diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 90966fb38..4df8df96b 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -3,7 +3,7 @@ { "name": "Langflow Dev Container", // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile - "image": "mcr.microsoft.com/devcontainers/python:1-3.10-bullseye", + "image": "mcr.microsoft.com/devcontainers/python:3.10", // Features to add to the dev container. More info: https://containers.dev/features. "features": { diff --git a/.github/workflows/pre-release-base.yml b/.github/workflows/pre-release-base.yml index 44ffc92ee..cbcfe3ad6 100644 --- a/.github/workflows/pre-release-base.yml +++ b/.github/workflows/pre-release-base.yml @@ -13,9 +13,12 @@ env: POETRY_VERSION: "1.8.2" jobs: - if_release: + release: + name: Release Langflow Base if: inputs.release_package == true runs-on: ubuntu-latest + outputs: + version: ${{ steps.check-version.outputs.version }} steps: - uses: actions/checkout@v4 - name: Install poetry @@ -49,6 +52,12 @@ jobs: POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }} run: | make publish base=true + docker_build: + name: Build Docker Image + runs-on: ubuntu-latest + needs: release + steps: + - uses: actions/checkout@v4 - name: Set up QEMU uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx @@ -65,4 +74,4 @@ jobs: push: true file: ./build_and_push_base.Dockerfile tags: | - logspace/langflow:base-${{ steps.check-version.outputs.version }} + langflowai/langflow:base-${{ needs.release.outputs.version }} diff --git a/.github/workflows/pre-release-langflow.yml b/.github/workflows/pre-release-langflow.yml index 4c0be75c9..d8e2041ab 100644 --- a/.github/workflows/pre-release-langflow.yml +++ b/.github/workflows/pre-release-langflow.yml @@ -17,9 +17,12 @@ env: POETRY_VERSION: "1.8.2" jobs: - if_release: + release: + name: Release Langflow if: inputs.release_package == true runs-on: ubuntu-latest + outputs: + version: ${{ steps.check-version.outputs.version }} steps: - uses: actions/checkout@v4 - name: Install poetry @@ -49,6 +52,18 @@ jobs: POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }} run: | make publish main=true + - name: Upload Artifact + uses: actions/upload-artifact@v2 + with: + name: dist + path: dist + + docker_build: + name: Build Docker Image + runs-on: ubuntu-latest + needs: release + steps: + - uses: actions/checkout@v4 - name: Set up QEMU uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx @@ -65,9 +80,18 @@ jobs: push: true file: ./build_and_push.Dockerfile tags: | - logspace/langflow:${{ steps.check-version.outputs.version }} - logspace/langflow:1.0-alpha + langflowai/langflow:${{ needs.release.outputs.version }} + langflowai/langflow:1.0-alpha + create_release: + name: Create Release + runs-on: ubuntu-latest + needs: [docker_build, release] + steps: + - uses: actions/download-artifact@v2 + with: + name: dist + path: dist - name: Create Release uses: ncipollo/release-action@v1 with: @@ -76,5 +100,5 @@ jobs: draft: false generateReleaseNotes: true prerelease: true - tag: v${{ steps.check-version.outputs.version }} + tag: v${{ needs.release.outputs.version }} commit: dev diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 5d522c230..c19a491c3 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -52,8 +52,8 @@ jobs: push: true file: ./build_and_push.Dockerfile tags: | - logspace/langflow:${{ steps.check-version.outputs.version }} - logspace/langflow:latest + langflowai/langflow:${{ steps.check-version.outputs.version }} + langflowai/langflow:latest - name: Create Release uses: ncipollo/release-action@v1 with: diff --git a/.github/workflows/typescript_test.yml b/.github/workflows/typescript_test.yml index a75327785..0cb3213d8 100644 --- a/.github/workflows/typescript_test.yml +++ b/.github/workflows/typescript_test.yml @@ -19,8 +19,8 @@ jobs: strategy: fail-fast: false matrix: - shardIndex: [1, 2, 3, 4] - shardTotal: [4] + shardIndex: [1] + shardTotal: [1] steps: - name: Checkout code uses: actions/checkout@v4 @@ -38,27 +38,6 @@ jobs: npm ci if: ${{ steps.setup-node.outputs.cache-hit != 'true' }} - # Attempt to restore the correct Playwright browser binaries based on the - # currently installed version of Playwright (The browser binary versions - # may change with Playwright versions). - # Note: Playwright's cache directory is hard coded because that's what it - # says to do in the docs. There doesn't appear to be a command that prints - # it out for us. - # - uses: actions/cache@v4 - # id: playwright-cache - # with: - # path: ${{ env.PLAYWRIGHT_BROWSERS_PATH }} - # key: "${{ runner.os }}-playwright-${{ hashFiles('src/frontend/package-lock.json') }}" - # # As a fallback, if the Playwright version has changed, try use the - # # most recently cached version. There's a good chance that at least one - # # of the browser binary versions haven't been updated, so Playwright can - # # skip installing that in the next step. - # # Note: When falling back to an old cache, `cache-hit` (used below) - # # will be `false`. This allows us to restore the potentially out of - # # date cache, but still let Playwright decide if it needs to download - # # new binaries or not. - # restore-keys: | - # ${{ runner.os }}-playwright- - name: Cache playwright binaries uses: actions/cache@v4 id: playwright-cache @@ -82,32 +61,26 @@ jobs: npx playwright install-deps if: steps.playwright-cache.outputs.cache-hit != 'true' - # If the Playwright browser binaries weren't able to be restored, we tell - # paywright to install everything for us. - # - name: Install Playwright's dependencies - # if: steps.playwright-cache.outputs.cache-hit != 'true' - # run: npx playwright install --with-deps - - - name: Install Poetry - run: pipx install "poetry==${{ env.POETRY_VERSION }}" - - - name: Set up Python - uses: actions/setup-python@v5 - id: setup-python + - name: Set up Python ${{ env.PYTHON_VERSION }} + Poetry ${{ env.POETRY_VERSION }} + uses: "./.github/actions/poetry_caching" with: python-version: ${{ env.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 ${{ env.PYTHON_VERSION }} poetry install - if: ${{ steps.setup-python.outputs.cache-hit != 'true' }} + + - name: create .env + run: | + touch .env + echo "${{ secrets.ENV_VARS }}" > .env - name: Run Playwright Tests run: | cd src/frontend - npx playwright test --shard=${{ matrix.shardIndex }}/${{ matrix.shardTotal }} + npx playwright test - name: Upload blob report to GitHub Actions Artifacts if: always() diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 69376ded9..3b89f7faa 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -17,23 +17,23 @@ diverse, inclusive, and healthy community. Examples of behavior that contributes to a positive environment for our community include: -* Demonstrating empathy and kindness toward other people -* Being respectful of differing opinions, viewpoints, and experiences -* Giving and gracefully accepting constructive feedback -* Accepting responsibility and apologizing to those affected by our mistakes, +- Demonstrating empathy and kindness toward other people +- Being respectful of differing opinions, viewpoints, and experiences +- Giving and gracefully accepting constructive feedback +- Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience -* Focusing on what is best not just for us as individuals, but for the +- Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: -* The use of sexualized language or imagery, and sexual attention or +- The use of sexualized language or imagery, and sexual attention or advances of any kind -* Trolling, insulting or derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or email +- Trolling, insulting or derogatory comments, and personal or political attacks +- Public or private harassment +- Publishing others' private information, such as a physical or email address, without their explicit permission -* Other conduct which could reasonably be considered inappropriate in a +- Other conduct which could reasonably be considered inappropriate in a professional setting ## Enforcement Responsibilities @@ -60,7 +60,7 @@ representative at an online or offline event. Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at -contact@logspace.ai. +contact@langflow.org. All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the @@ -106,7 +106,7 @@ Violating these terms may lead to a permanent ban. ### 4. Permanent Ban **Community Impact**: Demonstrating a pattern of violation of community -standards, including sustained inappropriate behavior, harassment of an +standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. **Consequence**: A permanent ban from any sort of public interaction within diff --git a/GCP_DEPLOYMENT.md b/GCP_DEPLOYMENT.md index 12b6b0239..9f17e550b 100644 --- a/GCP_DEPLOYMENT.md +++ b/GCP_DEPLOYMENT.md @@ -6,7 +6,7 @@ This guide will help you set up a Langflow development VM in a Google Cloud Plat ## 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/langflow-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/gcp&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. @@ -14,7 +14,7 @@ This script sets up a Debian-based VM with the Langflow package, Nginx, and the ## 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/langflow-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/gcp&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. diff --git a/LICENSE b/LICENSE index cd91750fb..ee3d6be71 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ MIT License -Copyright (c) 2024 Logspace +Copyright (c) 2024 Langflow Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/base.Dockerfile b/base.Dockerfile index 3946af99d..2325ece79 100644 --- a/base.Dockerfile +++ b/base.Dockerfile @@ -63,8 +63,10 @@ RUN --mount=type=cache,target=/root/.cache \ # copy project requirement files here to ensure they will be cached. WORKDIR $PYSETUP_PATH +# Copy just one file to avoid rebuilding the whole image COPY poetry.lock pyproject.toml ./ -COPY ./src/backend/langflow/main.py ./src/backend/langflow/main.py +COPY ./src/backend/langflow ./src/backend/langflow +COPY ./src/backend/base/pyproject.toml ./src/backend/base/pyproject.toml # Copy README.md to the build context COPY README.md . # install runtime deps - uses $POETRY_VIRTUALENVS_IN_PROJECT internally @@ -84,7 +86,7 @@ COPY --from=builder-base $POETRY_HOME $POETRY_HOME COPY --from=builder-base $PYSETUP_PATH $PYSETUP_PATH # Copy just one file to avoid rebuilding the whole image -COPY ./src/backend/langflow/__init__.py ./src/backend/langflow/__init__.py +COPY ./src/backend/langflow ./src/backend/langflow # quicker install as runtime deps are already installed RUN --mount=type=cache,target=/root/.cache \ poetry install --with=dev --extras deploy diff --git a/build_and_push.Dockerfile b/build_and_push.Dockerfile index 1c595e180..f552dd695 100644 --- a/build_and_push.Dockerfile +++ b/build_and_push.Dockerfile @@ -74,10 +74,11 @@ RUN $POETRY_HOME/bin/poetry build FROM python-base as final # Copy virtual environment and built .tar.gz from builder base +RUN useradd -m -u 1000 user COPY --from=builder-base /app/dist/*.tar.gz ./ - # Install the package from the .tar.gz -RUN pip install *.tar.gz +RUN python -m pip install *.tar.gz --user WORKDIR /app -CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"] +ENTRYPOINT ["python", "-m", "langflow", "run"] +CMD ["--host", "0.0.0.0", "--port", "7860"] \ No newline at end of file diff --git a/build_and_push_base.Dockerfile b/build_and_push_base.Dockerfile index f5092c81c..db6d0cf58 100644 --- a/build_and_push_base.Dockerfile +++ b/build_and_push_base.Dockerfile @@ -82,10 +82,11 @@ RUN cd src/backend/base && $POETRY_HOME/bin/poetry build --format sdist FROM python-base as final # Copy virtual environment and built .tar.gz from builder base +RUN useradd -m -u 1000 user COPY --from=builder-base /app/src/backend/base/dist/*.tar.gz ./ - # Install the package from the .tar.gz -RUN pip install *.tar.gz +RUN pip install *.tar.gz --user WORKDIR /app -CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"] +ENTRYPOINT ["python", "-m", "langflow", "run"] +CMD ["--host", "0.0.0.0", "--port", "7860"] \ No newline at end of file diff --git a/container-cmd-cdk.sh b/container-cmd-cdk.sh index 383dffb6b..dbab8f42f 100644 --- a/container-cmd-cdk.sh +++ b/container-cmd-cdk.sh @@ -1,5 +1,5 @@ export LANGFLOW_DATABASE_URL="mysql+pymysql://${username}:${password}@${host}:3306/${dbname}" # echo $LANGFLOW_DATABASE_URL -uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --log-level debug +uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --log-level debug --loop asyncio # python -m langflow run --host 0.0.0.0 --port 7860 \ No newline at end of file diff --git a/deploy/base.Dockerfile b/deploy/base.Dockerfile deleted file mode 100644 index 84b448e2d..000000000 --- a/deploy/base.Dockerfile +++ /dev/null @@ -1,92 +0,0 @@ - - -# syntax=docker/dockerfile:1 -# Keep this syntax directive! It's used to enable Docker BuildKit - -# Based on https://github.com/python-poetry/poetry/discussions/1879?sort=top#discussioncomment-216865 -# but I try to keep it updated (see history) - -################################ -# PYTHON-BASE -# Sets up all our shared environment variables -################################ -FROM python:3.10-slim as python-base - -# python -ENV PYTHONUNBUFFERED=1 \ - # prevents python creating .pyc files - PYTHONDONTWRITEBYTECODE=1 \ - \ - # pip - PIP_DISABLE_PIP_VERSION_CHECK=on \ - PIP_DEFAULT_TIMEOUT=100 \ - \ - # poetry - # https://python-poetry.org/docs/configuration/#using-environment-variables - POETRY_VERSION=1.8.2 \ - # make poetry install to this location - POETRY_HOME="/opt/poetry" \ - # make poetry create the virtual environment in the project's root - # it gets named `.venv` - POETRY_VIRTUALENVS_IN_PROJECT=true \ - # do not ask any interactive question - POETRY_NO_INTERACTION=1 \ - \ - # paths - # this is where our requirements + virtual environment will live - PYSETUP_PATH="/opt/pysetup" \ - VENV_PATH="/opt/pysetup/.venv" - - -# prepend poetry and venv to path -ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH" - - -################################ -# BUILDER-BASE -# Used to build deps + create our virtual environment -################################ -FROM python-base as builder-base -RUN apt-get update \ - && apt-get install --no-install-recommends -y \ - # deps for installing poetry - curl \ - # deps for building python deps - build-essential - -# 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 reuse it -RUN --mount=type=cache,target=/root/.cache \ - curl -sSL https://install.python-poetry.org | python3 - - -# copy project requirement files here to ensure they will be cached. -WORKDIR $PYSETUP_PATH -COPY ./poetry.lock ./pyproject.toml ./ -# Copy README.md to the build context -COPY ./README.md ./ -# install runtime deps - uses $POETRY_VIRTUALENVS_IN_PROJECT internally -RUN --mount=type=cache,target=/root/.cache \ - poetry install --without dev --extras deploy - - -################################ -# DEVELOPMENT -# Image used during development / testing -################################ -FROM python-base as development -WORKDIR $PYSETUP_PATH - -# copy in our built poetry + venv -COPY --from=builder-base $POETRY_HOME $POETRY_HOME -COPY --from=builder-base $PYSETUP_PATH $PYSETUP_PATH - -# Copy just one file to avoid rebuilding the whole image -COPY ./src/backend/langflow/__init__.py ./src/backend/langflow/__init__.py -# quicker install as runtime deps are already installed -RUN --mount=type=cache,target=/root/.cache \ - poetry install --with=dev --extras deploy - -# copy in our app code -COPY ./src/backend ./src/backend -COPY ./tests ./tests diff --git a/deploy/docker-compose.yml b/deploy/docker-compose.yml index 201f9bc97..b1a5f4f06 100644 --- a/deploy/docker-compose.yml +++ b/deploy/docker-compose.yml @@ -69,10 +69,7 @@ services: - traefik.http.routers.${STACK_NAME?Variable not set}-proxy-http.middlewares=${STACK_NAME?Variable not set}-www-redirect,${STACK_NAME?Variable not set}-https-redirect backend: &backend - image: "ogabrielluiz/langflow:latest" - build: - context: ../ - dockerfile: base.Dockerfile + image: "langflowai/langflow:latest" depends_on: - db - broker @@ -143,9 +140,6 @@ services: <<: *backend env_file: - .env - build: - context: ../ - dockerfile: base.Dockerfile command: celery -A langflow.worker.celery_app worker --loglevel=INFO --concurrency=1 -n lf-worker@%h -P eventlet healthcheck: test: "exit 0" @@ -158,9 +152,6 @@ services: - .env networks: - default - build: - context: ../ - dockerfile: base.Dockerfile environment: - FLOWER_PORT=5555 diff --git a/dev.Dockerfile b/dev.Dockerfile index ed3631516..3e5a1d95e 100644 --- a/dev.Dockerfile +++ b/dev.Dockerfile @@ -15,4 +15,4 @@ COPY ./ ./ # Install dependencies RUN poetry config virtualenvs.create false && poetry install --no-interaction --no-ansi -CMD ["uvicorn", "--factory", "src.backend.langflow.main:create_app", "--host", "0.0.0.0", "--port", "7860", "--reload", "--log-level", "debug"] +CMD ["uvicorn", "--factory", "langflow.main:create_app", "--host", "0.0.0.0", "--port", "7860", "--reload", "--log-level", "debug", "--loop", "asyncio"] diff --git a/docker-compose.debug.yml b/docker-compose.debug.yml index c73ff2a60..f81faf8d4 100644 --- a/docker-compose.debug.yml +++ b/docker-compose.debug.yml @@ -11,7 +11,7 @@ services: [ "sh", "-c", - "pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload", + "pip install debugpy -t /tmp && python /tmp/debugpy --wait-for-client --listen 0.0.0.0:5678 -m uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --loop asyncio", ] ports: - 7860:7860 diff --git a/docker-compose.yml b/docker-compose.yml index c74447899..cd7a3b338 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -13,7 +13,7 @@ services: - "7860:7860" volumes: - ./:/app - command: bash -c "uvicorn --factory src.backend.langflow.main:create_app --host 0.0.0.0 --port 7860 --reload" + command: bash -c "uvicorn --factory langflow.main:create_app --host 0.0.0.0 --port 7860 --reload --loop asyncio", networks: - langflow frontend: @@ -23,7 +23,7 @@ services: args: - BACKEND_URL=http://backend:7860 depends_on: - - backend + - backend environment: - VITE_PROXY_TARGET=http://backend:7860 ports: diff --git a/docker_example/Dockerfile b/docker_example/Dockerfile index 24b2b5dcc..e7f0b422a 100644 --- a/docker_example/Dockerfile +++ b/docker_example/Dockerfile @@ -1,3 +1,3 @@ -FROM logspace/langflow:latest +FROM langflowai/langflow:latest CMD ["python", "-m", "langflow", "run", "--host", "0.0.0.0", "--port", "7860"] diff --git a/docker_example/README.md b/docker_example/README.md index 9eadce1f3..c5d2adb26 100644 --- a/docker_example/README.md +++ b/docker_example/README.md @@ -35,7 +35,7 @@ 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. +The `langflow` service uses the `langflowai/langflow:latest` Docker image and exposes port 7860. It depends on the `postgres` service. Environment variables: @@ -62,4 +62,4 @@ Volumes: ## 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`. +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 `langflowai/langflow:latest` to `langflowai/langflow:1.0-alpha`. diff --git a/docker_example/docker-compose.yml b/docker_example/docker-compose.yml index 9a9c435de..a8fddac71 100644 --- a/docker_example/docker-compose.yml +++ b/docker_example/docker-compose.yml @@ -2,7 +2,7 @@ version: "3.8" services: langflow: - image: logspace/langflow:latest + image: langflowai/langflow:latest ports: - "7860:7860" depends_on: diff --git a/docker_example/pre.Dockerfile b/docker_example/pre.Dockerfile index 40fd4ce6d..a72b72595 100644 --- a/docker_example/pre.Dockerfile +++ b/docker_example/pre.Dockerfile @@ -1,3 +1,3 @@ -FROM logspace/langflow:1.0-alpha +FROM langflowai/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 index 577f7bb3a..b2fa00903 100644 --- a/docker_example/pre.docker-compose.yml +++ b/docker_example/pre.docker-compose.yml @@ -2,7 +2,7 @@ version: "3.8" services: langflow: - image: logspace/langflow:1.0-alpha + image: langflowai/langflow:1.0-alpha ports: - "7860:7860" depends_on: diff --git a/docs/docs/deployment/gcp-deployment.md b/docs/docs/deployment/gcp-deployment.md index bd2b48968..e126e785c 100644 --- a/docs/docs/deployment/gcp-deployment.md +++ b/docs/docs/deployment/gcp-deployment.md @@ -8,7 +8,7 @@ This guide will help you set up a Langflow development VM in a Google Cloud Plat ## 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/langflow-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/gcp&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. @@ -16,7 +16,7 @@ This script sets up a Debian-based VM with the Langflow package, Nginx, and the ## 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/genome21/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/genome21/langflow&working_dir=scripts/gcp&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. diff --git a/docs/docs/getting-started/blog-writer.mdx b/docs/docs/getting-started/blog-writer.