNode", + "position": { + "x": 634.8148772766217, + "y": 27.035057029045305 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-4-0125-preview", + "gpt-4-1106-preview", + "gpt-4-vision-preview", + "gpt-3.5-turbo-0125", + "gpt-3.5-turbo-1106" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": true, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "object", + "Text", + "str" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-k39HS", + "description": "Generates text using OpenAI LLMs.", + "display_name": "OpenAI" + }, + "selected": false, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 634.8148772766217, + "y": 27.035057029045305 + }, + "dragging": false + }, + { + "id": "ChatOutput-njtka", + "type": "genericNode", + "position": { + "x": 1193.250417197867, + "y": 71.88476890163852 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Display a chat message in the Interaction Panel.", + "icon": "ChatOutput", + "base_classes": [ + "Record", + "Text", + "str", + "object" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null + }, + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-njtka" + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 1193.250417197867, + "y": 71.88476890163852 + }, + "dragging": false + }, + { + "id": "ChatInput-P3fgL", + "type": "genericNode", + "position": { + "x": -495.2223093083827, + "y": -232.56998443685862 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Interaction Panel.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "hi" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Get chat inputs from the Interaction Panel.", + "icon": "ChatInput", + "base_classes": [ + "object", + "Record", + "str", + "Text" + ], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null + }, + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-P3fgL" + }, + "selected": false, + "width": 384, + "height": 375, + "positionAbsolute": { + "x": -495.2223093083827, + "y": -232.56998443685862 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "OpenAIModel-k39HS", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}", + "target": "ChatOutput-njtka", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-njtka", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "Text", + "str" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-k39HS" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-k39HS{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-k39HSœ}-ChatOutput-njtka{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-njtkaœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-uxBqP", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}", + "target": "OpenAIModel-k39HS", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-k39HS", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "Prompt", + "id": "Prompt-uxBqP" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-uxBqP{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-uxBqPœ}-OpenAIModel-k39HS{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-k39HSœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "ChatInput-P3fgL", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}", + "target": "Prompt-uxBqP", + "targetHandle": "{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "user_input", + "id": "Prompt-uxBqP", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "Record", + "str", + "Text" + ], + "dataType": "ChatInput", + "id": "ChatInput-P3fgL" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-P3fgL{œbaseClassesœ:[œobjectœ,œRecordœ,œstrœ,œTextœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-P3fgLœ}-Prompt-uxBqP{œfieldNameœ:œuser_inputœ,œidœ:œPrompt-uxBqPœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": 260.58251815500563, + "y": 318.2261172111936, + "zoom": 0.43514115784696294 + } + }, + "description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ", + "name": "Basic Prompting (Hello, World)", + "last_tested_version": "1.0.0a4", + "is_component": false } \ No newline at end of file diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json index 9663a78ef..6be06dd5d 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Blog Writter.json @@ -45,7 +45,9 @@ "name": "template", "display_name": "Template", "advanced": false, - "input_types": ["Text"], + "input_types": [ + "Text" + ], "dynamic": false, "info": "", "load_from_db": false, @@ -136,14 +138,24 @@ "is_input": null, "is_output": null, "is_composition": null, - "base_classes": ["object", "Text", "str"], + "base_classes": [ + "object", + "Text", + "str" + ], "name": "", "display_name": "Prompt", "documentation": "", "custom_fields": { - "template": ["reference_1", "reference_2", "instructions"] + "template": [ + "reference_1", + "reference_2", + "instructions" + ] }, - "output_types": ["Text"], + "output_types": [ + "Text" + ], "full_path": null, "field_formatters": {}, "frozen": false, @@ -210,7 +222,9 @@ "info": "", "load_from_db": false, "title_case": false, - "input_types": ["Text"], + "input_types": [ + "Text" + ], "value": [ "https://www.promptingguide.ai/techniques/prompt_chaining" ] @@ -219,13 +233,17 @@ }, "description": "Fetch content from one or more URLs.", "icon": "layout-template", - "base_classes": ["Record"], + "base_classes": [ + "Record" + ], "display_name": "URL", "documentation": "", "custom_fields": { "urls": null }, - "output_types": ["Record"], + "output_types": [ + "Record" + ], "field_formatters": {}, "frozen": false, "field_order": [], @@ -260,7 +278,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n", + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", "fileTypes": [], "file_path": "", "password": false, @@ -284,7 +302,9 @@ "name": "input_value", "display_name": "Message", "advanced": false, - "input_types": ["Text"], + "input_types": [ + "Text" + ], "dynamic": false, "info": "", "load_from_db": false, @@ -308,7 +328,9 @@ "info": "In case of Message being a Record, this template will be used to convert it to text.", "load_from_db": false, "title_case": false, - "input_types": ["Text"] + "input_types": [ + "Text" + ] }, "return_record": { "type": "bool", @@ -340,7 +362,10 @@ "fileTypes": [], "file_path": "", "password": false, - "options": ["Machine", "User"], + "options": [ + "Machine", + "User" + ], "name": "sender", "display_name": "Sender Type", "advanced": true, @@ -348,7 +373,9 @@ "info": "", "load_from_db": false, "title_case": false, - "input_types": ["Text"] + "input_types": [ + "Text" + ] }, "sender_name": { "type": "str", @@ -368,7 +395,9 @@ "info": "", "load_from_db": false, "title_case": false, - "input_types": ["Text"] + "input_types": [ + "Text" + ] }, "session_id": { "type": "str", @@ -387,13 +416,20 @@ "info": "If provided, the message will be stored in the memory.", "load_from_db": false, "title_case": false, - "input_types": ["Text"] + "input_types": [ + "Text" + ] }, "_type": "CustomComponent" }, "description": "Display a chat message in the Interaction Panel.", "icon": "ChatOutput", - "base_classes": ["Text", "Record", "object", "str"], + "base_classes": [ + "Text", + "Record", + "object", + "str" + ], "display_name": "Chat Output", "documentation": "", "custom_fields": { @@ -404,7 +440,10 @@ "return_record": null, "record_template": null }, - "output_types": ["Text", "Record"], + "output_types": [ + "Text", + "Record" + ], "field_formatters": {}, "frozen": false, "field_order": [], @@ -444,7 +483,9 @@ "info": "", "load_from_db": false, "title_case": false, - "input_types": ["Text"] + "input_types": [ + "Text" + ] }, "code": { "type": "code", @@ -453,7 +494,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", "fileTypes": [], "file_path": "", "password": false, @@ -529,7 +570,9 @@ "info": "", "load_from_db": false, "title_case": false, - "input_types": ["Text"] + "input_types": [ + "Text" + ] }, "openai_api_base": { "type": "str", @@ -548,7 +591,9 @@ "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", "load_from_db": false, "title_case": false, - "input_types": ["Text"] + "input_types": [ + "Text" + ] }, "openai_api_key": { "type": "str", @@ -567,7 +612,9 @@ "info": "The OpenAI API Key to use for the OpenAI model.", "load_from_db": false, "title_case": false, - "input_types": ["Text"], + "input_types": [ + "Text" + ], "value": "" }, "stream": { @@ -606,7 +653,9 @@ "info": "System message to pass to the model.", "load_from_db": false, "title_case": false, - "input_types": ["Text"] + "input_types": [ + "Text" + ] }, "temperature": { "type": "float", @@ -637,7 +686,11 @@ }, "description": "Generates text using OpenAI LLMs.", "icon": "OpenAI", - "base_classes": ["str", "Text", "object"], + "base_classes": [ + "str", + "Text", + "object" + ], "display_name": "OpenAI", "documentation": "", "custom_fields": { @@ -651,7 +704,9 @@ "stream": null, "system_message": null }, - "output_types": ["Text"], + "output_types": [ + "Text" + ], "field_formatters": {}, "frozen": false, "field_order": [ @@ -724,20 +779,28 @@ "info": "", "load_from_db": false, "title_case": false, - "input_types": ["Text"], - "value": ["https://www.promptingguide.ai/introduction/basics"] + "input_types": [ + "Text" + ], + "value": [ + "https://www.promptingguide.ai/introduction/basics" + ] }, "_type": "CustomComponent" }, "description": "Fetch content from one or more URLs.", "icon": "layout-template", - "base_classes": ["Record"], + "base_classes": [ + "Record" + ], "display_name": "URL", "documentation": "", "custom_fields": { "urls": null }, - "output_types": ["Record"], + "output_types": [ + "Record" + ], "field_formatters": {}, "frozen": false, "field_order": [], @@ -797,7 +860,10 @@ "name": "input_value", "display_name": "Value", "advanced": false, - "input_types": ["Record", "Text"], + "input_types": [ + "Record", + "Text" + ], "dynamic": false, "info": "Text or Record to be passed as input.", "load_from_db": false, @@ -821,20 +887,28 @@ "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", "load_from_db": false, "title_case": false, - "input_types": ["Text"] + "input_types": [ + "Text" + ] }, "_type": "CustomComponent" }, "description": "Get text inputs from the Interaction Panel.", "icon": "type", - "base_classes": ["object", "Text", "str"], + "base_classes": [ + "object", + "Text", + "str" + ], "display_name": "Instructions", "documentation": "", "custom_fields": { "input_value": null, "record_template": null }, - "output_types": ["Text"], + "output_types": [ + "Text" + ], "field_formatters": {}, "frozen": false, "field_order": [], @@ -863,11 +937,18 @@ "targetHandle": { "fieldName": "reference_2", "id": "Prompt-Rse03", - "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], "type": "str" }, "sourceHandle": { - "baseClasses": ["Record"], + "baseClasses": [ + "Record" + ], "dataType": "URL", "id": "URL-HYPkR" } @@ -887,11 +968,17 @@ "targetHandle": { "fieldName": "input_value", "id": "ChatOutput-JPlxl", - "inputTypes": ["Text"], + "inputTypes": [ + "Text" + ], "type": "str" }, "sourceHandle": { - "baseClasses": ["str", "Text", "object"], + "baseClasses": [ + "str", + "Text", + "object" + ], "dataType": "OpenAIModel", "id": "OpenAIModel-gi29P" } @@ -911,11 +998,18 @@ "targetHandle": { "fieldName": "reference_1", "id": "Prompt-Rse03", - "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], "type": "str" }, "sourceHandle": { - "baseClasses": ["Record"], + "baseClasses": [ + "Record" + ], "dataType": "URL", "id": "URL-2cX90" } @@ -935,11 +1029,20 @@ "targetHandle": { "fieldName": "instructions", "id": "Prompt-Rse03", - "inputTypes": ["Document", "BaseOutputParser", "Record", "Text"], + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], "type": "str" }, "sourceHandle": { - "baseClasses": ["object", "Text", "str"], + "baseClasses": [ + "object", + "Text", + "str" + ], "dataType": "TextInput", "id": "TextInput-og8Or" } @@ -959,11 +1062,17 @@ "targetHandle": { "fieldName": "input_value", "id": "OpenAIModel-gi29P", - "inputTypes": ["Text"], + "inputTypes": [ + "Text" + ], "type": "str" }, "sourceHandle": { - "baseClasses": ["object", "Text", "str"], + "baseClasses": [ + "object", + "Text", + "str" + ], "dataType": "Prompt", "id": "Prompt-Rse03" } @@ -986,4 +1095,4 @@ "name": "Blog Writer", "last_tested_version": "1.0.0a0", "is_component": false -} +} \ No newline at end of file diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json index b8449f430..592c05fac 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Document QA.json @@ -1,1031 +1,1031 @@ { - "id": "fecbce42-6f11-454c-8ab2-db6eddbbbb0f", - "data": { - "nodes": [ - { - "id": "Prompt-tHwPf", - "type": "genericNode", - "position": { - "x": 585.7906101139403, - "y": 117.52115876762832 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:\n{Question}\n\nAnswer:\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "Document": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "Document", - "display_name": "Document", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "Question": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "Question", - "display_name": "Question", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "str", - "Text" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "Document", - "Question" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-tHwPf", - "description": "A component for creating prompt templates using dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 479, - "positionAbsolute": { - "x": 585.7906101139403, - "y": 117.52115876762832 - }, - "dragging": false + "id": "fecbce42-6f11-454c-8ab2-db6eddbbbb0f", + "data": { + "nodes": [ + { + "id": "Prompt-tHwPf", + "type": "genericNode", + "position": { + "x": 585.7906101139403, + "y": 117.52115876762832 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "Answer user's questions based on the document below:\n\n---\n\n{Document}\n\n---\n\nQuestion:\n{Question}\n\nAnswer:\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "Document": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "Document", + "display_name": "Document", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "Question": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "Question", + "display_name": "Question", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "File-6TEsD", - "type": "genericNode", - "position": { - "x": -18.636536329280602, - "y": 3.951948774836353 - }, - "data": { - "type": "File", - "node": { - "template": { - "path": { - "type": "file", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [ - ".txt", - ".md", - ".mdx", - ".csv", - ".json", - ".yaml", - ".yml", - ".xml", - ".html", - ".htm", - ".pdf", - ".docx" - ], - "password": false, - "name": "path", - "display_name": "Path", - "advanced": false, - "dynamic": false, - "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"Files\"\n description = \"A generic file loader.\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "silent_errors": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "silent_errors", - "display_name": "Silent Errors", - "advanced": true, - "dynamic": false, - "info": "If true, errors will not raise an exception.", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "A generic file loader.", - "base_classes": [ - "Record" - ], - "display_name": "Files", - "documentation": "", - "custom_fields": { - "path": null, - "silent_errors": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "File-6TEsD" - }, - "selected": false, - "width": 384, - "height": 282, - "positionAbsolute": { - "x": -18.636536329280602, - "y": 3.951948774836353 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "object", + "str", + "Text" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "Document", + "Question" + ] }, - { - "id": "ChatInput-MsSJ9", - "type": "genericNode", - "position": { - "x": -28.80036300619821, - "y": 379.81180230285355 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Interaction Panel.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Interaction Panel.", - "icon": "ChatInput", - "base_classes": [ - "str", - "Record", - "Text", - "object" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-MsSJ9" - }, - "selected": true, - "width": 384, - "height": 377, - "positionAbsolute": { - "x": -28.80036300619821, - "y": 379.81180230285355 - }, - "dragging": false + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-tHwPf", + "description": "A component for creating prompt templates using dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 479, + "positionAbsolute": { + "x": 585.7906101139403, + "y": 117.52115876762832 + }, + "dragging": false + }, + { + "id": "File-6TEsD", + "type": "genericNode", + "position": { + "x": -18.636536329280602, + "y": 3.951948774836353 + }, + "data": { + "type": "File", + "node": { + "template": { + "path": { + "type": "file", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [ + ".txt", + ".md", + ".mdx", + ".csv", + ".json", + ".yaml", + ".yml", + ".xml", + ".html", + ".htm", + ".pdf", + ".docx" + ], + "password": false, + "name": "path", + "display_name": "Path", + "advanced": false, + "dynamic": false, + "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"Files\"\n description = \"A generic file loader.\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "silent_errors": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "silent_errors", + "display_name": "Silent Errors", + "advanced": true, + "dynamic": false, + "info": "If true, errors will not raise an exception.", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "id": "ChatOutput-F5Awj", - "type": "genericNode", - "position": { - "x": 1733.3012915204283, - "y": 168.76098809939327 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Interaction Panel.", - "icon": "ChatOutput", - "base_classes": [ - "str", - "Record", - "Text", - "object" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-F5Awj" - }, - "selected": false, - "width": 384, - "height": 385, - "positionAbsolute": { - "x": 1733.3012915204283, - "y": 168.76098809939327 - }, - "dragging": false + "description": "A generic file loader.", + "base_classes": [ + "Record" + ], + "display_name": "Files", + "documentation": "", + "custom_fields": { + "path": null, + "silent_errors": null }, - { - "id": "OpenAIModel-Bt067", - "type": "genericNode", - "position": { - "x": 1137.6078582863759, - "y": -14.41920034020356 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-4-turbo-preview", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-4-0125-preview", - "gpt-4-1106-preview", - "gpt-4-vision-preview", - "gpt-3.5-turbo-0125", - "gpt-3.5-turbo-1106" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": false, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "str", - "Text" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-Bt067" - }, - "selected": false, - "width": 384, - "height": 642, - "positionAbsolute": { - "x": 1137.6078582863759, - "y": -14.41920034020356 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "ChatInput-MsSJ9", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œRecordœ,œTextœ,œobjectœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-MsSJ9œ}", - "target": "Prompt-tHwPf", - "targetHandle": "{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "Question", - "id": "Prompt-tHwPf", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Record", - "Text", - "object" - ], - "dataType": "ChatInput", - "id": "ChatInput-MsSJ9" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-MsSJ9{œbaseClassesœ:[œstrœ,œRecordœ,œTextœ,œobjectœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-MsSJ9œ}-Prompt-tHwPf{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "File-6TEsD" + }, + "selected": false, + "width": 384, + "height": 282, + "positionAbsolute": { + "x": -18.636536329280602, + "y": 3.951948774836353 + }, + "dragging": false + }, + { + "id": "ChatInput-MsSJ9", + "type": "genericNode", + "position": { + "x": -28.80036300619821, + "y": 379.81180230285355 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Interaction Panel.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "source": "File-6TEsD", - "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-6TEsDœ}", - "target": "Prompt-tHwPf", - "targetHandle": "{œfieldNameœ:œDocumentœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "Document", - "id": "Prompt-tHwPf", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "File", - "id": "File-6TEsD" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-File-6TEsD{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-6TEsDœ}-Prompt-tHwPf{œfieldNameœ:œDocumentœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + "description": "Get chat inputs from the Interaction Panel.", + "icon": "ChatInput", + "base_classes": [ + "str", + "Record", + "Text", + "object" + ], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "source": "Prompt-tHwPf", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-tHwPfœ}", - "target": "OpenAIModel-Bt067", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Bt067œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-Bt067", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "Prompt", - "id": "Prompt-tHwPf" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-tHwPf{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-tHwPfœ}-OpenAIModel-Bt067{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Bt067œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-MsSJ9" + }, + "selected": true, + "width": 384, + "height": 377, + "positionAbsolute": { + "x": -28.80036300619821, + "y": 379.81180230285355 + }, + "dragging": false + }, + { + "id": "ChatOutput-F5Awj", + "type": "genericNode", + "position": { + "x": 1733.3012915204283, + "y": 168.76098809939327 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-Bt067", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Bt067œ}", - "target": "ChatOutput-F5Awj", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-F5Awjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-F5Awj", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "str", - "Text" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-Bt067" - } + "description": "Display a chat message in the Interaction Panel.", + "icon": "ChatOutput", + "base_classes": [ + "str", + "Record", + "Text", + "object" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null + }, + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-F5Awj" + }, + "selected": false, + "width": 384, + "height": 385, + "positionAbsolute": { + "x": 1733.3012915204283, + "y": 168.76098809939327 + }, + "dragging": false + }, + { + "id": "OpenAIModel-Bt067", + "type": "genericNode", + "position": { + "x": 1137.6078582863759, + "y": -14.41920034020356 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-4-turbo-preview", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-4-0125-preview", + "gpt-4-1106-preview", + "gpt-4-vision-preview", + "gpt-3.5-turbo-0125", + "gpt-3.5-turbo-1106" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": false, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIModel-Bt067{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Bt067œ}-ChatOutput-F5Awj{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-F5Awjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" - } - ], - "viewport": { - "x": 352.20899206064655, - "y": 56.054900898593075, - "zoom": 0.9023391400011 - } - }, - "description": "This flow integrates PDF reading with a language model to answer document-specific questions. Ideal for small-scale texts, it facilitates direct queries with immediate insights.", - "name": "Document QA", - "last_tested_version": "1.0.0a0", - "is_component": false + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "object", + "str", + "Text" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-Bt067" + }, + "selected": false, + "width": 384, + "height": 642, + "positionAbsolute": { + "x": 1137.6078582863759, + "y": -14.41920034020356 + }, + "dragging": false + } + ], + "edges": [ + { + "source": "ChatInput-MsSJ9", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œRecordœ,œTextœ,œobjectœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-MsSJ9œ}", + "target": "Prompt-tHwPf", + "targetHandle": "{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "Question", + "id": "Prompt-tHwPf", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "Record", + "Text", + "object" + ], + "dataType": "ChatInput", + "id": "ChatInput-MsSJ9" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-MsSJ9{œbaseClassesœ:[œstrœ,œRecordœ,œTextœ,œobjectœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-MsSJ9œ}-Prompt-tHwPf{œfieldNameœ:œQuestionœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "File-6TEsD", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-6TEsDœ}", + "target": "Prompt-tHwPf", + "targetHandle": "{œfieldNameœ:œDocumentœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "Document", + "id": "Prompt-tHwPf", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Record" + ], + "dataType": "File", + "id": "File-6TEsD" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-File-6TEsD{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-6TEsDœ}-Prompt-tHwPf{œfieldNameœ:œDocumentœ,œidœ:œPrompt-tHwPfœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "Prompt-tHwPf", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-tHwPfœ}", + "target": "OpenAIModel-Bt067", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Bt067œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-Bt067", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "Prompt", + "id": "Prompt-tHwPf" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-tHwPf{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-tHwPfœ}-OpenAIModel-Bt067{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-Bt067œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "OpenAIModel-Bt067", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Bt067œ}", + "target": "ChatOutput-F5Awj", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-F5Awjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-F5Awj", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "str", + "Text" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-Bt067" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-Bt067{œbaseClassesœ:[œobjectœ,œstrœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-Bt067œ}-ChatOutput-F5Awj{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-F5Awjœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": 352.20899206064655, + "y": 56.054900898593075, + "zoom": 0.9023391400011 + } + }, + "description": "This flow integrates PDF reading with a language model to answer document-specific questions. Ideal for small-scale texts, it facilitates direct queries with immediate insights.", + "name": "Document QA", + "last_tested_version": "1.0.0a0", + "is_component": false } \ No newline at end of file diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json index 92dbdd6f2..d424b97bc 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Memory Conversation.json @@ -4,1269 +4,1269 @@ "icon_bg_color": "#FFD700", "data": { "nodes": [ - { - "id": "ChatInput-t7F8v", - "type": "genericNode", - "position": { - "x": 1283.2700598313072, - "y": 982.5953650473145 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Interaction Panel.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": false, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "MySessionID" - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Interaction Panel.", - "icon": "ChatInput", - "base_classes": [ - "Text", - "object", - "Record", - "str" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-t7F8v" - }, - "selected": false, - "width": 384, - "height": 469, - "positionAbsolute": { - "x": 1283.2700598313072, - "y": 982.5953650473145 - }, - "dragging": false + { + "id": "ChatInput-t7F8v", + "type": "genericNode", + "position": { + "x": 1283.2700598313072, + "y": 982.5953650473145 }, - { - "id": "ChatOutput-P1jEe", - "type": "genericNode", - "position": { - "x": 3154.916355514023, - "y": 851.051882666333 + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Interaction Panel.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": false, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "MySessionID" + }, + "_type": "CustomComponent" }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": false, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "MySessionID" - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Interaction Panel.", - "icon": "ChatOutput", - "base_classes": [ - "Text", - "object", - "Record", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-P1jEe" + "description": "Get chat inputs from the Interaction Panel.", + "icon": "ChatInput", + "base_classes": [ + "Text", + "object", + "Record", + "str" + ], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - "selected": false, - "width": 384, - "height": 477, - "dragging": false, - "positionAbsolute": { - "x": 3154.916355514023, - "y": 851.051882666333 - } + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-t7F8v" }, - { - "id": "MemoryComponent-cdA1J", - "type": "genericNode", - "position": { - "x": 1289.9606870058817, - "y": 442.16804561053766 - }, - "data": { - "type": "MemoryComponent", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.memory import get_messages\n\n\nclass MemoryComponent(CustomComponent):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages given a specific Session ID.