0.6.7 Adds Dynamic Field Updates (#1458)
* Update docker-compose.yml Problems with Docker Compose not being able to find the backend * Bump vite from 4.5.1 to 4.5.2 in /src/frontend Bumps [vite](https://github.com/vitejs/vite/tree/HEAD/packages/vite) from 4.5.1 to 4.5.2. - [Release notes](https://github.com/vitejs/vite/releases) - [Changelog](https://github.com/vitejs/vite/blob/v4.5.2/packages/vite/CHANGELOG.md) - [Commits](https://github.com/vitejs/vite/commits/v4.5.2/packages/vite) --- updated-dependencies: - dependency-name: vite dependency-type: direct:development ... Signed-off-by: dependabot[bot] <support@github.com> * Refactor: remove flow if there is no changes * update group node function to reconnect edges when create groupNode * Remove console.log statements * Fix disallowed_special parameter in OpenAIEmbeddingsComponent * Refactor CharacterTextSplitterComponent to use typing and update return value * Update ChromaComponent configuration * Bump version to 0.6.7a1 in pyproject.toml * Add icon support to CustomComponent * Add icon property to APIClassType * Add emoji validation to icon field in custom components * add emoji icon * Fix: Error: cannot import name 'CreateTrace' from 'langfuse.callback' * Refactor langflow processing and langfuse callback initialization * Update version to 0.6.7a2 in pyproject.toml * Fix: Bring back loading to avoid white page error * Add dependabot.yml * Bump actions/checkout from 2 to 4 Bumps [actions/checkout](https://github.com/actions/checkout) from 2 to 4. - [Release notes](https://github.com/actions/checkout/releases) - [Changelog](https://github.com/actions/checkout/blob/main/CHANGELOG.md) - [Commits](https://github.com/actions/checkout/compare/v2...v4) --- updated-dependencies: - dependency-name: actions/checkout dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> * Bump github/codeql-action from 2 to 3 Bumps [github/codeql-action](https://github.com/github/codeql-action) from 2 to 3. - [Release notes](https://github.com/github/codeql-action/releases) - [Changelog](https://github.com/github/codeql-action/blob/main/CHANGELOG.md) - [Commits](https://github.com/github/codeql-action/compare/v2...v3) --- updated-dependencies: - dependency-name: github/codeql-action dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> * Bump actions/setup-python from 4 to 5 Bumps [actions/setup-python](https://github.com/actions/setup-python) from 4 to 5. - [Release notes](https://github.com/actions/setup-python/releases) - [Commits](https://github.com/actions/setup-python/compare/v4...v5) --- updated-dependencies: - dependency-name: actions/setup-python dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> * Bump actions/cache from 2 to 4 Bumps [actions/cache](https://github.com/actions/cache) from 2 to 4. - [Release notes](https://github.com/actions/cache/releases) - [Changelog](https://github.com/actions/cache/blob/main/RELEASES.md) - [Commits](https://github.com/actions/cache/compare/v2...v4) --- updated-dependencies: - dependency-name: actions/cache dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> * Bump actions/setup-node from 3 to 4 Bumps [actions/setup-node](https://github.com/actions/setup-node) from 3 to 4. - [Release notes](https://github.com/actions/setup-node/releases) - [Commits](https://github.com/actions/setup-node/compare/v3...v4) --- updated-dependencies: - dependency-name: actions/setup-node dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] <support@github.com> * makes function args not to be sorted by name * Add field_order property to CustomComponent * Refactor Template class in base.py * Update field_order to be an optional list * Refactor custom component field ordering * Update prompts.mdx Update broken link to all page building to complete * add icon regex * add isEmoji * Fix invalid emoji error handling * Fix invalid emoji validation in Component class * add logic to icon name * changing to useCallback function * Add HuggingFaceInferenceAPIEmbeddingsComponent class * Update QdrantComponent build method to handle pre-existing vector-stores * Update python-multipart version * Update dependencies in pyproject.toml * Add Python 3.11 support to lint and test workflows * Refactor import statements in Qdrant.py * Update dependencies in pyproject.toml * Fix documentation link and code formatting * Fix validation of icon field in Component class * Update imports and deactivate test * Fixed group nodes appearing at tooltip * Update imports and type annotations in several components * Remove Python 3.9 from matrix in test.yml * refactor: icon fragments functions * Default display_name to None * 🔧 chore(base.py): update serialize_display_name method to handle cases where display_name is not set and convert name to title case if title_case is True * Fix error handling and formatting in component.py and typesStore.ts * add controlX feature * Add files via upload * Fixed groupByFamily * Add LiteLLMComponent to the project * Add ChatLiteLLM component to backend * Update ChatLiteLLM import and add verbose option * Remove unused code in ChatLiteLLM.py * Rename LiteLLMComponent to ChatLiteLLMComponent * Changes some parameters for mypy linting compatibility * Update cookie settings for login and refresh_token functions * Update cookie settings for secure access * Update cookie settings for login and token refresh * Refactor authentication cookie settings * Update version to 0.6.7a3 in pyproject.toml * Fix formatting and import issues * Import litellm package and update ChatLiteLLMComponent class * Update version to 0.6.7a3 and fix formatting and import issues (#1445) * Update version to 0.6.7a3 in pyproject.toml * Fix formatting and import issues * Import litellm package and update ChatLiteLLMComponent class * Update login.py with new auth settings * Update version to 0.6.7a4 in pyproject.toml * Update version to 0.6.7a5 in pyproject.toml * Update Langflow README (#1456) * Update Langflow README * Refactor flow creation process * Update README.md * Removed some phrases, changed Creating Flows section * Update README.md with additional project references --------- Co-authored-by: Gabriel Luiz Freitas Almeida <gabriel@logspace.ai> * Add docs for field update, icon, and small fixes (#1459) * Refactor code formatting and improve error handling in utils.py * Refactor parameterComponent to include refresh button * Update Langflow description * Add new_langflow_demo.gif and remove langflow-demo.gif and langflow-screen.png * Update image source path in README.md * Add dynamic options and default value support to CustomComponent class * Update version number in pyproject.toml * Add title_case option to CustomComponent * Refactor HuggingFaceEndpointsComponent imports and handle model_kwargs parameter --------- Signed-off-by: dependabot[bot] <support@github.com> Co-authored-by: YoungWook KIM <ukng1024@gmail.com> Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: igorrCarvalho <igorsilvabhz6@gmail.com> Co-authored-by: anovazzi1 <otavio2204@gmail.com> Co-authored-by: Cristhian Zanforlin Lousa <72977554+Cristhianzl@users.noreply.github.com> Co-authored-by: cristhianzl <cristhian.lousa@gmail.com> Co-authored-by: Łukasz Gajownik <lukasz.gajownik@ordergroup.pl> Co-authored-by: Chris Bateman <chris-bateman@users.noreply.github.com> Co-authored-by: Ricardo Henriques <paxcalpt@gmail.com> Co-authored-by: Lucas Oliveira <lucas.edu.oli@hotmail.com> Co-authored-by: Carlos Coelho <80289056+carlosrcoelho@users.noreply.github.com> Co-authored-by: Lucas Oliveira <62335616+lucaseduoli@users.noreply.github.com>
This commit is contained in:
Gabriel Luiz Freitas Almeida
GitHub
parent
c3492b9064
commit
38ee9fdd89
63 changed files with 2740 additions and 2166 deletions
11
.github/dependabot.yml
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# Set update schedule for GitHub Actions
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version: 2
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updates:
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- package-ecosystem: "github-actions"
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directory: "/"
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schedule:
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# Check for updates to GitHub Actions every week
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interval: "monthly"
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.github/workflows/ci.yml
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.github/workflows/ci.yml
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@ -16,10 +16,10 @@ jobs:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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steps:
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- name: Checkout code
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uses: actions/checkout@v2
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uses: actions/checkout@v4
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- name: Cache Docker layers
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uses: actions/cache@v2
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uses: actions/cache@v4
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with:
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path: /tmp/.buildx-cache
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key: ${{ runner.os }}-buildx-${{ github.sha }}
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.github/workflows/codeql.yml
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@ -30,11 +30,11 @@ jobs:
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steps:
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- name: Checkout repository
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uses: actions/checkout@v3
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uses: actions/checkout@v4
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# Initializes the CodeQL tools for scanning.
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- name: Initialize CodeQL
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uses: github/codeql-action/init@v2
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uses: github/codeql-action/init@v3
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with:
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languages: ${{ matrix.language }}
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# If you wish to specify custom queries, you can do so here or in a config file.
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@ -48,7 +48,7 @@ jobs:
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# Autobuild attempts to build any compiled languages (C/C++, C#, Go, Java, or Swift).
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# If this step fails, then you should remove it and run the build manually (see below)
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- name: Autobuild
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uses: github/codeql-action/autobuild@v2
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uses: github/codeql-action/autobuild@v3
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# ℹ️ Command-line programs to run using the OS shell.
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# 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
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@ -61,6 +61,6 @@ jobs:
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# ./location_of_script_within_repo/buildscript.sh
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- name: Perform CodeQL Analysis
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uses: github/codeql-action/analyze@v2
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uses: github/codeql-action/analyze@v3
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with:
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category: "/language:${{matrix.language}}"
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.github/workflows/deploy_gh-pages.yml
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name: Deploy to GitHub Pages
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/setup-node@v3
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- uses: actions/checkout@v4
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- uses: actions/setup-node@v4
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with:
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node-version: 18
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cache: npm
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.github/workflows/lint.yml
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python-version:
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- "3.9"
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- "3.10"
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- "3.11"
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Install poetry
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run: |
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pipx install poetry==$POETRY_VERSION
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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cache: poetry
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.github/workflows/pre-release.yml
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if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'pre-release') }}
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Install poetry
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run: pipx install poetry==$POETRY_VERSION
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- name: Set up Python 3.10
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uses: actions/setup-python@v4
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uses: actions/setup-python@v5
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with:
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python-version: "3.10"
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cache: "poetry"
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if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'Release') }}
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Install poetry
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run: pipx install poetry==$POETRY_VERSION
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- name: Set up Python 3.10
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uses: actions/setup-python@v4
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uses: actions/setup-python@v5
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with:
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python-version: "3.10"
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cache: "poetry"
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.github/workflows/test.yml
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matrix:
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python-version:
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- "3.10"
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- "3.11"
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env:
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OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
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steps:
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- uses: actions/checkout@v3
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- uses: actions/checkout@v4
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- name: Install poetry
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run: pipx install poetry==$POETRY_VERSION
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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cache: "poetry"
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80
README.md
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README.md
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@ -1,46 +1,27 @@
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<!-- Title -->
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<!-- markdownlint-disable MD030 -->
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# ⛓️ Langflow
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~ An effortless way to experiment and prototype [LangChain](https://github.com/hwchase17/langchain) pipelines ~
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<h3>Discover a simpler & smarter way to build around Foundation Models</h3>
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<p>
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<img alt="GitHub Contributors" src="https://img.shields.io/github/contributors/logspace-ai/langflow" />
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<img alt="GitHub Last Commit" src="https://img.shields.io/github/last-commit/logspace-ai/langflow" />
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<img alt="" src="https://img.shields.io/github/repo-size/logspace-ai/langflow" />
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<img alt="GitHub Issues" src="https://img.shields.io/github/issues/logspace-ai/langflow" />
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<img alt="GitHub Pull Requests" src="https://img.shields.io/github/issues-pr/logspace-ai/langflow" />
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<img alt="Github License" src="https://img.shields.io/github/license/logspace-ai/langflow" />
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||||
</p>
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||||
[](https://github.com/logspace-ai/langflow/releases)
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[](https://github.com/logspace-ai/langflow/contributors)
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[](https://github.com/logspace-ai/langflow/last-commit)
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||||
[](https://github.com/logspace-ai/langflow/issues)
|
||||
[](https://github.com/logspace-ai/langflow/repo-size)
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||||
[](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/logspace-ai/langflow)
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||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://star-history.com/#logspace-ai/langflow)
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||||
[](https://github.com/logspace-ai/langflow/fork)
|
||||
[](https://twitter.com/langflow_ai)
|
||||
[](https://discord.com/invite/EqksyE2EX9)
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||||
[](https://huggingface.co/spaces/Logspace/Langflow)
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[](https://codespaces.new/logspace-ai/langflow)
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<p>
|
||||
<a href="https://discord.gg/EqksyE2EX9"><img alt="Discord Server" src="https://dcbadge.vercel.app/api/server/EqksyE2EX9?compact=true&style=flat"/></a>
|
||||
<a href="https://huggingface.co/spaces/Logspace/Langflow"><img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="HuggingFace Spaces"></a>
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</p>
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The easiest way to create and customize your flow
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<a href="https://github.com/logspace-ai/langflow">
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<img width="100%" src="https://github.com/logspace-ai/langflow/blob/dev/img/langflow-demo.gif?raw=true"></a>
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<p>
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</p>
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# Table of Contents
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- [⛓️ Langflow](#️-langflow)
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- [Table of Contents](#table-of-contents)
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- [📦 Installation](#-installation)
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- [Locally](#locally)
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- [HuggingFace Spaces](#huggingface-spaces)
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- [🖥️ Command Line Interface (CLI)](#️-command-line-interface-cli)
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- [Usage](#usage)
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- [Environment Variables](#environment-variables)
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- [Deployment](#deployment)
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- [Deploy Langflow on Google Cloud Platform](#deploy-langflow-on-google-cloud-platform)
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- [Deploy on Railway](#deploy-on-railway)
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- [Deploy on Render](#deploy-on-render)
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- [🎨 Creating Flows](#-creating-flows)
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- [👋 Contributing](#-contributing)
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- [📄 License](#-license)
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<img width="100%" src="https://github.com/logspace-ai/langflow/blob/dev/docs/static/img/new_langflow_demo.gif"></a>
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# 📦 Installation
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@ -65,7 +46,7 @@ This will install the following dependencies:
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|||
- [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
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- [sentence-transformers](https://github.com/UKPLab/sentence-transformers)
|
||||
|
||||
You can still use models from projects like LocalAI
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You can still use models from projects like LocalAI, Ollama, LM Studio, Jan and others.
|
||||
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||||
Next, run:
|
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@ -117,7 +98,7 @@ Each option is detailed below:
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- `--backend-only`: This parameter, with a default value of `False`, allows running only the backend server without the frontend. It can also be set using the `LANGFLOW_BACKEND_ONLY` environment variable.