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/getting-started/document-qa.mdx b/docs/docs/getting-started/document-qa.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/getting-started/huggingface-spaces.mdx b/docs/docs/getting-started/huggingface-spaces.mdx new file mode 100644 index 000000000..0bbbb48de --- /dev/null +++ b/docs/docs/getting-started/huggingface-spaces.mdx @@ -0,0 +1,27 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import Admonition from "@theme/Admonition"; + +# 🤗 HuggingFace Spaces + +Hugging Face provides a great alternative for running Langflow in their Spaces environment. This means you can run Langflow without any local installation required. + +The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Langflow/Langflow?duplicate=true) or [Langflow 1.0 Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) + +Remember to use a Chromium-based browser for the best experience. You'll be presented with the following screen: + + + +From here, just name your Space, define the visibility (Public or Private), and click on `Duplicate Space` to start the installation process. When that is done, you'll be redirected to the Space's main page to start using Langflow right away! + +Once you get Langflow running, click on New Project in the top right corner of the screen. Langflow provides a range of example flows to help you get started. + +To quickly try one of them, open a starter example, set up your API keys and click ⚡ Run, on the bottom right corner of the canvas. This will open up Langflow's Interaction Panel with the chat console, text inputs, and outputs. diff --git a/docs/docs/getting-started/install-langflow.mdx b/docs/docs/getting-started/install-langflow.mdx new file mode 100644 index 000000000..4571e2f98 --- /dev/null +++ b/docs/docs/getting-started/install-langflow.mdx @@ -0,0 +1,77 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import Admonition from "@theme/Admonition"; + +# 📦 Install Langflow + + + Langflow v1.0 is also available in a [HuggingFace Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) if you'd rather try it out before installing locally. + + +## Prerequisites + +Langflow requires the following programs installed on your system. + +* [Python 3.10](https://www.python.org/downloads/release/python-3100/) + +* [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/) + +## Install Langflow + +To install Langflow: + +pip: +```bash +python -m pip install langflow -U +``` + +pipx: +```bash +pipx install langflow --python python3.10 --fetch-missing-python +``` +Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually. + + +## Install Langflow pre-release + +Use `--force-reinstall` to ensure you have the latest version of Langflow and its dependencies. + +To install a pre-release version of Langflow: + +pip: +```bash +python -m pip install langflow --pre --force-reinstall +``` + +pipx: +```bash +pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall" +``` + +## Having a problem? + +If you encounter a problem, see [Possible Installation Issues](/migration/possible-installation-issues). + +To get help in the Langflow CLI: + +```bash +python -m langflow --help +``` + +## ⛓️ Run Langflow + +1. To run Langflow, enter the following command. +```bash +python -m langflow run +``` + +2. Confirm that a local Langflow instance starts by visiting `http://127.0.0.1:7860` in your browser. +```bash +│ Welcome to ⛓ Langflow │ +│ │ +│ Access http://127.0.0.1:7860 │ +│ Collaborate, and contribute at our GitHub Repo 🚀 │ +``` + +3. Continue on to the [Quickstart](./quickstart.mdx). \ No newline at end of file diff --git a/docs/docs/getting-started/memory-chatbot.mdx b/docs/docs/getting-started/memory-chatbot.mdx deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/docs/getting-started/new-to-llms.mdx b/docs/docs/getting-started/new-to-llms.mdx new file mode 100644 index 000000000..3a38a37f4 --- /dev/null +++ b/docs/docs/getting-started/new-to-llms.mdx @@ -0,0 +1,10 @@ +# 📚 New to LLMs? + +Large Language Models, or LLMs, are part of an exciting new world in computing. + +We made Langflow for anyone to create with LLMs, and hope you'll feel comfortable installing Langflow and [getting started](./quickstart.mdx). + +If you want to learn more about LLMs, prompt engineering, and AI models, Langflow recommends [promptingguide.ai](https://promptingguide.ai), an open-source repository of prompt engineering content maintained by AI experts. +PromptingGuide offers content for [beginners](https://www.promptingguide.ai/introduction/basics) and [experts](https://www.promptingguide.ai/techniques/cot), as well as the latest [research papers](https://www.promptingguide.ai/papers) and [test results](https://www.promptingguide.ai/research) fueling AI's progress. + +Wherever you are on your AI journey, it's helpful to keep Prompting Guide open in a tab. \ No newline at end of file diff --git a/docs/docs/getting-started/quickstart.mdx b/docs/docs/getting-started/quickstart.mdx new file mode 100644 index 000000000..1caf71077 --- /dev/null +++ b/docs/docs/getting-started/quickstart.mdx @@ -0,0 +1,119 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import ReactPlayer from "react-player"; +import Admonition from "@theme/Admonition"; + +# ⚡️ Quickstart + +This quickstart demonstrates how to install Langflow, run it locally, build a basic prompt flow, and modify that prompt for different outcomes. + +## Prerequisites + +* [Python 3.10](https://www.python.org/downloads/release/python-3100/) + +* [pip](https://pypi.org/project/pip/) or [pipx](https://pipx.pypa.io/stable/installation/) + +* [OpenAI API key](https://platform.openai.com) + +## Install Langflow + + + Langflow v1.0 is also available in a [HuggingFace Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) if you'd rather try it out before installing locally. This quickstart will run there, too. + + +1. To install Langflow, enter the following command in pip or pipx: + +pip: +```bash +python -m pip install langflow -U +``` + +pipx: +```bash +pipx install langflow --python python3.10 --fetch-missing-python +``` +Pipx can fetch the missing Python version for you with `--fetch-missing-python`, but you can also install the Python version manually. + +2. Start a local Langflow instance with the Langflow CLI: +```bash +langflow run +``` + +Or start Langflow with Python: +```bash +python -m langflow run +``` + +Result: +``` +│ Welcome to ⛓ Langflow │ +│ │ +│ Access http://127.0.0.1:7860 │ +│ Collaborate, and contribute at our GitHub Repo 🚀 │ +``` + +3. Go to `http://127.0.0.1:7860` and confirm the Langflow UI is available. + + + If you encounter a problem, see [Possible Installation Issues](/migration/possible-installation-issues). + + +## Create the basic prompting project + +Now that you have Langflow installed and running, let us formally welcome you to Langflow!👋 + +You will use Langflow's prompt tools to issue prompts to the OpenAI LLM. + +Prompts serve as the inputs to a large language model (LLM), acting as the interface between human instructions and computational tasks. + +By submitting natural language requests in a prompt to an LLM, you can obtain answers, generate text, and solve problems. + +1. From the Langflow dashboard, click **New Project**. +2. Select **Basic Prompting**. +3. The **Basic Prompting** flow is created. + + + +This flow allows you to chat with the **OpenAI** component via a **Prompt** component. +Examine the **Prompt** component. The **Template** field instructs the LLM to `Answer the user as if you were a pirate.` +This should be interesting... + +4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. + +## Run the basic prompting flow + +1. Click the **Run** button. +The **Interaction Panel** opens, where you can converse with your bot. +2. Type a message and press Enter. +The bot responds in a markedly piratical manner! + +## Modify the prompt for a different result + +1. To modify your prompt results, in the **Prompt** template, click the **Template** field. +The **Edit Prompt** window opens. +2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.` +3. Run the basic prompting flow again. +The response will be markedly different. + +## Next steps + +Well done! You've built your first prompt in Langflow. 🎉 + +By adding Langflow components to this prompt, you can build all sorts of interesting flows. + +* [Memory chatbot](/guides/memory-chatbot.mdx) +* [Blog writer](/guides/blog-writer.mdx) +* [Document QA](/guides/document-qa.mdx) + + diff --git a/docs/docs/getting-started/rag-with-astradb.mdx b/docs/docs/getting-started/rag-with-astradb.mdx deleted file mode 100644 index 01daa7b6f..000000000 --- a/docs/docs/getting-started/rag-with-astradb.mdx +++ /dev/null @@ -1,195 +0,0 @@ -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/guides/basic-prompting.mdx b/docs/docs/guides/basic-prompting.mdx index e69de29bb..08ec0259e 100644 --- a/docs/docs/guides/basic-prompting.mdx +++ b/docs/docs/guides/basic-prompting.mdx @@ -0,0 +1,83 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import ReactPlayer from "react-player"; + +# Basic prompting + +Prompts serve as the inputs to a large language model (LLM), acting as the interface between human instructions and computational tasks. + +By submitting natural language requests in a prompt to an LLM, you can obtain answers, generate text, and solve problems. + +This article demonstrates how to use Langflow's prompt tools to issue basic prompts to an LLM, and how various prompting strategies can affect your outcomes. + +## Prerequisites + +1. Install Langflow. +```bash +python -m pip install langflow --pre +``` + +2. Start a local Langflow instance with the Langflow CLI: +```bash +langflow run +``` +Or start Langflow with Python: +```bash +python -m langflow run +``` + +Result: +``` +│ Welcome to ⛓ Langflow │ +│ │ +│ Access http://127.0.0.1:7860 │ +│ Collaborate, and contribute at our GitHub Repo 🚀 │ +``` + +Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment. + +3. Create an [OpenAI API key](https://platform.openai.com). + +## Create the basic prompting project + +1. From the Langflow dashboard, click **New Project**. +2. Select **Basic Prompting**. +3. The **Basic Prompting** flow is created. + + + +This flow allows you to chat with the **OpenAI** component via a **Prompt** component. +Examine the **Prompt** component. The **Template** field instructs the LLM to `Answer the user as if you were a pirate.` +This should be interesting... + +4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. + +## Run the basic prompting flow + +1. Click the **Run** button. +The **Interaction Panel** opens, where you can converse with your bot. +2. Type a message and press Enter. +The bot responds in a markedly piratical manner! + +## Modify the prompt for a different result + +1. To modify your prompt results, in the **Prompt** template, click the **Template** field. +The **Edit Prompt** window opens. +2. Change `Answer the user as if you were a pirate` to a different character, perhaps `Answer the user as if you were Harold Abelson.` +3. Run the basic prompting flow again. +The response will be markedly different. + + + + diff --git a/docs/docs/guides/blog-writer.mdx b/docs/docs/guides/blog-writer.mdx index e69de29bb..7b336e7ba 100644 --- a/docs/docs/guides/blog-writer.mdx +++ b/docs/docs/guides/blog-writer.mdx @@ -0,0 +1,92 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import ReactPlayer from "react-player"; +import Admonition from "@theme/Admonition"; + +# Blog writer + +Build a blog writer with OpenAI that uses URLs for reference content. + +## Prerequisites + +1. Install Langflow. +```bash +python -m pip install langflow --pre +``` + +2. Start a local Langflow instance with the Langflow CLI: +```bash +langflow run +``` +Or start Langflow with Python: +```bash +python -m langflow run +``` + +Result: +```bash +│ Welcome to ⛓ Langflow │ +│ │ +│ Access http://127.0.0.1:7860 │ +│ Collaborate, and contribute at our GitHub Repo 🚀 │ +``` + +Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment. + +3. Create an [OpenAI API key](https://platform.openai.com). + +## Create the Blog Writer project + +1. From the Langflow dashboard, click **New Project**. +2. Select **Blog Writer**. +3. The **Blog Writer** flow is created. + + + +This flow creates a one-shot prompt flow with **Prompt**, **OpenAI**, and **Chat Output** components, and augments the flow with reference content and instructions from the **URL** and **Instructions** components. + +The **Prompt** component's default **Template** field looks like this: +```bash +Reference 1: + +{reference_1} + +--- + +Reference 2: + +{reference_2} + +--- + +{instructions} + +Blog: + +``` + +The `{instructions}` value is received from the **Value** field of the **Instructions** component. +The `reference_1` and `reference_2` values are received from the **URL** fields of the **URL** components. + +4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. + +## Run the Blog Writer flow + +1. Click the **Run** button. +The **Interaction Panel** opens, where you can run your one-shot flow. +2. Click the **Lighting Bolt** icon to run your flow. +3. The **OpenAI** component constructs a blog post with the **URL** items as context. +The default **URL** values are for web pages at `promptingguide.ai`, so your blog post will be about prompting LLMs. + +To write about something different, change the values in the **URL** components, and see what the LLM constructs. \ No newline at end of file diff --git a/docs/docs/guides/document-qa.mdx b/docs/docs/guides/document-qa.mdx index e69de29bb..105753b99 100644 --- a/docs/docs/guides/document-qa.mdx +++ b/docs/docs/guides/document-qa.mdx @@ -0,0 +1,82 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import ReactPlayer from "react-player"; +import Admonition from "@theme/Admonition"; + +# Document QA + +Build a question-and-answer chatbot with a document loaded from local memory. + +## Prerequisites + +1. Install Langflow. +```bash +python -m pip install langflow --pre +``` + +2. Start a local Langflow instance with the Langflow CLI: +```bash +langflow run +``` +Or start Langflow with Python: +```bash +python -m langflow run +``` + +Result: +``` +│ Welcome to ⛓ Langflow │ +│ │ +│ Access http://127.0.0.1:7860 │ +│ Collaborate, and contribute at our GitHub Repo 🚀 │ +``` + +Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment. + +3. Create an [OpenAI API key](https://platform.openai.com). + +## Create the Document QA project + +1. From the Langflow dashboard, click **New Project**. +2. Select **Document QA**. +3. The **Document QA** flow is created. + + + +This flow creates a basic chatbot with the **Chat Input**, **Prompt**, **OpenAI**, and **Chat Output** components. +This chatbot is augmented with the **Files** component, which loads a file from your local machine into the **Prompt** component as `{Document}`. +The **Prompt** component is instructed to answer questions based on the contents of `{Document}`. +Including a file with the prompt gives the **OpenAI** component context it may not otherwise have access to. + +4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. + +5. To select a document to load, in the **Files** component, click within the **Path** field. + 1. Select a local file, and then click **Open**. + 2. The file name appears in the field. + + The file must be of an extension type listed [here](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/base/data/utils.py#L13). + + +## Run the Document QA flow + +1. Click the **Run** button. +The **Interaction Panel** opens, where you can converse with your bot. +2. Type a message and press Enter. +For this example, we loaded an error log `.txt` file and asked, "What went wrong?" +The bot responded: +``` +The issue occurred during the execution of migrations in the application. Specifically, an error was raised by the Alembic library, indicating that new upgrade operations were detected that had not been accounted for in the existing migration scripts. The operation in question involved modifying the nullable property of a column (apikey, created_at) in the database, with details about the existing type (DATETIME()), existing server default, and other properties. +``` + +This result indicates that the bot received the loaded document and understood the context surrounding the vague question. It also correctly identified the issue in the error log, and followed up with appropriate troubleshooting suggestions. Nice! diff --git a/docs/docs/guides/memory-chatbot.mdx b/docs/docs/guides/memory-chatbot.mdx index e69de29bb..227b530ea 100644 --- a/docs/docs/guides/memory-chatbot.mdx +++ b/docs/docs/guides/memory-chatbot.mdx @@ -0,0 +1,99 @@ +import ThemedImage from "@theme/ThemedImage"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import ZoomableImage from "/src/theme/ZoomableImage.js"; +import ReactPlayer from "react-player"; + +# Memory chatbot + +This flow extends the [basic prompting flow](./basic-prompting.mdx) to include chat memory for unique SessionIDs. + +## Prerequisites + +1. Install Langflow. +```bash +python -m pip install langflow --pre +``` + +2. Start a local Langflow instance with the Langflow CLI: +```bash +langflow run +``` +Or start Langflow with Python: +```bash +python -m langflow run +``` + +Result: +``` +│ Welcome to ⛓ Langflow │ +│ │ +│ Access http://127.0.0.1:7860 │ +│ Collaborate, and contribute at our GitHub Repo 🚀 │ +``` + +Alternatively, go to [HuggingFace Spaces](https://docs.langflow.org/getting-started/hugging-face-spaces) or [Lightning.ai Studio](https://lightning.ai/ogabrielluiz-8j6t8/studios/langflow) for a pre-built Langflow test environment. + +3. Create an [OpenAI API key](https://platform.openai.com). + +## Create the memory chatbot project + +1. From the Langflow dashboard, click **New Project**. +2. Select **Memory Chatbot**. +3. The **Memory Chatbot** flow is created. + + + +This flow creates a basic chatbot with the **Chat Input**, **Prompt**, and **OpenAI** components. +This chatbot is augmented with the **Chat Memory** component, which stores messages submitted via **Chat Input** and prepends them to subsequent prompts to OpenAI via `{context}`. +The **Chat History** component gives the **OpenAI** component a memory of previous questions. + +4. To create an environment variable for the **OpenAI** component, in the **OpenAI API Key** field, click the **Globe** button, and then click **Add New Variable**. + 1. In the **Variable Name** field, enter `openai_api_key`. + 2. In the **Value** field, paste your OpenAI API Key (`sk-...