\"\n beta: bool = True\n icon = \"history\"\n\n def build_config(self):\n return {\n \"sender\": {\n \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n \"display_name\": \"Sender Type\",\n },\n \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n \"n_messages\": {\n \"display_name\": \"Number of Messages\",\n \"info\": \"Number of messages to retrieve.\",\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"info\": \"Session ID of the chat history.\",\n \"input_types\": [\"Text\"],\n },\n \"order\": {\n \"options\": [\"Ascending\", \"Descending\"],\n \"display_name\": \"Order\",\n \"info\": \"Order of the messages.\",\n \"advanced\": True,\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n sender: Optional[str] = \"Machine and User\",\n sender_name: Optional[str] = None,\n session_id: Optional[str] = None,\n n_messages: int = 5,\n order: Optional[str] = \"Descending\",\n record_template: Optional[str] = \"{sender_name}: {text}\",\n ) -> Text:\n order = \"DESC\" if order == \"Descending\" else \"ASC\"\n if sender == \"Machine and User\":\n sender = None\n messages = get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n messages_str = records_to_text(template=record_template, records=messages)\n self.status = messages_str\n return messages_str\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "n_messages": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 5, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "n_messages", - "display_name": "Number of Messages", - "advanced": false, - "dynamic": false, - "info": "Number of messages to retrieve.", - "load_from_db": false, - "title_case": false - }, - "order": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Descending", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Ascending", - "Descending" - ], - "name": "order", - "display_name": "Order", - "advanced": true, - "dynamic": false, - "info": "Order of the messages.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{sender_name}: {text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine and User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User", - "Machine and User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "Session ID of the chat history.", - "load_from_db": false, - "title_case": false, - "value": "MySessionID" - }, - "_type": "CustomComponent" - }, - "description": "Retrieves stored chat messages given a specific Session ID.", - "icon": "history", - "base_classes": [ - "str", - "Text", - "object" - ], - "display_name": "Chat Memory", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "session_id": null, - "n_messages": null, - "order": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": true - }, - "id": "MemoryComponent-cdA1J", - "description": "Retrieves stored chat messages given a specific Session ID.", - "display_name": "Chat Memory" - }, - "selected": false, - "width": 384, - "height": 489, - "dragging": false, - "positionAbsolute": { - "x": 1289.9606870058817, - "y": 442.16804561053766 - } + "selected": false, + "width": 384, + "height": 469, + "positionAbsolute": { + "x": 1283.2700598313072, + "y": 982.5953650473145 }, - { - "id": "Prompt-ODkUx", - "type": "genericNode", - "position": { - "x": 1894.594426342426, - "y": 753.3797365481901 - }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "{context}\n\nUser: {user_message}\nAI: ", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "context": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "context", - "display_name": "context", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "user_message": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "user_message", - "display_name": "user_message", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "Text", - "str", - "object" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "context", - "user_message" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-ODkUx", - "description": "A component for creating prompt templates using dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 477, - "dragging": false, - "positionAbsolute": { - "x": 1894.594426342426, - "y": 753.3797365481901 - } + "dragging": false + }, + { + "id": "ChatOutput-P1jEe", + "type": "genericNode", + "position": { + "x": 3154.916355514023, + "y": 851.051882666333 }, - { - "id": "OpenAIModel-9RykF", - "type": "genericNode", - "position": { - "x": 2561.5850334731617, - "y": 553.2745131130916 + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": false, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "MySessionID" + }, + "_type": "CustomComponent" }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-4-1106-preview", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-4-0125-preview", - "gpt-4-1106-preview", - "gpt-4-vision-preview", - "gpt-3.5-turbo-0125", - "gpt-3.5-turbo-1106" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "0.2", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "str", - "object", - "Text" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-9RykF" + "description": "Display a chat message in the Interaction Panel.", + "icon": "ChatOutput", + "base_classes": [ + "Text", + "object", + "Record", + "str" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - "selected": false, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 2561.5850334731617, - "y": 553.2745131130916 - }, - "dragging": false + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-P1jEe" }, - { - "id": "TextOutput-vrs6T", - "type": "genericNode", - "position": { - "x": 1911.4785906252087, - "y": 247.39079954376987 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Interaction Panel.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Interaction Panel.", - "icon": "type", - "base_classes": [ - "str", - "object", - "Text" - ], - "display_name": "Inspect Memory", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-vrs6T" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": 1911.4785906252087, - "y": 247.39079954376987 - }, - "dragging": false + "selected": false, + "width": 384, + "height": 477, + "dragging": false, + "positionAbsolute": { + "x": 3154.916355514023, + "y": 851.051882666333 } + }, + { + "id": "MemoryComponent-cdA1J", + "type": "genericNode", + "position": { + "x": 1289.9606870058817, + "y": 442.16804561053766 + }, + "data": { + "type": "MemoryComponent", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.memory import get_messages\n\n\nclass MemoryComponent(CustomComponent):\n display_name = \"Chat Memory\"\n description = \"Retrieves stored chat messages given a specific Session ID.\"\n beta: bool = True\n icon = \"history\"\n\n def build_config(self):\n return {\n \"sender\": {\n \"options\": [\"Machine\", \"User\", \"Machine and User\"],\n \"display_name\": \"Sender Type\",\n },\n \"sender_name\": {\"display_name\": \"Sender Name\", \"advanced\": True},\n \"n_messages\": {\n \"display_name\": \"Number of Messages\",\n \"info\": \"Number of messages to retrieve.\",\n },\n \"session_id\": {\n \"display_name\": \"Session ID\",\n \"info\": \"Session ID of the chat history.\",\n \"input_types\": [\"Text\"],\n },\n \"order\": {\n \"options\": [\"Ascending\", \"Descending\"],\n \"display_name\": \"Order\",\n \"info\": \"Order of the messages.\",\n \"advanced\": True,\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n sender: Optional[str] = \"Machine and User\",\n sender_name: Optional[str] = None,\n session_id: Optional[str] = None,\n n_messages: int = 5,\n order: Optional[str] = \"Descending\",\n record_template: Optional[str] = \"{sender_name}: {text}\",\n ) -> Text:\n order = \"DESC\" if order == \"Descending\" else \"ASC\"\n if sender == \"Machine and User\":\n sender = None\n messages = get_messages(\n sender=sender,\n sender_name=sender_name,\n session_id=session_id,\n limit=n_messages,\n order=order,\n )\n messages_str = records_to_text(template=record_template or \"\", records=messages)\n self.status = messages_str\n return messages_str\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "n_messages": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 5, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "n_messages", + "display_name": "Number of Messages", + "advanced": false, + "dynamic": false, + "info": "Number of messages to retrieve.", + "load_from_db": false, + "title_case": false + }, + "order": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Descending", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Ascending", + "Descending" + ], + "name": "order", + "display_name": "Order", + "advanced": true, + "dynamic": false, + "info": "Order of the messages.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{sender_name}: {text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine and User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User", + "Machine and User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "Session ID of the chat history.", + "load_from_db": false, + "title_case": false, + "value": "MySessionID" + }, + "_type": "CustomComponent" + }, + "description": "Retrieves stored chat messages given a specific Session ID.", + "icon": "history", + "base_classes": [ + "str", + "Text", + "object" + ], + "display_name": "Chat Memory", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "session_id": null, + "n_messages": null, + "order": null, + "record_template": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": true + }, + "id": "MemoryComponent-cdA1J", + "description": "Retrieves stored chat messages given a specific Session ID.", + "display_name": "Chat Memory" + }, + "selected": false, + "width": 384, + "height": 489, + "dragging": false, + "positionAbsolute": { + "x": 1289.9606870058817, + "y": 442.16804561053766 + } + }, + { + "id": "Prompt-ODkUx", + "type": "genericNode", + "position": { + "x": 1894.594426342426, + "y": 753.3797365481901 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "{context}\n\nUser: {user_message}\nAI: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "context": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "context", + "display_name": "context", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "user_message": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "user_message", + "display_name": "user_message", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } + }, + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "Text", + "str", + "object" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "context", + "user_message" + ] + }, + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-ODkUx", + "description": "A component for creating prompt templates using dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 477, + "dragging": false, + "positionAbsolute": { + "x": 1894.594426342426, + "y": 753.3797365481901 + } + }, + { + "id": "OpenAIModel-9RykF", + "type": "genericNode", + "position": { + "x": 2561.5850334731617, + "y": 553.2745131130916 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-4-1106-preview", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-4-0125-preview", + "gpt-4-1106-preview", + "gpt-4-vision-preview", + "gpt-3.5-turbo-0125", + "gpt-3.5-turbo-1106" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "0.2", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 + }, + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" + }, + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "str", + "object", + "Text" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-9RykF" + }, + "selected": false, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 2561.5850334731617, + "y": 553.2745131130916 + }, + "dragging": false + }, + { + "id": "TextOutput-vrs6T", + "type": "genericNode", + "position": { + "x": 1911.4785906252087, + "y": 247.39079954376987 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": [ + "Record", + "Text" + ], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Interaction Panel.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" + }, + "description": "Display a text output in the Interaction Panel.", + "icon": "type", + "base_classes": [ + "str", + "object", + "Text" + ], + "display_name": "Inspect Memory", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null + }, + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-vrs6T" + }, + "selected": false, + "width": 384, + "height": 289, + "positionAbsolute": { + "x": 1911.4785906252087, + "y": 247.39079954376987 + }, + "dragging": false + } ], "edges": [ - { - "source": "MemoryComponent-cdA1J", - "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}", - "target": "Prompt-ODkUx", - "targetHandle": "{œfieldNameœ:œcontextœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - 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{ - "source": "ChatInput-t7F8v", - "sourceHandle": "{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}", - "target": "Prompt-ODkUx", - "targetHandle": "{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "user_message", - "type": "str", - "id": "Prompt-ODkUx", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ] - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "object", - "Record", - "str" - ], - "dataType": "ChatInput", - "id": "ChatInput-t7F8v" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-t7F8v{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}-Prompt-ODkUx{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - "selected": false + "style": { + "stroke": "#555" }, - { - "source": "Prompt-ODkUx", - "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}", - "target": "OpenAIModel-9RykF", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-9RykF", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object" - ], - "dataType": "Prompt", - "id": "Prompt-ODkUx" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-Prompt-ODkUx{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}-OpenAIModel-9RykF{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-MemoryComponent-cdA1J{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}-Prompt-ODkUx{œfieldNameœ:œcontextœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "selected": false + }, + { + "source": "ChatInput-t7F8v", + "sourceHandle": "{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}", + "target": "Prompt-ODkUx", + "targetHandle": "{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "user_message", + "type": "str", + "id": "Prompt-ODkUx", + "inputTypes": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ] + }, + "sourceHandle": { + "baseClasses": [ + "Text", + "object", + "Record", + "str" + ], + "dataType": "ChatInput", + "id": "ChatInput-t7F8v" + } }, - 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"target": "TextOutput-vrs6T", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-vrs6T", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "str", - "Text", - "object" - ], - "dataType": "MemoryComponent", - "id": "MemoryComponent-cdA1J" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-foreground stroke-connection", - "id": "reactflow__edge-MemoryComponent-cdA1J{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}-TextOutput-vrs6T{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" - } + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-t7F8v{œbaseClassesœ:[œTextœ,œobjectœ,œRecordœ,œstrœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-t7F8vœ}-Prompt-ODkUx{œfieldNameœ:œuser_messageœ,œidœ:œPrompt-ODkUxœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", + "selected": false + }, + { + "source": "Prompt-ODkUx", + "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}", + "target": "OpenAIModel-9RykF", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "OpenAIModel-9RykF", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Text", + "str", + "object" + ], + "dataType": "Prompt", + "id": "Prompt-ODkUx" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-Prompt-ODkUx{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-ODkUxœ}-OpenAIModel-9RykF{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-9RykFœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "OpenAIModel-9RykF", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}", + "target": "ChatOutput-P1jEe", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-P1jEe", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "object", + "Text" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-9RykF" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIModel-9RykF{œbaseClassesœ:[œstrœ,œobjectœ,œTextœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-9RykFœ}-ChatOutput-P1jEe{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-P1jEeœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "MemoryComponent-cdA1J", + "sourceHandle": "{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}", + "target": "TextOutput-vrs6T", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "TextOutput-vrs6T", + "inputTypes": [ + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "str", + "Text", + "object" + ], + "dataType": "MemoryComponent", + "id": "MemoryComponent-cdA1J" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-foreground stroke-connection", + "id": "reactflow__edge-MemoryComponent-cdA1J{œbaseClassesœ:[œstrœ,œTextœ,œobjectœ],œdataTypeœ:œMemoryComponentœ,œidœ:œMemoryComponent-cdA1Jœ}-TextOutput-vrs6T{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-vrs6Tœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" + } ], "viewport": { - "x": -569.862554459756, - "y": -42.08339711050985, - "zoom": 0.4868590524514978 + "x": -569.862554459756, + "y": -42.08339711050985, + "zoom": 0.4868590524514978 } -}, + }, "description": "This project can be used as a starting point for building a Chat experience with user specific memory. You can set a different Session ID to start a new message history.", "name": "Memory Chatbot", "last_tested_version": "1.0.0a0", diff --git a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json index 43e8d729c..3b2411fec 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/Langflow Prompt Chaining.json @@ -256,7 +256,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n", + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", "fileTypes": [], "file_path": "", "password": false, @@ -452,7 +452,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n", + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", "fileTypes": [], "file_path": "", "password": false, @@ -647,7 +647,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Interaction Panel.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[str] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextInput(TextComponent):\n display_name = \"Text Input\"\n description = \"Get text inputs from the Interaction Panel.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as input.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Optional[Text] = \"\",\n record_template: Optional[str] = \"\",\n ) -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", "fileTypes": [], "file_path": "", "password": false, @@ -884,7 +884,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", "fileTypes": [], "file_path": "", "password": false, @@ -1270,7 +1270,7 @@ "list": false, "show": true, "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", "fileTypes": [], "file_path": "", "password": false, diff --git a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json index 5706a0fbf..4aabe3717 100644 --- a/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json +++ b/src/backend/base/langflow/initial_setup/starter_projects/VectorStore-RAG-Flows.json @@ -1,3403 +1,3409 @@ { - "id": "51e2b78a-199b-4054-9f32-e288eef6924c", - "data": { - "nodes": [ - { - "id": "ChatInput-yxMKE", - "type": "genericNode", - "position": { - "x": 1195.5276981160775, - "y": 209.421875 - }, - "data": { - "type": "ChatInput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Interaction Panel.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "value": "what is a line" - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "User", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Get chat inputs from the Interaction Panel.", - "icon": "ChatInput", - "base_classes": [ - "Text", - "str", - "object", - "Record" - ], - "display_name": "Chat Input", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatInput-yxMKE" - }, - "selected": false, - "width": 384, - "height": 383 + "id": "51e2b78a-199b-4054-9f32-e288eef6924c", + "data": { + "nodes": [ + { + "id": "ChatInput-yxMKE", + "type": "genericNode", + "position": { + "x": 1195.5276981160775, + "y": 209.421875 + }, + "data": { + "type": "ChatInput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatInput(ChatComponent):\n display_name = \"Chat Input\"\n description = \"Get chat inputs from the Interaction Panel.\"\n icon = \"ChatInput\"\n\n def build_config(self):\n build_config = super().build_config()\n build_config[\"input_value\"] = {\n \"input_types\": [],\n \"display_name\": \"Message\",\n \"multiline\": True,\n }\n\n return build_config\n\n def build(\n self,\n sender: Optional[str] = \"User\",\n sender_name: Optional[str] = \"User\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n ) -> Union[Text, Record]:\n return super().build_no_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "value": "what is a line" + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "User", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "id": "TextOutput-BDknO", - "type": "genericNode", - "position": { - "x": 2322.600672827879, - "y": 604.9467307442569 - }, - "data": { - "type": "TextOutput", - "node": { - "template": { - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Value", - "advanced": false, - "input_types": [ - "Record", - "Text" - ], - "dynamic": false, - "info": "Text or Record to be passed as output.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Interaction Panel.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a text output in the Interaction Panel.", - "icon": "type", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "Extracted Chunks", - "documentation": "", - "custom_fields": { - "input_value": null, - "record_template": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "TextOutput-BDknO" - }, - "selected": false, - "width": 384, - "height": 289, - "positionAbsolute": { - "x": 2322.600672827879, - "y": 604.9467307442569 - }, - "dragging": false + "description": "Get chat inputs from the Interaction Panel.", + "icon": "ChatInput", + "base_classes": [ + "Text", + "str", + "object", + "Record" + ], + "display_name": "Chat Input", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null }, - { - "id": "OpenAIEmbeddings-ZlOk1", - "type": "genericNode", - "position": { - "x": 1183.667250865064, - "y": 687.3171828430261 - }, - "data": { - "type": "OpenAIEmbeddings", - "node": { - "template": { - "allowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "allowed_special", - "display_name": "Allowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "client", - "display_name": "Client", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=openai_api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_headers": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_headers", - "display_name": "Default Headers", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_query": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_query", - "display_name": "Default Query", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "deployment": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "deployment", - "display_name": "Deployment", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "disallowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [ - "all" - ], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "disallowed_special", - "display_name": "Disallowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "embedding_ctx_length": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 8191, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding_ctx_length", - "display_name": "Embedding Context Length", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_retries": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 6, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_retries", - "display_name": "Max Retries", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "name": "model", - "display_name": "Model", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "openai_api_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_type", - "display_name": "OpenAI API Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_version": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_version", - "display_name": "OpenAI API Version", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_organization": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_organization", - "display_name": "OpenAI Organization", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_proxy": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_proxy", - "display_name": "OpenAI Proxy", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "request_timeout": { - "type": "float", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "request_timeout", - "display_name": "Request Timeout", - "advanced": true, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "show_progress_bar": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "show_progress_bar", - "display_name": "Show Progress Bar", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "skip_empty": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "skip_empty", - "display_name": "Skip Empty", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_enable": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_enable", - "display_name": "TikToken Enable", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_model_name", - "display_name": "TikToken Model Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Generate embeddings using OpenAI models.", - "base_classes": [ - "Embeddings" - ], - "display_name": "OpenAI Embeddings", - "documentation": "", - "custom_fields": { - "openai_api_key": null, - "default_headers": null, - "default_query": null, - "allowed_special": null, - "disallowed_special": null, - "chunk_size": null, - "client": null, - "deployment": null, - "embedding_ctx_length": null, - "max_retries": null, - "model": null, - "model_kwargs": null, - "openai_api_base": null, - "openai_api_type": null, - "openai_api_version": null, - "openai_organization": null, - "openai_proxy": null, - "request_timeout": null, - "show_progress_bar": null, - "skip_empty": null, - "tiktoken_enable": null, - "tiktoken_model_name": null - }, - "output_types": [ - "Embeddings" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "OpenAIEmbeddings-ZlOk1" - }, - "selected": false, - "width": 384, - "height": 383, - "dragging": false + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatInput-yxMKE" + }, + "selected": false, + "width": 384, + "height": 383 + }, + { + "id": "TextOutput-BDknO", + "type": "genericNode", + "position": { + "x": 2322.600672827879, + "y": 604.9467307442569 + }, + "data": { + "type": "TextOutput", + "node": { + "template": { + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Value", + "advanced": false, + "input_types": [ + "Record", + "Text" + ], + "dynamic": false, + "info": "Text or Record to be passed as output.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langflow.base.io.text import TextComponent\nfrom langflow.field_typing import Text\n\n\nclass TextOutput(TextComponent):\n display_name = \"Text Output\"\n description = \"Display a text output in the Interaction Panel.\"\n icon = \"type\"\n\n def build_config(self):\n return {\n \"input_value\": {\n \"display_name\": \"Value\",\n \"input_types\": [\"Record\", \"Text\"],\n \"info\": \"Text or Record to be passed as output.\",\n },\n \"record_template\": {\n \"display_name\": \"Record Template\",\n \"multiline\": True,\n \"info\": \"Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.