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- `--store`: This parameter, with a default value of `True`, enables the store features, use `--no-store` to deactivate it. It can be configured using the `LANGFLOW_STORE` environment variable.
|
||||
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These parameters are important for users who need to customize the behavior of Langflow, especially in development or specialized deployment scenarios. You may want to update the documentation to include these parameters for completeness and clarity.
|
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These parameters are important for users who need to customize the behavior of Langflow, especially in development or specialized deployment scenarios.
|
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||||
### Environment Variables
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@ -147,19 +128,19 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google
|
|||
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# 🎨 Creating Flows
|
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||||
Creating flows with Langflow is easy. Simply drag sidebar components onto the canvas and connect them together to create your pipeline. Langflow provides a range of [LangChain components](https://python.langchain.com/docs/integrations/components) to choose from, including LLMs, prompt serializers, agents, and chains.
|
||||
Creating flows with Langflow is easy. Simply drag components from the sidebar onto the canvas and connect them to start building your application.
|
||||
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||||
Explore by editing prompt parameters, link chains and agents, track an agent's thought process, and export your flow.
|
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Explore by editing prompt parameters, grouping components into a single high-level component, and building your own Custom Components.
|
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Once you're done, you can export your flow as a JSON file to use with LangChain.
|
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To do so, click the "Export" button in the top right corner of the canvas, then
|
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in Python, you can load the flow with:
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Once you’re done, you can export your flow as a JSON file.
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Load the flow with:
|
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```python
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from langflow import load_flow_from_json
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flow = load_flow_from_json("path/to/flow.json")
|
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# Now you can use it like any chain
|
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# Now you can use it
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flow("Hey, have you heard of Langflow?")
|
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```
|
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|
@ -167,15 +148,16 @@ flow("Hey, have you heard of Langflow?")
|
|||
|
||||
We welcome contributions from developers of all levels to our open-source project on GitHub. If you'd like to contribute, please check our [contributing guidelines](./CONTRIBUTING.md) and help make Langflow more accessible.
|
||||
|
||||
Join our [Discord](https://discord.com/invite/EqksyE2EX9) server to ask questions, make suggestions, and showcase your projects! 🦾
|
||||
|
||||
---
|
||||
|
||||
Join our [Discord](https://discord.com/invite/EqksyE2EX9) server to ask questions, make suggestions and showcase your projects! 🦾
|
||||
|
||||
<p>
|
||||
</p>
|
||||
|
||||
[](https://star-history.com/#logspace-ai/langflow&Date)
|
||||
|
||||
# 🌟 Contributors
|
||||
|
||||
[](https://github.com/logspace-ai/langflow/graphs/contributors)
|
||||
|
||||
# 📄 License
|
||||
|
||||
Langflow is released under the MIT License. See the LICENSE file for details.
|
||||
|
|
|
|||
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@ -22,6 +22,8 @@ services:
|
|||
dockerfile: ./cdk.Dockerfile
|
||||
args:
|
||||
- BACKEND_URL=http://backend:7860
|
||||
depends_on:
|
||||
- backend
|
||||
environment:
|
||||
- VITE_PROXY_TARGET=http://backend:7860
|
||||
ports:
|
||||
|
|
|
|||
|
|
@ -81,7 +81,18 @@ The CustomComponent class serves as the foundation for creating custom component
|
|||
| _`required: bool`_ | Makes the field required. |
|
||||
| _`info: str`_ | Adds a tooltip to the field. |
|
||||
| _`file_types: List[str]`_ | This is a requirement if the _`field_type`_ is _file_. Defines which file types will be accepted. For example, _json_, _yaml_ or _yml_. |
|
||||
| _`range_spec: langflow.field_typing.RangeSpec`_ | This is a requirement if the _`field_type`_ is _`float`_. Defines the range of values accepted and the step size. If none is defined, the default is _`[-1, 1, 0.1]`_. |
|
||||
| _`range_spec: langflow.field_typing.RangeSpec`_ | This is a requirement if the _`field_type`_ is _`float`_. Defines the range of values accepted and the step size. If none is defined, the default is _`[-1, 1, 0.1]`_. |
|
||||
| _`title_case: bool`_ | Formats the name of the field when _`display_name`_ is not defined. Set it to False to keep the name as you set it in the _`build`_ method. |
|
||||
|
||||
<Admonition type="info" label="Tip">
|
||||
|
||||
Keys _`options`_ and _`value`_ can receive a method or function that returns a list of strings or a string, respectively. This is useful when you want to dynamically generate the options or the default value of a field. A refresh button will appear next to the field in the component, allowing the user to update the options or the default value.
|
||||
|
||||
</Admonition>
|
||||
|
||||
|
||||
|
||||
|
||||
- The CustomComponent class also provides helpful methods for specific tasks (e.g., to load and use other flows from the Langflow platform):
|
||||
|
||||
| Method Name | Description |
|
||||
|
|
@ -94,7 +105,9 @@ The CustomComponent class serves as the foundation for creating custom component
|
|||
|
||||
| Attribute Name | Description |
|
||||
| -------------- | ----------------------------------------------------------------------------- |
|
||||
| _`repr_value`_ | Displays the value it receives in the _`build`_ method. Useful for debugging. |
|
||||
| _`status`_ | Displays the value it receives in the _`build`_ method. Useful for debugging. |
|
||||
| _`field_order`_ | Defines the order the fields will be displayed in the canvas. |
|
||||
| _`icon`_ | Defines the emoji (for example, _`:rocket:`_) that will be displayed in the canvas. |
|
||||
|
||||
<Admonition type="info" label="Tip">
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# 👋 Welcome to Langflow
|
||||
|
||||
Langflow is an easy way to prototype [LangChain](https://github.com/hwchase17/langchain) flows. The drag-and-drop feature allows quick and effortless experimentation, while the built-in chat interface facilitates real-time interaction. It provides options to edit prompt parameters, create chains and agents, track thought processes, and export flows.
|
||||
Langflow is an easy way to create flows. The drag-and-drop feature allows quick and effortless experimentation, while the built-in chat interface facilitates real-time interaction. It provides options to edit prompt parameters, create chains and agents, track thought processes, and export flows.
|
||||
|
||||
import ThemedImage from "@theme/ThemedImage";
|
||||
import useBaseUrl from "@docusaurus/useBaseUrl";
|
||||
|
|
@ -11,7 +11,7 @@ import ZoomableImage from "/src/theme/ZoomableImage.js";
|
|||
<ZoomableImage
|
||||
alt="Docusaurus themed image"
|
||||
sources={{
|
||||
light: "img/new_langflow.gif",
|
||||
light: "img/new_langflow_demo.gif",
|
||||
}}
|
||||
style={{ width: "100%" }}
|
||||
/>
|
||||
|
|
|
|||
BIN
docs/static/img/new_langflow_demo.gif
vendored
Normal file
BIN
docs/static/img/new_langflow_demo.gif
vendored
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 20 MiB |
Binary file not shown.
|
Before Width: | Height: | Size: 2 MiB |
Binary file not shown.
|
Before Width: | Height: | Size: 550 KiB |
2918
poetry.lock
generated
2918
poetry.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow"
|
||||
version = "0.6.6"
|
||||
version = "0.6.7"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Logspace <contact@logspace.ai>"]
|
||||
maintainers = [
|
||||
|
|
@ -25,17 +25,17 @@ documentation = "https://docs.langflow.org"
|
|||
langflow = "langflow.__main__:main"
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.9,<3.11"
|
||||
python = ">=3.9,<3.12"
|
||||
fastapi = "^0.109.0"
|
||||
uvicorn = "^0.27.0"
|
||||
beautifulsoup4 = "^4.12.2"
|
||||
google-search-results = "^2.4.1"
|
||||
google-api-python-client = "^2.79.0"
|
||||
google-api-python-client = "^2.118.0"
|
||||
typer = "^0.9.0"
|
||||
gunicorn = "^21.2.0"
|
||||
langchain = "~0.1.0"
|
||||
openai = "^1.11.0"
|
||||
pandas = "2.0.3"
|
||||
openai = "^1.12.0"
|
||||
pandas = "2.2.0"
|
||||
chromadb = "^0.4.0"
|
||||
huggingface-hub = { version = "^0.20.0", extras = ["inference"] }
|
||||
rich = "^13.7.0"
|
||||
|
|
@ -49,16 +49,15 @@ fake-useragent = "^1.4.0"
|
|||
docstring-parser = "^0.15"
|
||||
psycopg2-binary = "^2.9.6"
|
||||
pyarrow = "^14.0.0"
|
||||
tiktoken = "~0.5.0"
|
||||
tiktoken = "~0.6.0"
|
||||
wikipedia = "^1.4.0"
|
||||
qdrant-client = "^1.7.0"
|
||||
websockets = "^10.3"
|
||||
weaviate-client = "*"
|
||||
jina = "*"
|
||||
sentence-transformers = { version = "^2.3.1", optional = true }
|
||||
ctransformers = { version = "^0.2.10", optional = true }
|
||||
cohere = "^4.45.0"
|
||||
python-multipart = "^0.0.6"
|
||||
cohere = "^4.47.0"
|
||||
python-multipart = "^0.0.7"
|
||||
sqlmodel = "^0.0.14"
|
||||
faiss-cpu = "^1.7.4"
|
||||
anthropic = "^0.15.0"
|
||||
|
|
@ -67,17 +66,17 @@ multiprocess = "^0.70.14"
|
|||
cachetools = "^5.3.1"
|
||||
types-cachetools = "^5.3.0.5"
|
||||
platformdirs = "^4.2.0"
|
||||
pinecone-client = "^2.2.2"
|
||||
pinecone-client = "^3.0.3"
|
||||
pymongo = "^4.6.0"
|
||||
supabase = "^2.3.0"
|
||||
certifi = "^2023.11.17"
|
||||
google-cloud-aiplatform = "^1.36.0"
|
||||
google-cloud-aiplatform = "^1.42.0"
|
||||
psycopg = "^3.1.9"
|
||||
psycopg-binary = "^3.1.9"
|
||||
fastavro = "^1.8.0"
|
||||
langchain-experimental = "*"
|
||||
celery = { extras = ["redis"], version = "^5.3.6", optional = true }
|
||||
redis = { version = "^4.6.0", optional = true }
|
||||
redis = { version = "^5.0.1", optional = true }
|
||||
flower = { version = "^2.0.0", optional = true }
|
||||
alembic = "^1.13.0"
|
||||
passlib = "^1.7.4"
|
||||
|
|
@ -90,45 +89,45 @@ zep-python = "*"
|
|||
pywin32 = { version = "^306", markers = "sys_platform == 'win32'" }
|
||||
loguru = "^0.7.1"
|
||||
langfuse = "^2.9.0"
|
||||
pillow = "^10.0.0"
|
||||
metal-sdk = "^2.4.0"
|
||||
pillow = "^10.2.0"
|
||||
metal-sdk = "^2.5.0"
|
||||
markupsafe = "^2.1.3"
|
||||
extract-msg = "^0.45.0"
|
||||
extract-msg = "^0.47.0"
|
||||
# jq is not available for windows
|
||||
jq = { version = "^1.6.0", markers = "sys_platform != 'win32'" }
|
||||
boto3 = "^1.34.0"
|
||||
numexpr = "^2.8.6"
|
||||
qianfan = "0.2.0"
|
||||
qianfan = "0.3.0"
|
||||
pgvector = "^0.2.3"
|
||||
pyautogen = "^0.2.0"
|
||||
langchain-google-genai = "^0.0.6"
|
||||
elasticsearch = "^8.11.1"
|
||||
elasticsearch = "^8.12.0"
|
||||
pytube = "^15.0.0"
|
||||
llama-index = "^0.9.44"
|
||||
langchain-openai = "^0.0.5"
|
||||
llama-index = "0.9.48"
|
||||
langchain-openai = "^0.0.6"
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest-asyncio = "^0.23.1"
|
||||
types-redis = "^4.6.0.5"
|
||||
ipykernel = "^6.27.0"
|
||||
ipykernel = "^6.29.0"
|
||||
mypy = "^1.8.0"
|
||||
ruff = "^0.1.5"
|
||||
ruff = "^0.2.1"
|
||||
httpx = "*"
|
||||
pytest = "^7.4.2"
|
||||
pytest = "^8.0.0"
|
||||
types-requests = "^2.31.0"
|
||||
requests = "^2.31.0"
|
||||
pytest-cov = "^4.1.0"