`). + 3. Click **Save Variable**. + +## Run the memory chatbot flow + +1. Click the **Run** button. +The **Interaction Panel** opens, where you can converse with your bot. +2. Type a message and press Enter. +The bot will respond according to the template in the **Prompt** component. +3. Type more questions. In the **Outputs** log, your queries are logged in order. Up to 5 queries are stored by default. Try asking `What is the first subject I asked you about?` to see where the LLM's memory disappears. + +## Modify the Session ID field to have multiple conversations + +`SessionID` is a unique identifier in Langchain for a conversation session between a chatbot and a client. +A `SessionID` is created when a conversation is initiated, and then associated with all subsequent messages during that session. + +In the **Memory Chatbot** flow you created, the **Chat Memory** component references past interactions with **Chat Input** by **Session ID**. +You can demonstrate this by modifying the **Session ID** value to switch between conversation histories. + +1. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value from `MySessionID` to `AnotherSessionID`. +2. Click the **Run** button to run your flow. +In the **Interaction Panel**, you will have a new conversation. (You may need to clear the cache with the **Eraser** button). +3. Type a few questions to your bot. +4. In the **Session ID** field of the **Chat Memory** and **Chat Input** components, change the **Session ID** value back to `MySessionID`. +5. Run your flow. +The **Outputs** log of the **Interaction Panel** displays the history from your initial chat with `MySessionID`. + +## Store Session ID as a Langflow variable + +To store **Session ID** as a Langflow variable, in the **Session ID** field, click the **Globe** button, and then click **Add New Variable**. + +1. In the **Variable Name** field, enter a name like `customer_chat_emea`. +2. In the **Value** field, enter a value like `1B5EBD79-6E9C-4533-B2C8-7E4FF29E983B`. +3. Click **Save Variable**. +4. Apply this variable to **Chat Input**. + diff --git a/docs/docs/index.mdx b/docs/docs/index.mdx index 7ee26d0e0..1310e5ae6 100644 --- a/docs/docs/index.mdx +++ b/docs/docs/index.mdx @@ -5,7 +5,9 @@ import Admonition from "@theme/Admonition"; # 👋 Welcome to Langflow -Langflow is an easy way to build from simple to complex AI applications. It is a low-code platform that allows you to integrate AI into everything you do. +Langflow is a low-code platform that allows you to integrate AI into everything you do. + +Use Langflow's simple but powerful UI to build any AI application you can dream up, from simple to complex. {" "} @@ -20,96 +22,17 @@ Langflow is an easy way to build from simple to complex AI applications. It is a ## 🚀 First steps -## Installation +* [Install Langflow](/getting-started/install-langflow) - Install and start a local Langflow server. -Make sure you have **Python 3.10** installed on your system. +* [Quickstart](/getting-started/quickstart) - Install Langflow, create a flow, and run it. -You can install **Langflow** with [pipx](https://pipx.pypa.io/stable/installation/) or with pip. +* [HuggingFace Spaces](/getting-started/huggingface-spaces) - Duplicate the Langflow preview space and try it out before you install. -Pipx can fetch the missing Python version for you, but you can also install it manually. +* [New to LLMs?](/getting-started/new-to-llms) - Learn more about LLMs, prompting, and more at [promptingguide.ai](https://promptingguide.ai). -```bash -# Remember to check if you have Python 3.10 installed -python -m pip install langflow -U -# or -pipx install langflow --python python3.10 --fetch-missing-python -``` +## Learn more about Langflow 1.0 -Or you can install a pre-release version using: +Learn more about the exciting changes in Langflow 1.0, and how to migrate your existing Langflow projects. -```bash -python -m pip install langflow --pre --force-reinstall -# or -pipx install langflow --python python3.10 --fetch-missing-python --pip-args="--pre --force-reinstall" -``` - - -

- Please, check out our [Possible Installation Issues - section](/migration/possible-installation-issues) if you encounter any - problems. -

- - -We recommend using --force-reinstall to ensure you have the latest version of Langflow and its dependencies. - -### ⛓️ Running Langflow - -Langflow can be run in a variety of ways, including using the command-line interface (CLI) or HuggingFace Spaces. - -```bash -python -m langflow run # or langflow --help -``` - -#### 🤗 HuggingFace Spaces - -Hugging Face provides a great alternative for running Langflow in their Spaces environment. This means you can run Langflow without any local installation required. - -The first step is to go to the [Langflow Space](https://huggingface.co/spaces/Langflow/Langflow?duplicate=true) or [Langflow 1.0 Preview Space](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true) - -Remember to use a Chromium-based browser for the best experience. You'll be presented with the following screen: - - - -From here, just name your Space, define the visibility (Public or Private), and click on `Duplicate Space` to start the installation process. When that is done, you'll be redirected to the Space's main page to start using Langflow right away! - -Once you get Langflow running, click on New Project in the top right corner of the screen. Langflow provides a range of example flows to help you get started. - -To quickly try one of them, open a starter example, set up your API keys and click ⚡ Run, on the bottom right corner of the canvas. This will open up Langflow's Interaction Panel with the chat console, text inputs, and outputs. - -### 🖥️ Command Line Interface (CLI) - -Langflow provides a command-line interface (CLI) for easy management and configuration. - -#### Usage - -You can run the Langflow using the following command: - -```bash -langflow run [OPTIONS] -``` - -Find more information about the available options by running: - -```bash -python -m langflow --help -``` - -## Find out more about 1.0 - - -

- We are currently working on updating the documentation for Langflow 1.0. -

- - -To get you learning more about what's new and why you should be excited about Langflow 1.0, -go to [A new chapter for Langflow](/whats-new/a-new-chapter-langflow) and also come back often -to check out our [migration guides](/whats-new/migrating-to-one-point-zero) as we release them. +* [A new chapter for Langflow](/whats-new/a-new-chapter-langflow) +* [Migration guides](/whats-new/migrating-to-one-point-zero) \ No newline at end of file diff --git a/docs/docusaurus.config.js b/docs/docusaurus.config.js index f64b180c7..8f9302cfd 100644 --- a/docs/docusaurus.config.js +++ b/docs/docusaurus.config.js @@ -7,11 +7,11 @@ module.exports = { title: "Langflow Documentation", tagline: "Langflow is a GUI for LangChain, designed with react-flow", favicon: "img/favicon.ico", - url: "https://logspace-ai.github.io", + url: "https://langflow-ai.github.io", baseUrl: "/", onBrokenLinks: "throw", onBrokenMarkdownLinks: "warn", - organizationName: "logspace-ai", + organizationName: "langflow-ai", projectName: "langflow", trailingSlash: false, staticDirectories: ["static"], @@ -131,7 +131,7 @@ module.exports = { }, footer: { links: [], - copyright: `Copyright © ${new Date().getFullYear()} Logspace.`, + copyright: `Copyright © ${new Date().getFullYear()} Langflow.`, }, zoom: { selector: ".markdown :not(a) > img:not(.no-zoom)", diff --git a/docs/sidebars.js b/docs/sidebars.js index 74c3dc4c8..be7cc3e3a 100644 --- a/docs/sidebars.js +++ b/docs/sidebars.js @@ -6,14 +6,26 @@ module.exports = { collapsed: false, items: [ "index", - "getting-started/cli", - // "guides/basic-prompting", - // "guides/document-qa", - // "guides/blog-writer", - // "guides/memory-chatbot", + "getting-started/install-langflow", + "getting-started/quickstart", + "getting-started/huggingface-spaces", + "getting-started/new-to-llms", + ], + }, + + { + type: "category", + label: " Starter Projects", + collapsed: false, + items: [ + "guides/basic-prompting", + "guides/blog-writer", + "guides/document-qa", + "guides/memory-chatbot", "guides/rag-with-astradb", ], }, + { type: "category", label: " What's New", @@ -46,6 +58,7 @@ module.exports = { "migration/global-variables", // "migration/experimental-components", // "migration/state-management", + //"guides/rag-with-astradb", ], }, { @@ -53,6 +66,7 @@ module.exports = { label: "Guidelines", collapsed: false, items: [ + "getting-started/cli", "guidelines/login", "guidelines/api", "guidelines/components", diff --git a/docs/static/img/basic-prompting.png b/docs/static/img/basic-prompting.png new file mode 100644 index 000000000..76d658a2d Binary files /dev/null and b/docs/static/img/basic-prompting.png differ diff --git a/docs/static/img/blog-writer.png b/docs/static/img/blog-writer.png new file mode 100644 index 000000000..d7b0d3780 Binary files /dev/null and b/docs/static/img/blog-writer.png differ diff --git a/docs/static/img/document-qa.png b/docs/static/img/document-qa.png new file mode 100644 index 000000000..b1c36eb61 Binary files 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"zope.testing"] testing = ["coverage (>=5.0.3)", "zope.event", "zope.testing"] @@ -10246,4 +10260,4 @@ local = ["ctransformers", "llama-cpp-python", "sentence-transformers"] [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.12" -content-hash = "9dd152b30031767c522c77e2ad5fc4597a8d1590b13968af143bd382e056b2a1" +content-hash = "bec34397b534f882551511558c76785c7cd67e6a1eefc1d45f6a64d97175d886" diff --git a/pyproject.toml b/pyproject.toml index 85231375a..abf3be003 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,16 +1,16 @@ [tool.poetry] name = "langflow" -version = "1.0.0a18" +version = "1.0.0a28" description = "A Python package with a built-in web application" -authors = ["Logspace "] +authors = ["Langflow "] maintainers = [ - "Carlos Coelho ", + "Carlos Coelho ", "Cristhian Zanforlin ", - "Gabriel Almeida ", + "Gabriel Almeida ", "Igor Carvalho ", "Lucas Eduoli ", "Otávio Anovazzi ", - "Rodrigo Nader ", + "Rodrigo Nader ", ] repository = "https://github.com/langflow-ai/langflow" license = "MIT" @@ -81,6 +81,7 @@ chromadb = "^0.4.24" langchain-anthropic = "^0.1.6" langchain-astradb = "^0.1.0" langchain-openai = "^0.1.1" +zep-python = { version = "^2.0.0rc5", allow-prereleases = true } [tool.poetry.group.dev.dependencies] types-redis = "^4.6.0.5" diff --git a/scripts/gcp/deploy_langflow_gcp.sh b/scripts/gcp/deploy_langflow_gcp.sh index fbf87099a..cae2d0c93 100644 --- a/scripts/gcp/deploy_langflow_gcp.sh +++ b/scripts/gcp/deploy_langflow_gcp.sh @@ -35,7 +35,7 @@ fi # Create a firewall rule to allow IAP traffic firewall_iap_exists=$(gcloud compute firewall-rules list --filter="name=allow-iap" --format="value(name)") if [[ -z "$firewall_iap_exists" ]]; then - gcloud compute firewall-rules create allow-iap --network $VPC_NAME --allow tcp:80,tcp:443,tcp:22,:tcp:3389 --source-ranges 35.235.240.0/20 --direction INGRESS + gcloud compute firewall-rules create allow-iap --network $VPC_NAME --allow tcp:80,tcp:443,tcp:22,tcp:3389 --source-ranges 35.235.240.0/20 --direction INGRESS fi # Define the startup script as a multiline Bash here-doc diff --git a/src/backend/Dockerfile b/src/backend/Dockerfile index 8257d89b0..ba1f3c946 100644 --- a/src/backend/Dockerfile +++ b/src/backend/Dockerfile @@ -1,4 +1,4 @@ -FROM logspace/backend_build as backend_build +FROM langflowai/backend_build as backend_build FROM python:3.10-slim WORKDIR /app diff --git a/src/backend/base/langflow/alembic/versions/1a110b568907_replace_credential_table_with_variable.py b/src/backend/base/langflow/alembic/versions/1a110b568907_replace_credential_table_with_variable.py index e27d088d0..48d12ac30 100644 --- a/src/backend/base/langflow/alembic/versions/1a110b568907_replace_credential_table_with_variable.py +++ b/src/backend/base/langflow/alembic/versions/1a110b568907_replace_credential_table_with_variable.py @@ -5,6 +5,7 @@ Revises: 63b9c451fd30 Create Date: 2024-03-25 09:40:02.743453 """ + from typing import Sequence, Union import sqlalchemy as sa diff --git a/src/backend/base/langflow/alembic/versions/4e5980a44eaa_fix_date_times_again.py b/src/backend/base/langflow/alembic/versions/4e5980a44eaa_fix_date_times_again.py new file mode 100644 index 000000000..dc3e985ed --- /dev/null +++ b/src/backend/base/langflow/alembic/versions/4e5980a44eaa_fix_date_times_again.py @@ -0,0 +1,130 @@ +"""Fix date times again + +Revision ID: 4e5980a44eaa +Revises: 79e675cb6752 +Create Date: 2024-04-12 18:11:06.454037 + +""" + +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op +from loguru import logger +from sqlalchemy.dialects import postgresql +from sqlalchemy.engine.reflection import Inspector + +# revision identifiers, used by Alembic. +revision: str = "4e5980a44eaa" +down_revision: Union[str, None] = "79e675cb6752" +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 isinstance(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, + ) + else: + if created_at_column is None: + logger.warning("Column 'created_at' not found in table 'apikey'") + else: + logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'") + 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 isinstance(created_at_column["type"], postgresql.TIMESTAMP): + batch_op.alter_column( + "created_at", + existing_type=postgresql.TIMESTAMP(), + type_=sa.DateTime(timezone=True), + existing_nullable=True, + ) + else: + if created_at_column is None: + logger.warning("Column 'created_at' not found in table 'variable'") + else: + logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'") + if updated_at_column is not None and isinstance(updated_at_column["type"], postgresql.TIMESTAMP): + batch_op.alter_column( + "updated_at", + existing_type=postgresql.TIMESTAMP(), + type_=sa.DateTime(timezone=True), + existing_nullable=True, + ) + else: + if updated_at_column is None: + logger.warning("Column 'updated_at' not found in table 'variable'") + else: + logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'") + + # ### 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 isinstance(updated_at_column["type"], sa.DateTime): + batch_op.alter_column( + "updated_at", + existing_type=sa.DateTime(timezone=True), + type_=postgresql.TIMESTAMP(), + existing_nullable=True, + ) + else: + if updated_at_column is None: + logger.warning("Column 'updated_at' not found in table 'variable'") + else: + logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'") + if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime): + batch_op.alter_column( + "created_at", + existing_type=sa.DateTime(timezone=True), + type_=postgresql.TIMESTAMP(), + existing_nullable=True, + ) + else: + if created_at_column is None: + logger.warning("Column 'created_at' not found in table 'variable'") + else: + logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'") + + 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 isinstance(created_at_column["type"], sa.DateTime): + 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, + ) + else: + if created_at_column is None: + logger.warning("Column 'created_at' not found in table 'apikey'") + else: + logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'") + + # ### end Alembic commands ### diff --git a/src/backend/base/langflow/alembic/versions/58b28437a398_modify_nullable.py b/src/backend/base/langflow/alembic/versions/58b28437a398_modify_nullable.py new file mode 100644 index 000000000..ead1ca30a --- /dev/null +++ b/src/backend/base/langflow/alembic/versions/58b28437a398_modify_nullable.py @@ -0,0 +1,66 @@ +"""Modify nullable + +Revision ID: 58b28437a398 +Revises: 4e5980a44eaa +Create Date: 2024-04-13 10:57:23.061709 + +""" + +from typing import Sequence, Union + +import sqlalchemy as sa +from alembic import op +from loguru import logger +from sqlalchemy.engine.reflection import Inspector + +down_revision: Union[str, None] = "4e5980a44eaa" +branch_labels: Union[str, Sequence[str], None] = None +depends_on: Union[str, Sequence[str], None] = None + +# Revision identifiers, used by Alembic. +revision = "58b28437a398" +down_revision = "4e5980a44eaa" +branch_labels = None +depends_on = None + + +def upgrade(): + conn = op.get_bind() + inspector = Inspector.from_engine(conn) + tables = ["apikey", "variable"] # List of tables to modify + + for table_name in tables: + modify_nullable(conn, inspector, table_name, upgrade=True) + + +def downgrade(): + conn = op.get_bind() + inspector = Inspector.from_engine(conn) + tables = ["apikey", "variable"] # List of tables to revert + + for table_name in tables: + modify_nullable(conn, inspector, table_name, upgrade=False) + + +def modify_nullable(conn, inspector, table_name, upgrade=True): + columns = inspector.get_columns(table_name) + nullable_changes = {"apikey": {"created_at": False}, "variable": {"created_at": True, "updated_at": True}} + + if table_name in columns: + with op.batch_alter_table(table_name, schema=None) as batch_op: + for column_name, nullable_setting in nullable_changes.get(table_name, {}).items(): + column_info = next((col for col in columns if col["name"] == column_name), None) + if column_info: + current_nullable = column_info["nullable"] + target_nullable = nullable_setting if upgrade else not nullable_setting + + if current_nullable != target_nullable: + batch_op.alter_column( + column_name, existing_type=sa.DateTime(timezone=True), nullable=target_nullable + ) + else: + logger.info( + f"Column '{column_name}' in table '{table_name}' already has nullable={target_nullable}" + ) + else: + logger.warning(f"Column '{column_name}' not found in table '{table_name}'") 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 index 5f9de6034..0b6bc9218 100644 --- a/src/backend/base/langflow/alembic/versions/79e675cb6752_change_datetime_type.py +++ b/src/backend/base/langflow/alembic/versions/79e675cb6752_change_datetime_type.py @@ -5,13 +5,14 @@ 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 +from loguru import logger # revision identifiers, used by Alembic. revision: str = "79e675cb6752" @@ -28,7 +29,7 @@ def upgrade() -> None: 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(): + if created_at_column is not None and isinstance(created_at_column["type"], postgresql.TIMESTAMP): with op.batch_alter_table("apikey", schema=None) as batch_op: batch_op.alter_column( "created_at", @@ -36,25 +37,40 @@ def upgrade() -> None: type_=sa.DateTime(timezone=True), existing_nullable=False, ) + else: + if created_at_column is None: + logger.warning("Column 'created_at' not found in table 'apikey'") + else: + logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'") 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(): + if created_at_column is not None and isinstance(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(): + else: + if created_at_column is None: + logger.warning("Column 'created_at' not found in table 'variable'") + else: + logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'") + if updated_at_column is not None and isinstance(updated_at_column["type"], postgresql.TIMESTAMP): batch_op.alter_column( "updated_at", existing_type=postgresql.TIMESTAMP(), type_=sa.DateTime(timezone=True), existing_nullable=True, ) + else: + if updated_at_column is None: + logger.warning("Column 'updated_at' not