\",\n \"advanced\": True,\n },\n }\n\n def build(self, input_value: Optional[Text] = \"\", record_template: str = \"\") -> Text:\n return super().build(input_value=input_value, record_template=record_template)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "id": "OpenAIModel-EjXlN", - "type": "genericNode", - "position": { - "x": 3410.117202077183, - "y": 431.2038048137648 - }, - "data": { - "type": "OpenAIModel", - "node": { - "template": { - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=openai_api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_tokens": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 256, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_tokens", - "display_name": "Max Tokens", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "gpt-3.5-turbo", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "gpt-4-turbo-preview", - "gpt-3.5-turbo", - "gpt-4-0125-preview", - "gpt-4-1106-preview", - "gpt-4-vision-preview", - "gpt-3.5-turbo-0125", - "gpt-3.5-turbo-1106" - ], - "name": "model_name", - "display_name": "Model Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "The OpenAI API Key to use for the OpenAI model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "stream": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "stream", - "display_name": "Stream", - "advanced": true, - "dynamic": false, - "info": "Stream the response from the model. Streaming works only in Chat.", - "load_from_db": false, - "title_case": false - }, - "system_message": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "system_message", - "display_name": "System Message", - "advanced": true, - "dynamic": false, - "info": "System message to pass to the model.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "temperature": { - "type": "float", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 0.1, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "temperature", - "display_name": "Temperature", - "advanced": false, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "Generates text using OpenAI LLMs.", - "icon": "OpenAI", - "base_classes": [ - "object", - "Text", - "str" - ], - "display_name": "OpenAI", - "documentation": "", - "custom_fields": { - "input_value": null, - "openai_api_key": null, - "temperature": null, - "model_name": null, - "max_tokens": null, - "model_kwargs": null, - "openai_api_base": null, - "stream": null, - "system_message": null - }, - "output_types": [ - "Text" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "max_tokens", - "model_kwargs", - "model_name", - "openai_api_base", - "openai_api_key", - "temperature", - "input_value", - "system_message", - "stream" - ], - "beta": false - }, - "id": "OpenAIModel-EjXlN" - }, - "selected": true, - "width": 384, - "height": 563, - "positionAbsolute": { - "x": 3410.117202077183, - "y": 431.2038048137648 - }, - "dragging": false + "description": "Display a text output in the Interaction Panel.", + "icon": "type", + "base_classes": [ + "object", + "Text", + "str" + ], + "display_name": "Extracted Chunks", + "documentation": "", + "custom_fields": { + "input_value": null, + "record_template": null }, - { - "id": "Prompt-xeI6K", - "type": "genericNode", - "position": { - "x": 2969.0261961391298, - "y": 442.1613649809069 + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "TextOutput-BDknO" + }, + "selected": false, + "width": 384, + "height": 289, + "positionAbsolute": { + "x": 2322.600672827879, + "y": 604.9467307442569 + }, + "dragging": false + }, + { + "id": "OpenAIEmbeddings-ZlOk1", + "type": "genericNode", + "position": { + "x": 1183.667250865064, + "y": 687.3171828430261 + }, + "data": { + "type": "OpenAIEmbeddings", + "node": { + "template": { + "allowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "allowed_special", + "display_name": "Allowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "client", + "display_name": "Client", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_headers": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_headers", + "display_name": "Default Headers", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_query": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_query", + "display_name": "Default Query", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "deployment": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "deployment", + "display_name": "Deployment", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "disallowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [ + "all" + ], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "disallowed_special", + "display_name": "Disallowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "embedding_ctx_length": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 8191, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding_ctx_length", + "display_name": "Embedding Context Length", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_retries": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 6, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_retries", + "display_name": "Max Retries", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "text-embedding-3-small", + "text-embedding-3-large", + "text-embedding-ada-002" + ], + "name": "model", + "display_name": "Model", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "openai_api_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_type", + "display_name": "OpenAI API Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_version": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_version", + "display_name": "OpenAI API Version", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_organization": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_organization", + "display_name": "OpenAI Organization", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_proxy": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_proxy", + "display_name": "OpenAI Proxy", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "request_timeout": { + "type": "float", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "request_timeout", + "display_name": "Request Timeout", + "advanced": true, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "data": { - "type": "Prompt", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "template": { - "type": "prompt", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "template", - "display_name": "Template", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent", - "context": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "context", - "display_name": "context", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - }, - "question": { - "field_type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "question", - "display_name": "question", - "advanced": false, - "input_types": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "type": "str" - } - }, - "description": "Create a prompt template with dynamic variables.", - "icon": "prompts", - "is_input": null, - "is_output": null, - "is_composition": null, - "base_classes": [ - "object", - "Text", - "str" - ], - "name": "", - "display_name": "Prompt", - "documentation": "", - "custom_fields": { - "template": [ - "context", - "question" - ] - }, - "output_types": [ - "Text" - ], - "full_path": null, - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false, - "error": null - }, - "id": "Prompt-xeI6K", - "description": "Create a prompt template with dynamic variables.", - "display_name": "Prompt" - }, - "selected": false, - "width": 384, - "height": 477, - "positionAbsolute": { - "x": 2969.0261961391298, - "y": 442.1613649809069 - }, - "dragging": false + "load_from_db": false, + "title_case": false + }, + "show_progress_bar": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "show_progress_bar", + "display_name": "Show Progress Bar", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "skip_empty": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "skip_empty", + "display_name": "Skip Empty", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_enable": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_enable", + "display_name": "TikToken Enable", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "tiktoken_model_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "tiktoken_model_name", + "display_name": "TikToken Model Name", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "id": "ChatOutput-Q39I8", - "type": "genericNode", - "position": { - "x": 3887.2073667611485, - "y": 588.4801225794856 - }, - "data": { - "type": "ChatOutput", - "node": { - "template": { - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Message", - "advanced": false, - "input_types": [ - "Text" - ], - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "record_template": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "{text}", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "record_template", - "display_name": "Record Template", - "advanced": true, - "dynamic": false, - "info": "In case of Message being a Record, this template will be used to convert it to text.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "return_record": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "return_record", - "display_name": "Return Record", - "advanced": true, - "dynamic": false, - "info": "Return the message as a record containing the sender, sender_name, and session_id.", - "load_from_db": false, - "title_case": false - }, - "sender": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Machine", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Machine", - "User" - ], - "name": "sender", - "display_name": "Sender Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "sender_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "AI", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "sender_name", - "display_name": "Sender Name", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "session_id": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "session_id", - "display_name": "Session ID", - "advanced": true, - "dynamic": false, - "info": "If provided, the message will be stored in the memory.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Display a chat message in the Interaction Panel.", - "icon": "ChatOutput", - "base_classes": [ - "object", - "Text", - "Record", - "str" - ], - "display_name": "Chat Output", - "documentation": "", - "custom_fields": { - "sender": null, - "sender_name": null, - "input_value": null, - "session_id": null, - "return_record": null, - "record_template": null - }, - "output_types": [ - "Text", - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "ChatOutput-Q39I8" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 3887.2073667611485, - "y": 588.4801225794856 - }, - "dragging": false + "description": "Generate embeddings using OpenAI models.", + "base_classes": [ + "Embeddings" + ], + "display_name": "OpenAI Embeddings", + "documentation": "", + "custom_fields": { + "openai_api_key": null, + "default_headers": null, + "default_query": null, + "allowed_special": null, + "disallowed_special": null, + "chunk_size": null, + "client": null, + "deployment": null, + "embedding_ctx_length": null, + "max_retries": null, + "model": null, + "model_kwargs": null, + "openai_api_base": null, + "openai_api_type": null, + "openai_api_version": null, + "openai_organization": null, + "openai_proxy": null, + "request_timeout": null, + "show_progress_bar": null, + "skip_empty": null, + "tiktoken_enable": null, + "tiktoken_model_name": null }, - { - "id": "File-t0a6a", - "type": "genericNode", - "position": { - "x": 2257.233450682836, - "y": 1747.5389618367233 + "output_types": [ + "Embeddings" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "OpenAIEmbeddings-ZlOk1" + }, + "selected": false, + "width": 384, + "height": 383, + "dragging": false + }, + { + "id": "OpenAIModel-EjXlN", + "type": "genericNode", + "position": { + "x": 3410.117202077183, + "y": 431.2038048137648 + }, + "data": { + "type": "OpenAIModel", + "node": { + "template": { + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain_openai import ChatOpenAI\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.base.constants import STREAM_INFO_TEXT\nfrom langflow.base.models.model import LCModelComponent\nfrom langflow.field_typing import NestedDict, Text\n\n\nclass OpenAIModelComponent(LCModelComponent):\n display_name = \"OpenAI\"\n description = \"Generates text using OpenAI LLMs.\"\n icon = \"OpenAI\"\n\n field_order = [\n \"max_tokens\",\n \"model_kwargs\",\n \"model_name\",\n \"openai_api_base\",\n \"openai_api_key\",\n \"temperature\",\n \"input_value\",\n \"system_message\",\n \"stream\",\n ]\n\n def build_config(self):\n return {\n \"input_value\": {\"display_name\": \"Input\"},\n \"max_tokens\": {\n \"display_name\": \"Max Tokens\",\n \"advanced\": True,\n },\n \"model_kwargs\": {\n \"display_name\": \"Model Kwargs\",\n \"advanced\": True,\n },\n \"model_name\": {\n \"display_name\": \"Model Name\",\n \"advanced\": False,\n \"options\": [\n \"gpt-4-turbo-preview\",\n \"gpt-3.5-turbo\",\n \"gpt-4-0125-preview\",\n \"gpt-4-1106-preview\",\n \"gpt-4-vision-preview\",\n \"gpt-3.5-turbo-0125\",\n \"gpt-3.5-turbo-1106\",\n ],\n \"value\": \"gpt-4-turbo-preview\",\n },\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"advanced\": True,\n \"info\": (\n \"The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\\n\\n\"\n \"You can change this to use other APIs like JinaChat, LocalAI and Prem.\"\n ),\n },\n \"openai_api_key\": {\n \"display_name\": \"OpenAI API Key\",\n \"info\": \"The OpenAI API Key to use for the OpenAI model.\",\n \"advanced\": False,\n \"password\": True,\n },\n \"temperature\": {\n \"display_name\": \"Temperature\",\n \"advanced\": False,\n \"value\": 0.1,\n },\n \"stream\": {\n \"display_name\": \"Stream\",\n \"info\": STREAM_INFO_TEXT,\n \"advanced\": True,\n },\n \"system_message\": {\n \"display_name\": \"System Message\",\n \"info\": \"System message to pass to the model.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n input_value: Text,\n openai_api_key: str,\n temperature: float,\n model_name: str,\n max_tokens: Optional[int] = 256,\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n stream: bool = False,\n system_message: Optional[str] = None,\n ) -> Text:\n if not openai_api_base:\n openai_api_base = \"https://api.openai.com/v1\"\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n output = ChatOpenAI(\n max_tokens=max_tokens,\n model_kwargs=model_kwargs,\n model=model_name,\n base_url=openai_api_base,\n api_key=api_key,\n temperature=temperature,\n )\n\n return self.get_chat_result(output, stream, input_value, system_message)\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "max_tokens": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 256, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "max_tokens", + "display_name": "Max Tokens", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_kwargs": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "model_kwargs", + "display_name": "Model Kwargs", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "model_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "gpt-3.5-turbo", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "gpt-4-turbo-preview", + "gpt-3.5-turbo", + "gpt-4-0125-preview", + "gpt-4-1106-preview", + "gpt-4-vision-preview", + "gpt-3.5-turbo-0125", + "gpt-3.5-turbo-1106" + ], + "name": "model_name", + "display_name": "Model Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_base": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "openai_api_base", + "display_name": "OpenAI API Base", + "advanced": true, + "dynamic": false, + "info": "The base URL of the OpenAI API. Defaults to https://api.openai.com/v1.\n\nYou can change this to use other APIs like JinaChat, LocalAI and Prem.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "openai_api_key": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "openai_api_key", + "display_name": "OpenAI API Key", + "advanced": false, + "dynamic": false, + "info": "The OpenAI API Key to use for the OpenAI model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "stream": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "stream", + "display_name": "Stream", + "advanced": true, + "dynamic": false, + "info": "Stream the response from the model. Streaming works only in Chat.", + "load_from_db": false, + "title_case": false + }, + "system_message": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "system_message", + "display_name": "System Message", + "advanced": true, + "dynamic": false, + "info": "System message to pass to the model.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "temperature": { + "type": "float", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 0.1, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "temperature", + "display_name": "Temperature", + "advanced": false, + "dynamic": false, + "info": "", + "rangeSpec": { + "step_type": "float", + "min": -1, + "max": 1, + "step": 0.1 }, - "data": { - "type": "File", - "node": { - "template": { - "path": { - "type": "file", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [ - ".txt", - ".md", - ".mdx", - ".csv", - ".json", - ".yaml", - ".yml", - ".xml", - ".html", - ".htm", - ".pdf", - ".docx" - ], - "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf", - "password": false, - "name": "path", - "display_name": "Path", - "advanced": false, - "dynamic": false, - "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx", - "load_from_db": false, - "title_case": false, - "value": "" - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "silent_errors": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "silent_errors", - "display_name": "Silent Errors", - "advanced": true, - "dynamic": false, - "info": "If true, errors will not raise an exception.", - "load_from_db": false, - "title_case": false - }, - "_type": "CustomComponent" - }, - "description": "A generic file loader.", - "icon": "file-text", - "base_classes": [ - "Record" - ], - "display_name": "File", - "documentation": "", - "custom_fields": { - "path": null, - "silent_errors": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "File-t0a6a" - }, - "selected": false, - "width": 384, - "height": 281, - "positionAbsolute": { - "x": 2257.233450682836, - "y": 1747.5389618367233 - }, - "dragging": false + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "id": "RecursiveCharacterTextSplitter-tR9QM", - "type": "genericNode", - "position": { - "x": 2791.013514133929, - "y": 1462.9588953494142 - }, - "data": { - "type": "RecursiveCharacterTextSplitter", - "node": { - "template": { - "inputs": { - "type": "Document", - "required": true, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "inputs", - "display_name": "Input", - "advanced": false, - "input_types": [ - "Document", - "Record" - ], - "dynamic": false, - "info": "The texts to split.", - "load_from_db": false, - "title_case": false - }, - "chunk_overlap": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 200, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_overlap", - "display_name": "Chunk Overlap", - "advanced": false, - "dynamic": false, - "info": "The amount of overlap between chunks.", - "load_from_db": false, - "title_case": false - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": false, - "dynamic": false, - "info": "The maximum length of each chunk.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Optional\n\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "separators": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "separators", - "display_name": "Separators", - "advanced": false, - "dynamic": false, - "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": [ - "" - ] - }, - "_type": "CustomComponent" - }, - "description": "Split text into chunks of a specified length.", - "base_classes": [ - "Record" - ], - "display_name": "Recursive Character Text Splitter", - "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter", - "custom_fields": { - "inputs": null, - "separators": null, - "chunk_size": null, - "chunk_overlap": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "RecursiveCharacterTextSplitter-tR9QM" - }, - "selected": false, - "width": 384, - "height": 501, - "positionAbsolute": { - "x": 2791.013514133929, - "y": 1462.9588953494142 - }, - "dragging": false + "description": "Generates text using OpenAI LLMs.", + "icon": "OpenAI", + "base_classes": [ + "object", + "Text", + "str" + ], + "display_name": "OpenAI", + "documentation": "", + "custom_fields": { + "input_value": null, + "openai_api_key": null, + "temperature": null, + "model_name": null, + "max_tokens": null, + "model_kwargs": null, + "openai_api_base": null, + "stream": null, + "system_message": null }, - { - "id": "AstraDBSearch-41nRz", - "type": "genericNode", - "position": { - "x": 1723.976434815103, - "y": 277.03317407245913 - }, - "data": { - "type": "AstraDBSearch", - "node": { - "template": { - "embedding": { - "type": "Embeddings", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding", - "display_name": "Embedding", - "advanced": false, - "dynamic": false, - "info": "Embedding to use", - "load_from_db": false, - "title_case": false - }, - "input_value": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "input_value", - "display_name": "Input Value", - "advanced": false, - "dynamic": false, - "info": "Input value to search", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "api_endpoint": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "api_endpoint", - "display_name": "API Endpoint", - "advanced": false, - "dynamic": false, - "info": "API endpoint URL for the Astra DB service.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "batch_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "batch_size", - "display_name": "Batch Size", - "advanced": true, - "dynamic": false, - "info": "Optional number of records to process in a single batch.", - "load_from_db": false, - "title_case": false - }, - "bulk_delete_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_delete_concurrency", - "display_name": "Bulk Delete Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk delete operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_batch_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_batch_concurrency", - "display_name": "Bulk Insert Batch Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_overwrite_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_overwrite_concurrency", - "display_name": "Bulk Insert Overwrite Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "collection_indexing_policy": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_indexing_policy", - "display_name": "Collection Indexing Policy", - "advanced": true, - "dynamic": false, - "info": "Optional dictionary defining the indexing policy for the collection.", - "load_from_db": false, - "title_case": false - }, - "collection_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_name", - "display_name": "Collection Name", - "advanced": false, - "dynamic": false, - "info": "The name of the collection within Astra DB where the vectors will be stored.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "langflow" - }, - "metadata_indexing_exclude": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_exclude", - "display_name": "Metadata Indexing Exclude", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to exclude from the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metadata_indexing_include": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_include", - "display_name": "Metadata Indexing Include", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to include in the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metric": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metric", - "display_name": "Metric", - "advanced": true, - "dynamic": false, - "info": "Optional distance metric for vector comparisons in the vector store.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "namespace": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "namespace", - "display_name": "Namespace", - "advanced": true, - "dynamic": false, - "info": "Optional namespace within Astra DB to use for the collection.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "number_of_results": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 4, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "number_of_results", - "display_name": "Number of Results", - "advanced": true, - "dynamic": false, - "info": "Number of results to return.", - "load_from_db": false, - "title_case": false - }, - "pre_delete_collection": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "pre_delete_collection", - "display_name": "Pre Delete Collection", - "advanced": true, - "dynamic": false, - "info": "Boolean flag to determine whether to delete the collection before creating a new one.", - "load_from_db": false, - "title_case": false - }, - "search_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Similarity", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Similarity", - "MMR" - ], - "name": "search_type", - "display_name": "Search Type", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "setup_mode": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Sync", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Sync", - "Async", - "Off" - ], - "name": "setup_mode", - "display_name": "Setup Mode", - "advanced": true, - "dynamic": false, - "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "token": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "token", - "display_name": "Token", - "advanced": false, - "dynamic": false, - "info": "Authentication token for accessing Astra DB.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "_type": "CustomComponent" - }, - "description": "Searches an existing Astra DB Vector Store.", - "icon": "AstraDB", - "base_classes": [ - "Record" - ], - "display_name": "Astra DB Search", - "documentation": "", - "custom_fields": { - "embedding": null, - "collection_name": null, - "input_value": null, - "token": null, - "api_endpoint": null, - "search_type": null, - "number_of_results": null, - "namespace": null, - "metric": null, - "batch_size": null, - "bulk_insert_batch_concurrency": null, - "bulk_insert_overwrite_concurrency": null, - "bulk_delete_concurrency": null, - "setup_mode": null, - "pre_delete_collection": null, - "metadata_indexing_include": null, - "metadata_indexing_exclude": null, - "collection_indexing_policy": null - }, - "output_types": [ - "Record" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "token", - "api_endpoint", - "collection_name", - "input_value", - "embedding" - ], - "beta": false - }, - "id": "AstraDBSearch-41nRz" - }, - "selected": false, - "width": 384, - "height": 713, - "dragging": false, - "positionAbsolute": { - "x": 1723.976434815103, - "y": 277.03317407245913 - } + "output_types": [ + "Text" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "max_tokens", + "model_kwargs", + "model_name", + "openai_api_base", + "openai_api_key", + "temperature", + "input_value", + "system_message", + "stream" + ], + "beta": false + }, + "id": "OpenAIModel-EjXlN" + }, + "selected": true, + "width": 384, + "height": 563, + "positionAbsolute": { + "x": 3410.117202077183, + "y": 431.2038048137648 + }, + "dragging": false + }, + { + "id": "Prompt-xeI6K", + "type": "genericNode", + "position": { + "x": 2969.0261961391298, + "y": 442.1613649809069 + }, + "data": { + "type": "Prompt", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from langchain_core.prompts import PromptTemplate\n\nfrom langflow.field_typing import Prompt, TemplateField, Text\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass PromptComponent(CustomComponent):\n display_name: str = \"Prompt\"\n description: str = \"Create a prompt template with dynamic variables.\"\n icon = \"prompts\"\n\n def build_config(self):\n return {\n \"template\": TemplateField(display_name=\"Template\"),\n \"code\": TemplateField(advanced=True),\n }\n\n def build(\n self,\n template: Prompt,\n **kwargs,\n ) -> Text:\n from langflow.base.prompts.utils import dict_values_to_string\n\n prompt_template = PromptTemplate.from_template(Text(template))\n kwargs = dict_values_to_string(kwargs)\n kwargs = {k: \"\\n\".join(v) if isinstance(v, list) else v for k, v in kwargs.items()}\n try:\n formated_prompt = prompt_template.format(**kwargs)\n except Exception as exc:\n raise ValueError(f\"Error formatting prompt: {exc}\") from exc\n self.status = f'Prompt:\\n\"{formated_prompt}\"'\n return formated_prompt\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "template": { + "type": "prompt", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "{context}\n\n---\n\nGiven the context above, answer the question as best as possible.