|
||||
pandas-stubs = "^2.0.0.230412"
|
||||
types-pillow = "^9.5.0.2"
|
||||
pandas-stubs = "^2.1.4.231227"
|
||||
types-pillow = "^10.2.0.20240213"
|
||||
types-pyyaml = "^6.0.12.8"
|
||||
types-python-jose = "^3.3.4.8"
|
||||
types-passlib = "^1.7.7.13"
|
||||
locust = "^2.19.1"
|
||||
locust = "^2.23.1"
|
||||
pytest-mock = "^3.12.0"
|
||||
pytest-xdist = "^3.5.0"
|
||||
types-pywin32 = "^306.0.0.4"
|
||||
types-google-cloud-ndb = "^2.2.0.0"
|
||||
pytest-sugar = "^0.9.7"
|
||||
pytest-sugar = "^1.0.0"
|
||||
pytest-instafail = "^0.5.0"
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -2,13 +2,12 @@ import asyncio
|
|||
from typing import Any, Dict, List, Optional
|
||||
from uuid import UUID
|
||||
|
||||
from langchain.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
|
||||
from langchain.schema import AgentAction, AgentFinish
|
||||
from loguru import logger
|
||||
|
||||
from langchain_core.callbacks.base import AsyncCallbackHandler, BaseCallbackHandler
|
||||
from langflow.api.v1.schemas import ChatResponse, PromptResponse
|
||||
from langflow.services.deps import get_chat_service
|
||||
from langflow.utils.util import remove_ansi_escape_codes
|
||||
from loguru import logger
|
||||
|
||||
|
||||
# https://github.com/hwchase17/chat-langchain/blob/master/callback.py
|
||||
|
|
|
|||
|
|
@ -1,7 +1,5 @@
|
|||
from fastapi import APIRouter, Depends, HTTPException, Request, Response, status
|
||||
from fastapi.security import OAuth2PasswordRequestForm
|
||||
from sqlmodel import Session
|
||||
|
||||
from langflow.api.v1.schemas import Token
|
||||
from langflow.services.auth.utils import (
|
||||
authenticate_user,
|
||||
|
|
@ -10,6 +8,7 @@ from langflow.services.auth.utils import (
|
|||
create_user_tokens,
|
||||
)
|
||||
from langflow.services.deps import get_session, get_settings_service
|
||||
from sqlmodel import Session
|
||||
|
||||
router = APIRouter(tags=["Login"])
|
||||
|
||||
|
|
@ -20,7 +19,9 @@ async def login_to_get_access_token(
|
|||
form_data: OAuth2PasswordRequestForm = Depends(),
|
||||
db: Session = Depends(get_session),
|
||||
# _: Session = Depends(get_current_active_user)
|
||||
settings_service=Depends(get_settings_service),
|
||||
):
|
||||
auth_settings = settings_service.auth_settings
|
||||
try:
|
||||
user = authenticate_user(form_data.username, form_data.password, db)
|
||||
except Exception as exc:
|
||||
|
|
@ -33,8 +34,20 @@ async def login_to_get_access_token(
|
|||
|
||||
if user:
|
||||
tokens = create_user_tokens(user_id=user.id, db=db, update_last_login=True)
|
||||
response.set_cookie("refresh_token_lf", tokens["refresh_token"], httponly=True)
|
||||
response.set_cookie("access_token_lf", tokens["access_token"], httponly=False)
|
||||
response.set_cookie(
|
||||
"refresh_token_lf",
|
||||
tokens["refresh_token"],
|
||||
httponly=auth_settings.REFRESH_HTTPONLY,
|
||||
samesite=auth_settings.REFRESH_SAME_SITE,
|
||||
secure=auth_settings.REFRESH_SECURE,
|
||||
)
|
||||
response.set_cookie(
|
||||
"access_token_lf",
|
||||
tokens["access_token"],
|
||||
httponly=auth_settings.ACCESS_HTTPONLY,
|
||||
samesite=auth_settings.ACCESS_SAME_SITE,
|
||||
secure=auth_settings.ACCESS_SECURE,
|
||||
)
|
||||
return tokens
|
||||
else:
|
||||
raise HTTPException(
|
||||
|
|
@ -46,11 +59,20 @@ async def login_to_get_access_token(
|
|||
|
||||
@router.get("/auto_login")
|
||||
async def auto_login(
|
||||
response: Response, db: Session = Depends(get_session), settings_service=Depends(get_settings_service)
|
||||
response: Response,
|
||||
db: Session = Depends(get_session),
|
||||
settings_service=Depends(get_settings_service),
|
||||
):
|
||||
auth_settings = settings_service.auth_settings
|
||||
if settings_service.auth_settings.AUTO_LOGIN:
|
||||
tokens = create_user_longterm_token(db)
|
||||
response.set_cookie("access_token_lf", tokens["access_token"], httponly=False)
|
||||
response.set_cookie(
|
||||
"access_token_lf",
|
||||
tokens["access_token"],
|
||||
httponly=auth_settings.ACCESS_HTTPONLY,
|
||||
samesite=auth_settings.ACCESS_SAME_SITE,
|
||||
secure=auth_settings.ACCESS_SECURE,
|
||||
)
|
||||
return tokens
|
||||
|
||||
raise HTTPException(
|
||||
|
|
@ -63,12 +85,27 @@ async def auto_login(
|
|||
|
||||
|
||||
@router.post("/refresh")
|
||||
async def refresh_token(request: Request, response: Response):
|
||||
async def refresh_token(request: Request, response: Response, settings_service=Depends(get_settings_service)):
|
||||
auth_settings = settings_service.auth_settings
|
||||
|
||||
token = request.cookies.get("refresh_token_lf")
|
||||
|
||||
if token:
|
||||
tokens = create_refresh_token(token)
|
||||
response.set_cookie("refresh_token_lf", tokens["refresh_token"], httponly=True)
|
||||
response.set_cookie("access_token_lf", tokens["access_token"], httponly=False)
|
||||
response.set_cookie(
|
||||
"refresh_token_lf",
|
||||
tokens["refresh_token"],
|
||||
httponly=auth_settings.REFRESH_TOKEN_HTTPONLY,
|
||||
samesite=auth_settings.REFRESH_SAME_SITE,
|
||||
secure=auth_settings.REFRESH_SECURE,
|
||||
)
|
||||
response.set_cookie(
|
||||
"access_token_lf",
|
||||
tokens["access_token"],
|
||||
httponly=auth_settings.ACCESS_HTTPONLY,
|
||||
samesite=auth_settings.ACCESS_SAME_SITE,
|
||||
secure=auth_settings.ACCESS_SECURE,
|
||||
)
|
||||
return tokens
|
||||
else:
|
||||
raise HTTPException(
|
||||
|
|
|
|||
|
|
@ -1,10 +1,8 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Callable, Union
|
||||
|
||||
from langchain.chains import LLMCheckerChain
|
||||
from typing import Union, Callable
|
||||
from langflow.field_typing import (
|
||||
BaseLanguageModel,
|
||||
Chain,
|
||||
)
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, Chain
|
||||
|
||||
|
||||
class LLMCheckerChainComponent(CustomComponent):
|
||||
|
|
@ -21,4 +19,4 @@ class LLMCheckerChainComponent(CustomComponent):
|
|||
self,
|
||||
llm: BaseLanguageModel,
|
||||
) -> Union[Chain, Callable]:
|
||||
return LLMCheckerChain(llm=llm)
|
||||
return LLMCheckerChain.from_llm(llm=llm)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,8 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Any, Dict, List
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from typing import Optional, Dict, Any
|
||||
from langchain.document_loaders.directory import DirectoryLoader
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class DirectoryLoaderComponent(CustomComponent):
|
||||
|
|
@ -23,20 +25,18 @@ class DirectoryLoaderComponent(CustomComponent):
|
|||
self,
|
||||
glob: str,
|
||||
path: str,
|
||||
load_hidden: Optional[bool] = False,
|
||||
max_concurrency: Optional[int] = 10,
|
||||
metadata: Optional[dict] = {},
|
||||
recursive: Optional[bool] = True,
|
||||
silent_errors: Optional[bool] = False,
|
||||
use_multithreading: Optional[bool] = True,
|
||||
) -> Document:
|
||||
return Document(
|
||||
max_concurrency: int = 2,
|
||||
load_hidden: bool = False,
|
||||
recursive: bool = True,
|
||||
silent_errors: bool = False,
|
||||
use_multithreading: bool = True,
|
||||
) -> List[Document]:
|
||||
return DirectoryLoader(
|
||||
glob=glob,
|
||||
path=path,
|
||||
load_hidden=load_hidden,
|
||||
max_concurrency=max_concurrency,
|
||||
metadata=metadata,
|
||||
recursive=recursive,
|
||||
silent_errors=silent_errors,
|
||||
use_multithreading=use_multithreading,
|
||||
)
|
||||
).load()
|
||||
|
|
|
|||
|
|
@ -0,0 +1,42 @@
|
|||
from typing import Dict, Optional
|
||||
|
||||
from langchain_community.embeddings.huggingface import HuggingFaceInferenceAPIEmbeddings
|
||||
from langflow import CustomComponent
|
||||
from pydantic.v1.types import SecretStr
|
||||
|
||||
|
||||
class HuggingFaceInferenceAPIEmbeddingsComponent(CustomComponent):
|
||||
display_name = "HuggingFaceInferenceAPIEmbeddings"
|
||||
description = "HuggingFace sentence_transformers embedding models, API version."
|
||||
documentation = "https://github.com/huggingface/text-embeddings-inference"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"api_key": {"display_name": "API Key", "password": True, "advanced": True},
|
||||
"api_url": {"display_name": "API URL", "advanced": True},
|
||||
"model_name": {"display_name": "Model Name"},
|
||||
"cache_folder": {"display_name": "Cache Folder", "advanced": True},
|
||||
"encode_kwargs": {"display_name": "Encode Kwargs", "advanced": True, "field_type": "dict"},
|
||||
"model_kwargs": {"display_name": "Model Kwargs", "field_type": "dict", "advanced": True},
|
||||
"multi_process": {"display_name": "Multi Process", "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
api_key: Optional[str] = "",
|
||||
api_url: str = "http://localhost:8080",
|
||||
model_name: str = "BAAI/bge-large-en-v1.5",
|
||||
cache_folder: Optional[str] = None,
|
||||
encode_kwargs: Optional[Dict] = {},
|
||||
model_kwargs: Optional[Dict] = {},
|
||||
multi_process: bool = False,
|
||||
) -> HuggingFaceInferenceAPIEmbeddings:
|
||||
if api_key:
|
||||
secret_api_key = SecretStr(api_key)
|
||||
else:
|
||||
raise ValueError("API Key is required")
|
||||
return HuggingFaceInferenceAPIEmbeddings(
|
||||
api_key=secret_api_key,
|
||||
api_url=api_url,
|
||||
model_name=model_name,
|
||||
)
|
||||
|
|
@ -1,9 +1,9 @@
|
|||
from typing import Any, Callable, Dict, List, Optional, Union
|
||||
|
||||
from langchain_openai.embeddings.base import OpenAIEmbeddings
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import NestedDict
|
||||
from pydantic.v1.types import SecretStr
|
||||
|
||||
|
||||
class OpenAIEmbeddingsComponent(CustomComponent):
|
||||
|
|
@ -67,7 +67,7 @@ class OpenAIEmbeddingsComponent(CustomComponent):
|
|||
},
|
||||
"skip_empty": {"display_name": "Skip Empty", "advanced": True},
|
||||
"tiktoken_model_name": {"display_name": "TikToken Model Name"},
|
||||
"tikToken_enable": {"display_name": "TikToken Enable"},
|
||||
"tikToken_enable": {"display_name": "TikToken Enable", "advanced": True},
|
||||
}
|
||||
|
||||
def build(
|
||||
|
|
@ -92,15 +92,21 @@ class OpenAIEmbeddingsComponent(CustomComponent):
|
|||
request_timeout: Optional[float] = None,
|
||||
show_progress_bar: bool = False,
|
||||
skip_empty: bool = False,
|
||||
tikToken_enable: bool = True,
|
||||
tiktoken_enable: bool = True,
|
||||
tiktoken_model_name: Optional[str] = None,
|
||||
) -> Union[OpenAIEmbeddings, Callable]:
|
||||
# This is to avoid errors with Vector Stores (e.g Chroma)
|
||||
if disallowed_special == ["all"]:
|
||||
disallowed_special = "all" # type: ignore
|
||||
|
||||
api_key = SecretStr(openai_api_key) if openai_api_key else None
|
||||
|
||||
return OpenAIEmbeddings(
|
||||
tiktoken_enabled=tikToken_enable,
|
||||
tiktoken_enabled=tiktoken_enable,
|
||||
default_headers=default_headers,
|
||||
default_query=default_query,
|
||||
allowed_special=set(allowed_special),
|
||||
disallowed_special=set(disallowed_special),
|
||||
disallowed_special="all",
|
||||
chunk_size=chunk_size,
|
||||
client=client,
|
||||
deployment=deployment,
|
||||
|
|
@ -109,7 +115,7 @@ class OpenAIEmbeddingsComponent(CustomComponent):
|
|||
model=model,
|
||||
model_kwargs=model_kwargs,
|
||||
base_url=openai_api_base,
|
||||
api_key=openai_api_key,
|
||||
api_key=api_key,
|
||||
openai_api_type=openai_api_type,
|
||||
api_version=openai_api_version,
|
||||
organization=openai_organization,
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from pydantic import SecretStr
|
||||
from pydantic.v1.types import SecretStr
|
||||
from langflow import CustomComponent
|
||||
from typing import Optional, Union, Callable
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
|
|
|
|||
137
src/backend/langflow/components/llms/ChatLiteLLM.py
Normal file
137
src/backend/langflow/components/llms/ChatLiteLLM.py
Normal file
|
|
@ -0,0 +1,137 @@
|
|||
import os
|
||||
from typing import Any, Callable, Dict, Optional, Union
|
||||
|
||||
from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
|
||||
|
||||
class ChatLiteLLMComponent(CustomComponent):
|
||||
display_name = "ChatLiteLLM"
|
||||
description = "`LiteLLM` collection of large language models."