found in table 'variable'") + else: + logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'") # ### end Alembic commands ### @@ -69,25 +85,35 @@ def downgrade() -> None: 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): + if updated_at_column is not None and isinstance(updated_at_column["type"], sa.DateTime): 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): + else: + if updated_at_column is None: + logger.warning("Column 'updated_at' not found in table 'variable'") + else: + logger.warning(f"Column 'updated_at' has type {updated_at_column['type']} in table 'variable'") + if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime): batch_op.alter_column( "created_at", existing_type=sa.DateTime(timezone=True), type_=postgresql.TIMESTAMP(), existing_nullable=True, ) + else: + if created_at_column is None: + logger.warning("Column 'created_at' not found in table 'variable'") + else: + logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'variable'") 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): + if created_at_column is not None and isinstance(created_at_column["type"], sa.DateTime): with op.batch_alter_table("apikey", schema=None) as batch_op: batch_op.alter_column( "created_at", @@ -95,5 +121,10 @@ def downgrade() -> None: type_=postgresql.TIMESTAMP(), existing_nullable=False, ) + else: + if created_at_column is None: + logger.warning("Column 'created_at' not found in table 'apikey'") + else: + logger.warning(f"Column 'created_at' has type {created_at_column['type']} in table 'apikey'") # ### 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 index dbcf7fd9f..bfcd8e60b 100644 --- a/src/backend/base/langflow/alembic/versions/e3bc869fa272_fix_nullable.py +++ b/src/backend/base/langflow/alembic/versions/e3bc869fa272_fix_nullable.py @@ -26,19 +26,21 @@ def upgrade() -> None: # ### 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")] + columns = [column for column in inspector.get_columns("variable")] + column_names = [column["name"] for column in columns] 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, - ) + created_at_colunmn = next(column for column in columns if column["name"] == "created_at") + if created_at_colunmn["nullable"] is False: + 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 ### @@ -50,13 +52,17 @@ def downgrade() -> None: # ### commands auto generated by Alembic - please adjust! ### if "variable" not in table_names: return + columns = [column for column in inspector.get_columns("variable")] + column_names = [column["name"] for column in columns] 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, - ) + if "created_at" in column_names: + created_at_colunmn = next(column for column in columns if column["name"] == "created_at") + if created_at_colunmn["nullable"] is True: + 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 1db888970..e13d4333d 100644 --- a/src/backend/base/langflow/api/v1/endpoints.py +++ b/src/backend/base/langflow/api/v1/endpoints.py @@ -109,6 +109,7 @@ async def simplified_run_flow( This endpoint provides a powerful interface for executing flows with enhanced flexibility and efficiency, supporting a wide range of applications by allowing for dynamic input and output configuration along with performance optimizations through session management and caching. """ session_id = input_request.session_id + try: task_result: List[RunOutputs] = [] artifacts = {} @@ -127,8 +128,9 @@ async def simplified_run_flow( if flow.data is None: raise ValueError(f"Flow {flow_id} has no data") graph_data = flow.data - graph_data = process_tweaks(graph_data, input_request.tweaks or {}) - graph = Graph.from_payload(graph_data, flow_id=flow_id) + + graph_data = process_tweaks(graph_data, input_request.tweaks or {}, stream=stream) + graph = Graph.from_payload(graph_data, flow_id=flow_id, user_id=str(api_key_user.id)) inputs = [ InputValueRequest(components=[], input_value=input_request.input_value, type=input_request.input_type) ] diff --git a/src/backend/base/langflow/api/v1/schemas.py b/src/backend/base/langflow/api/v1/schemas.py index f5d0b97d1..0cc6274c2 100644 --- a/src/backend/base/langflow/api/v1/schemas.py +++ b/src/backend/base/langflow/api/v1/schemas.py @@ -26,7 +26,7 @@ class BuildStatus(Enum): class TweaksRequest(BaseModel): - tweaks: Optional[Dict[str, Dict[str, str]]] = Field(default_factory=dict) + tweaks: Optional[Dict[str, Dict[str, Any]]] = Field(default_factory=dict) class UpdateTemplateRequest(BaseModel): diff --git a/src/backend/base/langflow/api/v1/store.py b/src/backend/base/langflow/api/v1/store.py index 313b0638b..7d0e9bff9 100644 --- a/src/backend/base/langflow/api/v1/store.py +++ b/src/backend/base/langflow/api/v1/store.py @@ -2,6 +2,7 @@ from typing import Annotated, List, Optional, Union from uuid import UUID from fastapi import APIRouter, Depends, HTTPException, Query +from loguru import logger from langflow.api.utils import check_langflow_version from langflow.services.auth import utils as auth_utils @@ -27,8 +28,11 @@ def get_user_store_api_key( ): if not user.store_api_key: raise HTTPException(status_code=400, detail="You must have a store API key set.") - decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service) - return decrypted + try: + decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service) + return decrypted + except Exception as e: + raise HTTPException(status_code=500, detail="Failed to decrypt API key. Please set a new one.") from e def get_optional_user_store_api_key( @@ -37,8 +41,12 @@ def get_optional_user_store_api_key( ): if not user.store_api_key: return None - decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service) - return decrypted + try: + decrypted = auth_utils.decrypt_api_key(user.store_api_key, settings_service) + return decrypted + except Exception as e: + logger.error(f"Failed to decrypt API key: {e}") + return user.store_api_key @router.get("/check/") diff --git a/src/backend/base/langflow/base/agents/agent.py b/src/backend/base/langflow/base/agents/agent.py index 5ef498b66..ce40f1f51 100644 --- a/src/backend/base/langflow/base/agents/agent.py +++ b/src/backend/base/langflow/base/agents/agent.py @@ -1,13 +1,19 @@ from typing import List, Optional, Union, cast from langchain.agents import AgentExecutor, BaseMultiActionAgent, BaseSingleActionAgent +from langchain_core.messages import BaseMessage from langchain_core.runnables import Runnable +from langflow.base.agents.utils import get_agents_list, records_to_messages from langflow.custom import CustomComponent -from langflow.field_typing import BaseMemory, Text, Tool +from langflow.field_typing import Text, Tool +from langflow.schema.schema import Record class LCAgentComponent(CustomComponent): + def get_agents_list(self): + return get_agents_list() + def build_config(self): return { "lc": { @@ -42,9 +48,8 @@ class LCAgentComponent(CustomComponent): self, agent: Union[Runnable, BaseSingleActionAgent, BaseMultiActionAgent, AgentExecutor], inputs: str, - input_variables: list[str], tools: List[Tool], - memory: Optional[BaseMemory] = None, + message_history: Optional[List[Record]] = None, handle_parsing_errors: bool = True, output_key: str = "output", ) -> Text: @@ -55,13 +60,11 @@ class LCAgentComponent(CustomComponent): agent=agent, # type: ignore tools=tools, verbose=True, - memory=memory, handle_parsing_errors=handle_parsing_errors, ) - input_dict = {"input": inputs} - for var in input_variables: - if var not in ["agent_scratchpad", "input"]: - input_dict[var] = "" + input_dict: dict[str, str | list[BaseMessage]] = {"input": inputs} + if message_history: + input_dict["chat_history"] = records_to_messages(message_history) result = await runnable.ainvoke(input_dict) self.status = result if output_key in result: diff --git a/src/backend/base/langflow/base/agents/default_prompts.py b/src/backend/base/langflow/base/agents/default_prompts.py new file mode 100644 index 000000000..04e394813 --- /dev/null +++ b/src/backend/base/langflow/base/agents/default_prompts.py @@ -0,0 +1,23 @@ +XML_AGENT_PROMPT = """You are a helpful assistant. Help the user answer any questions. + + You have access to the following tools: + + {tools} + + In order to use a tool, you can use and tags. You will then get back a response in the form + For example, if you have a tool called 'search' that could run a google search, in order to search for the weather in SF you would respond: + + searchweather in SF + 64 degrees + + When you are done, respond with a final answer between . For example: + + The weather in SF is 64 degrees + + Begin! + + Previous Conversation: + {chat_history} + + Question: {input} + {agent_scratchpad}""" diff --git a/src/backend/base/langflow/base/agents/utils.py b/src/backend/base/langflow/base/agents/utils.py new file mode 100644 index 000000000..cb34d1cea --- /dev/null +++ b/src/backend/base/langflow/base/agents/utils.py @@ -0,0 +1,143 @@ +from typing import Any, Callable, Dict, List, Optional, Sequence, Union + +from langchain.agents import ( + create_json_chat_agent, + create_openai_tools_agent, + create_tool_calling_agent, + create_xml_agent, +) +from langchain.agents.xml.base import render_text_description +from langchain_core.language_models import BaseLanguageModel +from langchain_core.messages import BaseMessage +from langchain_core.prompts import BasePromptTemplate, ChatPromptTemplate +from langchain_core.tools import BaseTool +from pydantic import BaseModel + +from langflow.schema.schema import Record + +from .default_prompts import XML_AGENT_PROMPT + + +class AgentSpec(BaseModel): + func: Callable[ + [ + BaseLanguageModel, + Sequence[BaseTool], + BasePromptTemplate | ChatPromptTemplate, + Optional[Callable[[List[BaseTool]], str]], + Optional[Union[bool, List[str]]], + ], + Any, + ] + prompt: Optional[Any] = None + fields: List[str] + hub_repo: Optional[str] = None + + +def records_to_messages(records: List[Record]) -> List[BaseMessage]: + """ + Convert a list of records to a list of messages. + + Args: + records (List[Record]): The records to convert. + + Returns: + List[Message]: The records as messages. + """ + return [record.to_lc_message() for record in records] + + +def validate_and_create_xml_agent( + llm: BaseLanguageModel, + tools: Sequence[BaseTool], + prompt: BasePromptTemplate, + tools_renderer: Callable[[List[BaseTool]], str] = render_text_description, + *, + stop_sequence: Union[bool, List[str]] = True, +): + return create_xml_agent( + llm=llm, + tools=tools, + prompt=prompt, + tools_renderer=tools_renderer, + stop_sequence=stop_sequence, + ) + + +def validate_and_create_openai_tools_agent( + llm: BaseLanguageModel, + tools: Sequence[BaseTool], + prompt: ChatPromptTemplate, + tools_renderer: Callable[[List[BaseTool]], str] = render_text_description, + *, + stop_sequence: Union[bool, List[str]] = True, +): + return create_openai_tools_agent( + llm=llm, + tools=tools, + prompt=prompt, + ) + + +def validate_and_create_tool_calling_agent( + llm: BaseLanguageModel, + tools: Sequence[BaseTool], + prompt: ChatPromptTemplate, + tools_renderer: Callable[[List[BaseTool]], str] = render_text_description, + *, + stop_sequence: Union[bool, List[str]] = True, +): + return create_tool_calling_agent( + llm=llm, + tools=tools, + prompt=prompt, + ) + + +def validate_and_create_json_chat_agent( + llm: BaseLanguageModel, + tools: Sequence[BaseTool], + prompt: ChatPromptTemplate, + tools_renderer: Callable[[List[BaseTool]], str] = render_text_description, + *, + stop_sequence: Union[bool, List[str]] = True, +): + return create_json_chat_agent( + llm=llm, + tools=tools, + prompt=prompt, + tools_renderer=tools_renderer, + stop_sequence=stop_sequence, + ) + + +AGENTS: Dict[str, AgentSpec] = { + "Tool Calling Agent": AgentSpec( + func=validate_and_create_tool_calling_agent, + prompt=None, + fields=["llm", "tools", "prompt"], + hub_repo=None, + ), + "XML Agent": AgentSpec( + func=validate_and_create_xml_agent, + prompt=XML_AGENT_PROMPT, # Ensure XML_AGENT_PROMPT is properly defined and typed. + fields=["llm", "tools", "prompt", "tools_renderer", "stop_sequence"], + hub_repo="hwchase17/xml-agent-convo", + ), + "OpenAI Tools Agent": AgentSpec( + func=validate_and_create_openai_tools_agent, + prompt=None, + fields=["llm", "tools", "prompt"], + hub_repo=None, + ), + "JSON Chat Agent": AgentSpec( + func=validate_and_create_json_chat_agent, + prompt=None, + fields=["llm", "tools", "prompt", "tools_renderer", "stop_sequence"], + hub_repo="hwchase17/react-chat-json", + ), +} + + +def get_agents_list(): + return list(AGENTS.keys()) diff --git a/src/backend/base/langflow/base/constants.py b/src/backend/base/langflow/base/constants.py index c9a41a100..498b46f65 100644 --- a/src/backend/base/langflow/base/constants.py +++ b/src/backend/base/langflow/base/constants.py @@ -6,6 +6,7 @@ Constants: - NODE_FORMAT_ATTRIBUTES: A list of attributes used for formatting nodes. - FIELD_FORMAT_ATTRIBUTES: A list of attributes used for formatting fields. """ + STREAM_INFO_TEXT = "Stream the response from the model. Streaming works only in Chat." NODE_FORMAT_ATTRIBUTES = ["beta", "icon", "display_name", "description"] diff --git a/docs/docs/getting-started/basic-prompting.mdx b/src/backend/base/langflow/base/memory/__init__.py similarity index 100% rename from docs/docs/getting-started/basic-prompting.mdx rename to src/backend/base/langflow/base/memory/__init__.py diff --git a/src/backend/base/langflow/base/memory/memory.py b/src/backend/base/langflow/base/memory/memory.py new file mode 100644 index 000000000..836752509 --- /dev/null +++ b/src/backend/base/langflow/base/memory/memory.py @@ -0,0 +1,51 @@ +from typing import Optional + +from langflow.field_typing import Text +from langflow.helpers.record import records_to_text +from langflow.interface.custom.custom_component import CustomComponent +from langflow.schema.schema import Record + + +class BaseMemoryComponent(CustomComponent): + display_name = "Chat Memory" + description = "Retrieves stored chat messages given a specific Session ID." + beta: bool = True + icon = "history" + + def build_config(self): + return { + "sender": { + "options": ["Machine", "User", "Machine and User"], + "display_name": "Sender Type", + }, + "sender_name": {"display_name": "Sender Name", "advanced": True}, + "n_messages": { + "display_name": "Number of Messages", + "info": "Number of messages to retrieve.", + }, + "session_id": { + "display_name": "Session ID", + "info": "Session ID of the chat history.", + "input_types": ["Text"], + }, + "order": { + "options": ["Ascending", "Descending"], + "display_name": "Order", + "info": "Order of the messages.", + "advanced": True, + }, + "record_template": { + "display_name": "Record Template", + "multiline": True, + "info": "Template to convert Record to Text. If left empty, it will be dynamically set to the Record's text key.", + "advanced": True, + }, + } + + def get_messages(self, **kwargs) -> list[Record]: + raise NotImplementedError + + def add_message( + self, sender: str, sender_name: str, text: str, session_id: str, metadata: Optional[dict] = None, **kwargs + ): + raise NotImplementedError diff --git a/src/backend/base/langflow/base/models/model.py b/src/backend/base/langflow/base/models/model.py index b8ef38e07..977090818 100644 --- a/src/backend/base/langflow/base/models/model.py +++ b/src/backend/base/langflow/base/models/model.py @@ -2,7 +2,7 @@ from typing import Optional, Union from langchain_core.language_models.chat_models import BaseChatModel from langchain_core.language_models.llms import LLM -from langchain_core.messages import HumanMessage, SystemMessage +from langchain_core.messages import AIMessage, HumanMessage, SystemMessage from langflow.custom import CustomComponent @@ -31,6 +31,47 @@ class LCModelComponent(CustomComponent): self.status = result return result + def build_status_message(self, message: AIMessage): + """ + Builds a status message from an AIMessage object. + + Args: + message (AIMessage): The AIMessage object to build the status message from. + + Returns: + The status message. + """ + if message.response_metadata: + # Build a well formatted status message + content = message.content + response_metadata = message.response_metadata + openai_keys = ["token_usage", "model_name", "finish_reason"] + inner_openai_keys = ["completion_tokens", "prompt_tokens", "total_tokens"] + anthropic_keys = ["model", "usage", "stop_reason"] + inner_anthropic_keys = ["input_tokens", "output_tokens"] + if all(key in response_metadata for key in openai_keys) and all( + key in response_metadata["token_usage"] for key in inner_openai_keys + ): + token_usage = response_metadata["token_usage"] + completion_tokens = token_usage["completion_tokens"] + prompt_tokens = token_usage["prompt_tokens"] + total_tokens = token_usage["total_tokens"] + finish_reason = response_metadata["finish_reason"] + status_message = f"Tokens:\n- Input: {prompt_tokens}\nOutput: {completion_tokens}\nTotal Tokens: {total_tokens}\nStop Reason: {finish_reason}\nResponse: {content}" + elif all(key in response_metadata for key in anthropic_keys) and all( + key in response_metadata["usage"] for key in inner_anthropic_keys + ): + usage = response_metadata["usage"] + input_tokens = usage["input_tokens"] + output_tokens = usage["output_tokens"] + stop_reason = response_metadata["stop_reason"] + status_message = f"Tokens:\n- Input: {input_tokens}\n- Output: {output_tokens}\nStop Reason: {stop_reason}\nResponse: {content}" + else: + status_message = f"Response: {content}" + else: + status_message = f"Response: {message.content}" + return status_message + def get_chat_result( self, runnable: BaseChatModel, stream: bool, input_value: str, system_message: Optional[str] = None ): @@ -46,5 +87,9 @@ class LCModelComponent(CustomComponent): else: message = runnable.invoke(messages) result = message.content - self.status = result + if isinstance(message, AIMessage): + status_message = self.build_status_message(message) + self.status = status_message + else: + self.status = result return result diff --git a/src/backend/base/langflow/components/agents/ToolCallingAgent.py b/src/backend/base/langflow/components/agents/ToolCallingAgent.py new file mode 100644 index 000000000..b4a319e2f --- /dev/null +++ b/src/backend/base/langflow/components/agents/ToolCallingAgent.py @@ -0,0 +1,64 @@ +from typing import List, Optional + +from langchain.agents.tool_calling_agent.base import create_tool_calling_agent +from langchain_core.prompts import ChatPromptTemplate + +from langflow.base.agents.agent import LCAgentComponent +from langflow.field_typing import BaseLanguageModel, Text, Tool +from langflow.schema.schema import Record + + +class ToolCallingAgentComponent(LCAgentComponent): + display_name: str = "Tool Calling Agent" + description: str = "Agent that uses tools. Only models that are compatible with function calling are supported." + + def build_config(self): + return { + "llm": {"display_name": "LLM"}, + "tools": {"display_name": "Tools"}, + "user_prompt": { + "display_name": "Prompt", + "multiline": True, + "info": "This prompt must contain 'input' key.", + }, + "handle_parsing_errors": { + "display_name": "Handle Parsing Errors", + "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.", + "advanced": True, + }, + "memory": { + "display_name": "Memory", + "info": "Memory to use for the agent.", + }, + "input_value": { + "display_name": "Inputs", + "info": "Input text to pass to the agent.", + }, + } + + async def build( + self, + input_value: str, + llm: BaseLanguageModel, + tools: List[Tool], + user_prompt: str = "{input}", + message_history: Optional[List[Record]] = None, + system_message: str = "You are a helpful assistant", + handle_parsing_errors: bool = True, + ) -> Text: + if "input" not in user_prompt: + raise ValueError("Prompt must contain 'input' key.") + messages = [ + ("system", system_message), + ( + "placeholder", + "{chat_history}", + ), + ("human", user_prompt), + ("placeholder", "{agent_scratchpad}"), + ] + prompt = ChatPromptTemplate.from_messages(messages) + agent = create_tool_calling_agent(llm, tools, prompt) + result = await self.run_agent(agent, input_value, tools, message_history, handle_parsing_errors) + self.status = result + return result diff --git a/src/backend/base/langflow/components/agents/XMLAgent.py b/src/backend/base/langflow/components/agents/XMLAgent.py index 117399af9..76f96da53 100644 --- a/src/backend/base/langflow/components/agents/XMLAgent.py +++ b/src/backend/base/langflow/components/agents/XMLAgent.py @@ -1,10 +1,12 @@ from typing import List, Optional from langchain.agents import create_xml_agent -from langchain_core.prompts import PromptTemplate +from langchain_core.prompts import ChatPromptTemplate + from langflow.base.agents.agent import LCAgentComponent -from langflow.field_typing import BaseLanguageModel, BaseMemory, Text, Tool +from langflow.field_typing import BaseLanguageModel, Text, Tool +from langflow.schema.schema import Record class XMLAgentComponent(LCAgentComponent): @@ -15,7 +17,7 @@ class XMLAgentComponent(LCAgentComponent): return { "llm": {"display_name": "LLM"}, "tools": {"display_name": "Tools"}, - "prompt": { + "user_prompt": { "display_name": "Prompt", "multiline": True, "info": "This prompt must contain 'tools' and 'agent_scratchpad' keys.", @@ -43,6 +45,11 @@ class XMLAgentComponent(LCAgentComponent): Question: {input} {agent_scratchpad}""", }, + "system_message": { + "display_name": "System Message", + "info": "System message to be passed to the LLM.", + "advanced": True, + }, "tool_template": { "display_name": "Tool Template", "info": "Template for rendering tools in the prompt. Tools have 'name' and 'description' keys.", @@ -53,9 +60,9 @@ class XMLAgentComponent(LCAgentComponent): "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.", "advanced": True, }, - "memory": { - "display_name": "Memory", - "info": "Memory to use for the agent.", + "message_history": { + "display_name": "Message History", + "info": "Message history to pass to the agent.", }, "input_value": { "display_name": "Inputs", @@ -68,12 +75,13 @@ class XMLAgentComponent(LCAgentComponent): input_value: str, llm: BaseLanguageModel, tools: List[Tool], - prompt: str, - memory: Optional[BaseMemory] = None, + user_prompt: str = "{input}", + system_message: str = "You are a helpful assistant", + message_history: Optional[List[Record]] = None, tool_template: str = "{name}: {description}", handle_parsing_errors: bool = True, ) -> Text: - if "input" not in prompt: + if "input" not in user_prompt: raise ValueError("Prompt must contain 'input' key.") def render_tool_description(tools): @@ -81,9 +89,23 @@ class XMLAgentComponent(LCAgentComponent): [tool_template.format(name=tool.name, description=tool.description, args=tool.args) for tool in tools] ) - prompt_template = PromptTemplate.from_template(prompt) - input_variables = prompt_template.input_variables - agent = create_xml_agent(llm, tools, prompt_template, tools_renderer=render_tool_description) - result = await self.run_agent(agent, input_value, input_variables, tools, memory, handle_parsing_errors) + messages = [ + ("system", system_message), + ( + "placeholder", + "{chat_history}", + ), + ("human", user_prompt), + ("placeholder", "{agent_scratchpad}"), + ] + prompt = ChatPromptTemplate.from_messages(messages) + agent = create_xml_agent(llm, tools, prompt, tools_renderer=render_tool_description) + result = await self.run_agent( + agent=agent, + inputs=input_value, + tools=tools, + message_history=message_history, + handle_parsing_errors=handle_parsing_errors, + ) self.status = result return result diff --git a/src/backend/base/langflow/components/experimental/AgentComponent.py b/src/backend/base/langflow/components/experimental/AgentComponent.py new file mode 100644 index 000000000..9a6840a41 --- /dev/null +++ b/src/backend/base/langflow/components/experimental/AgentComponent.py @@ -0,0 +1,185 @@ +from typing import Any, List, Optional, cast + +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.prompts.chat import HumanMessagePromptTemplate, SystemMessagePromptTemplate + +from langflow.base.agents.agent import LCAgentComponent +from langflow.base.agents.utils import AGENTS, AgentSpec, get_agents_list +from langflow.field_typing import BaseLanguageModel, Text, Tool +from langflow.schema.dotdict import dotdict +from langflow.schema.schema import Record + + +class AgentComponent(LCAgentComponent): + display_name = "Agent" + description = "Run any LangChain agent using a simplified interface." + field_order = [ + "agent_name", + "llm", + "tools", + "prompt", + "tool_template", + "handle_parsing_errors", + "memory", + "input_value", + ] + + def build_config(self): + return { + "agent_name": { + "display_name": "Agent", + "info": "The agent to use.", + "refresh_button": True, + "real_time_refresh": True, + "options": get_agents_list(), + }, + "llm": {"display_name": "LLM"}, + "tools": {"display_name": "Tools"}, + "user_prompt": { + "display_name": "Prompt", + "multiline": True, + "info": "This prompt must contain 'tools' and 'agent_scratchpad' keys.", + }, + "system_message": { + "display_name": "System Message", + "info": "System message to be passed to the LLM.", + "advanced": True, + }, + "tool_template": { + "display_name": "Tool Template", + "info": "Template for rendering tools in the prompt. Tools have 'name' and 'description' keys.", + "advanced": True, + }, + "handle_parsing_errors": { + "display_name": "Handle Parsing Errors", + "info": "If True, the agent will handle parsing errors. If False, the agent will raise an error.", + "advanced": True, + }, + "message_history": { + "display_name": "Message History", + "info": "Message history to pass to the agent.", + }, + "input_value": { + "display_name": "Input", + "info": "Input text to pass to the agent.", + }, + "langchain_hub_api_key": { + "display_name": "LangChain Hub API Key", + "info": "API key to use for LangChain Hub. If provided, prompts will be fetched from LangChain Hub.", + "advanced": True, + }, + } + + def get_system_and_user_message_from_prompt(self, prompt: Any): + """ + Extracts the system message and user prompt from a given prompt object. + + Args: + prompt (Any): The prompt object from which to extract the system message and user prompt. + + Returns: + Tuple[Optional[str], Optional[str]]: A tuple containing the system message and user prompt. + If the prompt object does not have any messages, both values will be None. + """ + if hasattr(prompt, "messages"): + system_message = None + user_prompt = None + for message in prompt.messages: + if isinstance(message, SystemMessagePromptTemplate): + s_prompt = message.prompt + if isinstance(s_prompt, list): + s_template = " ".join([cast(str, s.template) for s in s_prompt if hasattr(s, "template")]) + elif hasattr(s_prompt, "template"): + s_template = s_prompt.template + system_message = s_template + elif isinstance(message, HumanMessagePromptTemplate): + h_prompt = message.prompt + if isinstance(h_prompt, list): + h_template = " ".join([cast(str, h.template) for h in h_prompt if hasattr(h, "template")]) + elif hasattr(h_prompt, "template"): + h_template = h_prompt.template + user_prompt = h_template + return system_message, user_prompt + return None, None + + def update_build_config(self, build_config: dotdict, field_value: Any, field_name: Text | None = None): + """ + Updates the build configuration based on the provided field value and field name. + + Args: + build_config (dotdict): The build configuration to be updated. + field_value (Any): The value of the field being updated. + field_name (Text | None, optional): The name of the field being updated. Defaults to None. + + Returns: + dotdict: The updated build configuration. + """ + if field_name == "agent": + build_config["agent"]["options"] = get_agents_list() + if field_value in AGENTS: + # if langchain_hub_api_key is provided, fetch the prompt from LangChain Hub + if build_config["langchain_hub_api_key"]["value"] and AGENTS[field_value].hub_repo: + from langchain import hub + + hub_repo: str | None = AGENTS[field_value].hub_repo + if hub_repo: + hub_api_key: str = build_config["langchain_hub_api_key"]["value"] + prompt = hub.pull(hub_repo, api_key=hub_api_key) + system_message, user_prompt = self.get_system_and_user_message_from_prompt(prompt) + if system_message: + build_config["system_message"]["value"] = system_message + if user_prompt: + build_config["user_prompt"]["value"] = user_prompt + + if AGENTS[field_value].prompt: + build_config["user_prompt"]["value"] = AGENTS[field_value].prompt + else: + build_config["user_prompt"]["value"] = "{input}" + fields = AGENTS[field_value].fields + for field in ["llm", "tools", "prompt", "tools_renderer"]: + if field not in fields: + build_config[field]["show"] = False + return build_config + + async def build( + self, + agent_name: str, + input_value: str, + llm: BaseLanguageModel, + tools: List[Tool], + system_message: str = "You are a helpful assistant. Help the user answer any questions.", + user_prompt: str = "{input}", + message_history: Optional[List[Record]] = None, + tool_template: str = "{name}: {description}", + handle_parsing_errors: bool = True, + ) -> Text: + agent_spec: Optional[AgentSpec] = AGENTS.get(agent_name) + if agent_spec is None: + raise ValueError(f"{agent_name} not found.") + + def render_tool_description(tools): + return "\n".join( + [tool_template.format(name=tool.name, description=tool.description, args=tool.args) for tool in tools] + ) + + messages = [ + ("system", system_message), + ( + "placeholder", + "{chat_history}", + ), + ("human", user_prompt), + ("placeholder", "{agent_scratchpad}"), + ] + prompt = ChatPromptTemplate.from_messages(messages) + agent_func = agent_spec.func + agent = agent_func(llm, tools, prompt, render_tool_description, True) + result = await self.run_agent( + agent=agent, + inputs=input_value, + tools=tools, + message_history=message_history, + handle_parsing_errors=handle_parsing_errors, + ) + self.status = result + return result diff --git a/src/backend/base/langflow/components/experimental/__init__.py b/src/backend/base/langflow/components/experimental/__init__.py index 54ca60a3b..a8e83125c 100644 --- a/src/backend/base/langflow/components/experimental/__init__.py +++ b/src/backend/base/langflow/components/experimental/__init__.py @@ -10,8 +10,10 @@ from .RunFlow import RunFlowComponent from .RunnableExecutor import RunnableExecComponent from .SQLExecutor import SQLExecutorComponent from .SubFlow import SubFlowComponent +from .AgentComponent import AgentComponent __all__ = [ + "AgentComponent", "ClearMessageHistoryComponent", "ExtractKeyFromRecordComponent", "FlowToolComponent", diff --git a/src/backend/base/langflow/components/helpers/CombineTextsUnsorted.py b/src/backend/base/langflow/components/helpers/CombineTextsUnsorted.py new file mode 100644 index 000000000..20cd4db29 --- /dev/null +++ b/src/backend/base/langflow/components/helpers/CombineTextsUnsorted.py @@ -0,0 +1,25 @@ +from langflow.interface.custom.custom_component import CustomComponent +from langflow.field_typing import Text + + +class CombineTextsUnsortedComponent(CustomComponent): + display_name = "Combine Texts (Unsorted)" + description = "Concatenate text sources into a single text chunk using a specified delimiter." + icon = "merge" + + def build_config(self): + return { + "texts": { + "display_name": "Texts", + "info": "The first text input to concatenate.", + }, + "delimiter": { + "display_name": "Delimiter", + "info": "A string used to separate the two text inputs. Defaults to a whitespace.", + }, + } + + def build(self, texts: list[str], delimiter: str = " ") -> Text: + combined = delimiter.join(texts) + self.status = combined + return combined diff --git a/src/backend/base/langflow/components/helpers/MemoryComponent.py b/src/backend/base/langflow/components/helpers/MemoryComponent.py index 9590fe8a3..6d19bfd59 100644 --- a/src/backend/base/langflow/components/helpers/MemoryComponent.py +++ b/src/backend/base/langflow/components/helpers/MemoryComponent.py @@ -1,12 +1,13 @@ from typing import Optional +from langflow.base.memory.memory import BaseMemoryComponent from langflow.field_typing import Text from langflow.helpers.record import records_to_text -from langflow.interface.custom.custom_component import CustomComponent from langflow.memory import get_messages +from langflow.schema.schema import Record -class MemoryComponent(CustomComponent): +class MemoryComponent(BaseMemoryComponent): display_name = "Chat Memory" description = "Retrieves stored chat messages given a specific Session ID." beta: bool = True @@ -42,6 +43,24 @@ class MemoryComponent(CustomComponent): }, } + def get_messages(self, **kwargs) -> list[Record]: + # Validate kwargs by checking if it contains the correct keys + if "sender" not in kwargs: + kwargs["sender"] = None + if "sender_name" not in kwargs: + kwargs["sender_name"] = None + if "session_id" not in kwargs: + kwargs["session_id"] = None + if "limit" not in kwargs: + kwargs["limit"] = 5 + if "order" not in kwargs: + kwargs["order"] = "Descending" + + kwargs["order"] = "DESC" if kwargs["order"] == "Descending" else "ASC" + if kwargs["sender"] == "Machine and User": + kwargs["sender"] = None + return get_messages(**kwargs) + def build( self, sender: Optional[str] = "Machine and User", @@ -51,10 +70,7 @@ class MemoryComponent(CustomComponent): order: Optional[str] = "Descending", record_template: Optional[str] = "{sender_name}: {text}", ) -> Text: - order = "DESC" if order == "Descending" else "ASC" - if sender == "Machine and User": - sender = None - messages = get_messages( + messages = self.get_messages( sender=sender, sender_name=sender_name, session_id=session_id, diff --git a/src/backend/base/langflow/components/memories/ZepMessageReader.py b/src/backend/base/langflow/components/memories/ZepMessageReader.py new file mode 100644 index 000000000..603e41972 --- /dev/null +++ b/src/backend/base/langflow/components/memories/ZepMessageReader.py @@ -0,0 +1,137 @@ +from typing import Optional, cast + +from langchain_community.chat_message_histories.zep import SearchScope, SearchType, ZepChatMessageHistory + +from langflow.base.memory.memory import BaseMemoryComponent +from langflow.field_typing import Text +from langflow.schema.schema import Record + + +class ZepMessageReaderComponent(BaseMemoryComponent): + display_name = "Zep Message Reader" + description = "Retrieves stored chat messages from Zep." + + def build_config(self): + return { + "session_id": { + "display_name": "Session ID", + "info": "Session ID of the chat history.", + "input_types": ["Text"], + }, + "url": { + "display_name": "Zep URL", + "info": "URL of the Zep instance.", + "input_types": ["Text"], + }, + "api_key": { + "display_name": "Zep API Key", + "info": "API Key for the Zep instance.", + "password": True, + }, + "query": { + "display_name": "Query", + "info": "Query to search for in the chat history.", + }, + "metadata": { + "display_name": "Metadata", + "info": "Optional metadata to attach to the message.", + "advanced": True, + }, + "search_scope": { + "options": ["Messages", "Summary"], + "display_name": "Search Scope", + "info": "Scope of the search.", + "advanced": True, + }, + "search_type": { + "options": ["Similarity", "MMR"], + "display_name": "Search Type", + "info": "Type of search.", + "advanced": True, + }, + "limit": { + "display_name": "Limit", + "info": "Limit of search results.", + "advanced": True, + }, + } + + def get_messages(self, **kwargs) -> list[Record]: + """ + Retrieves messages from the ZepChatMessageHistory memory. + + If a query is provided, the search method is used to search for messages in the memory, otherwise all messages are returned. + + Args: + memory (ZepChatMessageHistory): The ZepChatMessageHistory instance to retrieve messages from. + query (str, optional): The query string to search for messages. Defaults to None. + metadata (dict, optional): Additional metadata to filter the search results. Defaults to None. + search_scope (str, optional): The scope of the search. Can be 'messages' or 'summary'. Defaults to 'messages'. + search_type (str, optional): The type of search. Can be 'similarity' or 'exact'. Defaults to 'similarity'. + limit (int, optional): The maximum number of search results to return. Defaults to None. + + Returns: + list[Record]: A list of Record objects representing the search results. + """ + memory: ZepChatMessageHistory = cast(ZepChatMessageHistory, kwargs.get("memory")) + if not memory: + raise ValueError("ZepChatMessageHistory instance is required.") + query = kwargs.get("query") + search_scope = kwargs.get("search_scope", SearchScope.messages).lower() + search_type = kwargs.get("search_type", SearchType.similarity).lower() + limit = kwargs.get("limit") + + if query: + memory_search_results = memory.search( + query, + search_scope=search_scope, + search_type=search_type, + limit=limit, + ) + # Get the messages from the search results if the search scope is messages + result_dicts = [] + for result in memory_search_results: + result_dict = {} + if search_scope == SearchScope.messages: + result_dict["text"] = result.message + else: + result_dict["text"] = result.summary + result_dict["metadata"] = result.metadata + result_dict["score"] = result.score + result_dicts.append(result_dict) + results = [Record(data=result_dict) for result_dict in result_dicts] + else: + messages = memory.messages + results = [Record.from_lc_message(message) for message in messages] + return results + + def build( + self, + session_id: Text, + url: Optional[Text] = None, + api_key: Optional[Text] = None, + query: Optional[Text] = None, + search_scope: SearchScope = SearchScope.messages, + search_type: SearchType = SearchType.similarity, + limit: Optional[int] = None, + ) -> list[Record]: + try: + from zep_python import ZepClient + from zep_python.langchain import ZepChatMessageHistory + except ImportError: + raise ImportError( + "Could not import zep-python package. " "Please install it with `pip install zep-python`." + ) + if url == "": + url = None + zep_client = ZepClient(api_url=url, api_key=api_key) + memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client) + records = self.get_messages( + memory=memory, + query=query, + search_scope=search_scope, + search_type=search_type, + limit=limit, + ) + self.status = records + return records diff --git a/src/backend/base/langflow/components/memories/ZepMessageWriter.py b/src/backend/base/langflow/components/memories/ZepMessageWriter.py new file mode 100644 index 000000000..248dfd810 --- /dev/null +++ b/src/backend/base/langflow/components/memories/ZepMessageWriter.py @@ -0,0 +1,96 @@ +from typing import Optional, TYPE_CHECKING + + +from langflow.base.memory.memory import BaseMemoryComponent +from langflow.field_typing import Text +from langflow.schema.schema import Record + +if TYPE_CHECKING: + from zep_python.langchain import ZepChatMessageHistory + + +class ZepMessageWriterComponent(BaseMemoryComponent): + display_name = "Zep Message Writer" + description = "Writes a message to Zep." + + def build_config(self): + return { + "session_id": { + "display_name": "Session ID", + "info": "Session ID of the chat