\n\nQuestion: {question}\n\nAnswer: ", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "template", + "display_name": "Template", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent", + "context": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "context", + "display_name": "context", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + }, + "question": { + "field_type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "question", + "display_name": "question", + "advanced": false, + "input_types": [ + "Document", + "BaseOutputParser", + "Record", + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "type": "str" + } }, - { - "id": "AstraDB-eUCSS", - "type": "genericNode", - "position": { - "x": 3372.04958055989, - "y": 1611.0742035495277 - }, - "data": { - "type": "AstraDB", - "node": { - "template": { - "embedding": { - "type": "Embeddings", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding", - "display_name": "Embedding", - "advanced": false, - "dynamic": false, - "info": "Embedding to use", - "load_from_db": false, - "title_case": false - }, - "inputs": { - "type": "Record", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "inputs", - "display_name": "Inputs", - "advanced": false, - "dynamic": false, - "info": "Optional list of records to be processed and stored in the vector store.", - "load_from_db": false, - "title_case": false - }, - "api_endpoint": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "api_endpoint", - "display_name": "API Endpoint", - "advanced": false, - "dynamic": false, - "info": "API endpoint URL for the Astra DB service.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "batch_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "batch_size", - "display_name": "Batch Size", - "advanced": true, - "dynamic": false, - "info": "Optional number of records to process in a single batch.", - "load_from_db": false, - "title_case": false - }, - "bulk_delete_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_delete_concurrency", - "display_name": "Bulk Delete Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk delete operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_batch_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_batch_concurrency", - "display_name": "Bulk Insert Batch Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations.", - "load_from_db": false, - "title_case": false - }, - "bulk_insert_overwrite_concurrency": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "bulk_insert_overwrite_concurrency", - "display_name": "Bulk Insert Overwrite Concurrency", - "advanced": true, - "dynamic": false, - "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import List, Optional\n\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Async\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> VectorStore:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "collection_indexing_policy": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_indexing_policy", - "display_name": "Collection Indexing Policy", - "advanced": true, - "dynamic": false, - "info": "Optional dictionary defining the indexing policy for the collection.", - "load_from_db": false, - "title_case": false - }, - "collection_name": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "collection_name", - "display_name": "Collection Name", - "advanced": false, - "dynamic": false, - "info": "The name of the collection within Astra DB where the vectors will be stored.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "langflow" - }, - "metadata_indexing_exclude": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_exclude", - "display_name": "Metadata Indexing Exclude", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to exclude from the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metadata_indexing_include": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metadata_indexing_include", - "display_name": "Metadata Indexing Include", - "advanced": true, - "dynamic": false, - "info": "Optional list of metadata fields to include in the indexing.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "metric": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "metric", - "display_name": "Metric", - "advanced": true, - "dynamic": false, - "info": "Optional distance metric for vector comparisons in the vector store.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "namespace": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "namespace", - "display_name": "Namespace", - "advanced": true, - "dynamic": false, - "info": "Optional namespace within Astra DB to use for the collection.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "pre_delete_collection": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "pre_delete_collection", - "display_name": "Pre Delete Collection", - "advanced": true, - "dynamic": false, - "info": "Boolean flag to determine whether to delete the collection before creating a new one.", - "load_from_db": false, - "title_case": false - }, - "setup_mode": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "Async", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "Sync", - "Async", - "Off" - ], - "name": "setup_mode", - "display_name": "Setup Mode", - "advanced": true, - "dynamic": false, - "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "token": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "token", - "display_name": "Token", - "advanced": false, - "dynamic": false, - "info": "Authentication token for accessing Astra DB.", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "_type": "CustomComponent" - }, - "description": "Builds or loads an Astra DB Vector Store.", - "icon": "AstraDB", - "base_classes": [ - "VectorStore" - ], - "display_name": "Astra DB", - "documentation": "", - "custom_fields": { - "embedding": null, - "token": null, - "api_endpoint": null, - "collection_name": null, - "inputs": null, - "namespace": null, - "metric": null, - "batch_size": null, - "bulk_insert_batch_concurrency": null, - "bulk_insert_overwrite_concurrency": null, - "bulk_delete_concurrency": null, - "setup_mode": null, - "pre_delete_collection": null, - "metadata_indexing_include": null, - "metadata_indexing_exclude": null, - "collection_indexing_policy": null - }, - "output_types": [ - "VectorStore" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [ - "token", - "api_endpoint", - "collection_name", - "inputs", - "embedding" - ], - "beta": false - }, - "id": "AstraDB-eUCSS" - }, - "selected": false, - "width": 384, - "height": 573, - "positionAbsolute": { - "x": 3372.04958055989, - "y": 1611.0742035495277 - }, - "dragging": false + "description": "Create a prompt template with dynamic variables.", + "icon": "prompts", + "is_input": null, + "is_output": null, + "is_composition": null, + "base_classes": [ + "object", + "Text", + "str" + ], + "name": "", + "display_name": "Prompt", + "documentation": "", + "custom_fields": { + "template": [ + "context", + "question" + ] }, - { - "id": "OpenAIEmbeddings-9TPjc", - "type": "genericNode", - "position": { - "x": 2814.0402191223047, - "y": 1955.9268168273086 - }, - "data": { - "type": "OpenAIEmbeddings", - "node": { - "template": { - "allowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "allowed_special", - "display_name": "Allowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "chunk_size": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 1000, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "chunk_size", - "display_name": "Chunk Size", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "client": { - "type": "Any", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "client", - "display_name": "Client", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "code": { - "type": "code", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": true, - "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=openai_api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "code", - "advanced": true, - "dynamic": true, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_headers": { - "type": "dict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_headers", - "display_name": "Default Headers", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "default_query": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "default_query", - "display_name": "Default Query", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "deployment": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "name": "deployment", - "display_name": "Deployment", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "disallowed_special": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": [ - "all" - ], - "fileTypes": [], - "file_path": "", - "password": false, - "name": "disallowed_special", - "display_name": "Disallowed Special", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "embedding_ctx_length": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 8191, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "embedding_ctx_length", - "display_name": "Embedding Context Length", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "max_retries": { - "type": "int", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": 6, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "max_retries", - "display_name": "Max Retries", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "model": { - "type": "str", - "required": false, - "placeholder": "", - "list": true, - "show": true, - "multiline": false, - "value": "text-embedding-ada-002", - "fileTypes": [], - "file_path": "", - "password": false, - "options": [ - "text-embedding-3-small", - "text-embedding-3-large", - "text-embedding-ada-002" - ], - "name": "model", - "display_name": "Model", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "model_kwargs": { - "type": "NestedDict", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": {}, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "model_kwargs", - "display_name": "Model Kwargs", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "openai_api_base": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_base", - "display_name": "OpenAI API Base", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_key": { - "type": "str", - "required": true, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_key", - "display_name": "OpenAI API Key", - "advanced": false, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ], - "value": "" - }, - "openai_api_type": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": true, - "name": "openai_api_type", - "display_name": "OpenAI API Type", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_api_version": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_api_version", - "display_name": "OpenAI API Version", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_organization": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_organization", - "display_name": "OpenAI Organization", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "openai_proxy": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "openai_proxy", - "display_name": "OpenAI Proxy", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "request_timeout": { - "type": "float", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "request_timeout", - "display_name": "Request Timeout", - "advanced": true, - "dynamic": false, - "info": "", - "rangeSpec": { - "step_type": "float", - "min": -1, - "max": 1, - "step": 0.1 - }, - "load_from_db": false, - "title_case": false - }, - "show_progress_bar": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "show_progress_bar", - "display_name": "Show Progress Bar", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "skip_empty": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "skip_empty", - "display_name": "Skip Empty", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_enable": { - "type": "bool", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "value": true, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_enable", - "display_name": "TikToken Enable", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false - }, - "tiktoken_model_name": { - "type": "str", - "required": false, - "placeholder": "", - "list": false, - "show": true, - "multiline": false, - "fileTypes": [], - "file_path": "", - "password": false, - "name": "tiktoken_model_name", - "display_name": "TikToken Model Name", - "advanced": true, - "dynamic": false, - "info": "", - "load_from_db": false, - "title_case": false, - "input_types": [ - "Text" - ] - }, - "_type": "CustomComponent" - }, - "description": "Generate embeddings using OpenAI models.", - "base_classes": [ - "Embeddings" - ], - "display_name": "OpenAI Embeddings", - "documentation": "", - "custom_fields": { - "openai_api_key": null, - "default_headers": null, - "default_query": null, - "allowed_special": null, - "disallowed_special": null, - "chunk_size": null, - "client": null, - "deployment": null, - "embedding_ctx_length": null, - "max_retries": null, - "model": null, - "model_kwargs": null, - "openai_api_base": null, - "openai_api_type": null, - "openai_api_version": null, - "openai_organization": null, - "openai_proxy": null, - "request_timeout": null, - "show_progress_bar": null, - "skip_empty": null, - "tiktoken_enable": null, - "tiktoken_model_name": null - }, - "output_types": [ - "Embeddings" - ], - "field_formatters": {}, - "frozen": false, - "field_order": [], - "beta": false - }, - "id": "OpenAIEmbeddings-9TPjc" - }, - "selected": false, - "width": 384, - "height": 383, - "positionAbsolute": { - "x": 2814.0402191223047, - "y": 1955.9268168273086 - }, - "dragging": false - } - ], - "edges": [ - { - "source": "TextOutput-BDknO", - "target": "Prompt-xeI6K", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextOutputœ,œidœ:œTextOutput-BDknOœ}", - "targetHandle": "{œfieldNameœ:œcontextœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - "id": "reactflow__edge-TextOutput-BDknO{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œTextOutputœ,œidœ:œTextOutput-BDknOœ}-Prompt-xeI6K{œfieldNameœ:œcontextœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "context", - "id": "Prompt-xeI6K", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "TextOutput", - "id": "TextOutput-BDknO" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": [ + "Text" + ], + "full_path": null, + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false, + "error": null + }, + "id": "Prompt-xeI6K", + "description": "Create a prompt template with dynamic variables.", + "display_name": "Prompt" + }, + "selected": false, + "width": 384, + "height": 477, + "positionAbsolute": { + "x": 2969.0261961391298, + "y": 442.1613649809069 + }, + "dragging": false + }, + { + "id": "ChatOutput-Q39I8", + "type": "genericNode", + "position": { + "x": 3887.2073667611485, + "y": 588.4801225794856 + }, + "data": { + "type": "ChatOutput", + "node": { + "template": { + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional, Union\n\nfrom langflow.base.io.chat import ChatComponent\nfrom langflow.field_typing import Text\nfrom langflow.schema import Record\n\n\nclass ChatOutput(ChatComponent):\n display_name = \"Chat Output\"\n description = \"Display a chat message in the Interaction Panel.\"\n icon = \"ChatOutput\"\n\n def build(\n self,\n sender: Optional[str] = \"Machine\",\n sender_name: Optional[str] = \"AI\",\n input_value: Optional[str] = None,\n session_id: Optional[str] = None,\n return_record: Optional[bool] = False,\n record_template: Optional[str] = \"{text}\",\n ) -> Union[Text, Record]:\n return super().build_with_record(\n sender=sender,\n sender_name=sender_name,\n input_value=input_value,\n session_id=session_id,\n return_record=return_record,\n record_template=record_template or \"\",\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Message", + "advanced": false, + "input_types": [ + "Text" + ], + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "record_template": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "{text}", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "record_template", + "display_name": "Record Template", + "advanced": true, + "dynamic": false, + "info": "In case of Message being a Record, this template will be used to convert it to text.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "return_record": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "return_record", + "display_name": "Return Record", + "advanced": true, + "dynamic": false, + "info": "Return the message as a record containing the sender, sender_name, and session_id.", + "load_from_db": false, + "title_case": false + }, + "sender": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Machine", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Machine", + "User" + ], + "name": "sender", + "display_name": "Sender Type", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "sender_name": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "AI", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "sender_name", + "display_name": "Sender Name", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "session_id": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "session_id", + "display_name": "Session ID", + "advanced": true, + "dynamic": false, + "info": "If provided, the message will be stored in the memory.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "_type": "CustomComponent" }, - { - "source": "ChatInput-yxMKE", - "target": "Prompt-xeI6K", - "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}", - "targetHandle": "{œfieldNameœ:œquestionœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - "id": "reactflow__edge-ChatInput-yxMKE{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}-Prompt-xeI6K{œfieldNameœ:œquestionœ,œidœ:œPrompt-xeI6Kœ,œinputTypesœ:[œDocumentœ,œBaseOutputParserœ,œRecordœ,œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "question", - "id": "Prompt-xeI6K", - "inputTypes": [ - "Document", - "BaseOutputParser", - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object", - "Record" - ], - "dataType": "ChatInput", - "id": "ChatInput-yxMKE" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "description": "Display a chat message in the Interaction Panel.", + "icon": "ChatOutput", + "base_classes": [ + "object", + "Text", + "Record", + "str" + ], + "display_name": "Chat Output", + "documentation": "", + "custom_fields": { + "sender": null, + "sender_name": null, + "input_value": null, + "session_id": null, + "return_record": null, + "record_template": null }, - { - "source": "Prompt-xeI6K", - "target": "OpenAIModel-EjXlN", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-xeI6Kœ}", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-EjXlNœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "id": "reactflow__edge-Prompt-xeI6K{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œPromptœ,œidœ:œPrompt-xeI6Kœ}-OpenAIModel-EjXlN{œfieldNameœ:œinput_valueœ,œidœ:œOpenAIModel-EjXlNœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "OpenAIModel-EjXlN", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "Prompt", - "id": "Prompt-xeI6K" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": [ + "Text", + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "ChatOutput-Q39I8" + }, + "selected": false, + "width": 384, + "height": 383, + "positionAbsolute": { + "x": 3887.2073667611485, + "y": 588.4801225794856 + }, + "dragging": false + }, + { + "id": "File-t0a6a", + "type": "genericNode", + "position": { + "x": 2257.233450682836, + "y": 1747.5389618367233 + }, + "data": { + "type": "File", + "node": { + "template": { + "path": { + "type": "file", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [ + ".txt", + ".md", + ".mdx", + ".csv", + ".json", + ".yaml", + ".yml", + ".xml", + ".html", + ".htm", + ".pdf", + ".docx", + ".py", + ".sh", + ".sql", + ".js", + ".ts", + ".tsx" + ], + "file_path": "51e2b78a-199b-4054-9f32-e288eef6924c/Langflow conversation.pdf", + "password": false, + "name": "path", + "display_name": "Path", + "advanced": false, + "dynamic": false, + "info": "Supported file types: txt, md, mdx, csv, json, yaml, yml, xml, html, htm, pdf, docx, py, sh, sql, js, ts, tsx", + "load_from_db": false, + "title_case": false, + "value": "" + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from pathlib import Path\nfrom typing import Any, Dict\n\nfrom langflow.base.data.utils import TEXT_FILE_TYPES, parse_text_file_to_record\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\n\n\nclass FileComponent(CustomComponent):\n display_name = \"File\"\n description = \"A generic file loader.\"\n icon = \"file-text\"\n\n def build_config(self) -> Dict[str, Any]:\n return {\n \"path\": {\n \"display_name\": \"Path\",\n \"field_type\": \"file\",\n \"file_types\": TEXT_FILE_TYPES,\n \"info\": f\"Supported file types: {', '.join(TEXT_FILE_TYPES)}\",\n },\n \"silent_errors\": {\n \"display_name\": \"Silent Errors\",\n \"advanced\": True,\n \"info\": \"If true, errors will not raise an exception.\",\n },\n }\n\n def load_file(self, path: str, silent_errors: bool = False) -> Record:\n resolved_path = self.resolve_path(path)\n path_obj = Path(resolved_path)\n extension = path_obj.suffix[1:].lower()\n if extension == \"doc\":\n raise ValueError(\"doc files are not supported. Please save as .docx\")\n if extension not in TEXT_FILE_TYPES:\n raise ValueError(f\"Unsupported file type: {extension}\")\n record = parse_text_file_to_record(resolved_path, silent_errors)\n self.status = record if record else \"No data\"\n return record or Record()\n\n def build(\n self,\n path: str,\n silent_errors: bool = False,\n ) -> Record:\n record = self.load_file(path, silent_errors)\n self.status = record\n return record\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "silent_errors": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "silent_errors", + "display_name": "Silent Errors", + "advanced": true, + "dynamic": false, + "info": "If true, errors will not raise an exception.", + "load_from_db": false, + "title_case": false + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIModel-EjXlN", - "target": "ChatOutput-Q39I8", - "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-EjXlNœ}", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Q39I8œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "id": "reactflow__edge-OpenAIModel-EjXlN{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-EjXlNœ}-ChatOutput-Q39I8{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Q39I8œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "ChatOutput-Q39I8", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "object", - "Text", - "str" - ], - "dataType": "OpenAIModel", - "id": "OpenAIModel-EjXlN" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "description": "A generic file loader.", + "icon": "file-text", + "base_classes": [ + "Record" + ], + "display_name": "File", + "documentation": "", + "custom_fields": { + "path": null, + "silent_errors": null }, - { - "source": "File-t0a6a", - "target": "RecursiveCharacterTextSplitter-tR9QM", - "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-t0a6aœ}", - "targetHandle": "{œfieldNameœ:œinputsœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ,œinputTypesœ:[œDocumentœ,œRecordœ],œtypeœ:œDocumentœ}", - "id": "reactflow__edge-File-t0a6a{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-t0a6aœ}-RecursiveCharacterTextSplitter-tR9QM{œfieldNameœ:œinputsœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ,œinputTypesœ:[œDocumentœ,œRecordœ],œtypeœ:œDocumentœ}", - "data": { - "targetHandle": { - "fieldName": "inputs", - "id": "RecursiveCharacterTextSplitter-tR9QM", - "inputTypes": [ - "Document", - "Record" - ], - "type": "Document" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "File", - "id": "File-t0a6a" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "selected": false + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "File-t0a6a" + }, + "selected": false, + "width": 384, + "height": 281, + "positionAbsolute": { + "x": 2257.233450682836, + "y": 1747.5389618367233 + }, + "dragging": false + }, + { + "id": "RecursiveCharacterTextSplitter-tR9QM", + "type": "genericNode", + "position": { + "x": 2791.013514133929, + "y": 1462.9588953494142 + }, + "data": { + "type": "RecursiveCharacterTextSplitter", + "node": { + "template": { + "inputs": { + "type": "Document", + "required": true, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "inputs", + "display_name": "Input", + "advanced": false, + "input_types": [ + "Document", + "Record" + ], + "dynamic": false, + "info": "The texts to split.", + "load_from_db": false, + "title_case": false + }, + "chunk_overlap": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 200, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_overlap", + "display_name": "Chunk Overlap", + "advanced": false, + "dynamic": false, + "info": "The amount of overlap between chunks.", + "load_from_db": false, + "title_case": false + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": false, + "dynamic": false, + "info": "The maximum length of each chunk.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Optional\n\nfrom langchain.text_splitter import RecursiveCharacterTextSplitter\nfrom langchain_core.documents import Document\n\nfrom langflow.interface.custom.custom_component import CustomComponent\nfrom langflow.schema import Record\nfrom langflow.utils.util import build_loader_repr_from_records, unescape_string\n\n\nclass RecursiveCharacterTextSplitterComponent(CustomComponent):\n display_name: str = \"Recursive Character Text Splitter\"\n description: str = \"Split text into chunks of a specified length.\"\n documentation: str = \"https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter\"\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Input\",\n \"info\": \"The texts to split.\",\n \"input_types\": [\"Document\", \"Record\"],\n },\n \"separators\": {\n \"display_name\": \"Separators\",\n \"info\": 'The characters to split on.\\nIf left empty defaults to [\"\\\\n\\\\n\", \"\\\\n\", \" \", \"\"].',\n \"is_list\": True,\n },\n \"chunk_size\": {\n \"display_name\": \"Chunk Size\",\n \"info\": \"The maximum length of each chunk.\",\n \"field_type\": \"int\",\n \"value\": 1000,\n },\n \"chunk_overlap\": {\n \"display_name\": \"Chunk Overlap\",\n \"info\": \"The amount of overlap between chunks.\",\n \"field_type\": \"int\",\n \"value\": 200,\n },\n \"code\": {\"show\": False},\n }\n\n def build(\n self,\n inputs: list[Document],\n separators: Optional[list[str]] = None,\n chunk_size: Optional[int] = 1000,\n chunk_overlap: Optional[int] = 200,\n ) -> list[Record]:\n \"\"\"\n Split text into chunks of a specified length.\n\n Args:\n separators (list[str]): The characters to split on.\n chunk_size (int): The maximum length of each chunk.\n chunk_overlap (int): The amount of overlap between chunks.\n length_function (function): The function to use to calculate the length of the text.\n\n Returns:\n list[str]: The chunks of text.\n \"\"\"\n\n if separators == \"\":\n separators = None\n elif separators:\n # check if the separators list has escaped characters\n # if there are escaped characters, unescape them\n separators = [unescape_string(x) for x in separators]\n\n # Make sure chunk_size and chunk_overlap are ints\n if isinstance(chunk_size, str):\n chunk_size = int(chunk_size)\n if isinstance(chunk_overlap, str):\n chunk_overlap = int(chunk_overlap)\n splitter = RecursiveCharacterTextSplitter(\n separators=separators,\n chunk_size=chunk_size,\n chunk_overlap=chunk_overlap,\n )\n documents = []\n for _input in inputs:\n if isinstance(_input, Record):\n documents.append(_input.to_lc_document())\n else:\n documents.append(_input)\n docs = splitter.split_documents(documents)\n records = self.to_records(docs)\n self.repr_value = build_loader_repr_from_records(records)\n return records\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "separators": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "separators", + "display_name": "Separators", + "advanced": false, + "dynamic": false, + "info": "The characters to split on.