|
||||
documentation = "https://python.langchain.com/docs/integrations/chat/litellm"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"model": {
|
||||
"display_name": "Model name",
|
||||
"field_type": "str",
|
||||
"advanced": False,
|
||||
"required": True,
|
||||
"info": "The name of the model to use. For example, `gpt-3.5-turbo`.",
|
||||
},
|
||||
"api_key": {
|
||||
"display_name": "API key",
|
||||
"field_type": "str",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"password": True,
|
||||
},
|
||||
"streaming": {
|
||||
"display_name": "Streaming",
|
||||
"field_type": "bool",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"default": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"default": 0.7,
|
||||
},
|
||||
"model_kwargs": {
|
||||
"display_name": "Model kwargs",
|
||||
"field_type": "dict",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"default": {},
|
||||
},
|
||||
"top_p": {
|
||||
"display_name": "Top p",
|
||||
"field_type": "float",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
},
|
||||
"top_k": {
|
||||
"display_name": "Top k",
|
||||
"field_type": "int",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
},
|
||||
"n": {
|
||||
"display_name": "N",
|
||||
"field_type": "int",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"info": "Number of chat completions to generate for each prompt. "
|
||||
"Note that the API may not return the full n completions if duplicates are generated.",
|
||||
"default": 1,
|
||||
},
|
||||
"max_tokens": {
|
||||
"display_name": "Max tokens",
|
||||
"field_type": "int",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"default": 256,
|
||||
"info": "The maximum number of tokens to generate for each chat completion.",
|
||||
},
|
||||
"max_retries": {
|
||||
"display_name": "Max retries",
|
||||
"field_type": "int",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"default": 6,
|
||||
},
|
||||
"verbose": {
|
||||
"display_name": "Verbose",
|
||||
"field_type": "bool",
|
||||
"advanced": True,
|
||||
"required": False,
|
||||
"default": False,
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
model: str,
|
||||
api_key: str,
|
||||
streaming: bool = True,
|
||||
temperature: Optional[float] = 0.7,
|
||||
model_kwargs: Optional[Dict[str, Any]] = {},
|
||||
top_p: Optional[float] = None,
|
||||
top_k: Optional[int] = None,
|
||||
n: int = 1,
|
||||
max_tokens: int = 256,
|
||||
max_retries: int = 6,
|
||||
verbose: bool = False,
|
||||
) -> Union[BaseLanguageModel, Callable]:
|
||||
try:
|
||||
import litellm # type: ignore
|
||||
|
||||
litellm.drop_params = True
|
||||
litellm.set_verbose = verbose
|
||||
except ImportError:
|
||||
raise ChatLiteLLMException(
|
||||
"Could not import litellm python package. " "Please install it with `pip install litellm`"
|
||||
)
|
||||
if api_key:
|
||||
if "perplexity" in model:
|
||||
os.environ["PERPLEXITYAI_API_KEY"] = api_key
|
||||
elif "replicate" in model:
|
||||
os.environ["REPLICATE_API_KEY"] = api_key
|
||||
|
||||
LLM = ChatLiteLLM(
|
||||
model=model,
|
||||
client=None,
|
||||
streaming=streaming,
|
||||
temperature=temperature,
|
||||
model_kwargs=model_kwargs if model_kwargs is not None else {},
|
||||
top_p=top_p,
|
||||
top_k=top_k,
|
||||
n=n,
|
||||
max_tokens=max_tokens,
|
||||
max_retries=max_retries,
|
||||
)
|
||||
return LLM
|
||||
|
|
@ -1,9 +1,9 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_google_genai import ChatGoogleGenerativeAI # type: ignore
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel, RangeSpec, TemplateField
|
||||
from pydantic.v1.types import SecretStr
|
||||
|
||||
|
||||
class GoogleGenerativeAIComponent(CustomComponent):
|
||||
|
|
@ -63,10 +63,10 @@ class GoogleGenerativeAIComponent(CustomComponent):
|
|||
) -> BaseLanguageModel:
|
||||
return ChatGoogleGenerativeAI(
|
||||
model=model,
|
||||
max_output_tokens=max_output_tokens or None,
|
||||
max_output_tokens=max_output_tokens or None, # type: ignore
|
||||
temperature=temperature,
|
||||
top_k=top_k or None,
|
||||
top_p=top_p or None,
|
||||
top_p=top_p or None, # type: ignore
|
||||
n=n or 1,
|
||||
google_api_key=google_api_key,
|
||||
google_api_key=SecretStr(google_api_key),
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,8 @@
|
|||
from typing import Optional
|
||||
from langflow import CustomComponent
|
||||
from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint
|
||||
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class HuggingFaceEndpointsComponent(CustomComponent):
|
||||
|
|
@ -31,11 +32,11 @@ class HuggingFaceEndpointsComponent(CustomComponent):
|
|||
model_kwargs: Optional[dict] = None,
|
||||
) -> BaseLLM:
|
||||
try:
|
||||
output = HuggingFaceEndpoint(
|
||||
output = HuggingFaceEndpoint( # type: ignore
|
||||
endpoint_url=endpoint_url,
|
||||
task=task,
|
||||
huggingfacehub_api_token=huggingfacehub_api_token,
|
||||
model_kwargs=model_kwargs,
|
||||
model_kwargs=model_kwargs or {},
|
||||
)
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to HuggingFace Endpoints API.") from e
|
||||
|
|
|
|||
|
|
@ -1,7 +1,8 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import List
|
||||
|
||||
from langchain.text_splitter import CharacterTextSplitter
|
||||
from langchain_core.documents.base import Document
|
||||
from typing import List
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class CharacterTextSplitterComponent(CustomComponent):
|
||||
|
|
@ -23,8 +24,10 @@ class CharacterTextSplitterComponent(CustomComponent):
|
|||
chunk_size: int = 1000,
|
||||
separator: str = "\n",
|
||||
) -> List[Document]:
|
||||
return CharacterTextSplitter(
|
||||
docs = CharacterTextSplitter(
|
||||
chunk_overlap=chunk_overlap,
|
||||
chunk_size=chunk_size,
|
||||
separator=separator,
|
||||
).split_documents(documents)
|
||||
self.status = docs
|
||||
return docs
|
||||
|
|
|
|||
|
|
@ -1,8 +1,7 @@
|
|||
from langchain_community.agent_toolkits.openapi.toolkit import BaseToolkit, OpenAPIToolkit
|
||||
from langchain_community.utilities.requests import TextRequestsWrapper
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import AgentExecutor
|
||||
from typing import Callable
|
||||
from langchain_community.utilities.requests import TextRequestsWrapper
|
||||
from langchain_community.agent_toolkits.openapi.toolkit import OpenAPIToolkit
|
||||
|
||||
|
||||
class OpenAPIToolkitComponent(CustomComponent):
|
||||
|
|
@ -19,5 +18,5 @@ class OpenAPIToolkitComponent(CustomComponent):
|
|||
self,
|
||||
json_agent: AgentExecutor,
|
||||
requests_wrapper: TextRequestsWrapper,
|
||||
) -> Callable:
|
||||
) -> BaseToolkit:
|
||||
return OpenAPIToolkit(json_agent=json_agent, requests_wrapper=requests_wrapper)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Union, Callable
|
||||
from typing import Callable, Union
|
||||
|
||||
from langchain_community.utilities.google_search import GoogleSearchAPIWrapper
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class GoogleSearchAPIWrapperComponent(CustomComponent):
|
||||
|
|
@ -18,4 +19,4 @@ class GoogleSearchAPIWrapperComponent(CustomComponent):
|
|||
google_api_key: str,
|
||||
google_cse_id: str,
|
||||
) -> Union[GoogleSearchAPIWrapper, Callable]:
|
||||
return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id)
|
||||
return GoogleSearchAPIWrapper(google_api_key=google_api_key, google_cse_id=google_cse_id) # type: ignore
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
from langflow import CustomComponent
|
||||
from typing import Dict, Optional
|
||||
from typing import Dict
|
||||
|
||||
# Assuming the existence of GoogleSerperAPIWrapper class in the serper module
|
||||
# If this class does not exist, you would need to create it or import the appropriate class from another module
|
||||
from langchain_community.utilities.google_serper import GoogleSerperAPIWrapper
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
class GoogleSerperAPIWrapperComponent(CustomComponent):
|
||||
|
|
@ -42,6 +42,5 @@ class GoogleSerperAPIWrapperComponent(CustomComponent):
|
|||
def build(
|
||||
self,
|
||||
serper_api_key: str,
|
||||
result_key_for_type: Optional[Dict[str, str]] = None,
|
||||
) -> GoogleSerperAPIWrapper:
|
||||
return GoogleSerperAPIWrapper(result_key_for_type=result_key_for_type, serper_api_key=serper_api_key)
|
||||
return GoogleSerperAPIWrapper(serper_api_key=serper_api_key)
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ class ChromaComponent(CustomComponent):
|
|||
"collection_name": {"display_name": "Collection Name", "value": "langflow"},
|
||||
"persist": {"display_name": "Persist"},
|
||||
"persist_directory": {"display_name": "Persist Directory"},
|
||||
"code": {"show": False, "display_name": "Code"},
|
||||
"code": {"advanced": True, "display_name": "Code"},
|
||||
"documents": {"display_name": "Documents", "is_list": True},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"chroma_server_cors_allow_origins": {
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ import pinecone # type: ignore
|
|||
from langchain.schema import BaseRetriever
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
from langchain_community.vectorstores.pinecone import Pinecone
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import Document, Embeddings
|
||||
|
||||
|
|
@ -31,11 +30,11 @@ class PineconeComponent(CustomComponent):
|
|||
embedding: Embeddings,
|
||||
pinecone_env: str,
|
||||
documents: List[Document],
|
||||
text_key: str = "text",
|
||||
pool_threads: int = 4,
|
||||
index_name: Optional[str] = None,
|
||||
pinecone_api_key: Optional[str] = None,
|
||||
text_key: Optional[str] = "text",
|
||||
namespace: Optional[str] = "default",
|
||||
pool_threads: Optional[int] = None,
|
||||
) -> Union[VectorStore, Pinecone, BaseRetriever]:
|
||||
if pinecone_api_key is None or pinecone_env is None:
|
||||
raise ValueError("Pinecone API Key and Environment are required.")
|
||||
|
|
@ -43,6 +42,8 @@ class PineconeComponent(CustomComponent):
|
|||
raise ValueError("Pinecone API Key is required.")
|
||||
|
||||
pinecone.init(api_key=pinecone_api_key, environment=pinecone_env) # type: ignore
|
||||
if not index_name:
|
||||
raise ValueError("Index Name is required.")