history.", + "input_types": ["Text"], + }, + "url": { + "display_name": "Zep URL", + "info": "URL of the Zep instance.", + "input_types": ["Text"], + }, + "api_key": { + "display_name": "Zep API Key", + "info": "API Key for the Zep instance.", + "password": True, + }, + "limit": { + "display_name": "Limit", + "info": "Limit of search results.", + "advanced": True, + }, + "input_value": { + "display_name": "Input Record", + "info": "Record to write to Zep.", + }, + } + + def add_message( + self, sender: Text, sender_name: Text, text: Text, session_id: Text, metadata: dict | None = None, **kwargs + ): + """ + Adds a message to the ZepChatMessageHistory memory. + + Args: + sender (Text): The type of the message sender. Valid values are "Machine" or "User". + sender_name (Text): The name of the message sender. + text (Text): The content of the message. + session_id (Text): The session ID associated with the message. + metadata (dict | None, optional): Additional metadata for the message. Defaults to None. + **kwargs: Additional keyword arguments. + + Raises: + ValueError: If the ZepChatMessageHistory instance is not provided. + + """ + memory: ZepChatMessageHistory | None = kwargs.pop("memory", None) + if memory is None: + raise ValueError("ZepChatMessageHistory instance is required.") + if metadata is None: + metadata = {} + metadata["sender_name"] = sender_name + metadata.update(kwargs) + if sender == "Machine": + memory.add_ai_message(text, metadata=metadata) + elif sender == "User": + memory.add_user_message(text, metadata=metadata) + else: + raise ValueError(f"Invalid sender type: {sender}") + + def build( + self, + input_value: Record, + session_id: Text, + url: Optional[Text] = None, + api_key: Optional[Text] = None, + ) -> Record: + try: + from zep_python import ZepClient + from zep_python.langchain import ZepChatMessageHistory + except ImportError: + raise ImportError( + "Could not import zep-python package. " "Please install it with `pip install zep-python`." + ) + if url == "": + url = None + zep_client = ZepClient(api_url=url, api_key=api_key) + memory = ZepChatMessageHistory(session_id=session_id, zep_client=zep_client) + self.add_message(**input_value.data, memory=memory) + self.status = f"Added message to Zep memory for session {session_id}" + return input_value diff --git a/src/backend/base/langflow/components/models/AzureOpenAIModel.py b/src/backend/base/langflow/components/models/AzureOpenAIModel.py index 1440df4d0..b6d3607f3 100644 --- a/src/backend/base/langflow/components/models/AzureOpenAIModel.py +++ b/src/backend/base/langflow/components/models/AzureOpenAIModel.py @@ -105,7 +105,7 @@ class AzureChatOpenAIComponent(LCModelComponent): system_message: Optional[str] = None, max_tokens: Optional[int] = 1000, stream: bool = False, - ) -> BaseLanguageModel: + ) -> Text: if api_key: secret_api_key = SecretStr(api_key) else: diff --git a/src/backend/base/langflow/components/models/ChatLiteLLMModel.py b/src/backend/base/langflow/components/models/ChatLiteLLMModel.py index 5266c6935..23ac483cb 100644 --- a/src/backend/base/langflow/components/models/ChatLiteLLMModel.py +++ b/src/backend/base/langflow/components/models/ChatLiteLLMModel.py @@ -142,7 +142,7 @@ class ChatLiteLLMModelComponent(LCModelComponent): max_retries: int = 6, verbose: bool = False, system_message: Optional[str] = None, - ) -> BaseLanguageModel: + ) -> Text: try: import litellm # type: ignore diff --git a/src/backend/base/langflow/components/models/OpenAIModel.py b/src/backend/base/langflow/components/models/OpenAIModel.py index 1997170f5..641eeb755 100644 --- a/src/backend/base/langflow/components/models/OpenAIModel.py +++ b/src/backend/base/langflow/components/models/OpenAIModel.py @@ -40,6 +40,7 @@ class OpenAIModelComponent(LCModelComponent): "display_name": "Model Name", "advanced": False, "options": [ + "gpt-4-turbo-2024-04-09", "gpt-4-turbo-preview", "gpt-3.5-turbo", "gpt-4-0125-preview", diff --git a/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py b/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py index f47538f2e..fd5ccd1aa 100644 --- a/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py +++ b/src/backend/base/langflow/components/vectorsearch/WeaviateSearch.py @@ -61,10 +61,10 @@ class WeaviateSearchVectorStore(WeaviateVectorStoreComponent, LCVectorStoreCompo input_value: Text, search_type: str, url: str, + index_name: str, number_of_results: int = 4, search_by_text: bool = False, api_key: Optional[str] = None, - index_name: Optional[str] = None, text_key: str = "text", embedding: Optional[Embeddings] = None, attributes: Optional[list] = None, diff --git a/src/backend/base/langflow/components/vectorstores/AstraDB.py b/src/backend/base/langflow/components/vectorstores/AstraDB.py index 47fa2a881..3425c3a4e 100644 --- a/src/backend/base/langflow/components/vectorstores/AstraDB.py +++ b/src/backend/base/langflow/components/vectorstores/AstraDB.py @@ -1,5 +1,6 @@ -from typing import List, Optional +from typing import List, Optional, Union +from langchain.schema import BaseRetriever from langchain_astradb import AstraDBVectorStore from langchain_astradb.utils.astradb import SetupMode @@ -110,7 +111,7 @@ class AstraDBVectorStoreComponent(CustomComponent): metadata_indexing_include: Optional[List[str]] = None, metadata_indexing_exclude: Optional[List[str]] = None, collection_indexing_policy: Optional[dict] = None, - ) -> VectorStore: + ) -> Union[VectorStore, BaseRetriever]: try: setup_mode_value = SetupMode[setup_mode.upper()] except KeyError: diff --git a/src/backend/base/langflow/components/vectorstores/Weaviate.py b/src/backend/base/langflow/components/vectorstores/Weaviate.py index 3362a3fca..99ede77f7 100644 --- a/src/backend/base/langflow/components/vectorstores/Weaviate.py +++ b/src/backend/base/langflow/components/vectorstores/Weaviate.py @@ -4,6 +4,7 @@ import weaviate # type: ignore from langchain.embeddings.base import Embeddings from langchain.schema import BaseRetriever from langchain_community.vectorstores import VectorStore, Weaviate +from langchain_core.documents import Document from langflow.interface.custom.custom_component import CustomComponent from langflow.schema.schema import Record @@ -50,9 +51,9 @@ class WeaviateVectorStoreComponent(CustomComponent): def build( self, url: str, + index_name: str, search_by_text: bool = False, api_key: Optional[str] = None, - index_name: Optional[str] = None, text_key: str = "text", embedding: Optional[Embeddings] = None, inputs: Optional[Record] = None, @@ -78,11 +79,13 @@ class WeaviateVectorStoreComponent(CustomComponent): return pascal_case_word index_name = _to_pascal_case(index_name) if index_name else None - documents = [] + if not index_name: + raise ValueError("Index name is required") + documents: list[Document] = [] for _input in inputs or []: if isinstance(_input, Record): documents.append(_input.to_lc_document()) - else: + elif isinstance(_input, Document): documents.append(_input) if documents and embedding is not None: diff --git a/src/backend/base/langflow/graph/graph/base.py b/src/backend/base/langflow/graph/graph/base.py index cccb1739e..a0661f4f2 100644 --- a/src/backend/base/langflow/graph/graph/base.py +++ b/src/backend/base/langflow/graph/graph/base.py @@ -1,5 +1,7 @@ import asyncio +import uuid from collections import defaultdict, deque +from functools import partial from itertools import chain from typing import TYPE_CHECKING, Callable, Coroutine, Dict, Generator, List, Optional, Type, Union @@ -16,6 +18,7 @@ from langflow.graph.vertex.types import ChatVertex, FileToolVertex, LLMVertex, R from langflow.interface.tools.constants import FILE_TOOLS from langflow.schema import Record from langflow.schema.schema import INPUT_FIELD_NAME, InputType +from langflow.services.deps import get_chat_service if TYPE_CHECKING: from langflow.graph.schema import ResultData @@ -29,6 +32,7 @@ class Graph: nodes: List[Dict], edges: List[Dict[str, str]], flow_id: Optional[str] = None, + user_id: Optional[str] = None, ) -> None: """ Initializes a new instance of the Graph class. @@ -44,6 +48,7 @@ class Graph: self._runs = 0 self._updates = 0 self.flow_id = flow_id + self.user_id = user_id self._is_input_vertices: List[str] = [] self._is_output_vertices: List[str] = [] self._is_state_vertices: List[str] = [] @@ -164,13 +169,14 @@ class Graph: raise ValueError("Run ID not set") return self._run_id - def set_run_id(self, run_id: str): + def set_run_id(self, run_id: str | uuid.UUID): """ Sets the ID of the current run. Args: run_id (str): The run ID. """ + run_id = str(run_id) for vertex in self.vertices: self.state_manager.subscribe(run_id, vertex.update_graph_state) self._run_id = run_id @@ -446,7 +452,7 @@ class Graph: self.__init__(**state) @classmethod - def from_payload(cls, payload: Dict, flow_id: Optional[str] = None) -> "Graph": + def from_payload(cls, payload: Dict, flow_id: Optional[str] = None, user_id: Optional[str] = None) -> "Graph": """ Creates a graph from a payload. @@ -461,7 +467,7 @@ class Graph: try: vertices = payload["nodes"] edges = payload["edges"] - return cls(vertices, edges, flow_id) + return cls(vertices, edges, flow_id, user_id) except KeyError as exc: logger.exception(exc) if "nodes" not in payload and "edges" not in payload: @@ -748,31 +754,53 @@ class 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 + first_layer = self.sort_vertices(start_component_id=start_component_id) vertex_task_run_count: Dict[str, int] = {} - for layer_index, layer in enumerate(vertices_layers): + to_process = deque(first_layer) + layer_index = 0 + chat_service = get_chat_service() + run_id = uuid.uuid4() + self.set_run_id(run_id) + while to_process: + current_batch = list(to_process) # Copy current deque items to a list + to_process.clear() # Clear the deque for new items tasks = [] - for vertex_id in layer: + for vertex_id in current_batch: vertex = self.get_vertex(vertex_id) + lock = chat_service._cache_locks[self.run_id] + set_cache_coro = partial(chat_service.set_cache, flow_id=self.run_id) task = asyncio.create_task( - vertex.build(), + self.build_vertex( + lock=lock, + set_cache_coro=set_cache_coro, + vertex_id=vertex_id, + user_id=self.user_id, + inputs_dict={}, + ), name=f"{vertex.display_name} Run {vertex_task_run_count.get(vertex_id, 0)}", ) tasks.append(task) vertex_task_run_count[vertex_id] = vertex_task_run_count.get(vertex_id, 0) + 1 + logger.debug(f"Running layer {layer_index} with {len(tasks)} tasks") - await self._execute_tasks(tasks) + next_runnable_vertices = await self._execute_tasks(tasks) + to_process.extend(next_runnable_vertices) + logger.debug("Graph processing complete") return self - async def _execute_tasks(self, tasks): + async def _execute_tasks(self, tasks: List[asyncio.Task]) -> List[str]: """Executes tasks in parallel, handling exceptions for each task.""" results = [] for i, task in enumerate(asyncio.as_completed(tasks)): try: result = await task - results.append(result) + if isinstance(result, tuple) and len(result) == 7: + # Get the next runnable vertices + next_runnable_vertices = result[0] + results.extend(next_runnable_vertices) + else: + raise ValueError(f"Invalid result: {result}") except Exception as e: # Log the exception along with the task name for easier debugging # task_name = task.get_name() 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 fa049eec2..fce13f449 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 @@ -161,7 +161,7 @@ "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", + "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-2024-04-09\",\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, @@ -222,6 +222,7 @@ "file_path": "", "password": false, "options": [ + "gpt-4-turbo-2024-04-09", "gpt-4-turbo-preview", "gpt-3.5-turbo", "gpt-4-0125-preview", 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 6be06dd5d..8aa19c3df 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 @@ -494,7 +494,7 @@ "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", + "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-2024-04-09\",\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, @@ -555,6 +555,7 @@ "file_path": "", "password": false, "options": [ + "gpt-4-turbo-2024-04-09", "gpt-4-turbo-preview", "gpt-3.5-turbo", "gpt-4-0125-preview", 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 592c05fac..8291b96b4 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 @@ -651,7 +651,7 @@ "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", + "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-2024-04-09\",\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, @@ -712,6 +712,7 @@ "file_path": "", "password": false, "options": [ + "gpt-4-turbo-2024-04-09", "gpt-4-turbo-preview", "gpt-3.5-turbo", "gpt-4-0125-preview", 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 d424b97bc..69d5c61d5 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 @@ -377,7 +377,7 @@ "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", + "value": "from typing import Optional\n\nfrom langflow.base.memory.memory import BaseMemoryComponent\nfrom langflow.field_typing import Text\nfrom langflow.helpers.record import records_to_text\nfrom langflow.memory import get_messages\nfrom langflow.schema.schema import Record\n\n\nclass MemoryComponent(BaseMemoryComponent):\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 get_messages(self, **kwargs) -> list[Record]:\n # Validate kwargs by checking if it contains the correct keys\n if \"sender\" not in kwargs:\n kwargs[\"sender\"] = None\n if \"sender_name\" not in kwargs:\n kwargs[\"sender_name\"] = None\n if \"session_id\" not in kwargs:\n kwargs[\"session_id\"] = None\n if \"limit\" not in kwargs:\n kwargs[\"limit\"] = 5\n if \"order\" not in kwargs:\n kwargs[\"order\"] = \"Descending\"\n\n kwargs[\"order\"] = \"DESC\" if kwargs[\"order\"] == \"Descending\" else \"ASC\"\n if kwargs[\"sender\"] == \"Machine and User\":\n kwargs[\"sender\"] = None\n return get_messages(**kwargs)\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 messages = self.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, @@ -751,7 +751,7 @@ "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", + "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-2024-04-09\",\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, @@ -812,6 +812,7 @@ "file_path": "", "password": false, "options": [ + "gpt-4-turbo-2024-04-09", "gpt-4-turbo-preview", "gpt-3.5-turbo", "gpt-4-0125-preview", 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 3b2411fec..690c22e1e 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 @@ -884,7 +884,7 @@ "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", + "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-2024-04-09\",\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, @@ -945,6 +945,7 @@ "file_path": "", "password": false, "options": [ + "gpt-4-turbo-2024-04-09", "gpt-4-turbo-preview", "gpt-3.5-turbo", "gpt-4-0125-preview", @@ -1270,7 +1271,7 @@ "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", + "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-2024-04-09\",\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, @@ -1331,6 +1332,7 @@ "file_path": "", "password": false, "options": [ + "gpt-4-turbo-2024-04-09", "gpt-4-turbo-preview", "gpt-3.5-turbo", "gpt-4-0125-preview", 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 4aabe3717..69ac26c77 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 @@ -851,7 +851,7 @@ "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", + "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-2024-04-09\",\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, @@ -912,6 +912,7 @@ "file_path": "", "password": false, "options": [ + "gpt-4-turbo-2024-04-09", "gpt-4-turbo-preview", "gpt-3.5-turbo", "gpt-4-0125-preview", @@ -2302,7 +2303,7 @@ "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", + "value": "from typing import List, Optional, Union\n\nfrom langchain.schema import BaseRetriever\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 ) -> Union[VectorStore, BaseRetriever]:\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, diff --git a/src/backend/base/langflow/interface/initialize/vector_store.py b/src/backend/base/langflow/interface/initialize/vector_store.py index d01688165..619baa3f0 100644 --- a/src/backend/base/langflow/interface/initialize/vector_store.py +++ b/src/backend/base/langflow/interface/initialize/vector_store.py @@ -227,34 +227,11 @@ def initialize_qdrant(class_object: Type[Qdrant], params: dict): return class_object.from_documents(**params) -def initialize_elasticsearch(class_object: Type[ElasticsearchStore], params: dict): - """Initialize elastic and return the class object""" - if "index_name" not in params: - raise ValueError("Elasticsearch Index must be provided in the params") - if "es_url" not in params: - raise ValueError("Elasticsearch URL must be provided in the params") - if not docs_in_params(params): - existing_index_params = { - "embedding": params.pop("embedding"), - } - if "index_name" in params: - existing_index_params["index_name"] = params.pop("index_name") - if "es_url" in params: - existing_index_params["es_url"] = params.pop("es_url") - - return class_object.from_existing_index(**existing_index_params) - # If there are docs in the params, create a new index - if "texts" in params: - params["documents"] = params.pop("texts") - return class_object.from_documents(**params) - - vecstore_initializer: Dict[str, Callable[[Type[Any], dict], Any]] = { "Pinecone": initialize_pinecone, "Chroma": initialize_chroma, "Qdrant": initialize_qdrant, "Weaviate": initialize_weaviate, - "ElasticsearchStore": initialize_elasticsearch, "FAISS": initialize_faiss, "SupabaseVectorStore": initialize_supabase, "MongoDBAtlasVectorSearch": initialize_mongodb, diff --git a/src/backend/base/langflow/interface/run.py b/src/backend/base/langflow/interface/run.py index 2327b8725..283c752e8 100644 --- a/src/backend/base/langflow/interface/run.py +++ b/src/backend/base/langflow/interface/run.py @@ -1,9 +1,5 @@ -from typing import Dict, Tuple - from loguru import logger -from langflow.graph import Graph - def get_memory_key(langchain_object): """ diff --git a/src/backend/base/langflow/main.py b/src/backend/base/langflow/main.py index 1a369649e..81797dc80 100644 --- a/src/backend/base/langflow/main.py +++ b/src/backend/base/langflow/main.py @@ -11,6 +11,7 @@ from fastapi.responses import FileResponse from fastapi.staticfiles import StaticFiles from loguru import logger from rich import print as rprint +from starlette.middleware.base import BaseHTTPMiddleware from langflow.api import router from langflow.initial_setup.setup import create_or_update_starter_projects @@ -20,15 +21,38 @@ from langflow.services.utils import initialize_services, teardown_services from langflow.utils.logger import configure +class JavaScriptMIMETypeMiddleware(BaseHTTPMiddleware): + async def dispatch(self, request: Request, call_next): + try: + response = await call_next(request) + except Exception as exc: + logger.error(exc) + raise exc + if "files/" not in request.url.path and request.url.path.endswith(".js") and response.status_code == 200: + response.headers["Content-Type"] = "text/javascript" + return response + + def get_lifespan(fix_migration=False, socketio_server=None): + from langflow.version import __version__ # type: ignore + @asynccontextmanager async def lifespan(app: FastAPI): nest_asyncio.apply() - initialize_services(fix_migration=fix_migration, socketio_server=socketio_server) - setup_llm_caching() - LangfuseInstance.update() - create_or_update_starter_projects() - yield + # Startup message + if __version__: + rprint(f"[bold green]Starting Langflow v{__version__}...