\nIf left empty defaults to [\"\\n\\n\", \"\\n\", \" \", \"\"].", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": [ + "" + ] + }, + "_type": "CustomComponent" }, - { - "source": "OpenAIEmbeddings-ZlOk1", - "sourceHandle": "{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-ZlOk1œ}", - "target": "AstraDBSearch-41nRz", - "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", - "data": { - "targetHandle": { - "fieldName": "embedding", - "id": "AstraDBSearch-41nRz", - "inputTypes": null, - "type": "Embeddings" - }, - "sourceHandle": { - "baseClasses": [ - "Embeddings" - ], - "dataType": "OpenAIEmbeddings", - "id": "OpenAIEmbeddings-ZlOk1" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-ZlOk1œ}-AstraDBSearch-41nRz{œfieldNameœ:œembeddingœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}" + "description": "Split text into chunks of a specified length.", + "base_classes": [ + "Record" + ], + "display_name": "Recursive Character Text Splitter", + "documentation": "https://docs.langflow.org/components/text-splitters#recursivecharactertextsplitter", + "custom_fields": { + "inputs": null, + "separators": null, + "chunk_size": null, + "chunk_overlap": null }, - { - "source": "ChatInput-yxMKE", - "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}", - "target": "AstraDBSearch-41nRz", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "AstraDBSearch-41nRz", - "inputTypes": [ - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Text", - "str", - "object", - "Record" - ], - "dataType": "ChatInput", - "id": "ChatInput-yxMKE" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-ChatInput-yxMKE{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}-AstraDBSearch-41nRz{œfieldNameœ:œinput_valueœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [], + "beta": false + }, + "id": "RecursiveCharacterTextSplitter-tR9QM" + }, + "selected": false, + "width": 384, + "height": 501, + "positionAbsolute": { + "x": 2791.013514133929, + "y": 1462.9588953494142 + }, + "dragging": false + }, + { + "id": "AstraDBSearch-41nRz", + "type": "genericNode", + "position": { + "x": 1723.976434815103, + "y": 277.03317407245913 + }, + "data": { + "type": "AstraDBSearch", + "node": { + "template": { + "embedding": { + "type": "Embeddings", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding", + "display_name": "Embedding", + "advanced": false, + "dynamic": false, + "info": "Embedding to use", + "load_from_db": false, + "title_case": false + }, + "input_value": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "input_value", + "display_name": "Input Value", + "advanced": false, + "dynamic": false, + "info": "Input value to search", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "api_endpoint": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "api_endpoint", + "display_name": "API Endpoint", + "advanced": false, + "dynamic": false, + "info": "API endpoint URL for the Astra DB service.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "batch_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "batch_size", + "display_name": "Batch Size", + "advanced": true, + "dynamic": false, + "info": "Optional number of records to process in a single batch.", + "load_from_db": false, + "title_case": false + }, + "bulk_delete_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_delete_concurrency", + "display_name": "Bulk Delete Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk delete operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_batch_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_batch_concurrency", + "display_name": "Bulk Insert Batch Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_overwrite_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_overwrite_concurrency", + "display_name": "Bulk Insert Overwrite Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import List, Optional\n\nfrom langflow.components.vectorstores.AstraDB import AstraDBVectorStoreComponent\nfrom langflow.components.vectorstores.base.model import LCVectorStoreComponent\nfrom langflow.field_typing import Embeddings, Text\nfrom langflow.schema import Record\n\n\nclass AstraDBSearchComponent(LCVectorStoreComponent):\n display_name = \"Astra DB Search\"\n description = \"Searches an existing Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"input_value\", \"embedding\"]\n\n def build_config(self):\n return {\n \"search_type\": {\n \"display_name\": \"Search Type\",\n \"options\": [\"Similarity\", \"MMR\"],\n },\n \"input_value\": {\n \"display_name\": \"Input Value\",\n \"info\": \"Input value to search\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n \"number_of_results\": {\n \"display_name\": \"Number of Results\",\n \"info\": \"Number of results to return.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n collection_name: str,\n input_value: Text,\n token: str,\n api_endpoint: str,\n search_type: str = \"Similarity\",\n number_of_results: int = 4,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> List[Record]:\n vector_store = AstraDBVectorStoreComponent().build(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n try:\n return self.search_with_vector_store(input_value, search_type, vector_store, k=number_of_results)\n except KeyError as e:\n if \"content\" in str(e):\n raise ValueError(\n \"You should ingest data through Langflow (or LangChain) to query it in Langflow. Your collection does not contain a field name 'content'.\"\n )\n else:\n raise e\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "collection_indexing_policy": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_indexing_policy", + "display_name": "Collection Indexing Policy", + "advanced": true, + "dynamic": false, + "info": "Optional dictionary defining the indexing policy for the collection.", + "load_from_db": false, + "title_case": false + }, + "collection_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_name", + "display_name": "Collection Name", + "advanced": false, + "dynamic": false, + "info": "The name of the collection within Astra DB where the vectors will be stored.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "langflow" + }, + "metadata_indexing_exclude": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_exclude", + "display_name": "Metadata Indexing Exclude", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to exclude from the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metadata_indexing_include": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_include", + "display_name": "Metadata Indexing Include", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to include in the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metric": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metric", + "display_name": "Metric", + "advanced": true, + "dynamic": false, + "info": "Optional distance metric for vector comparisons in the vector store.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "namespace": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "namespace", + "display_name": "Namespace", + "advanced": true, + "dynamic": false, + "info": "Optional namespace within Astra DB to use for the collection.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "number_of_results": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 4, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "number_of_results", + "display_name": "Number of Results", + "advanced": true, + "dynamic": false, + "info": "Number of results to return.", + "load_from_db": false, + "title_case": false + }, + "pre_delete_collection": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "pre_delete_collection", + "display_name": "Pre Delete Collection", + "advanced": true, + "dynamic": false, + "info": "Boolean flag to determine whether to delete the collection before creating a new one.", + "load_from_db": false, + "title_case": false + }, + "search_type": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Similarity", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Similarity", + "MMR" + ], + "name": "search_type", + "display_name": "Search Type", + "advanced": false, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "setup_mode": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Sync", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Sync", + "Async", + "Off" + ], + "name": "setup_mode", + "display_name": "Setup Mode", + "advanced": true, + "dynamic": false, + "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "token": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "token", + "display_name": "Token", + "advanced": false, + "dynamic": false, + "info": "Authentication token for accessing Astra DB.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "_type": "CustomComponent" }, - { - "source": "RecursiveCharacterTextSplitter-tR9QM", - "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ}", - "target": "AstraDB-eUCSS", - "targetHandle": "{œfieldNameœ:œinputsœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œRecordœ}", - "data": { - "targetHandle": { - "fieldName": "inputs", - "id": "AstraDB-eUCSS", - "inputTypes": null, - "type": "Record" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "RecursiveCharacterTextSplitter", - "id": "RecursiveCharacterTextSplitter-tR9QM" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-RecursiveCharacterTextSplitter-tR9QM{œbaseClassesœ:[œRecordœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ}-AstraDB-eUCSS{œfieldNameœ:œinputsœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œRecordœ}", - "selected": false + "description": "Searches an existing Astra DB Vector Store.", + "icon": "AstraDB", + "base_classes": [ + "Record" + ], + "display_name": "Astra DB Search", + "documentation": "", + "custom_fields": { + "embedding": null, + "collection_name": null, + "input_value": null, + "token": null, + "api_endpoint": null, + "search_type": null, + "number_of_results": null, + "namespace": null, + "metric": null, + "batch_size": null, + "bulk_insert_batch_concurrency": null, + "bulk_insert_overwrite_concurrency": null, + "bulk_delete_concurrency": null, + "setup_mode": null, + "pre_delete_collection": null, + "metadata_indexing_include": null, + "metadata_indexing_exclude": null, + "collection_indexing_policy": null }, - { - "source": "OpenAIEmbeddings-9TPjc", - "sourceHandle": "{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-9TPjcœ}", - "target": "AstraDB-eUCSS", - "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", - "data": { - "targetHandle": { - "fieldName": "embedding", - "id": "AstraDB-eUCSS", - "inputTypes": null, - "type": "Embeddings" - }, - "sourceHandle": { - "baseClasses": [ - "Embeddings" - ], - "dataType": "OpenAIEmbeddings", - "id": "OpenAIEmbeddings-9TPjc" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-OpenAIEmbeddings-9TPjc{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-9TPjcœ}-AstraDB-eUCSS{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", - "selected": false - }, - { - "source": "AstraDBSearch-41nRz", - "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œAstraDBSearchœ,œidœ:œAstraDBSearch-41nRzœ}", - "target": "TextOutput-BDknO", - "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-BDknOœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", - "data": { - "targetHandle": { - "fieldName": "input_value", - "id": "TextOutput-BDknO", - "inputTypes": [ - "Record", - "Text" - ], - "type": "str" - }, - "sourceHandle": { - "baseClasses": [ - "Record" - ], - "dataType": "AstraDBSearch", - "id": "AstraDBSearch-41nRz" - } - }, - "style": { - "stroke": "#555" - }, - "className": "stroke-gray-900 stroke-connection", - "id": "reactflow__edge-AstraDBSearch-41nRz{œbaseClassesœ:[œRecordœ],œdataTypeœ:œAstraDBSearchœ,œidœ:œAstraDBSearch-41nRzœ}-TextOutput-BDknO{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-BDknOœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" - } - ], - "viewport": { - "x": -259.6782520315529, - "y": 90.3428735006047, - "zoom": 0.2687057134854984 + "output_types": [ + "Record" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "token", + "api_endpoint", + "collection_name", + "input_value", + "embedding" + ], + "beta": false + }, + "id": "AstraDBSearch-41nRz" + }, + "selected": false, + "width": 384, + "height": 713, + "dragging": false, + "positionAbsolute": { + "x": 1723.976434815103, + "y": 277.03317407245913 } - }, - "description": "Visit https://pre-release.langflow.org/guides/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.", - "name": "Vector Store RAG", - "last_tested_version": "1.0.0a0", - "is_component": false + }, + { + "id": "AstraDB-eUCSS", + "type": "genericNode", + "position": { + "x": 3372.04958055989, + "y": 1611.0742035495277 + }, + "data": { + "type": "AstraDB", + "node": { + "template": { + "embedding": { + "type": "Embeddings", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "embedding", + "display_name": "Embedding", + "advanced": false, + "dynamic": false, + "info": "Embedding to use", + "load_from_db": false, + "title_case": false + }, + "inputs": { + "type": "Record", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "inputs", + "display_name": "Inputs", + "advanced": false, + "dynamic": false, + "info": "Optional list of records to be processed and stored in the vector store.", + "load_from_db": false, + "title_case": false + }, + "api_endpoint": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "api_endpoint", + "display_name": "API Endpoint", + "advanced": false, + "dynamic": false, + "info": "API endpoint URL for the Astra DB service.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "batch_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "batch_size", + "display_name": "Batch Size", + "advanced": true, + "dynamic": false, + "info": "Optional number of records to process in a single batch.", + "load_from_db": false, + "title_case": false + }, + "bulk_delete_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_delete_concurrency", + "display_name": "Bulk Delete Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk delete operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_batch_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_batch_concurrency", + "display_name": "Bulk Insert Batch Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations.", + "load_from_db": false, + "title_case": false + }, + "bulk_insert_overwrite_concurrency": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "bulk_insert_overwrite_concurrency", + "display_name": "Bulk Insert Overwrite Concurrency", + "advanced": true, + "dynamic": false, + "info": "Optional concurrency level for bulk insert operations that overwrite existing records.", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import List, Optional\n\nfrom langchain_astradb import AstraDBVectorStore\nfrom langchain_astradb.utils.astradb import SetupMode\n\nfrom langflow.custom import CustomComponent\nfrom langflow.field_typing import Embeddings, VectorStore\nfrom langflow.schema import Record\n\n\nclass AstraDBVectorStoreComponent(CustomComponent):\n display_name = \"Astra DB\"\n description = \"Builds or loads an Astra DB Vector Store.\"\n icon = \"AstraDB\"\n field_order = [\"token\", \"api_endpoint\", \"collection_name\", \"inputs\", \"embedding\"]\n\n def build_config(self):\n return {\n \"inputs\": {\n \"display_name\": \"Inputs\",\n \"info\": \"Optional list of records to be processed and stored in the vector store.\",\n },\n \"embedding\": {\"display_name\": \"Embedding\", \"info\": \"Embedding to use\"},\n \"collection_name\": {\n \"display_name\": \"Collection Name\",\n \"info\": \"The name of the collection within Astra DB where the vectors will be stored.\",\n },\n \"token\": {\n \"display_name\": \"Token\",\n \"info\": \"Authentication token for accessing Astra DB.\",\n \"password\": True,\n },\n \"api_endpoint\": {\n \"display_name\": \"API Endpoint\",\n \"info\": \"API endpoint URL for the Astra DB service.\",\n },\n \"namespace\": {\n \"display_name\": \"Namespace\",\n \"info\": \"Optional namespace within Astra DB to use for the collection.\",\n \"advanced\": True,\n },\n \"metric\": {\n \"display_name\": \"Metric\",\n \"info\": \"Optional distance metric for vector comparisons in the vector store.\",\n \"advanced\": True,\n },\n \"batch_size\": {\n \"display_name\": \"Batch Size\",\n \"info\": \"Optional number of records to process in a single batch.\",\n \"advanced\": True,\n },\n \"bulk_insert_batch_concurrency\": {\n \"display_name\": \"Bulk Insert Batch Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations.\",\n \"advanced\": True,\n },\n \"bulk_insert_overwrite_concurrency\": {\n \"display_name\": \"Bulk Insert Overwrite Concurrency\",\n \"info\": \"Optional concurrency level for bulk insert operations that overwrite existing records.\",\n \"advanced\": True,\n },\n \"bulk_delete_concurrency\": {\n \"display_name\": \"Bulk Delete Concurrency\",\n \"info\": \"Optional concurrency level for bulk delete operations.\",\n \"advanced\": True,\n },\n \"setup_mode\": {\n \"display_name\": \"Setup Mode\",\n \"info\": \"Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.\",\n \"options\": [\"Sync\", \"Async\", \"Off\"],\n \"advanced\": True,\n },\n \"pre_delete_collection\": {\n \"display_name\": \"Pre Delete Collection\",\n \"info\": \"Boolean flag to determine whether to delete the collection before creating a new one.\",\n \"advanced\": True,\n },\n \"metadata_indexing_include\": {\n \"display_name\": \"Metadata Indexing Include\",\n \"info\": \"Optional list of metadata fields to include in the indexing.\",\n \"advanced\": True,\n },\n \"metadata_indexing_exclude\": {\n \"display_name\": \"Metadata Indexing Exclude\",\n \"info\": \"Optional list of metadata fields to exclude from the indexing.\",\n \"advanced\": True,\n },\n \"collection_indexing_policy\": {\n \"display_name\": \"Collection Indexing Policy\",\n \"info\": \"Optional dictionary defining the indexing policy for the collection.\",\n \"advanced\": True,\n },\n }\n\n def build(\n self,\n embedding: Embeddings,\n token: str,\n api_endpoint: str,\n collection_name: str,\n inputs: Optional[List[Record]] = None,\n namespace: Optional[str] = None,\n metric: Optional[str] = None,\n batch_size: Optional[int] = None,\n bulk_insert_batch_concurrency: Optional[int] = None,\n bulk_insert_overwrite_concurrency: Optional[int] = None,\n bulk_delete_concurrency: Optional[int] = None,\n setup_mode: str = \"Sync\",\n pre_delete_collection: bool = False,\n metadata_indexing_include: Optional[List[str]] = None,\n metadata_indexing_exclude: Optional[List[str]] = None,\n collection_indexing_policy: Optional[dict] = None,\n ) -> VectorStore:\n try:\n setup_mode_value = SetupMode[setup_mode.upper()]\n except KeyError:\n raise ValueError(f\"Invalid setup mode: {setup_mode}\")\n if inputs:\n documents = [_input.to_lc_document() for _input in inputs]\n\n vector_store = AstraDBVectorStore.from_documents(\n documents=documents,\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n else:\n vector_store = AstraDBVectorStore(\n embedding=embedding,\n collection_name=collection_name,\n token=token,\n api_endpoint=api_endpoint,\n namespace=namespace,\n metric=metric,\n batch_size=batch_size,\n bulk_insert_batch_concurrency=bulk_insert_batch_concurrency,\n bulk_insert_overwrite_concurrency=bulk_insert_overwrite_concurrency,\n bulk_delete_concurrency=bulk_delete_concurrency,\n setup_mode=setup_mode_value,\n pre_delete_collection=pre_delete_collection,\n metadata_indexing_include=metadata_indexing_include,\n metadata_indexing_exclude=metadata_indexing_exclude,\n collection_indexing_policy=collection_indexing_policy,\n )\n\n return vector_store\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "collection_indexing_policy": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_indexing_policy", + "display_name": "Collection Indexing Policy", + "advanced": true, + "dynamic": false, + "info": "Optional dictionary defining the indexing policy for the collection.", + "load_from_db": false, + "title_case": false + }, + "collection_name": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "collection_name", + "display_name": "Collection Name", + "advanced": false, + "dynamic": false, + "info": "The name of the collection within Astra DB where the vectors will be stored.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "langflow" + }, + "metadata_indexing_exclude": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_exclude", + "display_name": "Metadata Indexing Exclude", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to exclude from the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metadata_indexing_include": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metadata_indexing_include", + "display_name": "Metadata Indexing Include", + "advanced": true, + "dynamic": false, + "info": "Optional list of metadata fields to include in the indexing.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "metric": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "metric", + "display_name": "Metric", + "advanced": true, + "dynamic": false, + "info": "Optional distance metric for vector comparisons in the vector store.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "namespace": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "namespace", + "display_name": "Namespace", + "advanced": true, + "dynamic": false, + "info": "Optional namespace within Astra DB to use for the collection.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "pre_delete_collection": { + "type": "bool", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "pre_delete_collection", + "display_name": "Pre Delete Collection", + "advanced": true, + "dynamic": false, + "info": "Boolean flag to determine whether to delete the collection before creating a new one.", + "load_from_db": false, + "title_case": false + }, + "setup_mode": { + "type": "str", + "required": false, + "placeholder": "", + "list": true, + "show": true, + "multiline": false, + "value": "Sync", + "fileTypes": [], + "file_path": "", + "password": false, + "options": [ + "Sync", + "Async", + "Off" + ], + "name": "setup_mode", + "display_name": "Setup Mode", + "advanced": true, + "dynamic": false, + "info": "Configuration mode for setting up the vector store, with options like “Sync”, “Async”, or “Off”.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "token": { + "type": "str", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": true, + "name": "token", + "display_name": "Token", + "advanced": false, + "dynamic": false, + "info": "Authentication token for accessing Astra DB.", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ], + "value": "" + }, + "_type": "CustomComponent" + }, + "description": "Builds or loads an Astra DB Vector Store.", + "icon": "AstraDB", + "base_classes": [ + "VectorStore" + ], + "display_name": "Astra DB", + "documentation": "", + "custom_fields": { + "embedding": null, + "token": null, + "api_endpoint": null, + "collection_name": null, + "inputs": null, + "namespace": null, + "metric": null, + "batch_size": null, + "bulk_insert_batch_concurrency": null, + "bulk_insert_overwrite_concurrency": null, + "bulk_delete_concurrency": null, + "setup_mode": null, + "pre_delete_collection": null, + "metadata_indexing_include": null, + "metadata_indexing_exclude": null, + "collection_indexing_policy": null + }, + "output_types": [ + "VectorStore" + ], + "field_formatters": {}, + "frozen": false, + "field_order": [ + "token", + "api_endpoint", + "collection_name", + "inputs", + "embedding" + ], + "beta": false + }, + "id": "AstraDB-eUCSS" + }, + "selected": false, + "width": 384, + "height": 573, + "positionAbsolute": { + "x": 3372.04958055989, + "y": 1611.0742035495277 + }, + "dragging": false + }, + { + "id": "OpenAIEmbeddings-9TPjc", + "type": "genericNode", + "position": { + "x": 2814.0402191223047, + "y": 1955.9268168273086 + }, + "data": { + "type": "OpenAIEmbeddings", + "node": { + "template": { + "allowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "allowed_special", + "display_name": "Allowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "chunk_size": { + "type": "int", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": 1000, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "chunk_size", + "display_name": "Chunk Size", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "client": { + "type": "Any", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "client", + "display_name": "Client", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "code": { + "type": "code", + "required": true, + "placeholder": "", + "list": false, + "show": true, + "multiline": true, + "value": "from typing import Any, Dict, List, Optional\n\nfrom langchain_openai.embeddings.base import OpenAIEmbeddings\nfrom pydantic.v1 import SecretStr\n\nfrom langflow.field_typing import Embeddings, NestedDict\nfrom langflow.interface.custom.custom_component import CustomComponent\n\n\nclass OpenAIEmbeddingsComponent(CustomComponent):\n display_name = \"OpenAI Embeddings\"\n description = \"Generate embeddings using OpenAI models.\"\n\n def build_config(self):\n return {\n \"allowed_special\": {\n \"display_name\": \"Allowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"default_headers\": {\n \"display_name\": \"Default Headers\",\n \"advanced\": True,\n \"field_type\": \"dict\",\n },\n \"default_query\": {\n \"display_name\": \"Default Query\",\n \"advanced\": True,\n \"field_type\": \"NestedDict\",\n },\n \"disallowed_special\": {\n \"display_name\": \"Disallowed Special\",\n \"advanced\": True,\n \"field_type\": \"str\",\n \"is_list\": True,\n },\n \"chunk_size\": {\"display_name\": \"Chunk Size\", \"advanced\": True},\n \"client\": {\"display_name\": \"Client\", \"advanced\": True},\n \"deployment\": {\"display_name\": \"Deployment\", \"advanced\": True},\n \"embedding_ctx_length\": {\n \"display_name\": \"Embedding Context Length\",\n \"advanced\": True,\n },\n \"max_retries\": {\"display_name\": \"Max Retries\", \"advanced\": True},\n \"model\": {\n \"display_name\": \"Model\",\n \"advanced\": False,\n \"options\": [\n \"text-embedding-3-small\",\n \"text-embedding-3-large\",\n \"text-embedding-ada-002\",\n ],\n },\n \"model_kwargs\": {\"display_name\": \"Model Kwargs\", \"advanced\": True},\n \"openai_api_base\": {\n \"display_name\": \"OpenAI API Base\",\n \"password\": True,\n \"advanced\": True,\n },\n \"openai_api_key\": {\"display_name\": \"OpenAI API Key\", \"password\": True},\n \"openai_api_type\": {\n \"display_name\": \"OpenAI API Type\",\n \"advanced\": True,\n \"password\": True,\n },\n \"openai_api_version\": {\n \"display_name\": \"OpenAI API Version\",\n \"advanced\": True,\n },\n \"openai_organization\": {\n \"display_name\": \"OpenAI Organization\",\n \"advanced\": True,\n },\n \"openai_proxy\": {\"display_name\": \"OpenAI Proxy\", \"advanced\": True},\n \"request_timeout\": {\"display_name\": \"Request Timeout\", \"advanced\": True},\n \"show_progress_bar\": {\n \"display_name\": \"Show Progress Bar\",\n \"advanced\": True,\n },\n \"skip_empty\": {\"display_name\": \"Skip Empty\", \"advanced\": True},\n \"tiktoken_model_name\": {\n \"display_name\": \"TikToken Model Name\",\n \"advanced\": True,\n },\n \"tiktoken_enable\": {\"display_name\": \"TikToken Enable\", \"advanced\": True},\n }\n\n def build(\n self,\n openai_api_key: str,\n default_headers: Optional[Dict[str, str]] = None,\n default_query: Optional[NestedDict] = {},\n allowed_special: List[str] = [],\n disallowed_special: List[str] = [\"all\"],\n chunk_size: int = 1000,\n client: Optional[Any] = None,\n deployment: str = \"text-embedding-ada-002\",\n embedding_ctx_length: int = 8191,\n max_retries: int = 6,\n model: str = \"text-embedding-ada-002\",\n model_kwargs: NestedDict = {},\n openai_api_base: Optional[str] = None,\n openai_api_type: Optional[str] = None,\n openai_api_version: Optional[str] = None,\n openai_organization: Optional[str] = None,\n openai_proxy: Optional[str] = None,\n request_timeout: Optional[float] = None,\n show_progress_bar: bool = False,\n skip_empty: bool = False,\n tiktoken_enable: bool = True,\n tiktoken_model_name: Optional[str] = None,\n ) -> Embeddings:\n # This is to avoid errors with Vector Stores (e.g Chroma)\n if disallowed_special == [\"all\"]:\n disallowed_special = \"all\" # type: ignore\n if openai_api_key:\n api_key = SecretStr(openai_api_key)\n else:\n api_key = None\n\n return OpenAIEmbeddings(\n tiktoken_enabled=tiktoken_enable,\n default_headers=default_headers,\n default_query=default_query,\n allowed_special=set(allowed_special),\n disallowed_special=\"all\",\n chunk_size=chunk_size,\n client=client,\n deployment=deployment,\n embedding_ctx_length=embedding_ctx_length,\n max_retries=max_retries,\n model=model,\n model_kwargs=model_kwargs,\n base_url=openai_api_base,\n api_key=api_key,\n openai_api_type=openai_api_type,\n api_version=openai_api_version,\n organization=openai_organization,\n openai_proxy=openai_proxy,\n timeout=request_timeout,\n show_progress_bar=show_progress_bar,\n skip_empty=skip_empty,\n tiktoken_model_name=tiktoken_model_name,\n )\n", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "code", + "advanced": true, + "dynamic": true, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_headers": { + "type": "dict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_headers", + "display_name": "Default Headers", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "default_query": { + "type": "NestedDict", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": {}, + "fileTypes": [], + "file_path": "", + "password": false, + "name": "default_query", + "display_name": "Default Query", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false + }, + "deployment": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": "text-embedding-ada-002", + "fileTypes": [], + "file_path": "", + "password": false, + "name": "deployment", + "display_name": "Deployment", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "disallowed_special": { + "type": "str", + "required": false, + "placeholder": "", + "list": false, + "show": true, + "multiline": false, + "value": [ + "all" + ], + "fileTypes": [], + "file_path": "", + "password": false, + "name": "disallowed_special", + "display_name": "Disallowed Special", + "advanced": true, + "dynamic": false, + "info": "", + "load_from_db": false, + "title_case": false, + "input_types": [ + "Text" + ] + }, + "embedding_ctx_length": { + "type": 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"str" + ], + "dataType": "Prompt", + "id": "Prompt-xeI6K" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "OpenAIModel-EjXlN", + "target": "ChatOutput-Q39I8", + "sourceHandle": "{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-EjXlNœ}", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Q39I8œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "id": "reactflow__edge-OpenAIModel-EjXlN{œbaseClassesœ:[œobjectœ,œTextœ,œstrœ],œdataTypeœ:œOpenAIModelœ,œidœ:œOpenAIModel-EjXlNœ}-ChatOutput-Q39I8{œfieldNameœ:œinput_valueœ,œidœ:œChatOutput-Q39I8œ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "ChatOutput-Q39I8", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "object", + "Text", + "str" + ], + "dataType": "OpenAIModel", + "id": "OpenAIModel-EjXlN" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "File-t0a6a", + "target": "RecursiveCharacterTextSplitter-tR9QM", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-t0a6aœ}", + "targetHandle": "{œfieldNameœ:œinputsœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ,œinputTypesœ:[œDocumentœ,œRecordœ],œtypeœ:œDocumentœ}", + "id": "reactflow__edge-File-t0a6a{œbaseClassesœ:[œRecordœ],œdataTypeœ:œFileœ,œidœ:œFile-t0a6aœ}-RecursiveCharacterTextSplitter-tR9QM{œfieldNameœ:œinputsœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ,œinputTypesœ:[œDocumentœ,œRecordœ],œtypeœ:œDocumentœ}", + "data": { + "targetHandle": { + "fieldName": "inputs", + "id": "RecursiveCharacterTextSplitter-tR9QM", + "inputTypes": [ + "Document", + "Record" + ], + "type": "Document" + }, + "sourceHandle": { + "baseClasses": [ + "Record" + ], + "dataType": "File", + "id": "File-t0a6a" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "selected": false + }, + { + "source": "OpenAIEmbeddings-ZlOk1", + "sourceHandle": "{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-ZlOk1œ}", + "target": "AstraDBSearch-41nRz", + "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", + "data": { + "targetHandle": { + "fieldName": "embedding", + "id": "AstraDBSearch-41nRz", + "inputTypes": null, + "type": "Embeddings" + }, + "sourceHandle": { + "baseClasses": [ + "Embeddings" + ], + "dataType": "OpenAIEmbeddings", + "id": "OpenAIEmbeddings-ZlOk1" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIEmbeddings-ZlOk1{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-ZlOk1œ}-AstraDBSearch-41nRz{œfieldNameœ:œembeddingœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}" + }, + { + "source": "ChatInput-yxMKE", + "sourceHandle": "{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}", + "target": "AstraDBSearch-41nRz", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "AstraDBSearch-41nRz", + "inputTypes": [ + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Text", + "str", + "object", + "Record" + ], + "dataType": "ChatInput", + "id": "ChatInput-yxMKE" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-ChatInput-yxMKE{œbaseClassesœ:[œTextœ,œstrœ,œobjectœ,œRecordœ],œdataTypeœ:œChatInputœ,œidœ:œChatInput-yxMKEœ}-AstraDBSearch-41nRz{œfieldNameœ:œinput_valueœ,œidœ:œAstraDBSearch-41nRzœ,œinputTypesœ:[œTextœ],œtypeœ:œstrœ}" + }, + { + "source": "RecursiveCharacterTextSplitter-tR9QM", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ}", + "target": "AstraDB-eUCSS", + "targetHandle": "{œfieldNameœ:œinputsœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œRecordœ}", + "data": { + "targetHandle": { + "fieldName": "inputs", + "id": "AstraDB-eUCSS", + "inputTypes": null, + "type": "Record" + }, + "sourceHandle": { + "baseClasses": [ + "Record" + ], + "dataType": "RecursiveCharacterTextSplitter", + "id": "RecursiveCharacterTextSplitter-tR9QM" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-RecursiveCharacterTextSplitter-tR9QM{œbaseClassesœ:[œRecordœ],œdataTypeœ:œRecursiveCharacterTextSplitterœ,œidœ:œRecursiveCharacterTextSplitter-tR9QMœ}-AstraDB-eUCSS{œfieldNameœ:œinputsœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œRecordœ}", + "selected": false + }, + { + "source": "OpenAIEmbeddings-9TPjc", + "sourceHandle": "{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-9TPjcœ}", + "target": "AstraDB-eUCSS", + "targetHandle": "{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", + "data": { + "targetHandle": { + "fieldName": "embedding", + "id": "AstraDB-eUCSS", + "inputTypes": null, + "type": "Embeddings" + }, + "sourceHandle": { + "baseClasses": [ + "Embeddings" + ], + "dataType": "OpenAIEmbeddings", + "id": "OpenAIEmbeddings-9TPjc" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-OpenAIEmbeddings-9TPjc{œbaseClassesœ:[œEmbeddingsœ],œdataTypeœ:œOpenAIEmbeddingsœ,œidœ:œOpenAIEmbeddings-9TPjcœ}-AstraDB-eUCSS{œfieldNameœ:œembeddingœ,œidœ:œAstraDB-eUCSSœ,œinputTypesœ:null,œtypeœ:œEmbeddingsœ}", + "selected": false + }, + { + "source": "AstraDBSearch-41nRz", + "sourceHandle": "{œbaseClassesœ:[œRecordœ],œdataTypeœ:œAstraDBSearchœ,œidœ:œAstraDBSearch-41nRzœ}", + "target": "TextOutput-BDknO", + "targetHandle": "{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-BDknOœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}", + "data": { + "targetHandle": { + "fieldName": "input_value", + "id": "TextOutput-BDknO", + "inputTypes": [ + "Record", + "Text" + ], + "type": "str" + }, + "sourceHandle": { + "baseClasses": [ + "Record" + ], + "dataType": "AstraDBSearch", + "id": "AstraDBSearch-41nRz" + } + }, + "style": { + "stroke": "#555" + }, + "className": "stroke-gray-900 stroke-connection", + "id": "reactflow__edge-AstraDBSearch-41nRz{œbaseClassesœ:[œRecordœ],œdataTypeœ:œAstraDBSearchœ,œidœ:œAstraDBSearch-41nRzœ}-TextOutput-BDknO{œfieldNameœ:œinput_valueœ,œidœ:œTextOutput-BDknOœ,œinputTypesœ:[œRecordœ,œTextœ],œtypeœ:œstrœ}" + } + ], + "viewport": { + "x": -259.6782520315529, + "y": 90.3428735006047, + "zoom": 0.2687057134854984 + } + }, + "description": "Visit https://pre-release.langflow.org/guides/rag-with-astradb for a detailed guide of this project.\nThis project give you both Ingestion and RAG in a single file. You'll need to visit https://astra.datastax.com/ to create an Astra DB instance, your Token and grab an API Endpoint.\nRunning this project requires you to add a file in the Files component, then define a Collection Name and click on the Play icon on the Astra DB component. \n\nAfter the ingestion ends you are ready to click on the Run button at the lower left corner and start asking questions about your data.", + "name": "Vector Store RAG", + "last_tested_version": "1.0.0a0", + "is_component": false } \ No newline at end of file diff --git a/src/backend/base/langflow/interface/agents/base.py b/src/backend/base/langflow/interface/agents/base.py index 5d50b3aff..ee510580c 100644 --- a/src/backend/base/langflow/interface/agents/base.py +++ b/src/backend/base/langflow/interface/agents/base.py @@ -1,14 +1,14 @@ from typing import ClassVar, Dict, List, Optional from langchain.agents import types -from loguru import logger - from langflow.interface.agents.custom import CUSTOM_AGENTS from langflow.interface.base import LangChainTypeCreator +from langflow.interface.utils import build_template_from_class from langflow.legacy_custom.customs import get_custom_nodes from langflow.services.deps import get_settings_service from langflow.template.frontend_node.agents import AgentFrontendNode -from langflow.utils.util import build_template_from_class, build_template_from_method +from langflow.utils.util import build_template_from_method +from loguru import logger class AgentCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/chains/base.py b/src/backend/base/langflow/interface/chains/base.py index fb008f0a7..e69b93614 100644 --- a/src/backend/base/langflow/interface/chains/base.py +++ b/src/backend/base/langflow/interface/chains/base.py @@ -2,14 +2,14 @@ from typing import Any, ClassVar, Dict, List, Optional, Type from langchain import chains from langchain_experimental.sql import SQLDatabaseChain -from loguru import logger - from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class +from langflow.interface.utils import build_template_from_class from langflow.legacy_custom.customs import get_custom_nodes from langflow.services.deps import get_settings_service from langflow.template.frontend_node.chains import ChainFrontendNode -from langflow.utils.util import build_template_from_class, build_template_from_method +from langflow.utils.util import build_template_from_method +from loguru import logger # Assuming necessary imports for Field, Template, and FrontendNode classes diff --git a/src/backend/base/langflow/interface/document_loaders/base.py b/src/backend/base/langflow/interface/document_loaders/base.py index 24108c9b4..11bf0db42 100644 --- a/src/backend/base/langflow/interface/document_loaders/base.py +++ b/src/backend/base/langflow/interface/document_loaders/base.py @@ -4,9 +4,9 @@ from loguru import logger from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import documentloaders_type_to_cls_dict +from langflow.interface.utils import build_template_from_class from langflow.services.deps import get_settings_service from langflow.template.frontend_node.documentloaders import DocumentLoaderFrontNode -from langflow.utils.util import build_template_from_class class DocumentLoaderCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/embeddings/base.py b/src/backend/base/langflow/interface/embeddings/base.py index 061321092..5fd7ad3b0 100644 --- a/src/backend/base/langflow/interface/embeddings/base.py +++ b/src/backend/base/langflow/interface/embeddings/base.py @@ -4,10 +4,10 @@ from loguru import logger from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import embedding_type_to_cls_dict +from langflow.interface.utils import build_template_from_class from langflow.services.deps import get_settings_service from langflow.template.frontend_node.base import FrontendNode from langflow.template.frontend_node.embeddings import EmbeddingFrontendNode -from langflow.utils.util import build_template_from_class class EmbeddingCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/llms/base.py b/src/backend/base/langflow/interface/llms/base.py index ba611da8d..b7d91d674 100644 --- a/src/backend/base/langflow/interface/llms/base.py +++ b/src/backend/base/langflow/interface/llms/base.py @@ -4,9 +4,9 @@ from loguru import logger from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import llm_type_to_cls_dict +from langflow.interface.utils import build_template_from_class from langflow.services.deps import get_settings_service from langflow.template.frontend_node.llms import LLMFrontendNode -from langflow.utils.util import build_template_from_class class LLMCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/memories/base.py b/src/backend/base/langflow/interface/memories/base.py index b63c7d3f0..ea0eabbf1 100644 --- a/src/backend/base/langflow/interface/memories/base.py +++ b/src/backend/base/langflow/interface/memories/base.py @@ -1,14 +1,14 @@ from typing import ClassVar, Dict, List, Optional, Type -from loguru import logger - from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import memory_type_to_cls_dict +from langflow.interface.utils import build_template_from_class from langflow.legacy_custom.customs import get_custom_nodes from langflow.services.deps import get_settings_service from langflow.template.frontend_node.base import FrontendNode from langflow.template.frontend_node.memories import MemoryFrontendNode -from langflow.utils.util import build_template_from_class, build_template_from_method +from langflow.utils.util import build_template_from_method +from loguru import logger class MemoryCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/output_parsers/base.py b/src/backend/base/langflow/interface/output_parsers/base.py index 503b47642..b38bba87e 100644 --- a/src/backend/base/langflow/interface/output_parsers/base.py +++ b/src/backend/base/langflow/interface/output_parsers/base.py @@ -1,13 +1,13 @@ from typing import ClassVar, Dict, List, Optional, Type from langchain import output_parsers -from loguru import logger - from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class +from langflow.interface.utils import build_template_from_class from langflow.services.deps import get_settings_service from langflow.template.frontend_node.output_parsers import OutputParserFrontendNode -from langflow.utils.util import build_template_from_class, build_template_from_method +from langflow.utils.util import build_template_from_method +from loguru import logger class OutputParserCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/prompts/base.py b/src/backend/base/langflow/interface/prompts/base.py index 2b448c362..b9662e0cd 100644 --- a/src/backend/base/langflow/interface/prompts/base.py +++ b/src/backend/base/langflow/interface/prompts/base.py @@ -2,13 +2,12 @@ from typing import Dict, List, Optional, Type from langchain import prompts from loguru import logger - from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class +from langflow.interface.utils import build_template_from_class from langflow.legacy_custom.customs import get_custom_nodes from langflow.services.deps import get_settings_service from langflow.template.frontend_node.prompts import PromptFrontendNode -from langflow.utils.util import build_template_from_class class PromptCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/retrievers/base.py b/src/backend/base/langflow/interface/retrievers/base.py index a0be56b9b..6eefe18db 100644 --- a/src/backend/base/langflow/interface/retrievers/base.py +++ b/src/backend/base/langflow/interface/retrievers/base.py @@ -1,13 +1,13 @@ from typing import Any, ClassVar, Dict, List, Optional, Type from langchain_community import retrievers -from loguru import logger - from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class +from langflow.interface.utils import build_template_from_class from langflow.services.deps import get_settings_service from langflow.template.frontend_node.retrievers import RetrieverFrontendNode -from langflow.utils.util import build_template_from_class, build_template_from_method +from langflow.utils.util import build_template_from_method +from loguru import logger class RetrieverCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/run.py b/src/backend/base/langflow/interface/run.py index aec602ac7..2327b8725 100644 --- a/src/backend/base/langflow/interface/run.py +++ b/src/backend/base/langflow/interface/run.py @@ -5,17 +5,6 @@ from loguru import logger from langflow.graph import Graph -async def build_sorted_vertices(data_graph, flow_id: str) -> Tuple[Graph, Dict]: - """ - Build langchain object from data_graph. - """ - - logger.debug("Building langchain object") - graph = Graph.from_payload(data_graph, flow_id=flow_id) - - return graph, {} - - def get_memory_key(langchain_object): """ Given a LangChain object, this function retrieves the current memory key from the object's memory attribute. diff --git a/src/backend/base/langflow/interface/text_splitters/base.py b/src/backend/base/langflow/interface/text_splitters/base.py index 497377cde..69d9799b3 100644 --- a/src/backend/base/langflow/interface/text_splitters/base.py +++ b/src/backend/base/langflow/interface/text_splitters/base.py @@ -4,9 +4,9 @@ from loguru import logger from langflow.interface.base import LangChainTypeCreator from langflow.interface.custom_lists import textsplitter_type_to_cls_dict +from langflow.interface.utils import build_template_from_class from langflow.services.deps import get_settings_service from langflow.template.frontend_node.textsplitters import TextSplittersFrontendNode -from langflow.utils.util import build_template_from_class class TextSplitterCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/toolkits/base.py b/src/backend/base/langflow/interface/toolkits/base.py index efcfefaa4..eca7ae3b7 100644 --- a/src/backend/base/langflow/interface/toolkits/base.py +++ b/src/backend/base/langflow/interface/toolkits/base.py @@ -3,11 +3,10 @@ from typing import Callable, Dict, List, Optional from langchain.agents import agent_toolkits from loguru import logger - from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class, import_module +from langflow.interface.utils import build_template_from_class from langflow.services.deps import get_settings_service -from langflow.utils.util import build_template_from_class class ToolkitCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/tools/base.py b/src/backend/base/langflow/interface/tools/base.py index b4c933baa..f64192a3b 100644 --- a/src/backend/base/langflow/interface/tools/base.py +++ b/src/backend/base/langflow/interface/tools/base.py @@ -11,7 +11,7 @@ from langflow.template.field.base import TemplateField from langflow.template.template.base import Template from langflow.utils import util from langflow.utils.logger import logger -from langflow.utils.util import build_template_from_class +from langflow.interface.utils import build_template_from_class TOOL_INPUTS = { "str": TemplateField( diff --git a/src/backend/base/langflow/interface/utilities/base.py b/src/backend/base/langflow/interface/utilities/base.py index 1b7b69421..474bf8ca2 100644 --- a/src/backend/base/langflow/interface/utilities/base.py +++ b/src/backend/base/langflow/interface/utilities/base.py @@ -2,13 +2,12 @@ from typing import Dict, List, Optional, Type from langchain_community import utilities from loguru import logger - from langflow.interface.base import LangChainTypeCreator from langflow.interface.importing.utils import import_class +from langflow.interface.utils import build_template_from_class from langflow.legacy_custom.customs import get_custom_nodes from langflow.services.deps import get_settings_service from langflow.template.frontend_node.utilities import UtilitiesFrontendNode -from langflow.utils.util import build_template_from_class class UtilityCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/interface/utils.py b/src/backend/base/langflow/interface/utils.py index 8e7f476f5..aa4e3f7e1 100644 --- a/src/backend/base/langflow/interface/utils.py +++ b/src/backend/base/langflow/interface/utils.py @@ -3,14 +3,16 @@ import json import os import re from io import BytesIO +from typing import Dict import yaml +from docstring_parser import parse from langchain.base_language import BaseLanguageModel -from loguru import logger -from PIL.Image import Image - from langflow.services.chat.config import ChatConfig from langflow.services.deps import get_settings_service +from langflow.utils.util import format_dict, get_base_classes, get_default_factory +from loguru import logger +from PIL.Image import Image def load_file_into_dict(file_path: str) -> dict: @@ -100,7 +102,6 @@ def setup_llm_caching(): def set_langchain_cache(settings): from langchain.globals import set_llm_cache - from langflow.interface.importing.utils import import_class if cache_type := os.getenv("LANGFLOW_LANGCHAIN_CACHE"): @@ -114,3 +115,51 @@ def set_langchain_cache(settings): logger.warning(f"Could not import {cache_type}. ") else: logger.info("No LLM cache set.") + + +def build_template_from_class(name: str, type_to_cls_dict: Dict, add_function: bool = False): + classes = [item.__name__ for item in type_to_cls_dict.values()] + + # Raise error if name is not in chains + if name not in classes: + raise ValueError(f"{name} not found.") + + for _type, v in type_to_cls_dict.items(): + if v.__name__ == name: + _class = v + + # Get the docstring + docs = parse(_class.__doc__) + + variables = {"_type": _type} + + if "__fields__" in _class.__dict__: + for class_field_items, value in _class.__fields__.items(): + if class_field_items in ["callback_manager"]: + continue + variables[class_field_items] = {} + for name_, value_ in value.__repr_args__(): + if name_ == "default_factory": + try: + variables[class_field_items]["default"] = get_default_factory( + module=_class.__base__.__module__, + function=value_, + ) + except Exception: + variables[class_field_items]["default"] = None + elif name_ not in ["name"]: + variables[class_field_items][name_] = value_ + + variables[class_field_items]["placeholder"] = ( + docs.params[class_field_items] if class_field_items in docs.params else "" + ) + base_classes = get_base_classes(_class) + # Adding function to base classes to allow + # the output to be a function + if add_function: + base_classes.append("Callable") + return { + "template": format_dict(variables, name), + "description": docs.short_description or "", + "base_classes": base_classes, + } diff --git a/src/backend/base/langflow/interface/wrappers/base.py b/src/backend/base/langflow/interface/wrappers/base.py index 204a5153e..b850d345f 100644 --- a/src/backend/base/langflow/interface/wrappers/base.py +++ b/src/backend/base/langflow/interface/wrappers/base.py @@ -1,10 +1,9 @@ from typing import Dict, List, Optional from langchain_community.utilities import requests -from loguru import logger - from langflow.interface.base import LangChainTypeCreator -from langflow.utils.util import build_template_from_class +from langflow.interface.utils import build_template_from_class +from loguru import logger class WrapperCreator(LangChainTypeCreator): diff --git a/src/backend/base/langflow/main.py b/src/backend/base/langflow/main.py index eff774f57..1a369649e 100644 --- a/src/backend/base/langflow/main.py +++ b/src/backend/base/langflow/main.py @@ -10,6 +10,7 @@ from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import FileResponse from fastapi.staticfiles import StaticFiles from loguru import logger +from rich import print as rprint from langflow.api import router from langflow.initial_setup.setup import create_or_update_starter_projects @@ -28,6 +29,8 @@ def get_lifespan(fix_migration=False, socketio_server=None): LangfuseInstance.update() create_or_update_starter_projects() yield + # Shutdown message + rprint("[bold red]Shutting down Langflow...[/bold red]") teardown_services() return lifespan diff --git a/src/backend/base/langflow/processing/load.py b/src/backend/base/langflow/processing/load.py index 43d9ecc78..a2be59d95 100644 --- a/src/backend/base/langflow/processing/load.py +++ b/src/backend/base/langflow/processing/load.py @@ -2,21 +2,55 @@ import json from pathlib import Path from typing import List, Optional, Union +from dotenv import load_dotenv from langflow.graph import Graph from langflow.graph.schema import RunOutputs from langflow.processing.process import process_tweaks, run_graph +from langflow.utils.logger import configure +from langflow.utils.util import update_settings -def load_flow_from_json(flow: Union[Path, str, dict], tweaks: Optional[dict] = None) -> Graph: +def load_flow_from_json( + flow: Union[Path, str, dict], + tweaks: Optional[dict] = None, + log_level: Optional[str] = None, + log_file: Optional[str] = None, + env_file: Optional[str] = None, + cache: Optional[str] = None, + disable_logs: Optional[bool] = True, +) -> Graph: """ - Load flow from a JSON file or a JSON object. + Load a flow graph from a JSON file or a JSON object. + + Args: + flow (Union[Path, str, dict]): The flow to load. It can be a file path (str or Path object) + or a JSON object (dict). + tweaks (Optional[dict]): Optional tweaks to apply to the loaded flow graph. + log_level (Optional[str]): Optional log level to configure for the flow processing. + log_file (Optional[str]): Optional log file to configure for the flow processing. + env_file (Optional[str]): Optional .env file to override environment variables. + cache (Optional[str]): Optional cache path to update the flow settings. + disable_logs (Optional[bool], default=True): Optional flag to disable logs during flow processing. + If log_level or log_file are set, disable_logs is not used. + + Returns: + Graph: The loaded flow graph as a Graph object. + + Raises: + TypeError: If the input is neither a file path (str or Path object) nor a JSON object (dict). - :param flow: JSON file path or JSON object - :param tweaks: Optional tweaks to be processed - :param build: If True, build the graph, otherwise return the graph object - :return: Langchain object or Graph object depending on the build parameter """ # If input is a file path, load JSON from the file + log_file_path = Path(log_file) if log_file else None + configure(log_level=log_level, log_file=log_file_path, disable=disable_logs) + + # override env variables with .env file + if env_file: + load_dotenv(env_file, override=True) + + # Update settings with cache and components path + update_settings(cache=cache) + if isinstance(flow, (str, Path)): with open(flow, "r", encoding="utf-8") as f: flow_graph = json.load(f) @@ -41,22 +75,44 @@ def run_flow_from_json( input_type: str = "chat", output_type: str = "chat", output_component: Optional[str] = None, + log_level: Optional[str] = None, + log_file: Optional[str] = None, + env_file: Optional[str] = None, + cache: Optional[str] = None, + disable_logs: Optional[bool] = True, ) -> List[RunOutputs]: """ - Runs a JSON flow by loading it from a file or dictionary and executing it with the given input value. + Run a flow from a JSON file or dictionary. Args: - flow (Union[Path, str, dict]): The path to the JSON file, or the JSON dictionary representing the flow. + flow (Union[Path, str, dict]): The path to the JSON file or the JSON dictionary representing the flow. input_value (str): The input value to be processed by the flow. tweaks (Optional[dict], optional): Optional tweaks to be applied to the flow. Defaults to None. input_type (str, optional): The type of the input value. Defaults to "chat". output_type (str, optional): The type of the output value. Defaults to "chat". - output_component (Optional[str], optional): The specific output component to retrieve. Defaults to None. + output_component (Optional[str], optional): The specific component to output. Defaults to None. + log_level (Optional[str], optional): The log level to use. Defaults to None. + log_file (Optional[str], optional): The log file to write logs to. Defaults to None. + env_file (Optional[str], optional): The environment file to load. Defaults to None. + cache (Optional[str], optional): The cache directory to use. Defaults to None. + disable_logs (Optional[bool], optional): Whether to disable logs. Defaults to True. Returns: - None: The result of running the flow. + List[RunOutputs]: A list of RunOutputs objects representing the results of running the flow. """ - graph = load_flow_from_json(flow, tweaks) + # Set all streaming to false + if tweaks is None: + tweaks = {} + tweaks["stream"] = False + graph = load_flow_from_json( + flow=flow, + tweaks=tweaks, + log_level=log_level, + log_file=log_file, + env_file=env_file, + cache=cache, + disable_logs=disable_logs, + ) result = run_graph( graph=graph, input_value=input_value, diff --git a/src/backend/base/langflow/processing/process.py b/src/backend/base/langflow/processing/process.py index c05163933..bd35527b4 100644 --- a/src/backend/base/langflow/processing/process.py +++ b/src/backend/base/langflow/processing/process.py @@ -9,11 +9,12 @@ from langflow.graph.graph.base import Graph from langflow.graph.schema import RunOutputs from langflow.graph.vertex.base import Vertex from langflow.interface.run import get_memory_key, update_memory_keys +from langflow.schema.graph import InputValue, Tweaks from langflow.schema.schema import INPUT_FIELD_NAME from langflow.services.session.service import SessionService if TYPE_CHECKING: - from langflow.api.v1.schemas import InputValueRequest, Tweaks + from langflow.api.v1.schemas import InputValueRequest def fix_memory_inputs(langchain_object): @@ -188,7 +189,7 @@ def run_graph( List[RunOutputs]: A list of RunOutputs objects representing the outputs of the graph. """ - inputs = [InputValueRequest(components=[], input_value=input_value, type=input_type)] + inputs = [InputValue(components=[], input_value=input_value, type=input_type)] if output_component: outputs = [output_component] else: @@ -245,7 +246,7 @@ def apply_tweaks(node: Dict[str, Any], node_tweaks: Dict[str, Any]) -> None: logger.warning(f"Node {node.get('id')} does not have a tweak named {tweak_name}") continue if tweak_name in template_data: - key = tweak_name if tweak_name == "file_path" else "value" + key = "file_path" if template_data[tweak_name]["type"] == "file" else "value" template_data[tweak_name][key] = tweak_value diff --git a/src/backend/base/langflow/schema/graph.py b/src/backend/base/langflow/schema/graph.py new file mode 100644 index 000000000..a515b3dd2 --- /dev/null +++ b/src/backend/base/langflow/schema/graph.py @@ -0,0 +1,43 @@ +from typing import List, Optional, Union + +from langflow.schema.schema import InputType +from pydantic import BaseModel, Field, RootModel + + +class InputValue(BaseModel): + components: Optional[List[str]] = [] + input_value: Optional[str] = None + type: Optional[InputType] = Field( + "any", + description="Defines on which components the input value should be applied. 