|
||||
if documents:
|
||||
return Pinecone.from_documents(
|
||||
documents=documents,
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from typing import List, Optional, Union
|
||||
from typing import Optional, Union
|
||||
|
||||
from langchain.schema import BaseRetriever
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
|
|
@ -15,7 +15,7 @@ class QdrantComponent(CustomComponent):
|
|||
return {
|
||||
"documents": {"display_name": "Documents"},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"api_key": {"display_name": "API Key", "password": True},
|
||||
"api_key": {"display_name": "API Key", "password": True, "advanced": True},
|
||||
"collection_name": {"display_name": "Collection Name"},
|
||||
"content_payload_key": {"display_name": "Content Payload Key", "advanced": True},
|
||||
"distance_func": {"display_name": "Distance Function", "advanced": True},
|
||||
|
|
@ -36,41 +36,68 @@ class QdrantComponent(CustomComponent):
|
|||
def build(
|
||||
self,
|
||||
embedding: Embeddings,
|
||||
documents: List[Document],
|
||||
collection_name: str,
|
||||
documents: Optional[Document] = None,
|
||||
api_key: Optional[str] = None,
|
||||
collection_name: Optional[str] = None,
|
||||
content_payload_key: str = "page_content",
|
||||
distance_func: str = "Cosine",
|
||||
grpc_port: Optional[int] = 6334,
|
||||
host: Optional[str] = None,
|
||||
grpc_port: int = 6334,
|
||||
https: bool = False,
|
||||
location: str = ":memory:",
|
||||
host: Optional[str] = None,
|
||||
location: Optional[str] = None,
|
||||
metadata_payload_key: str = "metadata",
|
||||
path: Optional[str] = None,
|
||||
port: Optional[int] = 6333,
|
||||
prefer_grpc: bool = False,
|
||||
prefix: Optional[str] = None,
|
||||
search_kwargs: Optional[NestedDict] = None,
|
||||
timeout: Optional[float] = None,
|
||||
timeout: Optional[int] = None,
|
||||
url: Optional[str] = None,
|
||||
) -> Union[VectorStore, Qdrant, BaseRetriever]:
|
||||
return Qdrant.from_documents(
|
||||
documents=documents,
|
||||
embedding=embedding,
|
||||
api_key=api_key,
|
||||
collection_name=collection_name,
|
||||
content_payload_key=content_payload_key,
|
||||
distance_func=distance_func,
|
||||
grpc_port=grpc_port,
|
||||
host=host,
|
||||
https=https,
|
||||
location=location,
|
||||
metadata_payload_key=metadata_payload_key,
|
||||
path=path,
|
||||
port=port,
|
||||
prefer_grpc=prefer_grpc,
|
||||
prefix=prefix,
|
||||
search_kwargs=search_kwargs,
|
||||
timeout=timeout,
|
||||
url=url,
|
||||
)
|
||||
if documents is None:
|
||||
from qdrant_client import QdrantClient
|
||||
|
||||
client = QdrantClient(
|
||||
location=location,
|
||||
url=host,
|
||||
port=port,
|
||||
grpc_port=grpc_port,
|
||||
https=https,
|
||||
prefix=prefix,
|
||||
timeout=timeout,
|
||||
prefer_grpc=prefer_grpc,
|
||||
metadata_payload_key=metadata_payload_key,
|
||||
content_payload_key=content_payload_key,
|
||||
api_key=api_key,
|
||||
collection_name=collection_name,
|
||||
host=host,
|
||||
path=path,
|
||||
)
|
||||
vs = Qdrant(
|
||||
client=client,
|
||||
collection_name=collection_name,
|
||||
embeddings=embedding,
|
||||
)
|
||||
return vs
|
||||
else:
|
||||
vs = Qdrant.from_documents(
|
||||
documents=documents, # type: ignore
|
||||
embedding=embedding,
|
||||
api_key=api_key,
|
||||
collection_name=collection_name,
|
||||
content_payload_key=content_payload_key,
|
||||
distance_func=distance_func,
|
||||
grpc_port=grpc_port,
|
||||
host=host,
|
||||
https=https,
|
||||
location=location,
|
||||
metadata_payload_key=metadata_payload_key,
|
||||
path=path,
|
||||
port=port,
|
||||
prefer_grpc=prefer_grpc,
|
||||
prefix=prefix,
|
||||
search_kwargs=search_kwargs,
|
||||
timeout=timeout,
|
||||
url=url,
|
||||
)
|
||||
return vs
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ from langchain_community.vectorstores import VectorStore
|
|||
from langchain_community.vectorstores.redis import Redis
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
|
|
@ -31,6 +30,7 @@ class RedisComponent(CustomComponent):
|
|||
"code": {"show": False, "display_name": "Code"},
|
||||
"documents": {"display_name": "Documents", "is_list": True},
|
||||
"embedding": {"display_name": "Embedding"},
|
||||
"schema": {"display_name": "Schema", "file_types": [".yaml"]},
|
||||
"redis_server_url": {
|
||||
"display_name": "Redis Server Connection String",
|
||||
"advanced": False,
|
||||
|
|
@ -43,6 +43,7 @@ class RedisComponent(CustomComponent):
|
|||
embedding: Embeddings,
|
||||
redis_server_url: str,
|
||||
redis_index_name: str,
|
||||
schema: Optional[str] = None,
|
||||
documents: Optional[Document] = None,
|
||||
) -> Union[VectorStore, BaseRetriever]:
|
||||
"""
|
||||
|
|
@ -58,10 +59,12 @@ class RedisComponent(CustomComponent):
|
|||
- VectorStore: The Vector Store object.
|
||||
"""
|
||||
if documents is None:
|
||||
if schema is None:
|
||||
raise ValueError("If no documents are provided, a schema must be provided.")
|
||||
redis_vs = Redis.from_existing_index(
|
||||
embedding=embedding,
|
||||
index_name=redis_index_name,
|
||||
schema=None,
|
||||
schema=schema,
|
||||
key_prefix=None,
|
||||
redis_url=redis_server_url,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -6,7 +6,6 @@ from typing import List, Optional, Union
|
|||
from langchain_community.embeddings import FakeEmbeddings
|
||||
from langchain_community.vectorstores.vectara import Vectara
|
||||
from langchain_core.vectorstores import VectorStore
|
||||
|
||||
from langflow import CustomComponent
|
||||
from langflow.field_typing import BaseRetriever, Document
|
||||
|
||||
|
|
@ -46,7 +45,7 @@ class VectaraComponent(CustomComponent):
|
|||
|
||||
if documents is not None:
|
||||
return Vectara.from_documents(
|
||||
documents=documents,
|
||||
documents=documents, # type: ignore
|
||||
embedding=FakeEmbeddings(size=768),
|
||||
vectara_customer_id=vectara_customer_id,
|
||||
vectara_corpus_id=vectara_corpus_id,
|
||||
|
|
|
|||
|
|
@ -5,7 +5,6 @@ from langchain_community.vectorstores import VectorStore
|
|||
from langchain_community.vectorstores.pgvector import PGVector
|
||||
from langchain_core.documents import Document
|
||||
from langchain_core.retrievers import BaseRetriever
|
||||
|
||||
from langflow import CustomComponent
|
||||
|
||||
|
||||
|
|
@ -63,13 +62,13 @@ class PGVectorComponent(CustomComponent):
|
|||
collection_name=collection_name,
|
||||
connection_string=pg_server_url,
|
||||
)
|
||||
|
||||
vector_store = PGVector.from_documents(
|
||||
embedding=embedding,
|
||||
documents=documents,
|
||||
collection_name=collection_name,
|
||||
connection_string=pg_server_url,
|
||||
)
|
||||
else:
|
||||
vector_store = PGVector.from_documents(
|
||||
embedding=embedding,
|
||||
documents=documents, # type: ignore
|
||||
collection_name=collection_name,
|
||||
connection_string=pg_server_url,
|
||||
)
|
||||
except Exception as e:
|
||||
raise RuntimeError(f"Failed to build PGVector: {e}")
|
||||
return vector_store
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ import operator
|
|||
import warnings
|
||||
from typing import Any, ClassVar, Optional
|
||||
|
||||
import emoji
|
||||
from cachetools import TTLCache, cachedmethod
|
||||
from fastapi import HTTPException
|
||||
|
||||
|
|
@ -35,6 +36,10 @@ class Component:
|
|||
else:
|
||||
setattr(self, key, value)
|
||||
|
||||
# Validate the emoji at the icon field
|
||||
if hasattr(self, "icon") and self.icon:
|
||||
self.icon = self.validate_icon(self.icon)
|
||||
|
||||
def __setattr__(self, key, value):
|
||||
if key == "_user_id" and hasattr(self, "_user_id"):
|
||||
warnings.warn("user_id is immutable and cannot be changed.")
|
||||
|
|
@ -82,7 +87,23 @@ class Component:
|
|||
elif "documentation" in item_name:
|
||||
template_config["documentation"] = ast.literal_eval(item_value)
|
||||
|
||||
elif "icon" in item_name:
|
||||
icon_str = ast.literal_eval(item_value)
|
||||
template_config["icon"] = self.validate_icon(icon_str)
|
||||
|
||||
return template_config
|
||||
|
||||
def validate_icon(self, value: str):
|
||||
# we are going to use the emoji library to validate the emoji
|
||||
# emojis can be defined using the :emoji_name: syntax
|
||||
if not value.startswith(":") or not value.endswith(":"):
|
||||
warnings.warn("Invalid emoji. Please use the :emoji_name: syntax.")
|
||||
return value
|
||||
emoji_value = emoji.emojize(value, variant="emoji_type")
|
||||
if value == emoji_value:
|
||||
warnings.warn(f"Invalid emoji. {value} is not a valid emoji.")
|
||||
return value
|
||||
return emoji_value
|
||||
|
||||
def build(self, *args: Any, **kwargs: Any) -> Any:
|
||||
raise NotImplementedError
|
||||
|
|
|
|||
|
|
@ -5,36 +5,46 @@ from uuid import UUID
|
|||
import yaml
|
||||
from cachetools import TTLCache, cachedmethod
|
||||
from fastapi import HTTPException
|
||||
|
||||
from langflow.interface.custom.code_parser.utils import (
|
||||
extract_inner_type_from_generic_alias,
|
||||
extract_union_types_from_generic_alias,
|
||||
)
|
||||
from langflow.interface.custom.custom_component.component import Component
|
||||
from langflow.services.database.models.flow import Flow
|
||||
from langflow.services.database.utils import session_getter
|
||||
from langflow.services.deps import get_credential_service, get_db_service
|
||||
from langflow.utils import validate
|
||||
|
||||
from .component import Component
|
||||
|
||||
|
||||
class CustomComponent(Component):
|
||||
display_name: Optional[str] = None
|
||||
"""The display name of the component. Defaults to None."""
|
||||
description: Optional[str] = None
|
||||
"""The description of the component. Defaults to None."""
|
||||
icon: Optional[str] = None
|
||||
"""The icon of the component. It should be an emoji. Defaults to None."""
|
||||
code: Optional[str] = None
|
||||
"""The code of the component. Defaults to None."""
|
||||
field_config: dict = {}
|
||||
"""The field configuration of the component. Defaults to an empty dictionary."""
|
||||
field_order: Optional[List[str]] = None
|
||||
"""The field order of the component. Defaults to an empty list."""
|
||||
code_class_base_inheritance: ClassVar[str] = "CustomComponent"
|
||||
function_entrypoint_name: ClassVar[str] = "build"
|
||||
function: Optional[Callable] = None
|
||||
repr_value: Optional[Any] = ""
|
||||
user_id: Optional[Union[UUID, str]] = None
|
||||
status: Optional[Any] = None
|
||||
"""The status of the component. This is displayed on the frontend. Defaults to None."""
|
||||
_tree: Optional[dict] = None
|
||||
|
||||
def __init__(self, **data):
|
||||
self.cache = TTLCache(maxsize=1024, ttl=60)
|
||||
super().__init__(**data)
|
||||
|
||||
def _get_field_order(self):
|
||||
return self.field_order or list(self.field_config.keys())
|
||||
|
||||
def custom_repr(self):
|
||||
if self.repr_value == "":
|
||||
self.repr_value = self.status
|
||||
|
|
|
|||
|
|
@ -17,7 +17,9 @@ from langflow.interface.custom.directory_reader.utils import (
|
|||
)
|
||||
from langflow.interface.importing.utils import eval_custom_component_code
|
||||
from langflow.template.field.base import TemplateField
|
||||
from langflow.template.frontend_node.custom_components import CustomComponentFrontendNode
|
||||
from langflow.template.frontend_node.custom_components import (
|
||||
CustomComponentFrontendNode,
|
||||
)
|
||||
from langflow.utils.util import get_base_classes
|
||||
from loguru import logger
|
||||
|
||||
|
|
@ -43,6 +45,21 @@ def add_output_types(frontend_node: CustomComponentFrontendNode, return_types: L
|
|||
frontend_node.add_output_type(return_type)
|
||||
|
||||
|
||||
def reorder_fields(frontend_node: CustomComponentFrontendNode, field_order: List[str]):
|
||||
"""Reorder fields in the frontend node based on the specified field_order."""