[/bold green]") + else: + rprint("[bold green]Starting Langflow...[/bold green]") + try: + initialize_services(fix_migration=fix_migration, socketio_server=socketio_server) + setup_llm_caching() + LangfuseInstance.update() + create_or_update_starter_projects() + yield + except Exception as exc: + if "langflow migration --fix" not in str(exc): + logger.error(exc) # Shutdown message rprint("[bold red]Shutting down Langflow...[/bold red]") teardown_services() @@ -52,6 +76,7 @@ def create_app(): allow_methods=["*"], allow_headers=["*"], ) + app.add_middleware(JavaScriptMIMETypeMiddleware) @app.middleware("http") async def flatten_query_string_lists(request: Request, call_next): diff --git a/src/backend/base/langflow/processing/load.py b/src/backend/base/langflow/processing/load.py index a2be59d95..cf354c3b3 100644 --- a/src/backend/base/langflow/processing/load.py +++ b/src/backend/base/langflow/processing/load.py @@ -3,6 +3,8 @@ from pathlib import Path from typing import List, Optional, Union from dotenv import load_dotenv +from loguru import logger + from langflow.graph import Graph from langflow.graph.schema import RunOutputs from langflow.processing.process import process_tweaks, run_graph @@ -101,6 +103,12 @@ def run_flow_from_json( List[RunOutputs]: A list of RunOutputs objects representing the results of running the flow. """ # Set all streaming to false + try: + import nest_asyncio # type: ignore + + nest_asyncio.apply() + except Exception as e: + logger.warning(f"Could not apply nest_asyncio: {e}") if tweaks is None: tweaks = {} tweaks["stream"] = False diff --git a/src/backend/base/langflow/processing/process.py b/src/backend/base/langflow/processing/process.py index bd35527b4..2d8356159 100644 --- a/src/backend/base/langflow/processing/process.py +++ b/src/backend/base/langflow/processing/process.py @@ -1,5 +1,6 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union + from langchain.agents import AgentExecutor from langchain.schema import AgentAction from loguru import logger @@ -13,6 +14,7 @@ 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 @@ -243,7 +245,6 @@ def apply_tweaks(node: Dict[str, Any], node_tweaks: Dict[str, Any]) -> None: for tweak_name, tweak_value in node_tweaks.items(): if tweak_name not in template_data: - logger.warning(f"Node {node.get('id')} does not have a tweak named {tweak_name}") continue if tweak_name in template_data: key = "file_path" if template_data[tweak_name]["type"] == "file" else "value" @@ -256,27 +257,33 @@ def apply_tweaks_on_vertex(vertex: Vertex, node_tweaks: Dict[str, Any]) -> None: vertex.params[tweak_name] = tweak_value -def process_tweaks(graph_data: Dict[str, Any], tweaks: Union["Tweaks", Dict[str, Dict[str, Any]]]) -> Dict[str, Any]: +def process_tweaks( + graph_data: Dict[str, Any], tweaks: Union["Tweaks", Dict[str, Dict[str, Any]]], stream: bool = False +) -> Dict[str, Any]: """ This function is used to tweak the graph data using the node id and the tweaks dict. :param graph_data: The dictionary containing the graph data. It must contain a 'data' key with 'nodes' as its child or directly contain 'nodes' key. Each node should have an 'id' and 'data'. :param tweaks: The dictionary containing the tweaks. The keys can be the node id or the name of the tweak. - The values can be a dictionary containing the tweaks for the node or the value of the tweak. + The values can be a dictionary containing the tweaks for the node or the value of the tweak. + :param stream: A boolean flag indicating whether streaming should be deactivated across all components or not. Default is False. :return: The modified graph_data dictionary. - :raises ValueError: If the input is not in the expected format. """ + tweaks_dict = {} if not isinstance(tweaks, dict): - tweaks = tweaks.model_dump() - - nodes = validate_input(graph_data, tweaks) + tweaks_dict = tweaks.model_dump() + else: + tweaks_dict = tweaks + if "stream" not in tweaks_dict: + tweaks_dict["stream"] = stream + nodes = validate_input(graph_data, tweaks_dict) nodes_map = {node.get("id"): node for node in nodes} nodes_display_name_map = {node.get("data", {}).get("node", {}).get("display_name"): node for node in nodes} all_nodes_tweaks = {} - for key, value in tweaks.items(): + for key, value in tweaks_dict.items(): if isinstance(value, dict): if node := nodes_map.get(key): apply_tweaks(node, value) diff --git a/src/backend/base/langflow/schema/graph.py b/src/backend/base/langflow/schema/graph.py index a515b3dd2..6b6cca66a 100644 --- a/src/backend/base/langflow/schema/graph.py +++ b/src/backend/base/langflow/schema/graph.py @@ -1,7 +1,8 @@ -from typing import List, Optional, Union +from typing import Any, List, Optional, Union + +from pydantic import BaseModel, Field, RootModel from langflow.schema.schema import InputType -from pydantic import BaseModel, Field, RootModel class InputValue(BaseModel): @@ -14,7 +15,7 @@ class InputValue(BaseModel): class Tweaks(RootModel): - root: dict[str, Union[str, dict[str, str]]] = Field( + root: dict[str, Union[str, dict[str, Any]]] = 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 = { diff --git a/src/backend/base/langflow/schema/schema.py b/src/backend/base/langflow/schema/schema.py index a48e72339..1474c24ee 100644 --- a/src/backend/base/langflow/schema/schema.py +++ b/src/backend/base/langflow/schema/schema.py @@ -1,7 +1,8 @@ import copy -from typing import Literal, Optional +from typing import Literal, Optional, cast from langchain_core.documents import Document +from langchain_core.messages import AIMessage, BaseMessage, HumanMessage from pydantic import BaseModel, model_validator @@ -54,6 +55,21 @@ class Record(BaseModel): data["text"] = document.page_content return cls(data=data, text_key="text") + @classmethod + def from_lc_message(cls, message: BaseMessage) -> "Record": + """ + Converts a BaseMessage to a Record. + + Args: + message (BaseMessage): The BaseMessage to convert. + + Returns: + Record: The converted Record. + """ + data: dict = {"text": message.content} + data["metadata"] = cast(dict, message.to_json()) + return cls(data=data, text_key="text") + def __add__(self, other: "Record") -> "Record": """ Combines the data of two records by attempting to add values for overlapping keys @@ -85,6 +101,26 @@ class Record(BaseModel): text = self.data.pop(self.text_key, self.default_value) return Document(page_content=text, metadata=self.data) + def to_lc_message(self) -> BaseMessage: + """ + Converts the Record to a BaseMessage. + + Returns: + BaseMessage: The converted BaseMessage. + """ + # The idea of this function is to be a helper to convert a Record to a BaseMessage + # It will use the "sender" key to determine if the message is Human or AI + # If the key is not present, it will default to AI + # But first we check if all required keys are present in the data dictionary + # they are: "text", "sender" + if not all(key in self.data for key in ["text", "sender"]): + raise ValueError(f"Missing required keys ('text', 'sender') in Record: {self.data}") + sender = self.data.get("sender", "Machine") + text = self.data.get("text", "") + if sender == "User": + return HumanMessage(content=text) + return AIMessage(content=text) + def __getattr__(self, key): """ Allows attribute-like access to the data dictionary. diff --git a/src/backend/base/langflow/services/database/models/api_key/crud.py b/src/backend/base/langflow/services/database/models/api_key/crud.py index 33bdc7579..37fe08cd1 100644 --- a/src/backend/base/langflow/services/database/models/api_key/crud.py +++ b/src/backend/base/langflow/services/database/models/api_key/crud.py @@ -24,6 +24,7 @@ def create_api_key(session: Session, api_key_create: ApiKeyCreate, user_id: UUID api_key=generated_api_key, name=api_key_create.name, user_id=user_id, + created_at=api_key_create.created_at or datetime.datetime.now(datetime.timezone.utc), ) session.add(api_key) 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 e110258b9..be4e3ed4d 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 @@ -3,17 +3,18 @@ from typing import TYPE_CHECKING, Optional from uuid import UUID, uuid4 from pydantic import field_validator, validator -from sqlmodel import Field, Relationship, SQLModel, Column, func, DateTime +from sqlmodel import Column, DateTime, Field, Relationship, SQLModel, func if TYPE_CHECKING: from langflow.services.database.models.user import User +def utc_now(): + return datetime.now(timezone.utc) + + class ApiKeyBase(SQLModel): name: Optional[str] = Field(index=True, nullable=True, default=None) - 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) @@ -21,7 +22,9 @@ class ApiKeyBase(SQLModel): class ApiKey(ApiKeyBase, table=True): id: UUID = Field(default_factory=uuid4, primary_key=True, unique=True) - + created_at: Optional[datetime] = Field( + default=None, sa_column=Column(DateTime(timezone=True), server_default=func.now(), nullable=False) + ) api_key: str = Field(index=True, unique=True) # User relationship # Delete API keys when user is deleted @@ -34,10 +37,11 @@ class ApiKey(ApiKeyBase, table=True): class ApiKeyCreate(ApiKeyBase): api_key: Optional[str] = None user_id: Optional[UUID] = None + created_at: Optional[datetime] = Field(default_factory=utc_now) @field_validator("created_at", mode="before") def set_created_at(cls, v): - return v or datetime.now(timezone.utc) + return v or utc_now() class UnmaskedApiKeyRead(ApiKeyBase): 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 1fab92b4c..e253bb665 100644 --- a/src/backend/base/langflow/services/database/models/variable/model.py +++ b/src/backend/base/langflow/services/database/models/variable/model.py @@ -25,7 +25,7 @@ class Variable(VariableBase, table=True): description="Unique ID for the variable", ) # name is unique per user - created_at: datetime = Field( + created_at: Optional[datetime] = Field( default=None, sa_column=Column(DateTime(timezone=True), server_default=func.now(), nullable=True), description="Creation time of the variable", diff --git a/src/backend/base/langflow/services/database/service.py b/src/backend/base/langflow/services/database/service.py index 876f9f30e..5ca20c642 100644 --- a/src/backend/base/langflow/services/database/service.py +++ b/src/backend/base/langflow/services/database/service.py @@ -133,7 +133,7 @@ class DatabaseService(Service): alembic_cfg = Config(stdout=buffer) # alembic_cfg.attributes["connection"] = session alembic_cfg.set_main_option("script_location", str(self.script_location)) - alembic_cfg.set_main_option("sqlalchemy.url", self.database_url) + alembic_cfg.set_main_option("sqlalchemy.url", self.database_url.replace('%', '%%')) should_initialize_alembic = False with Session(self.engine) as session: diff --git a/src/backend/base/langflow/services/monitor/utils.py b/src/backend/base/langflow/services/monitor/utils.py index a71653e82..7b6948c1a 100644 --- a/src/backend/base/langflow/services/monitor/utils.py +++ b/src/backend/base/langflow/services/monitor/utils.py @@ -101,10 +101,16 @@ def add_row_to_table( conn.execute(insert_sql, values) except Exception as e: # Log values types + column_error_message = "" for key, value in validated_dict.items(): logger.error(f"{key}: {type(value)}") + if value in str(e): + column_error_message = f"Column: {key} Value: {value} Error: {e}" - logger.error(f"Error adding row to table: {e}") + if column_error_message: + logger.error(f"Error adding row to {table_name}: {column_error_message}") + else: + logger.error(f"Error adding row to {table_name}: {e}") async def log_message( diff --git a/src/backend/base/langflow/services/settings/base.py b/src/backend/base/langflow/services/settings/base.py index 97f69c6c7..db6ba1022 100644 --- a/src/backend/base/langflow/services/settings/base.py +++ b/src/backend/base/langflow/services/settings/base.py @@ -121,7 +121,7 @@ class Settings(BaseSettings): # Define the app name and author app_name = "langflow" - app_author = "logspace" + app_author = "langflow" # Get the cache directory for the application cache_dir = user_cache_dir(app_name, app_author) diff --git a/src/backend/base/langflow/services/utils.py b/src/backend/base/langflow/services/utils.py index 34b3415ad..3d2be2f6d 100644 --- a/src/backend/base/langflow/services/utils.py +++ b/src/backend/base/langflow/services/utils.py @@ -163,7 +163,6 @@ def initialize_services(fix_migration: bool = False, socketio_server=None): try: initialize_database(fix_migration=fix_migration) except Exception as exc: - logger.error(exc) raise exc setup_superuser(get_service(ServiceType.SETTINGS_SERVICE), next(get_session())) try: diff --git a/src/backend/base/langflow/utils/logger.py b/src/backend/base/langflow/utils/logger.py index bc81eaa77..48783710a 100644 --- a/src/backend/base/langflow/utils/logger.py +++ b/src/backend/base/langflow/utils/logger.py @@ -54,22 +54,24 @@ def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None, if not log_file: cache_dir = Path(user_cache_dir("langflow")) + logger.debug(f"Cache directory: {cache_dir}") log_file = cache_dir / "langflow.log" + logger.debug(f"Log file: {log_file}") + try: + log_file = Path(log_file) + log_file.parent.mkdir(parents=True, exist_ok=True) - log_file = Path(log_file) - log_file.parent.mkdir(parents=True, exist_ok=True) - - logger.add( - sink=str(log_file), - level=log_level.upper(), - format=log_format, - rotation="10 MB", # Log rotation based on file size - serialize=True, - ) + logger.add( + sink=str(log_file), + level=log_level.upper(), + format=log_format, + rotation="10 MB", # Log rotation based on file size + serialize=True, + ) + except Exception as exc: + logger.error(f"Error setting up log file: {exc}") logger.debug(f"Logger set up with log level: {log_level}") - if log_file: - logger.debug(f"Log file: {log_file}") setup_uvicorn_logger() setup_gunicorn_logger() diff --git a/src/backend/base/poetry.lock b/src/backend/base/poetry.lock index 40cc1669a..5195d0849 100644 --- a/src/backend/base/poetry.lock +++ b/src/backend/base/poetry.lock @@ -2,87 +2,87 @@ [[package]] name = "aiohttp" -version = "3.9.4" +version = "3.9.5" description = "Async http client/server framework (asyncio)" optional = false python-versions = ">=3.8" files = [ - {file = "aiohttp-3.9.4-cp310-cp310-macosx_10_9_universal2.whl", hash = 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package with a built-in web application" -authors = ["Logspace "] +authors = ["Langflow "] maintainers = [ - "Carlos Coelho ", + "Carlos Coelho ", "Cristhian Zanforlin ", - "Gabriel Almeida ", + "Gabriel Almeida ", "Igor Carvalho ", "Lucas Eduoli ", "Otávio Anovazzi ", - "Rodrigo Nader ", + "Rodrigo Nader ", ] repository = "https://github.com/langflow-ai/langflow" license = "MIT" @@ -29,8 +29,9 @@ python = ">=3.10,<3.12" fastapi = "^0.110.1" httpx = "*" uvicorn = "^0.29.0" -gunicorn = "^21.2.0" -langchain = "~0.1.14" +gunicorn = "^22.0.0" +langchain = "~0.1.16" +langchainhub = "~0.1.15" sqlmodel = "^0.0.16" loguru = "^0.7.1" rich = "^13.7.0" diff --git a/src/frontend/.gitignore b/src/frontend/.gitignore index a6dee659a..285b976e3 100644 --- a/src/frontend/.gitignore +++ b/src/frontend/.gitignore @@ -28,3 +28,7 @@ yarn-error.log* /playwright-report/ /blob-report/ /playwright/.cache/ +/test-results/ +/playwright-report/ +/blob-report/ +/playwright/.cache/ diff --git a/src/frontend/cdk.Dockerfile b/src/frontend/cdk.Dockerfile index 0b33e09b8..fb6562976 100644 --- a/src/frontend/cdk.Dockerfile +++ b/src/frontend/cdk.Dockerfile @@ -1,5 +1,5 @@ #baseline -FROM --platform=linux/amd64 node:19-bullseye-slim AS base +FROM --platform=linux/amd64 node:21-bookworm-slim AS base RUN mkdir -p /home/node/app RUN chown -R node:node /home/node && chmod -R 770 /home/node RUN apt-get update && apt-get install -y jq curl diff --git a/src/frontend/dev.Dockerfile b/src/frontend/dev.Dockerfile index 8678b02dd..533726ef0 100644 --- a/src/frontend/dev.Dockerfile +++ b/src/frontend/dev.Dockerfile @@ -1,5 +1,5 @@ #baseline -FROM node:19-bullseye-slim AS base +FROM node:21-bookworm-slim AS base RUN mkdir -p /home/node/app RUN chown -R node:node /home/node && chmod -R 770 /home/node RUN apt-get update && apt-get install -y jq diff --git a/src/frontend/package-lock.json b/src/frontend/package-lock.json index 4e3967f1b..59ba17bba 100644 --- a/src/frontend/package-lock.json +++ 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"resolved": "https://registry.npmjs.org/warning/-/warning-4.0.3.tgz", + "integrity": "sha512-rpJyN222KWIvHJ/F53XSZv0Zl/accqHR8et1kpaMTD/fLCRxtV8iX8czMzY7sVZupTI3zcUTg8eycS2kNF9l6w==", + "dependencies": { + "loose-envify": "^1.0.0" + } + }, "node_modules/wcwidth": { "version": "1.0.1", "resolved": "https://registry.npmjs.org/wcwidth/-/wcwidth-1.0.1.tgz", @@ -13016,6 +13558,35 @@ "resolved": "https://registry.npmjs.org/wicked-good-xpath/-/wicked-good-xpath-1.3.0.tgz", "integrity": "sha512-Gd9+TUn5nXdwj/hFsPVx5cuHHiF5Bwuc30jZ4+ronF1qHK5O7HD0sgmXWSEgwKquT3ClLoKPVbO6qGwVwLzvAw==" }, + "node_modules/wide-align": { + "version": "1.1.5", + "resolved": "https://registry.npmjs.org/wide-align/-/wide-align-1.1.5.tgz", + "integrity": "sha512-eDMORYaPNZ4sQIuuYPDHdQvf4gyCF9rEEV/yPxGfwPkRodwEgiMUUXTx/dex+Me0wxx53S+NgUHaP7y3MGlDmg==", + "optional": true, + "dependencies": { + "string-width": "^1.0.2 || 2 || 3 || 4" + } + }, + "node_modules/wide-align/node_modules/emoji-regex": { + "version": "8.0.0", + "resolved": "https://registry.npmjs.org/emoji-regex/-/emoji-regex-8.0.0.tgz", + "integrity": "sha512-MSjYzcWNOA0ewAHpz0MxpYFvwg6yjy1NG3xteoqz644VCo/RPgnr1/GGt+ic3iJTzQ8Eu3TdM14SawnVUmGE6A==", + "optional": true + }, + "node_modules/wide-align/node_modules/string-width": { + "version": "4.2.3", + "resolved": "https://registry.npmjs.org/string-width/-/string-width-4.2.3.tgz", + "integrity": "sha512-wKyQRQpjJ0sIp62ErSZdGsjMJWsap5oRNihHhu6G7JVO/9jIB6UyevL+tXuOqrng8j/cxKTWyWUwvSTriiZz/g==", + "optional": true, + "dependencies": { + "emoji-regex": "^8.0.0", + "is-fullwidth-code-point": "^3.0.0", + "strip-ansi": "^6.0.1" + }, + "engines": { + "node": ">=8" + } + }, "node_modules/wrap-ansi": { "version": "8.1.0", "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-8.1.0.tgz", @@ -13137,7 +13708,7 @@ "version": "1.0.2", "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz", "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ==", - "dev": true + "devOptional": true }, "node_modules/ws": { "version": "8.16.0", diff --git a/src/frontend/package.json b/src/frontend/package.json index e298a84fb..94ef0f9b2 100644 --- a/src/frontend/package.json +++ b/src/frontend/package.json @@ -26,6 +26,7 @@ "@tailwindcss/line-clamp": "^0.4.4", "@types/axios": "^0.14.0", "ace-builds": "^1.24.1", + "ag-grid-react": "^31.2.1", "ansi-to-html": "^0.7.2", "axios": "^1.5.0", "base64-js": "^1.5.1", @@ -33,12 +34,15 @@ "clsx": "^1.2.1", "cmdk": "^1.0.0", "dompurify": "^3.0.5", + "dotenv": "^16.4.5", "esbuild": "^0.17.19", + "file-saver": "^2.0.5", "framer-motion": "^11.0.6", "lodash": "^4.17.21", "lucide-react": "^0.331.0", "million": "^3.0.6", "moment": "^2.29.4", + "openseadragon": "^4.1.1", "playwright": "^1.42.0", "react": "^18.2.0", "react-ace": "^10.1.0", @@ -49,6 +53,7 @@ "react-icons": "^5.0.1", "react-laag": "^2.0.5", "react-markdown": "^8.0.7", + "react-pdf": "^7.7.1", "react-router-dom": "^6.15.0", "react-syntax-highlighter": "^15.5.0", "react18-json-view": "^0.2.3", @@ -93,7 +98,7 @@ }, "proxy": "http://127.0.0.1:7860", "devDependencies": { - "@playwright/test": "^1.42.0", + "@playwright/test": "^1.43.1", "@swc/cli": "^0.1.62", "@swc/core": "^1.3.80", "@tailwindcss/typography": "^0.5.9", diff --git a/src/frontend/playwright.config.ts b/src/frontend/playwright.config.ts index 649c42390..9535e0a15 100644 --- a/src/frontend/playwright.config.ts +++ b/src/frontend/playwright.config.ts @@ -1,14 +1,17 @@ import { defineConfig, devices } from "@playwright/test"; +import * as dotenv from "dotenv"; +import path from "path"; +dotenv.config(); +dotenv.config({ path: path.resolve(__dirname, "../../.env") }); /** * Read environment variables from file. * https://github.com/motdotla/dotenv */ -// require('dotenv').config(); - /** * See https://playwright.dev/docs/test-configuration. */ + export default defineConfig({ testDir: "./tests", /* Run tests in files in parallel */ @@ -18,7 +21,7 @@ export default defineConfig({ /* Retry on CI only */ retries: process.env.CI ? 