'any' applies to all input components.", + ) + + +class Tweaks(RootModel): + root: dict[str, Union[str, dict[str, str]]] = Field( + description="A dictionary of tweaks to adjust the flow's execution. Allows customizing flow behavior dynamically. All tweaks are overridden by the input values.", + ) + model_config = { + "json_schema_extra": { + "examples": [ + { + "parameter_name": "value", + "Component Name": {"parameter_name": "value"}, + "component_id": {"parameter_name": "value"}, + } + ] + } + } + + # This should behave like a dict + def __getitem__(self, key): + return self.root[key] + + def __setitem__(self, key, value): + self.root[key] = value + + def __delitem__(self, key): + del self.root[key] + + def items(self): + return self.root.items() diff --git a/src/backend/base/langflow/server.py b/src/backend/base/langflow/server.py index 6a1509dc0..67061fbdd 100644 --- a/src/backend/base/langflow/server.py +++ b/src/backend/base/langflow/server.py @@ -1,6 +1,8 @@ +import asyncio import logging +import signal -from gunicorn import glogging +from gunicorn import glogging # type: ignore from gunicorn.app.base import BaseApplication # type: ignore from uvicorn.workers import UvicornWorker @@ -10,6 +12,21 @@ from langflow.utils.logger import InterceptHandler # type: ignore class LangflowUvicornWorker(UvicornWorker): CONFIG_KWARGS = {"loop": "asyncio"} + def _install_sigint_handler(self) -> None: + """Install a SIGQUIT handler on workers. + + - https://github.com/encode/uvicorn/issues/1116 + - https://github.com/benoitc/gunicorn/issues/2604 + """ + + loop = asyncio.get_running_loop() + loop.add_signal_handler(signal.SIGINT, self.handle_exit, signal.SIGINT, None) + + async def _serve(self) -> None: + # We do this to not log the "Worker (pid:XXXXX) was sent SIGINT" + self._install_sigint_handler() + await super()._serve() + class Logger(glogging.Logger): """Implements and overrides the gunicorn logging interface. @@ -35,11 +52,7 @@ class LangflowApplication(BaseApplication): super().__init__() def load_config(self): - config = { - key: value - for key, value in self.options.items() - if key in self.cfg.settings and value is not None - } + config = {key: value for key, value in self.options.items() if key in self.cfg.settings and value is not None} for key, value in config.items(): self.cfg.set(key.lower(), value) diff --git a/src/backend/base/langflow/services/auth/utils.py b/src/backend/base/langflow/services/auth/utils.py index d89330856..d2290036e 100644 --- a/src/backend/base/langflow/services/auth/utils.py +++ b/src/backend/base/langflow/services/auth/utils.py @@ -11,7 +11,11 @@ from starlette.websockets import WebSocket from langflow.services.database.models.api_key.crud import check_key from langflow.services.database.models.api_key.model import ApiKey -from langflow.services.database.models.user.crud import get_user_by_id, get_user_by_username, update_user_last_login_at +from langflow.services.database.models.user.crud import ( + get_user_by_id, + get_user_by_username, + update_user_last_login_at, +) from langflow.services.database.models.user.model import User from langflow.services.deps import get_session, get_settings_service @@ -207,7 +211,7 @@ def create_super_user( return super_user -def create_user_longterm_token(db: Session = Depends(get_session)) -> dict: +def create_user_longterm_token(db: Session = Depends(get_session)) -> tuple[UUID, dict]: settings_service = get_settings_service() username = settings_service.auth_settings.SUPERUSER password = settings_service.auth_settings.SUPERUSER_PASSWORD @@ -227,7 +231,7 @@ def create_user_longterm_token(db: Session = Depends(get_session)) -> dict: # Update: last_login_at update_user_last_login_at(super_user.id, db) - return { + return super_user.id, { "access_token": access_token, "refresh_token": None, "token_type": "bearer", diff --git a/src/backend/base/langflow/services/database/models/api_key/model.py b/src/backend/base/langflow/services/database/models/api_key/model.py index 226794cbe..e110258b9 100644 --- a/src/backend/base/langflow/services/database/models/api_key/model.py +++ b/src/backend/base/langflow/services/database/models/api_key/model.py @@ -1,9 +1,9 @@ -from datetime import datetime +from datetime import datetime, timezone from typing import TYPE_CHECKING, Optional from uuid import UUID, uuid4 -from pydantic import validator -from sqlmodel import Field, Relationship, SQLModel +from pydantic import field_validator, validator +from sqlmodel import Field, Relationship, SQLModel, Column, func, DateTime if TYPE_CHECKING: from langflow.services.database.models.user import User @@ -11,7 +11,9 @@ if TYPE_CHECKING: class ApiKeyBase(SQLModel): name: Optional[str] = Field(index=True, nullable=True, default=None) - created_at: datetime = Field(default_factory=datetime.utcnow) + created_at: datetime = Field( + default=None, sa_column=Column(DateTime(timezone=True), server_default=func.now(), nullable=False) + ) last_used_at: Optional[datetime] = Field(default=None, nullable=True) total_uses: int = Field(default=0) is_active: bool = Field(default=True) @@ -33,6 +35,10 @@ class ApiKeyCreate(ApiKeyBase): api_key: Optional[str] = None user_id: Optional[UUID] = None + @field_validator("created_at", mode="before") + def set_created_at(cls, v): + return v or datetime.now(timezone.utc) + class UnmaskedApiKeyRead(ApiKeyBase): id: UUID diff --git a/src/backend/base/langflow/services/database/models/flow/model.py b/src/backend/base/langflow/services/database/models/flow/model.py index cbfdec563..e814d1f3a 100644 --- a/src/backend/base/langflow/services/database/models/flow/model.py +++ b/src/backend/base/langflow/services/database/models/flow/model.py @@ -1,14 +1,15 @@ # Path: src/backend/langflow/services/database/models/flow/model.py +import warnings from datetime import datetime from typing import TYPE_CHECKING, Dict, Optional from uuid import UUID, uuid4 +import emoji from emoji import purely_emoji # type: ignore from pydantic import field_serializer, field_validator from sqlmodel import JSON, Column, Field, Relationship, SQLModel -from langflow.interface.custom.attributes import validate_icon from langflow.schema.schema import Record if TYPE_CHECKING: @@ -45,12 +46,25 @@ class FlowBase(SQLModel): # emoji pattern in Python if v is None: return v + # we are going to use the emoji library to validate the emoji + # emojis can be defined using the :emoji_name: syntax - emoji = validate_icon(v) + if not v.startswith(":") and not v.endswith(":"): + return v + elif not v.startswith(":") or not v.endswith(":"): + # emoji should have both starting and ending colons + # so if one of them is missing, we will raise + raise ValueError(f"Invalid emoji. {v} is not a valid emoji.") - if purely_emoji(emoji): + emoji_value = emoji.emojize(v, variant="emoji_type") + if v == emoji_value: + warnings.warn(f"Invalid emoji. {v} is not a valid emoji.") + icon = v + icon = emoji_value + + if purely_emoji(icon): # this is indeed an emoji - return emoji + return icon # otherwise it should be a valid lucide icon if v is not None and not isinstance(v, str): raise ValueError("Icon must be a string") diff --git a/src/backend/base/langflow/services/database/models/variable/model.py b/src/backend/base/langflow/services/database/models/variable/model.py index 1c314d756..1fab92b4c 100644 --- a/src/backend/base/langflow/services/database/models/variable/model.py +++ b/src/backend/base/langflow/services/database/models/variable/model.py @@ -2,8 +2,7 @@ from datetime import datetime, timezone from typing import TYPE_CHECKING, Optional from uuid import UUID, uuid4 -from sqlmodel import Field, Relationship, SQLModel - +from sqlmodel import Column, DateTime, Field, Relationship, SQLModel, func if TYPE_CHECKING: from langflow.services.database.models.user.model import User @@ -26,15 +25,25 @@ class Variable(VariableBase, table=True): description="Unique ID for the variable", ) # name is unique per user - created_at: datetime = Field(default_factory=utc_now, description="Creation time of the variable") - updated_at: Optional[datetime] = Field(None, description="Last update time of the variable") + created_at: datetime = Field( + default=None, + sa_column=Column(DateTime(timezone=True), server_default=func.now(), nullable=True), + description="Creation time of the variable", + ) + updated_at: Optional[datetime] = Field( + default=None, + sa_column=Column(DateTime(timezone=True), nullable=True), + description="Last update time of the variable", + ) # foreign key to user table user_id: UUID = Field(description="User ID associated with this variable", foreign_key="user.id") user: "User" = Relationship(back_populates="variables") class VariableCreate(VariableBase): - type: Optional[str] = Field(None, description="Type of the variable") + created_at: Optional[datetime] = Field(default_factory=utc_now, description="Creation time of the variable") + + updated_at: Optional[datetime] = Field(default_factory=utc_now, description="Creation time of the variable") class VariableRead(SQLModel): diff --git a/src/backend/base/langflow/services/database/service.py b/src/backend/base/langflow/services/database/service.py index f06f6edc7..876f9f30e 100644 --- a/src/backend/base/langflow/services/database/service.py +++ b/src/backend/base/langflow/services/database/service.py @@ -6,16 +6,17 @@ from typing import TYPE_CHECKING import sqlalchemy as sa from alembic import command, util from alembic.config import Config +from loguru import logger +from sqlalchemy import inspect +from sqlalchemy.exc import OperationalError +from sqlmodel import Session, SQLModel, create_engine, select, text + from langflow.services.base import Service from langflow.services.database import models # noqa from langflow.services.database.models.user.crud import get_user_by_username from langflow.services.database.utils import Result, TableResults from langflow.services.deps import get_settings_service from langflow.services.utils import teardown_superuser -from loguru import logger -from sqlalchemy import inspect -from sqlalchemy.exc import OperationalError -from sqlmodel import Session, SQLModel, create_engine, select, text if TYPE_CHECKING: from sqlalchemy.engine import Engine @@ -36,10 +37,7 @@ class DatabaseService(Service): def _create_engine(self) -> "Engine": """Create the engine for the database.""" settings_service = get_settings_service() - if ( - settings_service.settings.DATABASE_URL - and settings_service.settings.DATABASE_URL.startswith("sqlite") - ): + if settings_service.settings.DATABASE_URL and settings_service.settings.DATABASE_URL.startswith("sqlite"): connect_args = {"check_same_thread": False} else: connect_args = {} @@ -51,9 +49,7 @@ class DatabaseService(Service): def __exit__(self, exc_type, exc_value, traceback): if exc_type is not None: # If an exception has been raised - logger.error( - f"Session rollback because of exception: {exc_type.__name__} {exc_value}" - ) + logger.error(f"Session rollback because of exception: {exc_type.__name__} {exc_value}") self._session.rollback() else: self._session.commit() @@ -70,9 +66,7 @@ class DatabaseService(Service): settings_service = get_settings_service() if settings_service.auth_settings.AUTO_LOGIN: with Session(self.engine) as session: - flows = session.exec( - select(models.Flow).where(models.Flow.user_id is None) - ).all() + flows = session.exec(select(models.Flow).where(models.Flow.user_id is None)).all() if flows: logger.debug("Migrating flows to default superuser") username = settings_service.auth_settings.SUPERUSER @@ -102,9 +96,7 @@ class DatabaseService(Service): expected_columns = list(model.model_fields.keys()) try: - available_columns = [ - col["name"] for col in inspector.get_columns(table) - ] + available_columns = [col["name"] for col in inspector.get_columns(table)] except sa.exc.NoSuchTableError: logger.debug(f"Missing table: {table}") return False @@ -168,9 +160,7 @@ class DatabaseService(Service): buffer.write(f"{datetime.now().isoformat()}: Checking migrations\n") command.check(alembic_cfg) except Exception as exc: - if isinstance( - exc, (util.exc.CommandError, util.exc.AutogenerateDiffsDetected) - ): + if isinstance(exc, (util.exc.CommandError, util.exc.AutogenerateDiffsDetected)): command.upgrade(alembic_cfg, "head") time.sleep(3) @@ -206,11 +196,11 @@ class DatabaseService(Service): # This method is used for testing purposes only # We will check that all models are in the database # and that the database is up to date with all columns - sql_models = [models.Flow, models.User, models.ApiKey] - return [ - TableResults(sql_model.__tablename__, self.check_table(sql_model)) - for sql_model in sql_models + # get all models that are subclasses of SQLModel + sql_models = [ + model for model in models.__dict__.values() if isinstance(model, type) and issubclass(model, SQLModel) ] + return [TableResults(sql_model.__tablename__, self.check_table(sql_model)) for sql_model in sql_models] def check_table(self, model): results = [] @@ -219,9 +209,7 @@ class DatabaseService(Service): expected_columns = list(model.__fields__.keys()) available_columns = [] try: - available_columns = [ - col["name"] for col in inspector.get_columns(table_name) - ] + available_columns = [col["name"] for col in inspector.get_columns(table_name)] results.append(Result(name=table_name, type="table", success=True)) except sa.exc.NoSuchTableError: logger.error(f"Missing table: {table_name}") @@ -252,9 +240,7 @@ class DatabaseService(Service): try: table.create(self.engine, checkfirst=True) except OperationalError as oe: - logger.warning( - f"Table {table} already exists, skipping. Exception: {oe}" - ) + logger.warning(f"Table {table} already exists, skipping. Exception: {oe}") except Exception as exc: logger.error(f"Error creating table {table}: {exc}") raise RuntimeError(f"Error creating table {table}") from exc @@ -266,9 +252,7 @@ class DatabaseService(Service): if table not in table_names: logger.error("Something went wrong creating the database and tables.") logger.error("Please check your database settings.") - raise RuntimeError( - "Something went wrong creating the database and tables." - ) + raise RuntimeError("Something went wrong creating the database and tables.") logger.debug("Database and tables created successfully") diff --git a/src/backend/base/langflow/services/deps.py b/src/backend/base/langflow/services/deps.py index 23a78e320..beafdec30 100644 --- a/src/backend/base/langflow/services/deps.py +++ b/src/backend/base/langflow/services/deps.py @@ -1,7 +1,7 @@ from contextlib import contextmanager from typing import TYPE_CHECKING, Generator -from langflow.services import ServiceType, service_manager +from langflow.services.schema import ServiceType if TYPE_CHECKING: from sqlmodel import Session @@ -21,7 +21,7 @@ if TYPE_CHECKING: from langflow.services.variable.service import VariableService -def get_service(service_type: ServiceType): +def get_service(service_type: ServiceType, default=None): """ Retrieves the service instance for the given service type. @@ -32,7 +32,13 @@ def get_service(service_type: ServiceType): Any: The service instance. """ - return service_manager.get(service_type) # type: ignore + from langflow.services.manager import service_manager + + if not service_manager.factories: + #! This is a workaround to ensure that the service manager is initialized + #! Not optimal, but it works for now + service_manager.register_factories() + return service_manager.get(service_type, default) # type: ignore def get_state_service() -> "StateService": @@ -42,7 +48,9 @@ def get_state_service() -> "StateService": Returns: The StateService instance. """ - return service_manager.get(ServiceType.STATE_SERVICE) # type: ignore + from langflow.services.state.factory import StateServiceFactory + + return get_service(ServiceType.STATE_SERVICE, StateServiceFactory()) # type: ignore def get_socket_service() -> "SocketIOService": @@ -52,7 +60,7 @@ def get_socket_service() -> "SocketIOService": Returns: SocketIOService: The SocketIOService instance. """ - return service_manager.get(ServiceType.SOCKETIO_SERVICE) # type: ignore + return get_service(ServiceType.SOCKETIO_SERVICE) # type: ignore def get_storage_service() -> "StorageService": @@ -62,7 +70,9 @@ def get_storage_service() -> "StorageService": Returns: The storage service instance. """ - return service_manager.get(ServiceType.STORAGE_SERVICE) # type: ignore + from langflow.services.storage.factory import StorageServiceFactory + + return get_service(ServiceType.STORAGE_SERVICE, default=StorageServiceFactory()) # type: ignore def get_variable_service() -> "VariableService": @@ -73,7 +83,9 @@ def get_variable_service() -> "VariableService": The VariableService instance. """ - return service_manager.get(ServiceType.VARIABLE_SERVICE) # type: ignore + from langflow.services.variable.factory import VariableServiceFactory + + return get_service(ServiceType.VARIABLE_SERVICE, VariableServiceFactory()) # type: ignore def get_plugins_service() -> "PluginService": @@ -83,7 +95,7 @@ def get_plugins_service() -> "PluginService": Returns: PluginService: The PluginService instance. """ - return service_manager.get(ServiceType.PLUGIN_SERVICE) # type: ignore + return get_service(ServiceType.PLUGIN_SERVICE) # type: ignore def get_settings_service() -> "SettingsService": @@ -98,14 +110,9 @@ def get_settings_service() -> "SettingsService": Raises: ValueError: If the service cannot be retrieved or initialized. """ - try: - return service_manager.get(ServiceType.SETTINGS_SERVICE) # type: ignore - except ValueError: - # initialize settings service - from langflow.services.manager import initialize_settings_service + from langflow.services.settings.factory import SettingsServiceFactory - initialize_settings_service() - return service_manager.get(ServiceType.SETTINGS_SERVICE) # type: ignore + return get_service(ServiceType.SETTINGS_SERVICE, SettingsServiceFactory()) # type: ignore def get_db_service() -> "DatabaseService": @@ -116,7 +123,9 @@ def get_db_service() -> "DatabaseService": The DatabaseService instance. """ - return service_manager.get(ServiceType.DATABASE_SERVICE) # type: ignore + from langflow.services.database.factory import DatabaseServiceFactory + + return get_service(ServiceType.DATABASE_SERVICE, DatabaseServiceFactory()) # type: ignore def get_session() -> Generator["Session", None, None]: @@ -165,7 +174,9 @@ def get_cache_service() -> "CacheService": Returns: The cache service instance. """ - return service_manager.get(ServiceType.CACHE_SERVICE) # type: ignore + from langflow.services.cache.factory import CacheServiceFactory + + return get_service(ServiceType.CACHE_SERVICE, CacheServiceFactory()) # type: ignore def get_session_service() -> "SessionService": @@ -175,7 +186,9 @@ def get_session_service() -> "SessionService": Returns: The session service instance. """ - return service_manager.get(ServiceType.SESSION_SERVICE) # type: ignore + from langflow.services.session.factory import SessionServiceFactory + + return get_service(ServiceType.SESSION_SERVICE, SessionServiceFactory()) # type: ignore def get_monitor_service() -> "MonitorService": @@ -185,7 +198,9 @@ def get_monitor_service() -> "MonitorService": Returns: MonitorService: The MonitorService instance. """ - return service_manager.get(ServiceType.MONITOR_SERVICE) # type: ignore + from langflow.services.monitor.factory import MonitorServiceFactory + + return get_service(ServiceType.MONITOR_SERVICE, MonitorServiceFactory()) # type: ignore def get_task_service() -> "TaskService": @@ -196,7 +211,9 @@ def get_task_service() -> "TaskService": The TaskService instance. """ - return service_manager.get(ServiceType.TASK_SERVICE) # type: ignore + from langflow.services.task.factory import TaskServiceFactory + + return get_service(ServiceType.TASK_SERVICE, TaskServiceFactory()) # type: ignore def get_chat_service() -> "ChatService": @@ -206,7 +223,7 @@ def get_chat_service() -> "ChatService": Returns: ChatService: The chat service instance. """ - return service_manager.get(ServiceType.CHAT_SERVICE) # type: ignore + return get_service(ServiceType.CHAT_SERVICE) # type: ignore def get_store_service() -> "StoreService": @@ -216,4 +233,4 @@ def get_store_service() -> "StoreService": Returns: StoreService: The StoreService instance. """ - return service_manager.get(ServiceType.STORE_SERVICE) # type: ignore + return get_service(ServiceType.STORE_SERVICE) # type: ignore diff --git a/src/backend/base/langflow/services/manager.py b/src/backend/base/langflow/services/manager.py index 20186f7da..cd27754d1 100644 --- a/src/backend/base/langflow/services/manager.py +++ b/src/backend/base/langflow/services/manager.py @@ -1,13 +1,20 @@ -from typing import TYPE_CHECKING, Dict +import importlib +import inspect +from typing import TYPE_CHECKING, Dict, Optional from loguru import logger if TYPE_CHECKING: from langflow.services.base import Service + from langflow.services.factory import ServiceFactory from langflow.services.schema import ServiceType +class NoFactoryRegisteredError(Exception): + pass + + class ServiceManager: """ Manages the creation of different services. @@ -16,6 +23,15 @@ class ServiceManager: def __init__(self): self.services: Dict[str, "Service"] = {} self.factories = {} + self.register_factories() + + def register_factories(self): + for factory in self.get_factories(): + try: + self.register_factory(factory) + except Exception as exc: + logger.exception(exc) + logger.error(f"Error initializing {factory}: {exc}") def register_factory( self, @@ -28,24 +44,28 @@ class ServiceManager: service_name = service_factory.service_class.name self.factories[service_name] = service_factory - def get(self, service_name: "ServiceType") -> "Service": + def get(self, service_name: "ServiceType", default: Optional["ServiceFactory"] = None) -> "Service": """ Get (or create) a service by its name. """ + if service_name not in self.services: - self._create_service(service_name) + self._create_service(service_name, default) return self.services[service_name] - def _create_service(self, service_name: "ServiceType"): + def _create_service(self, service_name: "ServiceType", default: Optional["ServiceFactory"] = None): """ Create a new service given its name, handling dependencies. """ logger.debug(f"Create service {service_name}") - self._validate_service_creation(service_name) + self._validate_service_creation(service_name, default) # Create dependencies first factory = self.factories.get(service_name) + if factory is None and default is not None: + self.register_factory(default) + factory = default for dependency in factory.dependencies: if dependency not in self.services: self._create_service(dependency) @@ -57,12 +77,12 @@ class ServiceManager: self.services[service_name] = self.factories[service_name].create(**dependent_services) self.services[service_name].set_ready() - def _validate_service_creation(self, service_name: "ServiceType"): + def _validate_service_creation(self, service_name: "ServiceType", default: Optional["ServiceFactory"] = None): """ Validate whether the service can be created. """ - if service_name not in self.factories: - raise ValueError(f"No factory registered for the service class '{service_name.name}'") + if service_name not in self.factories and default is None: + raise NoFactoryRegisteredError(f"No factory registered for the service class '{service_name.name}'") def update(self, service_name: "ServiceType"): """ @@ -88,6 +108,34 @@ class ServiceManager: self.services = {} self.factories = {} + @staticmethod + def get_factories(): + from langflow.services.factory import ServiceFactory + from langflow.services.schema import ServiceType + + service_names = [ServiceType(service_type).value.replace("_service", "") for service_type in ServiceType] + base_module = "langflow.services" + factories = [] + + for name in service_names: + try: + module_name = f"{base_module}.{name}.factory" + module = importlib.import_module(module_name) + + # Find all classes in the module that are subclasses of ServiceFactory + for name, obj in inspect.getmembers(module, inspect.isclass): + if issubclass(obj, ServiceFactory) and obj is not ServiceFactory: + factories.append(obj()) + break + + except Exception as exc: + logger.exception(exc) + raise RuntimeError( + f"Could not initialize services. Please check your settings. Error in {name}." + ) from exc + + return factories + service_manager = ServiceManager() @@ -106,9 +154,7 @@ def initialize_session_service(): Initialize the session manager. """ from langflow.services.cache import factory as cache_factory - from langflow.services.session import ( - factory as session_service_factory, - ) # type: ignore + from langflow.services.session import factory as session_service_factory # type: ignore initialize_settings_service() diff --git a/src/backend/base/langflow/services/schema.py b/src/backend/base/langflow/services/schema.py index fe0d7022e..511e395d2 100644 --- a/src/backend/base/langflow/services/schema.py +++ b/src/backend/base/langflow/services/schema.py @@ -19,5 +19,5 @@ class ServiceType(str, Enum): VARIABLE_SERVICE = "variable_service" STORAGE_SERVICE = "storage_service" MONITOR_SERVICE = "monitor_service" - SOCKETIO_SERVICE = "socket_service" + # SOCKETIO_SERVICE = "socket_service" STATE_SERVICE = "state_service" diff --git a/src/backend/base/langflow/services/session/service.py b/src/backend/base/langflow/services/session/service.py index 0c925db19..e0feea4e0 100644 --- a/src/backend/base/langflow/services/session/service.py +++ b/src/backend/base/langflow/services/session/service.py @@ -1,6 +1,5 @@ from typing import Coroutine, Optional -from langflow.interface.run import build_sorted_vertices from langflow.services.base import Service from langflow.services.cache.base import CacheService from langflow.services.session.utils import compute_dict_hash, session_id_generator @@ -25,8 +24,10 @@ class SessionService(Service): if data_graph is None: return (None, None) # If not cached, build the graph and cache it - graph, artifacts = await build_sorted_vertices(data_graph, flow_id) + from langflow.graph.graph.base import Graph + graph = Graph.from_payload(data_graph, flow_id=flow_id) + artifacts: dict = {} await self.cache_service.set(key, (graph, artifacts)) return graph, artifacts diff --git a/src/backend/base/langflow/services/settings/base.py b/src/backend/base/langflow/services/settings/base.py index 9c7ab21c4..97f69c6c7 100644 --- a/src/backend/base/langflow/services/settings/base.py +++ b/src/backend/base/langflow/services/settings/base.py @@ -3,18 +3,60 @@ import json import os from pathlib import Path from shutil import copy2 -from typing import List, Optional +from typing import Any, List, Optional, Tuple, Type import orjson import yaml from loguru import logger from pydantic import field_validator, validator -from pydantic_settings import BaseSettings, SettingsConfigDict +from pydantic.fields import FieldInfo +from pydantic_settings import BaseSettings, EnvSettingsSource, PydanticBaseSettingsSource, SettingsConfigDict + +from langflow.services.settings.constants import VARIABLES_TO_GET_FROM_ENVIRONMENT # BASE_COMPONENTS_PATH = str(Path(__file__).parent / "components") BASE_COMPONENTS_PATH = str(Path(__file__).parent.parent.parent / "components") +def is_list_of_any(field: FieldInfo) -> bool: + """ + Check if the given field is a list or an optional list of any type. + + Args: + field (FieldInfo): The field to be checked. + + Returns: + bool: True if the field is a list or a list of any type, False otherwise. + """ + if field.annotation is None: + return False + try: + if hasattr(field.annotation, "__args__"): + union_args = field.annotation.__args__ + else: + union_args = [] + + return field.annotation.__origin__ == list or any( + arg.