|
||||
if not field_order:
|
||||
return
|
||||
|
||||
# Create a dictionary for O(1) lookup time.
|
||||
field_dict = {field.name: field for field in frontend_node.template.fields}
|
||||
reordered_fields = [field_dict[name] for name in field_order if name in field_dict]
|
||||
# Add any fields that are not in the field_order list
|
||||
for field in frontend_node.template.fields:
|
||||
if field.name not in field_order:
|
||||
reordered_fields.append(field)
|
||||
frontend_node.template.fields = reordered_fields
|
||||
|
||||
|
||||
def add_base_classes(frontend_node: CustomComponentFrontendNode, return_types: List[str]):
|
||||
"""Add base classes to the frontend node"""
|
||||
for return_type_instance in return_types:
|
||||
|
|
@ -106,7 +123,7 @@ def add_new_custom_field(
|
|||
):
|
||||
# Check field_config if any of the keys are in it
|
||||
# if it is, update the value
|
||||
display_name = field_config.pop("display_name", field_name)
|
||||
display_name = field_config.pop("display_name", None)
|
||||
field_type = field_config.pop("field_type", field_type)
|
||||
field_contains_list = "list" in field_type.lower()
|
||||
field_type = process_type(field_type)
|
||||
|
|
@ -149,9 +166,6 @@ def add_extra_fields(frontend_node, field_config, function_args):
|
|||
if not function_args:
|
||||
return
|
||||
|
||||
# sort function_args which is a list of dicts
|
||||
function_args.sort(key=lambda x: x["name"])
|
||||
|
||||
for extra_field in function_args:
|
||||
if "name" not in extra_field or extra_field["name"] == "self":
|
||||
continue
|
||||
|
|
@ -175,7 +189,11 @@ def get_field_dict(field: Union[TemplateField, dict]):
|
|||
return field
|
||||
|
||||
|
||||
def run_build_config(custom_component: CustomComponent, user_id: Optional[Union[str, UUID]] = None, update_field=None):
|
||||
def run_build_config(
|
||||
custom_component: CustomComponent,
|
||||
user_id: Optional[Union[str, UUID]] = None,
|
||||
update_field=None,
|
||||
):
|
||||
"""Build the field configuration for a custom component"""
|
||||
|
||||
try:
|
||||
|
|
@ -196,7 +214,8 @@ def run_build_config(custom_component: CustomComponent, user_id: Optional[Union[
|
|||
) from exc
|
||||
|
||||
try:
|
||||
build_config: Dict = custom_class(user_id=user_id).build_config()
|
||||
custom_instance = custom_class(user_id=user_id)
|
||||
build_config: Dict = custom_instance.build_config()
|
||||
|
||||
for field_name, field in build_config.items():
|
||||
# Allow user to build TemplateField as well
|
||||
|
|
@ -210,7 +229,7 @@ def run_build_config(custom_component: CustomComponent, user_id: Optional[Union[
|
|||
except Exception as exc:
|
||||
logger.error(f"Error while getting build_config: {str(exc)}")
|
||||
|
||||
return build_config
|
||||
return build_config, custom_instance
|
||||
|
||||
except Exception as exc:
|
||||
logger.error(f"Error while building field config: {str(exc)}")
|
||||
|
|
@ -231,6 +250,7 @@ def sanitize_template_config(template_config):
|
|||
"beta",
|
||||
"documentation",
|
||||
"output_types",
|
||||
"icon",
|
||||
}
|
||||
for key in template_config.copy():
|
||||
if key not in attributes:
|
||||
|
|
@ -280,7 +300,7 @@ def build_custom_component_template(
|
|||
logger.debug("Built base frontend node")
|
||||
|
||||
logger.debug("Updated attributes")
|
||||
field_config = run_build_config(custom_component, user_id=user_id, update_field=update_field)
|
||||
field_config, custom_instance = run_build_config(custom_component, user_id=user_id, update_field=update_field)
|
||||
logger.debug("Built field config")
|
||||
entrypoint_args = custom_component.get_function_entrypoint_args
|
||||
|
||||
|
|
@ -291,6 +311,9 @@ def build_custom_component_template(
|
|||
add_base_classes(frontend_node, custom_component.get_function_entrypoint_return_type)
|
||||
add_output_types(frontend_node, custom_component.get_function_entrypoint_return_type)
|
||||
logger.debug("Added base classes")
|
||||
|
||||
reorder_fields(frontend_node, custom_instance._get_field_order())
|
||||
|
||||
return frontend_node.to_dict(add_name=False)
|
||||
except Exception as exc:
|
||||
if isinstance(exc, HTTPException):
|
||||
|
|
@ -349,7 +372,7 @@ def update_field_dict(field_dict):
|
|||
field_dict["refresh"] = True
|
||||
|
||||
if "value" in field_dict and callable(field_dict["value"]):
|
||||
field_dict["value"] = field_dict["value"](field_dict.get("options", []))
|
||||
field_dict["value"] = field_dict["value"]()
|
||||
field_dict["refresh"] = True
|
||||
|
||||
# Let's check if "range_spec" is a RangeSpec object
|
||||
|
|
@ -359,7 +382,16 @@ def update_field_dict(field_dict):
|
|||
|
||||
def sanitize_field_config(field_config: Dict):
|
||||
# If any of the already existing keys are in field_config, remove them
|
||||
for key in ["name", "field_type", "value", "required", "placeholder", "display_name", "advanced", "show"]:
|
||||
for key in [
|
||||
"name",
|
||||
"field_type",
|
||||
"value",
|
||||
"required",
|
||||
"placeholder",
|
||||
"display_name",
|
||||
"advanced",
|
||||
"show",
|
||||
]:
|
||||
field_config.pop(key, None)
|
||||
return field_config
|
||||
|
||||
|
|
|
|||
|
|
@ -2,10 +2,11 @@ from typing import TYPE_CHECKING, List, Union
|
|||
|
||||
from langchain.agents.agent import AgentExecutor
|
||||
from langchain.callbacks.base import BaseCallbackHandler
|
||||
from loguru import logger
|
||||
|
||||
from langflow.api.v1.callback import AsyncStreamingLLMCallbackHandler, StreamingLLMCallbackHandler
|
||||
from langflow.processing.process import fix_memory_inputs, format_actions
|
||||
from langflow.services.deps import get_plugins_service
|
||||
from loguru import logger
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langfuse.callback import CallbackHandler # type: ignore
|
||||
|
|
@ -28,13 +29,12 @@ def setup_callbacks(sync, trace_id, **kwargs):
|
|||
|
||||
def get_langfuse_callback(trace_id):
|
||||
from langflow.services.deps import get_plugins_service
|
||||
from langfuse.callback import CreateTrace
|
||||
|
||||
logger.debug("Initializing langfuse callback")
|
||||
if langfuse := get_plugins_service().get("langfuse"):
|
||||
logger.debug("Langfuse credentials found")
|
||||
try:
|
||||
trace = langfuse.trace(CreateTrace(name="langflow-" + trace_id, id=trace_id))
|
||||
trace = langfuse.trace(name="langflow-" + trace_id, id=trace_id)
|
||||
return trace.getNewHandler()
|
||||
except Exception as exc:
|
||||
logger.error(f"Error initializing langfuse callback: {exc}")
|
||||
|
|
|
|||
|
|
@ -64,14 +64,13 @@ class LangfusePlugin(CallbackPlugin):
|
|||
def get_callback(self, _id: Optional[str] = None):
|
||||
if _id is None:
|
||||
_id = "default"
|
||||
from langfuse.callback import CreateTrace # type: ignore
|
||||
|
||||
logger.debug("Initializing langfuse callback")
|
||||
|
||||
try:
|
||||
langfuse_instance = self.get()
|
||||
if langfuse_instance is not None and hasattr(langfuse_instance, "trace"):
|
||||
trace = langfuse_instance.trace(CreateTrace(name="langflow-" + _id, id=_id))
|
||||
trace = langfuse_instance.trace(name="langflow-" + _id, id=_id)
|
||||
if trace:
|
||||
return trace.getNewHandler()
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,10 @@ import secrets
|
|||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from langflow.services.settings.constants import DEFAULT_SUPERUSER, DEFAULT_SUPERUSER_PASSWORD
|
||||
from langflow.services.settings.constants import (
|
||||
DEFAULT_SUPERUSER,
|
||||
DEFAULT_SUPERUSER_PASSWORD,
|
||||
)
|
||||
from langflow.services.settings.utils import read_secret_from_file, write_secret_to_file
|
||||
from loguru import logger
|
||||
from passlib.context import CryptContext
|
||||
|
|
@ -34,6 +37,19 @@ class AuthSettings(BaseSettings):
|
|||
SUPERUSER: str = DEFAULT_SUPERUSER
|
||||
SUPERUSER_PASSWORD: str = DEFAULT_SUPERUSER_PASSWORD
|
||||
|
||||
REFRESH_SAME_SITE: str = "none"
|
||||
"""The SameSite attribute of the refresh token cookie."""
|
||||
REFRESH_SECURE: bool = True
|
||||
"""The Secure attribute of the refresh token cookie."""
|
||||
REFRESH_HTTPONLY: bool = True
|
||||
"""The HttpOnly attribute of the refresh token cookie."""
|
||||
ACCESS_SAME_SITE: str = "none"
|
||||
"""The SameSite attribute of the access token cookie."""
|
||||
ACCESS_SECURE: bool = True
|
||||
"""The Secure attribute of the access token cookie."""
|
||||
ACCESS_HTTPONLY: bool = False
|
||||
"""The HttpOnly attribute of the access token cookie."""
|
||||
|
||||
pwd_context: CryptContext = CryptContext(schemes=["bcrypt"], deprecated="auto")
|
||||
|
||||
class Config:
|
||||
|
|
|
|||
|
|
@ -1,8 +1,7 @@
|
|||
from typing import Any, Callable, Optional, Union
|
||||
|
||||
from pydantic import BaseModel, ConfigDict, Field, field_serializer
|
||||
|
||||
from langflow.field_typing.range_spec import RangeSpec
|
||||
from pydantic import BaseModel, ConfigDict, Field, field_serializer
|
||||
|
||||
|
||||
class TemplateField(BaseModel):
|
||||
|
|
@ -64,6 +63,9 @@ class TemplateField(BaseModel):
|
|||
range_spec: Optional[RangeSpec] = Field(default=None, serialization_alias="rangeSpec")
|
||||
"""Range specification for the field. Defaults to None."""
|
||||
|
||||
title_case: bool = True
|
||||
"""Specifies if the field should be displayed in title case. Defaults to True."""
|
||||
|
||||
def to_dict(self):
|
||||
return self.model_dump(by_alias=True, exclude_none=True)
|
||||
|
||||
|
|
@ -76,3 +78,15 @@ class TemplateField(BaseModel):
|
|||
if value == "float" and self.range_spec is None:
|
||||
self.range_spec = RangeSpec()
|
||||
return value
|
||||
|
||||
@field_serializer("display_name")
|
||||
def serialize_display_name(self, value, _info):
|
||||
# If display_name is not set, use name and convert to title case
|
||||
# if title_case is True
|
||||
if value is None:
|
||||
# name is probably a snake_case string
|
||||
# Ex: "file_path" -> "File Path"
|
||||
value = self.name.replace("_", " ")
|
||||
if self.title_case:
|
||||
value = value.title()
|
||||
return value
|
||||
|
|
|
|||
|
|
@ -2,13 +2,12 @@ import re
|
|||
from collections import defaultdict
|
||||
from typing import ClassVar, Dict, List, Optional, Union
|
||||
|
||||
from pydantic import BaseModel, Field, field_serializer, model_serializer
|
||||
|
||||
from langflow.template.field.base import TemplateField
|
||||
from langflow.template.frontend_node.constants import CLASSES_TO_REMOVE, FORCE_SHOW_FIELDS
|
||||
from langflow.template.frontend_node.formatter import field_formatters
|
||||
from langflow.template.template.base import Template
|
||||
from langflow.utils import constants
|
||||
from pydantic import BaseModel, Field, field_serializer, model_serializer
|
||||
|
||||
|
||||
class FieldFormatters(BaseModel):
|
||||
|
|
@ -43,6 +42,7 @@ class FrontendNode(BaseModel):
|
|||
_format_template: bool = True
|
||||
template: Template
|
||||
description: Optional[str] = None
|
||||
icon: Optional[str] = None
|
||||
base_classes: List[str]
|
||||
name: str = ""
|
||||
display_name: Optional[str] = ""
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
from typing import Callable, Union
|
||||
|
||||
from pydantic import BaseModel, model_serializer
|
||||
|
||||
from langflow.template.field.base import TemplateField
|
||||
from langflow.utils.constants import DIRECT_TYPES
|
||||
from pydantic import BaseModel, model_serializer
|
||||
|
||||
|
||||
class Template(BaseModel):
|
||||
type_name: str
|
||||
fields: list[TemplateField]
|
||||
field_order: list[str] = []
|
||||
|
||||
def process_fields(
|
||||
self,
|
||||
|
|
@ -30,6 +30,7 @@ class Template(BaseModel):
|
|||
for field in self.fields:
|
||||
result[field.name] = field.model_dump(by_alias=True, exclude_none=True)
|
||||
result["_type"] = result.pop("type_name")
|
||||
result.pop("field_order", None)
|
||||
return result