2 : 0, /* Opt out of parallel tests on CI. */ - workers: 3, + workers: 1, /* Reporter to use. See https://playwright.dev/docs/test-reporters */ timeout: 120 * 1000, // reporter: [ @@ -40,24 +43,32 @@ export default defineConfig({ projects: [ { name: "chromium", - use: { ...devices["Desktop Chrome"] }, + use: { + ...devices["Desktop Chrome"], + contextOptions: { + // chromium-specific permissions + permissions: ["clipboard-read", "clipboard-write"], + }, + }, }, { name: "firefox", - use: { ...devices["Desktop Firefox"] }, + use: { + ...devices["Desktop Firefox"], + launchOptions: { + firefoxUserPrefs: { + "dom.events.asyncClipboard.readText": true, + "dom.events.testing.asyncClipboard": true, + }, + }, + }, }, - - // { - // name: "webkit", - // use: { ...devices["Desktop Safari"] }, - // }, ], - /* Run your local dev server before starting the tests */ webServer: [ { command: - "poetry run uvicorn --factory langflow.main:create_app --host 127.0.0.1 --port 7860", + "poetry run uvicorn --factory langflow.main:create_app --host 127.0.0.1 --port 7860 --loop asyncio", port: 7860, env: { LANGFLOW_DATABASE_URL: "sqlite:///./temp", @@ -65,7 +76,7 @@ export default defineConfig({ }, stdout: "ignore", - reuseExistingServer: !process.env.CI, + reuseExistingServer: true, timeout: 120 * 1000, }, { diff --git a/src/frontend/src/components/ImageViewer/index.tsx b/src/frontend/src/components/ImageViewer/index.tsx new file mode 100644 index 000000000..6adda4e3e --- /dev/null +++ b/src/frontend/src/components/ImageViewer/index.tsx @@ -0,0 +1,141 @@ +import { useEffect, useRef, useState } from "react"; +import ForwardedIconComponent from "../genericIconComponent"; +import useFlowStore from "../../stores/flowStore"; +import OpenSeadragon from 'openseadragon'; +import { Separator } from "../ui/separator"; +import { saveAs } from 'file-saver' +import useAlertStore from "../../stores/alertStore"; +import { IMGViewErrorMSG, IMGViewErrorTitle } from "../../constants/constants"; + +export default function ImageViewer({image }) { + const viewerRef = useRef(null); + const [errorDownloading, setErrordownloading] = useState(false) + const setErrorList = useAlertStore(state => state.setErrorData); + const [initialMsg, setInicialMsg] = useState("Please build your flow"); + + + useEffect(() => { + try { + if (viewerRef.current) { + // Initialize OpenSeadragon viewer + const viewer = OpenSeadragon({ + element: viewerRef.current, + prefixUrl: 'https://cdnjs.cloudflare.com/ajax/libs/openseadragon/2.4.2/images/', // Optional: Set the path to OpenSeadragon images + tileSources: {type: 'image', url: image}, + defaultZoomLevel: 1, + maxZoomPixelRatio: 4, + showNavigationControl: false, + }); + const zoomInButton = document.getElementById('zoom-in-button'); + const zoomOutButton = document.getElementById('zoom-out-button'); + const homeButton = document.getElementById('home-button'); + const fullPageButton = document.getElementById('full-page-button'); + + zoomInButton!.addEventListener('click', () => viewer.viewport.zoomBy(1.2)); + zoomOutButton!.addEventListener('click', () => viewer.viewport.zoomBy(0.8)); + homeButton!.addEventListener('click', () => viewer.viewport.goHome()); + fullPageButton!.addEventListener('click', () => viewer.setFullScreen(true)); + + // Optionally, you can set additional viewer options here + + // Cleanup function + return () => { + viewer.destroy(); + zoomInButton!.removeEventListener('click', () => viewer.viewport.zoomBy(1.2)); + zoomOutButton!.removeEventListener('click', () => viewer.viewport.zoomBy(0.8)); + homeButton!.removeEventListener('click', () => viewer.viewport.goHome()); + fullPageButton!.removeEventListener('click', () => viewer.setFullScreen(true)); + }; + } + } catch (error) { + console.error('Error initializing OpenSeadragon:', error); + } + }, [image]); + + function download() { + const imageUrl = image; + // Fetch the image data + fetch(imageUrl) + .then(response => response.blob()) + .then(blob => { + // Save the image using FileSaver.js + saveAs(blob, 'image.jpg'); + }) + .catch(error => { + setErrorList({title: "There was an error downloading your image"}) + console.error('Error downloading image:', error) + }); + } + + return ( + image === "" ? ( +
+
+ + {IMGViewErrorTitle} +
+
+
+
+ {IMGViewErrorMSG} +
+
+
+
+ ) : ( + <> +
+
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+ +
+ + + +
+
+
+ + ) + ); +} \ No newline at end of file diff --git a/src/frontend/src/components/csvOutputComponent/helpers/convert-data-function.ts b/src/frontend/src/components/csvOutputComponent/helpers/convert-data-function.ts new file mode 100644 index 000000000..9c9211ac6 --- /dev/null +++ b/src/frontend/src/components/csvOutputComponent/helpers/convert-data-function.ts @@ -0,0 +1,27 @@ +export const convertCSVToData = (csvFile, csvSeparator: string) => { + const lines = csvFile.data.trim().split("\n"); + const headers = lines[0].trim().split(csvSeparator); + + + const initialRowData: any = []; + const initialColDefs = headers.map((header) => ({ + field: header.trim(), + wrapText: true, + autoHeight: true, + height: "100%", + })); + + for (let i = 1; i < lines.length; i++) { + const data = lines[i].trim().split(csvSeparator); + const rowDataEntry: any = {}; + + for (let j = 0; j < headers.length; j++) { + const value = isNaN(data[j]) ? data[j] : parseFloat(data[j]); + rowDataEntry[headers[j].trim()] = value; + } + + initialRowData.push(rowDataEntry); + } + + return { rowData: initialRowData, colDefs: initialColDefs }; +}; diff --git a/src/frontend/src/components/csvOutputComponent/index.tsx b/src/frontend/src/components/csvOutputComponent/index.tsx new file mode 100644 index 000000000..dbe527bc0 --- /dev/null +++ b/src/frontend/src/components/csvOutputComponent/index.tsx @@ -0,0 +1,182 @@ +import "ag-grid-community/styles/ag-grid.css"; // Mandatory CSS required by the grid +import "ag-grid-community/styles/ag-theme-balham.css"; // Optional Theme applied to the grid +import { AgGridReact } from "ag-grid-react"; +import { useCallback, useEffect, useMemo, useState } from "react"; +import { + CSVError, + CSVNoDataError, + CSVViewErrorTitle, +} from "../../constants/constants"; +import { useDarkStore } from "../../stores/darkStore"; +import { FlowPoolObjectType } from "../../types/chat"; +import { NodeType } from "../../types/flow"; +import ForwardedIconComponent from "../genericIconComponent"; +import Loading from "../ui/loading"; +import { convertCSVToData } from "./helpers/convert-data-function"; + +function CsvOutputComponent({ + csvNode, + flowPool, +}: { + csvNode: NodeType; + flowPool: FlowPoolObjectType; +}) { + const csvNodeArtifacts = flowPool?.data?.artifacts?.repr; + const jsonString = csvNodeArtifacts?.replace(/'/g, '"'); + let file = null; + try { + file = JSON?.parse(jsonString) || ""; + } catch (e) { + console.log("Error parsing JSON"); + } + + if (!file) { + return ( +
+
+ + {CSVViewErrorTitle} +
+
+
+
{CSVError}
+
+
+
+ ); + } + + const separator = csvNode?.data?.node?.template?.separator?.value || ","; + + const dark = useDarkStore.getState().dark; + + const [rowData, setRowData] = useState([]); + const [colDefs, setColDefs] = useState([]); + + const [status, setStatus] = useState("loading"); + var currentRowHeight: number; + var minRowHeight = 25; + const defaultColDef = useMemo(() => { + return { + width: 200, + editable: true, + filter: true, + }; + }, []); + + useEffect(() => { + setStatus("loading"); + if (file) { + const { rowData: data, colDefs: columns } = convertCSVToData( + file, + separator + ); + setRowData(data); + setColDefs(columns); + + setTimeout(() => { + setStatus("loaded"); + }, 1000); + } else { + setStatus("nodata"); + } + }, [separator]); + + const getRowHeight = useCallback(() => { + return currentRowHeight; + }, []); + + const onGridReady = useCallback((params: any) => { + minRowHeight = params.api.getSizesForCurrentTheme().rowHeight; + currentRowHeight = minRowHeight; + }, []); + + const updateRowHeight = (params: { api: any }) => { + const bodyViewport = document.querySelector(".ag-body-viewport"); + if (!bodyViewport) { + return; + } + var gridHeight = bodyViewport.clientHeight; + var renderedRowCount = params.api.getDisplayedRowCount(); + + if (renderedRowCount * minRowHeight >= gridHeight) { + if (currentRowHeight !== minRowHeight) { + currentRowHeight = minRowHeight; + params.api.resetRowHeights(); + } + } else { + currentRowHeight = Math.floor(gridHeight / renderedRowCount); + params.api.resetRowHeights(); + } + }; + + const onFirstDataRendered = useCallback( + (params: any) => { + updateRowHeight(params); + }, + [updateRowHeight] + ); + + const onGridSizeChanged = useCallback( + (params: any) => { + updateRowHeight(params); + }, + [updateRowHeight] + ); + + return ( +
+ {status === "nodata" && ( +
+
+ + {CSVViewErrorTitle} +
+
+
+
{CSVNoDataError}
+
+
+
+ )} + {status === "error" && ( +
+
+ + {CSVViewErrorTitle} +
+
+
+
{CSVError}
+
+
+
+ )} + + {status === "loaded" && ( +
+ +
+ )} + {status === "loading" && ( +
+ +
+ )} +
+ ); +} + +export default CsvOutputComponent; diff --git a/src/frontend/src/components/headerComponent/components/menuBar/index.tsx b/src/frontend/src/components/headerComponent/components/menuBar/index.tsx index 756143217..d61f64620 100644 --- a/src/frontend/src/components/headerComponent/components/menuBar/index.tsx +++ b/src/frontend/src/components/headerComponent/components/menuBar/index.tsx @@ -83,7 +83,9 @@ export const MenuBar = ({ diff --git a/src/frontend/src/components/pdfViewer/Error/index.tsx b/src/frontend/src/components/pdfViewer/Error/index.tsx new file mode 100644 index 000000000..76c35bcd0 --- /dev/null +++ b/src/frontend/src/components/pdfViewer/Error/index.tsx @@ -0,0 +1,23 @@ +import { CHAT_FIRST_INITIAL_TEXT, CHAT_SECOND_INITIAL_TEXT, PDFCheckFlow, PDFLoadErrorTitle } from "../../../constants/constants"; +import IconComponent from "../../genericIconComponent"; + + +export default function Error(): JSX.Element { + return ( +
+
+ + + {PDFLoadErrorTitle} + +
+
+ + {PDFCheckFlow}{" "} + +
+
+ +
+ ); +} \ No newline at end of file diff --git a/src/frontend/src/components/pdfViewer/index.tsx b/src/frontend/src/components/pdfViewer/index.tsx new file mode 100644 index 000000000..725dec395 --- /dev/null +++ b/src/frontend/src/components/pdfViewer/index.tsx @@ -0,0 +1,155 @@ +import { useEffect, useRef, useState } from "react"; +import { Document, Page, pdfjs } from "react-pdf"; +import "react-pdf/dist/esm/Page/AnnotationLayer.css"; +import "react-pdf/dist/esm/Page/TextLayer.css"; +import IconComponent from "../genericIconComponent"; +import Loading from "../ui/loading"; +import Error from "./Error"; +import NoDataPdf from "./noData"; + +pdfjs.GlobalWorkerOptions.workerSrc = `//unpkg.com/pdfjs-dist@${pdfjs.version}/build/pdf.worker.min.js`; + +export default function PdfViewer({ pdf }: { pdf: string }): JSX.Element { + const [numPages, setNumPages] = useState(-1); + const [pageNumber, setPageNumber] = useState(1); + const [scale, setScale] = useState(1); + const [width, setWidth] = useState(undefined); + const [showControl, setShowControl] = useState(false); + const container = useRef(null); + + //shortcuts to change page + useEffect(() => { + function handleKeyDown(event: KeyboardEvent) { + if (event.key === "ArrowLeft") { + if (pageNumber > 1) previousPage(); + } else if (event.key === "ArrowRight") { + if (pageNumber < numPages) nextPage(); + } + } + document.addEventListener("keydown", handleKeyDown); + return () => { + document.removeEventListener("keydown", handleKeyDown); + }; + }, [pageNumber]); + + function onDocumentLoadSuccess({ numPages }) { + setNumPages(numPages); + setPageNumber(1); + } + + function changePage(offset) { + setPageNumber((prevPageNumber) => prevPageNumber + offset); + } + + function previousPage() { + changePage(-1); + } + + function nextPage() { + changePage(1); + } + + //set handle scale in % to real number + function handleScaleChange(e) { + //check if e is a number + if (isNaN(e) || e < 0.1) return; + // round to 2 decimal places + e = Math.round(e * 10) / 10; + + setScale(e); + } + + function zoomIn() { + handleScaleChange(scale + 0.1); + } + function zoomOut() { + if (scale > 0.1) handleScaleChange(scale - 0.1); + } + + function handlePageLoad(page) { + if (!container.current) return; + const containerWidth = container.current.clientWidth; + const pageWidth = page.width; + if (containerWidth > pageWidth) { + setWidth(containerWidth - 10); + } + } + + return ( +
setShowControl(true)} + onMouseLeave={(_) => setShowControl(false)} + className="flex h-full w-full flex-col items-center justify-end overflow-clip rounded-lg border border-border" + > +
+ + +
+ } + onLoadSuccess={onDocumentLoadSuccess} + file={pdf} + noData={} + error={} + className="h-full w-full" + > + + +
+
0 ? "" : " hidden") + } + > +
+ +

+ {pageNumber || (numPages ? 1 : "--")}/{numPages || "--"} +

+ +

|

+ + handleScaleChange(e.target.value)} + value={scale} + /> + +
+
+
+ ); +} diff --git a/src/frontend/src/components/pdfViewer/noData/index.tsx b/src/frontend/src/components/pdfViewer/noData/index.tsx new file mode 100644 index 000000000..b5edee0bb --- /dev/null +++ b/src/frontend/src/components/pdfViewer/noData/index.tsx @@ -0,0 +1,17 @@ +import { PDFErrorTitle, PDFLoadError } from "../../../constants/constants"; + +export default function NoDataPdf(): JSX.Element { + return ( +
+
+ + 📄 {PDFErrorTitle} + +
+
+ {PDFLoadError} +
+
+
+ ); +} diff --git a/src/frontend/src/constants/constants.ts b/src/frontend/src/constants/constants.ts index c8d7626ca..49483875c 100644 --- a/src/frontend/src/constants/constants.ts +++ b/src/frontend/src/constants/constants.ts @@ -161,6 +161,29 @@ export const IMPORT_DIALOG_SUBTITLE = */ export const TOOLTIP_EMPTY = "No compatible components found."; +export const CSVViewErrorTitle = "CSV output"; + +export const CSVNoDataError = "No data available"; + +export const PDFViewConstant = "Expand the ouptut to see the PDF"; + +export const CSVError = "Error loading CSV"; + +export const PDFLoadErrorTitle = "Error loading PDF"; + +export const PDFCheckFlow = "Please check your flow and try again"; + +export const PDFErrorTitle = "PDF Output"; + +export const PDFLoadError = "Run the flow to see the pdf"; + +export const IMGViewConstant = "Expand the view to see the image"; + +export const IMGViewErrorMSG = + "Run the flow or inform a valid url to see your image"; + +export const IMGViewErrorTitle = "Image output"; + /** * The base text for subtitle of code dialog * @constant @@ -688,8 +711,14 @@ export const LANGFLOW_SUPPORTED_TYPES = new Set([ export const priorityFields = new Set(["code", "template"]); -export const INPUT_TYPES = new Set(["ChatInput", "TextInput"]); -export const OUTPUT_TYPES = new Set(["ChatOutput", "TextOutput"]); +export const INPUT_TYPES = new Set(["ChatInput", "TextInput", "KeyPairInput"]); +export const OUTPUT_TYPES = new Set([ + "ChatOutput", + "TextOutput", + "PDFOutput", + "ImageOutput", + "CSVOutput", +]); export const CHAT_FIRST_INITIAL_TEXT = "Start a conversation and click the agent's thoughts"; diff --git a/src/frontend/src/controllers/API/api.tsx b/src/frontend/src/controllers/API/api.tsx index 033a0e648..8084dfeb9 100644 --- a/src/frontend/src/controllers/API/api.tsx +++ b/src/frontend/src/controllers/API/api.tsx @@ -24,6 +24,9 @@ function ApiInterceptor() { async (error: AxiosError) => { if (error.response?.status === 403 || error.response?.status === 401) { if (!autoLogin) { + if (error?.config?.url?.includes("github")) { + return Promise.reject(error); + } const stillRefresh = checkErrorCount(); if (!stillRefresh) { return Promise.reject(error); diff --git a/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx b/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx index 37c9461b5..dd02d19ca 100644 --- a/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx +++ b/src/frontend/src/modals/IOModal/components/IOFieldView/index.tsx @@ -1,5 +1,17 @@ import { cloneDeep } from "lodash"; +import ImageViewer from "../../../../components/ImageViewer"; +import CsvOutputComponent from "../../../../components/csvOutputComponent"; +import PdfViewer from "../../../../components/pdfViewer"; +import { + Select, + SelectContent, + SelectGroup, + SelectItem, + SelectTrigger, + SelectValue, +} from "../../../../components/ui/select"; import { Textarea } from "../../../../components/ui/textarea"; +import { PDFViewConstant } from "../../../../constants/constants"; import { InputOutput } from "../../../../constants/enums"; import useFlowStore from "../../../../stores/flowStore"; import { IOFieldViewProps } from "../../../../types/components"; @@ -15,6 +27,19 @@ export default function IOFieldView({ const setNode = useFlowStore((state) => state.setNode); const flowPool = useFlowStore((state) => state.flowPool); const node = nodes.find((node) => node.id === fieldId); + const flowPoolNode = (flowPool[node!.id] ?? [])[ + (flowPool[node!.id]?.length ?? 1) - 1 + ]; + const handleChangeSelect = (e) => { + if (node) { + let newNode = cloneDeep(node); + if (newNode.data.node.template.separator) { + newNode.data.node.template.separator.value = e; + setNode(newNode.id, newNode); + } + } + }; + function handleOutputType() { if (!node) return <>"No node found!"; switch (type) { @@ -91,6 +116,58 @@ export default function IOFieldView({ readOnly /> ); + case "PDFOutput": + return left ? ( +
{PDFViewConstant}
+ ) : ( + + ); + case "CSVOutput": + return left ? ( + <> +
+ Expand the ouptut to see the CSV +
+
+ CSV separator + +
+ + ) : ( + <> + + + ); + case "ImageOutput": + return left ? ( +
Expand the view to see the image
+ ) : ( + + ); default: return ( diff --git a/src/frontend/src/modals/IOModal/components/chatView/chatMessage/index.tsx b/src/frontend/src/modals/IOModal/components/chatView/chatMessage/index.tsx index b15b61df3..93a0db3c3 100644 --- a/src/frontend/src/modals/IOModal/components/chatView/chatMessage/index.tsx +++ b/src/frontend/src/modals/IOModal/components/chatView/chatMessage/index.tsx @@ -135,7 +135,12 @@ export default function ChatMessage({ alt={!chat.isSend ? "robot_image" : "male_technology"} /> - + {chat.sender_name} diff --git a/src/frontend/src/modals/shareModal/index.tsx b/src/frontend/src/modals/shareModal/index.tsx index d40a5a399..89f1f2569 100644 --- a/src/frontend/src/modals/shareModal/index.tsx +++ b/src/frontend/src/modals/shareModal/index.tsx @@ -26,6 +26,7 @@ import { getTagsIds } from "../../utils/storeUtils"; import ConfirmationModal from "../ConfirmationModal"; import BaseModal from "../baseModal"; import { useHotkeys } from "react-hotkeys-hook"; +import ExportModal from "../exportModal"; export default function ShareModal({ component, @@ -215,9 +216,8 @@ export default function ShareModal({ {children ? children : <>} Share { setSharePublic(event); }} + data-testid="public-checkbox" />