__origin__ == list for arg in union_args if hasattr(arg, "__origin__") + ) + except AttributeError: + return False + + +class MyCustomSource(EnvSettingsSource): + def prepare_field_value(self, field_name: str, field: FieldInfo, value: Any, value_is_complex: bool) -> Any: + # allow comma-separated list parsing + + # fieldInfo contains the annotation of the field + if is_list_of_any(field): + if isinstance(value, str): + value = value.split(",") + if isinstance(value, list): + return value + + return super().prepare_field_value(field_name, field, value, value_is_complex) + + class Settings(BaseSettings): CHAINS: dict = {} AGENTS: dict = {} @@ -58,15 +100,20 @@ class Settings(BaseSettings): STORE: Optional[bool] = True STORE_URL: Optional[str] = "https://api.langflow.store" - DOWNLOAD_WEBHOOK_URL: Optional[ - str - ] = "https://api.langflow.store/flows/trigger/ec611a61-8460-4438-b187-a4f65e5559d4" + DOWNLOAD_WEBHOOK_URL: Optional[str] = ( + "https://api.langflow.store/flows/trigger/ec611a61-8460-4438-b187-a4f65e5559d4" + ) LIKE_WEBHOOK_URL: Optional[str] = "https://api.langflow.store/flows/trigger/64275852-ec00-45c1-984e-3bff814732da" STORAGE_TYPE: str = "local" CELERY_ENABLED: bool = False + store_environment_variables: bool = True + """Whether to store environment variables as Global Variables in the database.""" + variables_to_get_from_environment: list[str] = VARIABLES_TO_GET_FROM_ENVIRONMENT + """List of environment variables to get from the environment and store in the database.""" + @validator("CONFIG_DIR", pre=True, allow_reuse=True) def set_langflow_dir(cls, value): if not value: @@ -104,21 +151,50 @@ class Settings(BaseSettings): # if there is a database in that location if not values["CONFIG_DIR"]: raise ValueError("CONFIG_DIR not set, please set it or provide a DATABASE_URL") + from langflow.version import is_pre_release # type: ignore - new_path = f"{values['CONFIG_DIR']}/langflow.db" - if Path("./langflow.db").exists(): + pre_db_file_name = "langflow-pre.db" + db_file_name = "langflow.db" + new_pre_path = f"{values['CONFIG_DIR']}/{pre_db_file_name}" + new_path = f"{values['CONFIG_DIR']}/{db_file_name}" + final_path = None + if is_pre_release: + if Path(new_pre_path).exists(): + final_path = new_pre_path + elif Path(new_path).exists(): + # We need to copy the current db to the new location + logger.debug("Copying existing database to new location") + copy2(new_path, new_pre_path) + logger.debug(f"Copied existing database to {new_pre_path}") + elif Path(f"./{db_file_name}").exists(): + logger.debug("Copying existing database to new location") + copy2(f"./{db_file_name}", new_pre_path) + logger.debug(f"Copied existing database to {new_pre_path}") + else: + logger.debug(f"Database already exists at {new_pre_path}, using it") + final_path = new_pre_path + else: if Path(new_path).exists(): logger.debug(f"Database already exists at {new_path}, using it") - else: + final_path = new_path + elif Path("./{db_file_name}").exists(): try: logger.debug("Copying existing database to new location") - copy2("./langflow.db", new_path) + copy2("./{db_file_name}", new_path) logger.debug(f"Copied existing database to {new_path}") except Exception: logger.error("Failed to copy database, using default path") - new_path = "./langflow.db" + new_path = "./{db_file_name}" + else: + final_path = new_path - value = f"sqlite:///{new_path}" + if final_path is None: + if is_pre_release: + final_path = new_pre_path + else: + final_path = new_path + + value = f"sqlite:///{final_path}" return value @@ -149,14 +225,6 @@ class Settings(BaseSettings): model_config = SettingsConfigDict(validate_assignment=True, extra="ignore", env_prefix="LANGFLOW_") - # @model_validator() - # @classmethod - # def validate_lists(cls, values): - # for key, value in values.items(): - # if key != "dev" and not value: - # values[key] = [] - # return values - def update_from_yaml(self, file_path: str, dev: bool = False): new_settings = load_settings_from_yaml(file_path) self.CHAINS = new_settings.CHAINS or {} @@ -209,6 +277,17 @@ class Settings(BaseSettings): logger.debug(f"Updated {key}") logger.debug(f"{key}: {getattr(self, key)}") + @classmethod + def settings_customise_sources( + cls, + settings_cls: Type[BaseSettings], + init_settings: PydanticBaseSettingsSource, + env_settings: PydanticBaseSettingsSource, + dotenv_settings: PydanticBaseSettingsSource, + file_secret_settings: PydanticBaseSettingsSource, + ) -> Tuple[PydanticBaseSettingsSource, ...]: + return (MyCustomSource(settings_cls),) + def save_settings_to_yaml(settings: Settings, file_path: str): with open(file_path, "w") as f: diff --git a/src/backend/base/langflow/services/settings/constants.py b/src/backend/base/langflow/services/settings/constants.py index 6cf7d4823..323f53a01 100644 --- a/src/backend/base/langflow/services/settings/constants.py +++ b/src/backend/base/langflow/services/settings/constants.py @@ -1,2 +1,21 @@ DEFAULT_SUPERUSER = "langflow" DEFAULT_SUPERUSER_PASSWORD = "langflow" +VARIABLES_TO_GET_FROM_ENVIRONMENT = [ + "OPENAI_API_KEY", + "ANTHROPIC_API_KEY", + "GOOGLE_API_KEY", + "AZURE_OPENAI_API_KEY", + "AZURE_OPENAI_API_VERSION", + "AZURE_OPENAI_API_INSTANCE_NAME", + "AZURE_OPENAI_API_DEPLOYMENT_NAME", + "AZURE_OPENAI_API_EMBEDDINGS_DEPLOYMENT_NAME", + "ASTRA_DB_APPLICATION_TOKEN", + "ASTRA_DB_API_ENDPOINT", + "COHERE_API_KEY", + "HUGGINGFACEHUB_API_TOKEN", + "SEARCHAPI_API_KEY", + "SERPAPI_API_KEY", + "VECTARA_CUSTOMER_ID", + "VECTARA_CORPUS_ID", + "VECTARA_API_KEY", +] diff --git a/src/backend/base/langflow/services/utils.py b/src/backend/base/langflow/services/utils.py index 10c1a359e..34b3415ad 100644 --- a/src/backend/base/langflow/services/utils.py +++ b/src/backend/base/langflow/services/utils.py @@ -1,41 +1,13 @@ -import importlib -import inspect - from loguru import logger from sqlmodel import Session, select from langflow.services.auth.utils import create_super_user, verify_password +from langflow.services.cache.factory import CacheServiceFactory from langflow.services.database.utils import initialize_database -from langflow.services.factory import ServiceFactory -from langflow.services.manager import service_manager from langflow.services.schema import ServiceType from langflow.services.settings.constants import DEFAULT_SUPERUSER, DEFAULT_SUPERUSER_PASSWORD -from langflow.services.socket.utils import set_socketio_server -from .deps import get_db_service, get_session, get_settings_service - - -def get_factories(): - service_names = [ServiceType(service_type).value.replace("_service", "") for service_type in ServiceType] - base_module = "langflow.services" - factories = [] - - for name in service_names: - try: - module_name = f"{base_module}.{name}.factory" - module = importlib.import_module(module_name) - - # Find all classes in the module that are subclasses of ServiceFactory - for name, obj in inspect.getmembers(module, inspect.isclass): - if issubclass(obj, ServiceFactory) and obj is not ServiceFactory: - factories.append(obj()) - break - - except Exception as exc: - logger.exception(exc) - raise RuntimeError(f"Could not initialize services. Please check your settings. Error in {name}.") from exc - - return factories +from .deps import get_db_service, get_service, get_session, get_settings_service def get_or_create_super_user(session: Session, username, password, is_default): @@ -145,6 +117,8 @@ def teardown_services(): except Exception as exc: logger.exception(exc) try: + from langflow.services.manager import service_manager + service_manager.teardown() except Exception as exc: logger.exception(exc) @@ -156,7 +130,7 @@ def initialize_settings_service(): """ from langflow.services.settings import factory as settings_factory - service_manager.register_factory(settings_factory.SettingsServiceFactory()) + get_service(ServiceType.SETTINGS_SERVICE, settings_factory.SettingsServiceFactory()) def initialize_session_service(): @@ -168,11 +142,13 @@ def initialize_session_service(): initialize_settings_service() - service_manager.register_factory( + get_service( + ServiceType.CACHE_SERVICE, cache_factory.CacheServiceFactory(), ) - service_manager.register_factory( + get_service( + ServiceType.SESSION_SERVICE, session_service_factory.SessionServiceFactory(), ) @@ -181,27 +157,17 @@ def initialize_services(fix_migration: bool = False, socketio_server=None): """ Initialize all the services needed. """ - for factory in get_factories(): - try: - service_manager.register_factory(factory) - except Exception as exc: - logger.exception(exc) - logger.error(f"Error initializing {factory}: {exc}") - # Test cache connection - service_manager.get(ServiceType.CACHE_SERVICE) + get_service(ServiceType.CACHE_SERVICE, default=CacheServiceFactory()) # Setup the superuser try: initialize_database(fix_migration=fix_migration) except Exception as exc: logger.error(exc) raise exc - setup_superuser(service_manager.get(ServiceType.SETTINGS_SERVICE), next(get_session())) + setup_superuser(get_service(ServiceType.SETTINGS_SERVICE), next(get_session())) try: get_db_service().migrate_flows_if_auto_login() except Exception as exc: logger.error(f"Error migrating flows: {exc}") raise RuntimeError("Error migrating flows") from exc - - # Initialize the SocketIO service - set_socketio_server(socketio_server) diff --git a/src/backend/base/langflow/services/variable/service.py b/src/backend/base/langflow/services/variable/service.py index 23d1cd806..ef23a95a2 100644 --- a/src/backend/base/langflow/services/variable/service.py +++ b/src/backend/base/langflow/services/variable/service.py @@ -1,12 +1,14 @@ +import os from typing import TYPE_CHECKING, Optional, Union from uuid import UUID from fastapi import Depends +from loguru import logger from sqlmodel import Session, select from langflow.services.auth import utils as auth_utils from langflow.services.base import Service -from langflow.services.database.models.variable.model import Variable +from langflow.services.database.models.variable.model import Variable, VariableCreate from langflow.services.deps import get_session if TYPE_CHECKING: @@ -19,7 +21,28 @@ class VariableService(Service): def __init__(self, settings_service: "SettingsService"): self.settings_service = settings_service - def get_variable(self, user_id: Union[UUID, str], name: str, session: Session = Depends(get_session)) -> str: + def initialize_user_variables(self, user_id: Union[UUID, str], session: Session = Depends(get_session)): + # Check for environment variables that should be stored in the database + should_or_should_not = "Should" if self.settings_service.settings.store_environment_variables else "Should not" + logger.info(f"{should_or_should_not} store environment variables in the database.") + if self.settings_service.settings.store_environment_variables: + for var in self.settings_service.settings.variables_to_get_from_environment: + if var in os.environ: + logger.debug(f"Creating {var} variable from environment.") + try: + self.create_variable(user_id, var, os.environ[var], _type="Credential", session=session) + except Exception as e: + logger.error(f"Error creating {var} variable: {e}") + + else: + logger.info("Skipping environment variable storage.") + + def get_variable( + self, + user_id: Union[UUID, str], + name: str, + session: Session = Depends(get_session), + ) -> str: # we get the credential from the database # credential = session.query(Variable).filter(Variable.user_id == user_id, Variable.name == name).first() variable = session.exec(select(Variable).where(Variable.user_id == user_id, Variable.name == name)).first() @@ -34,7 +57,11 @@ class VariableService(Service): return [variable.name for variable in variables] def update_variable( - self, user_id: Union[UUID, str], name: str, value: str, session: Session = Depends(get_session) + self, + user_id: Union[UUID, str], + name: str, + value: str, + session: Session = Depends(get_session), ): variable = session.exec(select(Variable).where(Variable.user_id == user_id, Variable.name == name)).first() if not variable: @@ -46,7 +73,12 @@ class VariableService(Service): session.refresh(variable) return variable - def delete_variable(self, user_id: Union[UUID, str], name: str, session: Session = Depends(get_session)): + def delete_variable( + self, + user_id: Union[UUID, str], + name: str, + session: Session = Depends(get_session), + ): variable = session.exec(select(Variable).where(Variable.user_id == user_id, Variable.name == name)).first() if not variable: raise ValueError(f"{name} variable not found.") @@ -55,11 +87,19 @@ class VariableService(Service): return variable def create_variable( - self, user_id: Union[UUID, str], name: str, value: str, session: Session = Depends(get_session) + self, + user_id: Union[UUID, str], + name: str, + value: str, + _type: str = "Generic", + session: Session = Depends(get_session), ): - variable = Variable( - user_id=user_id, name=name, value=auth_utils.encrypt_api_key(value, settings_service=self.settings_service) + variable_base = VariableCreate( + name=name, + type=_type, + value=auth_utils.encrypt_api_key(value, settings_service=self.settings_service), ) + variable = Variable.model_validate(variable_base, from_attributes=True, update={"user_id": user_id}) session.add(variable) session.commit() session.refresh(variable) diff --git a/src/backend/base/langflow/template/frontend_node/llms.py b/src/backend/base/langflow/template/frontend_node/llms.py index e33c4a60a..7bf5a8cb6 100644 --- a/src/backend/base/langflow/template/frontend_node/llms.py +++ b/src/backend/base/langflow/template/frontend_node/llms.py @@ -63,8 +63,8 @@ class LLMFrontendNode(FrontendNode): field.info = OPENAI_API_BASE_INFO def add_extra_base_classes(self) -> None: - if "BaseLLM" not in self.base_classes: - self.base_classes.append("BaseLLM") + if "BaseLanguageModel" not in self.base_classes: + self.base_classes.append("BaseLanguageModel") @staticmethod def format_azure_field(field: TemplateField): diff --git a/src/backend/base/langflow/utils/logger.py b/src/backend/base/langflow/utils/logger.py index a656a8462..bc81eaa77 100644 --- a/src/backend/base/langflow/utils/logger.py +++ b/src/backend/base/langflow/utils/logger.py @@ -25,11 +25,10 @@ def patching(record): record["extra"]["serialized"] = serialize(record) -def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None): - if ( - os.getenv("LANGFLOW_LOG_LEVEL", "").upper() in VALID_LOG_LEVELS - and log_level is None - ): +def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None, disable: Optional[bool] = False): + if disable and log_level is None and log_file is None: + logger.disable("langflow") + if os.getenv("LANGFLOW_LOG_LEVEL", "").upper() in VALID_LOG_LEVELS and log_level is None: log_level = os.getenv("LANGFLOW_LOG_LEVEL") if log_level is None: log_level = "ERROR" @@ -77,11 +76,7 @@ def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None): def setup_uvicorn_logger(): - loggers = ( - logging.getLogger(name) - for name in logging.root.manager.loggerDict - if name.startswith("uvicorn.") - ) + loggers = (logging.getLogger(name) for name in logging.root.manager.loggerDict if name.startswith("uvicorn.")) for uvicorn_logger in loggers: uvicorn_logger.handlers = [] logging.getLogger("uvicorn").handlers = [InterceptHandler()] @@ -111,6 +106,4 @@ class InterceptHandler(logging.Handler): frame = frame.f_back depth += 1 - logger.opt(depth=depth, exception=record.exc_info).log( - level, record.getMessage() - ) + logger.opt(depth=depth, exception=record.exc_info).log(level, record.getMessage()) diff --git a/src/backend/base/langflow/utils/util.py b/src/backend/base/langflow/utils/util.py index 4ec2c526c..a4cce8ea3 100644 --- a/src/backend/base/langflow/utils/util.py +++ b/src/backend/base/langflow/utils/util.py @@ -2,13 +2,15 @@ import importlib import inspect import re from functools import wraps +from pathlib import Path from typing import Any, Dict, List, Optional, Union from docstring_parser import parse - from langflow.schema.schema import Record +from langflow.services.deps import get_settings_service from langflow.template.frontend_node.constants import FORCE_SHOW_FIELDS from langflow.utils import constants +from langflow.utils.logger import logger def unescape_string(s: str): @@ -66,54 +68,6 @@ def build_template_from_function(name: str, type_to_loader_dict: Dict, add_funct } -def build_template_from_class(name: str, type_to_cls_dict: Dict, add_function: bool = False): - classes = [item.__name__ for item in type_to_cls_dict.values()] - - # Raise error if name is not in chains - if name not in classes: - raise ValueError(f"{name} not found.") - - for _type, v in type_to_cls_dict.items(): - if v.__name__ == name: - _class = v - - # Get the docstring - docs = parse(_class.__doc__) - - variables = {"_type": _type} - - if "__fields__" in _class.__dict__: - for class_field_items, value in _class.__fields__.items(): - if class_field_items in ["callback_manager"]: - continue - variables[class_field_items] = {} - for name_, value_ in value.__repr_args__(): - if name_ == "default_factory": - try: - variables[class_field_items]["default"] = get_default_factory( - module=_class.__base__.__module__, - function=value_, - ) - except Exception: - variables[class_field_items]["default"] = None - elif name_ not in ["name"]: - variables[class_field_items][name_] = value_ - - variables[class_field_items]["placeholder"] = ( - docs.params[class_field_items] if class_field_items in docs.params else "" - ) - base_classes = get_base_classes(_class) - # Adding function to base classes to allow - # the output to be a function - if add_function: - base_classes.append("Callable") - return { - "template": format_dict(variables, name), - "description": docs.short_description or "", - "base_classes": base_classes, - } - - def build_template_from_method( class_name: str, method_name: str, @@ -462,3 +416,35 @@ def build_loader_repr_from_records(records: List[Record]) -> str: \nAvg. Record Length (characters): {int(avg_length)} Records: {records[:3]}...""" return "0 records" + + +def update_settings( + config: Optional[str] = None, + cache: Optional[str] = None, + dev: bool = False, + remove_api_keys: bool = False, + components_path: Optional[Path] = None, + store: bool = True, +): + """Update the settings from a config file.""" + from langflow.services.utils import initialize_settings_service + + # Check for database_url in the environment variables + + initialize_settings_service() + settings_service = get_settings_service() + if config: + logger.debug(f"Loading settings from {config}") + settings_service.settings.update_from_yaml(config, dev=dev) + if remove_api_keys: + logger.debug(f"Setting remove_api_keys to {remove_api_keys}") + settings_service.settings.update_settings(REMOVE_API_KEYS=remove_api_keys) + if cache: + logger.debug(f"Setting cache to {cache}") + settings_service.settings.update_settings(CACHE=cache) + if components_path: + 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"Gustavo Schaedler ", "Igor Carvalho ", "Lucas Eduoli ", "Otávio Anovazzi ", "Rodrigo Nader ", ] -repository = "https://github.com/logspace-ai/langflow" +repository = "https://github.com/langflow-ai/langflow" license = "MIT" readme = "README.md" keywords = ["nlp", "langchain", "openai", "gpt", "gui"] @@ -43,7 +42,7 @@ typer = "^0.12.0" cachetools = "^5.3.1" platformdirs = "^4.2.0" python-multipart = "^0.0.7" -orjson = "3.9.15" +orjson = "3.10.0" alembic = "^1.13.0" passlib = "^1.7.4" bcrypt = "4.0.1" @@ -60,30 +59,7 @@ pypdf = "^4.1.0" nest-asyncio = "^1.6.0" emoji = "^2.11.0" cryptography = "^42.0.5" - - -[tool.poetry.group.dev.dependencies] -pytest-asyncio = "^0.23.1" -types-redis = "^4.6.0.5" -ipykernel = "^6.26.0" -mypy = "^1.7.1" -ruff = "^0.3.5" -httpx = "*" -pytest = "^8.1.1" -types-requests = "^2.31.0" -requests = "^2.31.0" -pytest-cov = "^5.0.0" -pandas-stubs = "^2.2.1.230412" -types-pillow = "^10.2.0.0" -types-pyyaml = "^6.0.12.8" -types-python-jose = "^3.3.4.8" -types-passlib = "^1.7.7.13" -locust = "^2.24.1" -pytest-mock = "^3.14.0" -pytest-xdist = "^3.5.0" -types-pywin32 = "^306.0.0.4" -types-google-cloud-ndb = "^2.3.0.0" -pytest-sugar = "^1.0.0" +asyncer = "^0.0.5" [tool.poetry.extras] diff --git a/src/backend/langflow/version/__init__.py b/src/backend/langflow/version/__init__.py index 5c0cf285c..336c63fea 100644 --- a/src/backend/langflow/version/__init__.py +++ b/src/backend/langflow/version/__init__.py @@ -1 +1 @@ -from .version import __version__ # noqa: F401 +from .version import __version__, is_pre_release # noqa: F401 diff --git a/src/backend/langflow/version/version.py b/src/backend/langflow/version/version.py index 540b465f9..dce827029 100644 --- a/src/backend/langflow/version/version.py +++ b/src/backend/langflow/version/version.py @@ -2,6 +2,9 @@ from importlib import metadata try: __version__ = metadata.version("langflow") + # Check if the version is a pre-release version + is_pre_release = any(label in __version__ for label in ["a", "b", "rc", "dev", "post"]) except metadata.PackageNotFoundError: __version__ = "" + is_pre_release = False del metadata diff --git a/src/frontend/index.html b/src/frontend/index.html index d4b03effb..c99fd9f2f 100644 --- a/src/frontend/index.html +++ b/src/frontend/index.html @@ -11,7 +11,7 @@ > Langflow - +
diff --git a/src/frontend/playwright.config.ts b/src/frontend/playwright.config.ts index 59f30c4ba..649c42390 100644 --- a/src/frontend/playwright.config.ts +++ b/src/frontend/playwright.config.ts @@ -18,7 +18,7 @@ export default defineConfig({ /* Retry on CI only */ retries: process.env.CI ? 2 : 0, /* Opt out of parallel tests on CI. */ - workers: process.env.CI ? 2 : undefined, + workers: 3, /* Reporter to use. See https://playwright.dev/docs/test-reporters */ timeout: 120 * 1000, // reporter: [ diff --git a/src/frontend/src/App.tsx b/src/frontend/src/App.tsx index da3c50325..6c5af17df 100644 --- a/src/frontend/src/App.tsx +++ b/src/frontend/src/App.tsx @@ -1,9 +1,8 @@ import { useContext, useEffect, useState } from "react"; -import "reactflow/dist/style.css"; -import "./App.css"; - import { ErrorBoundary } from "react-error-boundary"; import { useNavigate } from "react-router-dom"; +import "reactflow/dist/style.css"; +import "./App.css"; import ErrorAlert from "./alerts/error"; import NoticeAlert from "./alerts/notice"; import SuccessAlert from "./alerts/success"; @@ -15,7 +14,7 @@ import { FETCH_ERROR_MESSAGE, } from "./constants/constants"; import { AuthContext } from "./contexts/authContext"; -import { getGlobalVariables, getHealth } from "./controllers/API"; +import { autoLogin, getGlobalVariables, getHealth } from "./controllers/API"; import Router from "./routes"; import useAlertStore from "./stores/alertStore"; import { useDarkStore } from "./stores/darkStore"; @@ -38,8 +37,10 @@ export default function App() { removeFromTempNotificationList(id); }; - const { isAuthenticated } = useContext(AuthContext); + const { isAuthenticated, login, setUserData, setAutoLogin, getUser } = + useContext(AuthContext); const refreshFlows = useFlowsManagerStore((state) => state.refreshFlows); + const setLoading = useAlertStore((state) => state.setLoading); const fetchApiData = useStoreStore((state) => state.fetchApiData); const getTypes = useTypesStore((state) => state.getTypes); const refreshVersion = useDarkStore((state) => state.refreshVersion); @@ -49,27 +50,59 @@ export default function App() { ); const checkHasStore = useStoreStore((state) => state.checkHasStore); const navigate = useNavigate(); + const dark = useDarkStore((state) => state.dark); const [isLoadingHealth, setIsLoadingHealth] = useState(false); useEffect(() => { - refreshStars(); - refreshVersion(); - - // If the user is authenticated, fetch the types. This code is important to check if the user is auth because of the execution order of the useEffect hooks. - if (isAuthenticated === true) { - // get data from db - getTypes().then(() => { - refreshFlows(); - }); - getGlobalVariables().then((res) => { - setGlobalVariables(res); - }); - checkHasStore(); - fetchApiData(); + if (!dark) { + document.getElementById("body")!.classList.remove("dark"); + } else { + document.getElementById("body")!.classList.add("dark"); } + }, [dark]); + + useEffect(() => { + const isLoginPage = location.pathname.includes("login"); + + autoLogin() + .then(async (user) => { + if (user && user["access_token"]) { + user["refresh_token"] = "auto"; + login(user["access_token"]); + setUserData(user); + setAutoLogin(true); + setLoading(false); + await Promise.all([refreshStars(), refreshVersion(), fetchData()]); + } + }) + .catch(async () => { + setAutoLogin(false); + if (isAuthenticated && !isLoginPage) { + getUser(); + await Promise.all([refreshStars(), refreshVersion(), fetchData()]); + } else { + setLoading(false); + useFlowsManagerStore.setState({ isLoading: false }); + } + }); }, [isAuthenticated]); + const fetchData = async () => { + if (isAuthenticated) { + try { + await getTypes(); + refreshFlows(); + const res = await getGlobalVariables(); + setGlobalVariables(res); + checkHasStore(); + fetchApiData(); + } catch (error) { + console.error("Failed to fetch data:", error); + } + } + }; + useEffect(() => { checkApplicationHealth(); // Timer to call getHealth every 5 seconds diff --git a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx index 6a5029101..eef861758 100644 --- a/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx +++ b/src/frontend/src/CustomNodes/GenericNode/components/parameterComponent/index.tsx @@ -252,7 +252,12 @@ export default function ParameterComponent({ nodeIconsLucide[item.family] ?? nodeIconsLucide["unknown"]; return ( -
+
{index === 0 && ( {left ? INPUT_HANDLER_HOVER : OUTPUT_HANDLER_HOVER} )} @@ -276,10 +281,16 @@ export default function ParameterComponent({ }} />
- + {nodeNames[item.family] ?? "Other"}{" "} {item?.display_name && item?.display_name?.length > 0 ? ( - + {" "} {item.display_name === "" ? "" : " - "} {item.display_name.split(", ").length > 2 @@ -296,7 +307,10 @@ export default function ParameterComponent({ : item.display_name} ) : ( - + {" "} {item.type === "" ? "" : " - "} {item.type.split(", ").length > 2 @@ -319,7 +333,9 @@ export default function ParameterComponent({ }); } else { //@ts-ignore - refHtml.current = {TOOLTIP_EMPTY}; + refHtml.current = ( + {TOOLTIP_EMPTY} + ); } } // If optionalHandle is an empty list, then it is not an optional handle @@ -343,6 +359,9 @@ export default function ParameterComponent({ side={left ? "left" : "right"} >
) : ( diff --git a/src/frontend/src/components/CrashErrorComponent/index.tsx b/src/frontend/src/components/CrashErrorComponent/index.tsx index 57dc83d83..978c9b340 100644 --- a/src/frontend/src/components/CrashErrorComponent/index.tsx +++ b/src/frontend/src/components/CrashErrorComponent/index.tsx @@ -28,7 +28,7 @@ export default function CrashErrorComponent({

Please report errors with detailed tracebacks on the{" "} Restart Langflow diff --git a/src/frontend/src/components/cardComponent/index.tsx b/src/frontend/src/components/cardComponent/index.tsx index 9d389fb7e..2ec60e0a2 100644 --- a/src/frontend/src/components/cardComponent/index.tsx +++ b/src/frontend/src/components/cardComponent/index.tsx @@ -163,20 +163,26 @@ export default function CollectionCardComponent({ - {data?.metadata?.total ?? 0} + + {data?.metadata?.total ?? 0} + )} - {likes_count ?? 0} + + {likes_count ?? 0} + - {downloads_count ?? 0} + + {downloads_count ?? 0} + @@ -286,6 +292,7 @@ export default function CollectionCardComponent({ } handleLike(); }} + data-testid={`like-${data.name}`} > ) : node.data.node.template[ diff --git a/src/frontend/src/components/dropdownComponent/index.tsx b/src/frontend/src/components/dropdownComponent/index.tsx index e0c0d39cf..67b9c5d82 100644 --- a/src/frontend/src/components/dropdownComponent/index.tsx +++ b/src/frontend/src/components/dropdownComponent/index.tsx @@ -43,7 +43,7 @@ export default function Dropdown({ role="combobox" ref={refButton} aria-expanded={open} - data-test={`${id ?? ""}`} + data-testid={`${id ?? ""}`} className={cn( editNode ? "dropdown-component-outline" @@ -52,11 +52,14 @@ export default function Dropdown({ editNode ? "input-edit-node" : "py-2" )} > - {value && - value !== "" && - options.find((option) => option === value) - ? options.find((option) => option === value) - : "Choose an option..."} + + {value && + value !== "" && + options.find((option) => option === value) + ? options.find((option) => option === value) + : "Choose an option..."} + + state.setDark); const stars = useDarkStore((state) => state.stars); - useEffect(() => { - if (dark) { - document.getElementById("body")!.classList.add("dark"); - } else { - document.getElementById("body")!.classList.remove("dark"); - } - window.localStorage.setItem("isDark", dark.toString()); - }, [dark]); - async function checkForChanges(nodes: Node[]): Promise { if (nodes.length === 0) { await removeFlow(id!); @@ -103,6 +94,7 @@ export default function Header(): JSX.Element { onClick={() => { checkForChanges(nodes); }} + data-testid="button-store" >

@@ -113,7 +105,7 @@ export default function Header(): JSX.Element {
{ if (disabled && value.length > 0 && value[0] !== "") { @@ -46,6 +47,10 @@ export default function InputListComponent({ newInputList[idx] = event.target.value; onChange(newInputList); }} + data-testid={ + `input-list-input${editNode ? "-edit" : ""}_${componentName}-` + + idx + } /> {idx === value.length - 1 ? (
) : myData.node?.template[templateParam] @@ -559,6 +564,7 @@ const EditNodeModal = forwardRef(