|
||||
|
||||
# For backwards compatibility
|
||||
|
|
|
|||
890
src/frontend/package-lock.json
generated
890
src/frontend/package-lock.json
generated
File diff suppressed because it is too large
Load diff
|
|
@ -121,6 +121,6 @@
|
|||
"pretty-quick": "^3.1.3",
|
||||
"tailwindcss": "^3.3.3",
|
||||
"typescript": "^5.2.2",
|
||||
"vite": "^4.5.1"
|
||||
"vite": "^4.5.2"
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -34,6 +34,7 @@ export default function App() {
|
|||
const setSuccessOpen = useAlertStore((state) => state.setSuccessOpen);
|
||||
const loading = useAlertStore((state) => state.loading);
|
||||
const [fetchError, setFetchError] = useState(false);
|
||||
const isLoading = useFlowsManagerStore((state) => state.isLoading);
|
||||
|
||||
// Initialize state variable for the list of alerts
|
||||
const [alertsList, setAlertsList] = useState<
|
||||
|
|
@ -170,7 +171,7 @@ export default function App() {
|
|||
description={FETCH_ERROR_DESCRIPION}
|
||||
message={FETCH_ERROR_MESSAGE}
|
||||
></FetchErrorComponent>
|
||||
) : loading ? (
|
||||
) : isLoading ? (
|
||||
<div className="loading-page-panel">
|
||||
<LoadingComponent remSize={50} />
|
||||
</div>
|
||||
|
|
|
|||
|
|
@ -403,13 +403,27 @@ export default function ParameterComponent({
|
|||
data-testid={"textarea-" + data.node.template[name].name}
|
||||
/>
|
||||
) : (
|
||||
<InputComponent
|
||||
id={"input-" + index}
|
||||
disabled={disabled}
|
||||
password={data.node?.template[name].password ?? false}
|
||||
value={data.node?.template[name].value ?? ""}
|
||||
onChange={handleOnNewValue}
|
||||
/>
|
||||
<div className="mt-2 flex w-full items-center">
|
||||
<div className="w-5/6 flex-grow">
|
||||
<InputComponent
|
||||
id={"input-" + index}
|
||||
disabled={disabled}
|
||||
password={data.node?.template[name].password ?? false}
|
||||
value={data.node?.template[name].value ?? ""}
|
||||
onChange={handleOnNewValue}
|
||||
/>
|
||||
</div>
|
||||
{data.node?.template[name].refresh && (
|
||||
<button
|
||||
className="extra-side-bar-buttons ml-2 mt-1 w-1/6"
|
||||
onClick={() => {
|
||||
handleUpdateValues(name, data);
|
||||
}}
|
||||
>
|
||||
<IconComponent name="RefreshCcw" />
|
||||
</button>
|
||||
)}
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
) : left === true && type === "bool" ? (
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
import { useEffect, useState } from "react";
|
||||
import { useCallback, useEffect, useState } from "react";
|
||||
import { NodeToolbar } from "reactflow";
|
||||
import ShadTooltip from "../../components/ShadTooltipComponent";
|
||||
import Tooltip from "../../components/TooltipComponent";
|
||||
|
|
@ -107,6 +107,39 @@ export default function GenericNode({
|
|||
|
||||
const nameEditable = data.node?.flow || data.type === "CustomComponent";
|
||||
|
||||
const emojiRegex = /\p{Emoji}/u;
|
||||
const isEmoji = emojiRegex.test(data?.node?.icon!);
|
||||
|
||||
const iconNodeRender = useCallback(() => {
|
||||
const iconElement = data?.node?.icon;
|
||||
const iconColor = nodeColors[types[data.type]];
|
||||
const iconName =
|
||||
iconElement || (data.node?.flow ? "group_components" : name);
|
||||
const iconClassName = `generic-node-icon ${
|
||||
!showNode ? "absolute inset-x-6 h-12 w-12" : ""
|
||||
}`;
|
||||
|
||||
if (iconElement && isEmoji) {
|
||||
return nodeIconFragment(iconElement);
|
||||
} else {
|
||||
return checkNodeIconFragment(iconColor, iconName, iconClassName);
|
||||
}
|
||||
}, [data, isEmoji, name, showNode]);
|
||||
|
||||
const nodeIconFragment = (icon) => {
|
||||
return <span className="text-lg">{icon}</span>;
|
||||
};
|
||||
|
||||
const checkNodeIconFragment = (iconColor, iconName, iconClassName) => {
|
||||
return (
|
||||
<IconComponent
|
||||
name={iconName}
|
||||
className={iconClassName}
|
||||
iconColor={iconColor}
|
||||
/>
|
||||
);
|
||||
};
|
||||
|
||||
return (
|
||||
<>
|
||||
<NodeToolbar>
|
||||
|
|
@ -156,14 +189,7 @@ export default function GenericNode({
|
|||
(!showNode && "justify-center")
|
||||
}
|
||||
>
|
||||
<IconComponent
|
||||
name={data.node?.flow ? "group_components" : name}
|
||||
className={
|
||||
"generic-node-icon " +
|
||||
(!showNode ? "absolute inset-x-6 h-12 w-12" : "")
|
||||
}
|
||||
iconColor={`${nodeColors[types[data.type]]}`}
|
||||
/>
|
||||
{iconNodeRender()}
|
||||
{showNode && (
|
||||
<div className="generic-node-tooltip-div">
|
||||
{nameEditable && inputName ? (
|
||||
|
|
|
|||
|
|
@ -8,19 +8,26 @@ import {
|
|||
} from "../../../ui/dropdown-menu";
|
||||
|
||||
import { useNavigate } from "react-router-dom";
|
||||
import { Node } from "reactflow";
|
||||
import FlowSettingsModal from "../../../../modals/flowSettingsModal";
|
||||
import useAlertStore from "../../../../stores/alertStore";
|
||||
import useFlowStore from "../../../../stores/flowStore";
|
||||
import useFlowsManagerStore from "../../../../stores/flowsManagerStore";
|
||||
import IconComponent from "../../../genericIconComponent";
|
||||
import { Button } from "../../../ui/button";
|
||||
|
||||
export const MenuBar = (): JSX.Element => {
|
||||
export const MenuBar = ({
|
||||
removeFunction,
|
||||
}: {
|
||||
removeFunction: (nodes: Node[]) => void;
|
||||
}): JSX.Element => {
|
||||
const addFlow = useFlowsManagerStore((state) => state.addFlow);
|
||||
const currentFlow = useFlowsManagerStore((state) => state.currentFlow);
|
||||
const setErrorData = useAlertStore((state) => state.setErrorData);
|
||||
const undo = useFlowsManagerStore((state) => state.undo);
|
||||
const redo = useFlowsManagerStore((state) => state.redo);
|
||||
const [openSettings, setOpenSettings] = useState(false);
|
||||
const n = useFlowStore((state) => state.nodes);
|
||||
|
||||
const navigate = useNavigate();
|
||||
|
||||
|
|
@ -39,6 +46,7 @@ export const MenuBar = (): JSX.Element => {
|
|||
<div className="round-button-div">
|
||||
<button
|
||||
onClick={() => {
|
||||
removeFunction(n);
|
||||
navigate(-1);
|
||||
}}
|
||||
>
|
||||
|
|
|
|||
|
|
@ -1,12 +1,15 @@
|
|||
import { useContext, useEffect } from "react";
|
||||
import { FaDiscord, FaGithub, FaTwitter } from "react-icons/fa";
|
||||
import { Link, useLocation, useNavigate } from "react-router-dom";
|
||||
import { Link, useLocation, useNavigate, useParams } from "react-router-dom";
|
||||
import AlertDropdown from "../../alerts/alertDropDown";
|
||||
import { USER_PROJECTS_HEADER } from "../../constants/constants";
|
||||
import { AuthContext } from "../../contexts/authContext";
|
||||
|
||||
import { Node } from "reactflow";
|
||||
import useAlertStore from "../../stores/alertStore";
|
||||
import { useDarkStore } from "../../stores/darkStore";
|
||||
import useFlowStore from "../../stores/flowStore";
|
||||
import useFlowsManagerStore from "../../stores/flowsManagerStore";
|
||||
import { useStoreStore } from "../../stores/storeStore";
|
||||
import { gradients } from "../../utils/styleUtils";
|
||||
import IconComponent from "../genericIconComponent";
|
||||
|
|
@ -27,8 +30,10 @@ export default function Header(): JSX.Element {
|
|||
const location = useLocation();
|
||||
const { logout, autoLogin, isAdmin, userData } = useContext(AuthContext);
|
||||
const navigate = useNavigate();
|
||||
|
||||
const removeFlow = useFlowsManagerStore((store) => store.removeFlow);
|
||||
const hasStore = useStoreStore((state) => state.hasStore);
|
||||
const { id } = useParams();
|
||||
const n = useFlowStore((state) => state.nodes);
|
||||
|
||||
const dark = useDarkStore((state) => state.dark);
|
||||
const setDark = useDarkStore((state) => state.setDark);
|
||||
|
|
@ -43,13 +48,19 @@ export default function Header(): JSX.Element {
|
|||
window.localStorage.setItem("isDark", dark.toString());
|
||||
}, [dark]);
|
||||
|
||||
async function checkForChanges(nodes: Node[]): Promise<void> {
|
||||
if (nodes.length === 0) {
|
||||
await removeFlow(id!);
|
||||
}
|
||||
}
|
||||
|
||||
return (
|
||||
<div className="header-arrangement">
|
||||
<div className="header-start-display lg:w-[30%]">
|
||||
<Link to="/">
|
||||
<Link to="/" onClick={() => checkForChanges(n)}>
|
||||
<span className="ml-4 text-2xl">⛓️</span>
|
||||
</Link>
|
||||
<MenuBar />
|
||||
<MenuBar removeFunction={checkForChanges} />
|
||||
</div>
|
||||
<div className="round-button-div">
|
||||
<Link to="/">
|
||||
|
|
@ -62,6 +73,9 @@ export default function Header(): JSX.Element {
|
|||
: "secondary"
|
||||
}
|
||||
size="sm"
|
||||
onClick={() => {
|
||||
checkForChanges(n);
|
||||
}}
|
||||
>
|
||||
<IconComponent name="Home" className="h-4 w-4" />
|
||||
<div className="hidden flex-1 md:block">{USER_PROJECTS_HEADER}</div>
|
||||
|
|
@ -85,6 +99,9 @@ export default function Header(): JSX.Element {
|
|||
className="gap-2"
|
||||
variant={location.pathname === "/store" ? "primary" : "secondary"}
|
||||
size="sm"
|
||||
onClick={() => {
|
||||
checkForChanges(n);
|
||||
}}
|
||||
>
|
||||
<IconComponent name="Store" className="h-4 w-4" />
|
||||
<div className="flex-1">Store</div>
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ import {
|
|||
generateNodeFromFlow,
|
||||
getNodeId,
|
||||
isValidConnection,
|
||||
reconnectEdges,
|
||||
validateSelection,
|
||||
} from "../../../../utils/reactflowUtils";
|
||||
import { getRandomName, isWrappedWithClass } from "../../../../utils/utils";
|
||||
|
|
@ -107,6 +108,13 @@ export default function Page({
|
|||
) {
|
||||
event.preventDefault();
|
||||
setLastCopiedSelection(_.cloneDeep(lastSelection));
|
||||
} else if (
|
||||
(event.ctrlKey || event.metaKey) &&
|
||||
event.key === "x" &&
|
||||
lastSelection
|
||||
) {
|
||||
event.preventDefault();
|
||||
setLastCopiedSelection(_.cloneDeep(lastSelection), true);
|
||||
} else if (
|
||||
(event.ctrlKey || event.metaKey) &&
|
||||
event.key === "v" &&
|
||||
|
|
@ -379,7 +387,7 @@ export default function Page({
|
|||
if (
|
||||
validateSelection(lastSelection!, edges).length === 0
|
||||
) {
|
||||
const { newFlow } = generateFlow(
|
||||
const { newFlow, removedEdges } = generateFlow(
|
||||
lastSelection!,
|
||||
nodes,
|
||||
edges,
|
||||
|
|
@ -389,6 +397,10 @@ export default function Page({
|
|||
newFlow,
|
||||
getNodeId
|
||||
);
|
||||
const newEdges = reconnectEdges(
|
||||
newGroupNode,
|
||||
removedEdges
|
||||
);
|
||||
setNodes((oldNodes) => [
|
||||
...oldNodes.filter(
|
||||
(oldNodes) =>
|
||||
|
|
@ -399,16 +411,17 @@ export default function Page({
|
|||
),
|
||||
newGroupNode,
|
||||
]);
|
||||
setEdges((oldEdges) =>
|
||||
oldEdges.filter(
|
||||
setEdges((oldEdges) => [
|
||||
...oldEdges.filter(
|
||||
(oldEdge) =>
|
||||
!lastSelection!.nodes.some(
|
||||
(selectionNode) =>
|
||||
selectionNode.id === oldEdge.target ||
|
||||
selectionNode.id === oldEdge.source
|
||||
)
|
||||
)
|
||||
);
|
||||
),
|
||||
...newEdges,
|
||||
]);
|
||||
} else {
|
||||
setErrorData({
|
||||
title: "Invalid selection",
|
||||
|
|
|
|||
|
|
@ -94,12 +94,6 @@ export default function ComponentsComponent({
|
|||
setPageSize(10);
|
||||
}
|
||||
|
||||
useEffect(() => {
|
||||
setTimeout(() => {
|
||||
setLoadingScreen(false);
|
||||
}, 600);
|
||||
}, []);
|
||||
|
||||
return (
|
||||
<CardsWrapComponent
|
||||
onFileDrop={onFileDrop}
|
||||
|
|
@ -107,7 +101,7 @@ export default function ComponentsComponent({
|
|||
>
|
||||
<div className="flex h-full w-full flex-col justify-between">
|
||||
<div className="flex w-full flex-col gap-4">
|
||||
{!loadingScreen && data.length === 0 ? (
|
||||
{!isLoading && data.length === 0 ? (
|
||||
<div className="mt-6 flex w-full items-center justify-center text-center">
|
||||
<div className="flex-max-width h-full flex-col">
|
||||
<div className="flex w-full flex-col gap-4">
|
||||
|
|
@ -136,7 +130,7 @@ export default function ComponentsComponent({
|
|||
</div>
|
||||
) : (
|
||||
<div className="grid w-full gap-4 md:grid-cols-2 lg:grid-cols-2">
|
||||
{loadingScreen === false && data?.length > 0 ? (
|
||||
{isLoading === false && data?.length > 0 ? (
|
||||
data?.map((item, idx) => (
|
||||
<CollectionCardComponent
|
||||
onDelete={() => {
|
||||
|
|
@ -185,7 +179,7 @@ export default function ComponentsComponent({
|
|||
</div>
|
||||
)}
|
||||
</div>
|
||||
{!loadingScreen && data.length > 0 && (
|
||||
{!isLoading && data.length > 0 && (
|
||||
<div className="relative py-6">
|
||||
<PaginatorComponent
|
||||
storeComponent={true}
|
||||
|
|
|
|||
|
|
@ -247,7 +247,29 @@ const useFlowStore = create<FlowStoreType>((set, get) => ({
|
|||
});
|
||||
get().setEdges(newEdges);
|
||||
},
|
||||
setLastCopiedSelection: (newSelection) => {
|
||||
setLastCopiedSelection: (newSelection, isCrop = false) => {
|
||||
if (isCrop) {
|
||||
const nodesIdsSelected = newSelection!.nodes.map((node) => node.id);
|
||||
const edgesIdsSelected = newSelection!.edges.map((edge) => edge.id);
|
||||
|
||||
nodesIdsSelected.forEach((id) => {
|
||||
get().deleteNode(id);
|
||||
});
|
||||
|
||||
edgesIdsSelected.forEach((id) => {
|
||||
get().deleteEdge(id);
|
||||
});
|
||||
|
||||
const newNodes = get().nodes.filter(
|
||||
(node) => !nodesIdsSelected.includes(node.id)
|
||||
);
|
||||
const newEdges = get().edges.filter(
|
||||
(edge) => !edgesIdsSelected.includes(edge.id)
|
||||
);
|
||||
|
||||
set({ nodes: newNodes, edges: newEdges });
|
||||
}
|
||||
|
||||
set({ lastCopiedSelection: newSelection });
|
||||
},
|
||||
cleanFlow: () => {
|
||||
|
|
|
|||
|
|
@ -62,10 +62,10 @@ const useFlowsManagerStore = create<FlowsManagerStoreType>((set, get) => ({
|
|||
if (dbData) {
|
||||
const { data, flows } = processFlows(dbData, false);
|
||||
get().setFlows(flows);
|
||||
set({ isLoading: false });
|
||||
useTypesStore.setState((state) => ({
|
||||
data: { ...state.data, ["saved_components"]: data },
|
||||
}));
|
||||
set({ isLoading: false });
|
||||
resolve();
|
||||
}
|
||||
})
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ import { APIDataType } from "../types/api";
|
|||
import { TypesStoreType } from "../types/zustand/types";
|
||||
import { templatesGenerator, typesGenerator } from "../utils/reactflowUtils";
|
||||
import useAlertStore from "./alertStore";
|
||||
import useFlowsManagerStore from "./flowsManagerStore";
|
||||
|
||||
export const useTypesStore = create<TypesStoreType>((set, get) => ({
|
||||
types: {},
|
||||
|
|
@ -11,6 +12,8 @@ export const useTypesStore = create<TypesStoreType>((set, get) => ({
|
|||
data: {},
|
||||
getTypes: () => {
|
||||
return new Promise<void>(async (resolve, reject) => {
|
||||
const setLoading = useFlowsManagerStore.getState().setIsLoading;
|
||||
setLoading(true);
|
||||
getAll()
|
||||
.then((response) => {
|
||||
const data = response.data;
|
||||
|
|
@ -20,6 +23,7 @@ export const useTypesStore = create<TypesStoreType>((set, get) => ({
|
|||
data: { ...old.data, ...data },
|
||||
templates: templatesGenerator(data),
|
||||
}));
|
||||
setLoading(false);
|
||||
resolve();
|
||||
})
|
||||
.catch((error) => {
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ export type APIClassType = {
|
|||
description: string;
|
||||
template: APITemplateType;
|
||||
display_name: string;
|
||||
icon?: string;
|
||||
input_types?: Array<string>;
|
||||
output_types?: Array<string>;
|
||||
custom_fields?: CustomFieldsType;
|
||||
|
|
|
|||
|
|
@ -46,7 +46,8 @@ export type FlowStoreType = {
|
|||
) => void;
|
||||
lastCopiedSelection: { nodes: any; edges: any } | null;
|
||||
setLastCopiedSelection: (
|
||||
newSelection: { nodes: any; edges: any } | null
|
||||
newSelection: { nodes: any; edges: any } | null,
|
||||
isCrop?: boolean
|
||||
) => void;
|
||||
isBuilt: boolean;
|
||||
setIsBuilt: (isBuilt: boolean) => void;
|
||||
|
|
|
|||
|
|
@ -37,7 +37,6 @@ import {
|
|||
createRandomKey,
|
||||
getFieldTitle,
|
||||
getRandomDescription,
|
||||
getRandomName,
|
||||
toTitleCase,
|
||||
} from "./utils";
|
||||
const uid = new ShortUniqueId({ length: 5 });
|
||||
|
|
@ -600,8 +599,7 @@ export function generateFlow(
|
|||
const newFlowData = { nodes, edges, viewport: { zoom: 1, x: 0, y: 0 } };
|
||||
const uid = new ShortUniqueId({ length: 5 });
|
||||
/* remove edges that are not connected to selected nodes on both ends
|
||||
in future we can save this edges to when ungrouping reconect to the old nodes
|
||||
*/
|
||||
*/
|
||||
newFlowData.edges = selection.edges.filter(
|
||||
(edge) =>
|
||||
selection.nodes.some((node) => node.id === edge.target) &&
|
||||
|
|
@ -622,12 +620,48 @@ export function generateFlow(
|
|||
// in the future we can use a better aproach using a set
|
||||
return {
|
||||
newFlow,
|
||||
removedEdges: selection.edges.filter(
|
||||
(edge) => !newFlowData.edges.includes(edge)
|
||||
removedEdges: edges.filter(
|
||||
(edge) =>
|
||||
(selection.nodes.some((node) => node.id === edge.target) ||
|
||||
selection.nodes.some((node) => node.id === edge.source)) &&
|
||||
newFlowData.edges.every((e) => e.id !== edge.id)
|
||||
),
|
||||
};
|
||||
}
|
||||
|
||||
export function reconnectEdges(groupNode: NodeType, excludedEdges: Edge[]) {
|
||||
let newEdges = cloneDeep(excludedEdges);
|
||||
if (!groupNode.data.node!.flow) return [];
|
||||
const { nodes, edges } = groupNode.data.node!.flow!.data!;
|
||||
const lastNode = findLastNode(groupNode.data.node!.flow!.data!);
|
||||
newEdges.forEach((edge) => {
|
||||
if (lastNode && edge.source === lastNode.id) {
|
||||
edge.source = groupNode.id;
|
||||
let newSourceHandle: sourceHandleType = scapeJSONParse(
|
||||
edge.sourceHandle!
|
||||
);
|
||||
newSourceHandle.id = groupNode.id;
|
||||
edge.sourceHandle = scapedJSONStringfy(newSourceHandle);
|
||||
edge.data.sourceHandle = newSourceHandle;
|
||||
}
|
||||
if (nodes.some((node) => node.id === edge.target)) {
|
||||
const targetNode = nodes.find((node) => node.id === edge.target)!;
|
||||
console.log("targetNode", targetNode);
|
||||
const targetHandle: targetHandleType = scapeJSONParse(edge.targetHandle!);
|
||||
console.log("targetHandle", targetHandle);
|
||||
const proxy = { id: targetNode.id, field: targetHandle.fieldName };
|
||||
let newTargetHandle: targetHandleType = cloneDeep(targetHandle);
|
||||
newTargetHandle.id = groupNode.id;
|
||||
newTargetHandle.proxy = proxy;
|
||||
edge.target = groupNode.id;
|
||||
newTargetHandle.fieldName = targetHandle.fieldName + "_" + targetNode.id;
|
||||
edge.targetHandle = scapedJSONStringfy(newTargetHandle);
|
||||
edge.data.targetHandle = newTargetHandle;
|
||||
}
|
||||
});
|
||||
return newEdges;
|
||||
}
|
||||
|
||||
export function filterFlow(
|
||||
selection: OnSelectionChangeParams,
|
||||
setNodes: (update: Node[] | ((oldState: Node[]) => Node[])) => void,
|
||||
|
|
@ -1185,7 +1219,7 @@ export const createNewFlow = (
|
|||
) => {
|
||||
return {
|
||||
description: flow?.description ?? getRandomDescription(),
|
||||
name: flow?.name ?? getRandomName(),
|
||||
name: flow?.name ? flow.name : "Untitled document",
|
||||
data: flowData,
|
||||
id: "",
|
||||
is_component: flow?.is_component ?? false,
|
||||
|
|
|
|||
|
|
@ -136,17 +136,20 @@ export function groupByFamily(
|
|||
((!excludeTypes.has(template.type) &&
|
||||
baseClassesSet.has(template.type)) ||
|
||||
(template.input_types &&
|
||||
template.input_types.some((inputType) => {
|
||||
baseClassesSet.has(inputType);
|
||||
})))
|
||||
template.input_types.some((inputType) =>
|
||||
baseClassesSet.has(inputType)
|
||||
)))
|
||||
);
|
||||
};
|
||||
|
||||
if (flow) {
|
||||
// se existir o flow
|
||||
for (const node of flow) {
|
||||
// para cada node do flow
|
||||
if (node!.data!.node!.flow) break; // não faz nada se o node for um group
|
||||
const nodeData = node.data;
|
||||
|
||||
const foundNode = checkedNodes.get(nodeData.type);
|
||||
const foundNode = checkedNodes.get(nodeData.type); // verifica se o tipo do node já foi checado
|
||||
checkedNodes.set(nodeData.type, {
|
||||
hasBaseClassInTemplate:
|
||||
foundNode?.hasBaseClassInTemplate ||
|
||||
|
|
@ -155,7 +158,7 @@ export function groupByFamily(
|
|||
foundNode?.hasBaseClassInBaseClasses ||
|
||||
nodeData.node!.base_classes.some((baseClass) =>
|
||||
baseClassesSet.has(baseClass)
|
||||
),
|
||||
), //seta como anterior ou verifica se o node tem base class
|
||||
displayName: nodeData.node?.display_name,
|
||||
});
|
||||
}
|
||||
|
|
|
|||
|
|
@ -545,35 +545,36 @@ def test_async_task_processing(distributed_client, added_flow, created_api_key):
|
|||
assert "Gabriel" in task_status_json["result"]["text"], task_status_json["result"]
|
||||
|
||||
|
||||
# ! Deactivating this until updating the test
|
||||
# Test function without loop
|
||||
@pytest.mark.async_test
|
||||
def test_async_task_processing_vector_store(client, added_vector_store, created_api_key):
|
||||
headers = {"x-api-key": created_api_key.api_key}
|
||||
post_data = {"inputs": {"input": "How do I upload examples?"}}
|
||||
# @pytest.mark.async_test
|
||||
# def test_async_task_processing_vector_store(client, added_vector_store, created_api_key):
|
||||
# headers = {"x-api-key": created_api_key.api_key}
|
||||
# post_data = {"inputs": {"input": "How do I upload examples?"}}
|
||||
|
||||
# Run the /api/v1/process/{flow_id} endpoint with sync=False
|
||||
response = client.post(
|
||||
f"api/v1/process/{added_vector_store.get('id')}",
|
||||
headers=headers,
|
||||
json={**post_data, "sync": False},
|
||||
)
|
||||
assert response.status_code == 200, response.json()
|
||||
assert "result" in response.json()
|
||||
assert "FAILURE" not in response.json()["result"]
|
||||
# # Run the /api/v1/process/{flow_id} endpoint with sync=False
|
||||
# response = client.post(
|
||||
# f"api/v1/process/{added_vector_store.get('id')}",
|
||||
# headers=headers,
|
||||
# json={**post_data, "sync": False},
|
||||
# )
|
||||
# assert response.status_code == 200, response.json()
|
||||
# assert "result" in response.json()
|
||||
# assert "FAILURE" not in response.json()["result"]
|
||||
|
||||
# Extract the task ID from the response
|
||||
task = response.json().get("task")
|
||||
task_id = task.get("id")
|
||||
task_href = task.get("href")
|
||||
assert task_id is not None
|
||||
assert task_href is not None
|
||||
assert task_href == f"api/v1/task/{task_id}"
|
||||
# # Extract the task ID from the response
|
||||
# task = response.json().get("task")
|
||||
# task_id = task.get("id")
|
||||
# task_href = task.get("href")
|
||||
# assert task_id is not None
|
||||
# assert task_href is not None
|
||||
# assert task_href == f"api/v1/task/{task_id}"
|
||||
|
||||
# Polling the task status using the helper function
|
||||
task_status_json = poll_task_status(client, headers, task_href)
|
||||
assert task_status_json is not None, "Task did not complete in time"
|
||||
# # Polling the task status using the helper function
|
||||
# task_status_json = poll_task_status(client, headers, task_href)
|
||||
# assert task_status_json is not None, "Task did not complete in time"
|
||||
|
||||
# Validate that the task completed successfully and the result is as expected
|
||||
assert "result" in task_status_json, task_status_json
|
||||
assert "output" in task_status_json["result"], task_status_json["result"]
|
||||
assert "Langflow" in task_status_json["result"]["output"], task_status_json["result"]
|
||||
# # Validate that the task completed successfully and the result is as expected
|
||||
# assert "result" in task_status_json, task_status_json
|
||||
# assert "output" in task_status_json["result"], task_status_json["result"]
|
||||
# assert "Langflow" in task_status_json["result"]["output"], task_status_json["result"]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue