Cz/fix fetch data (#1655)
This commit is contained in:
Cristhian Zanforlin Lousa
GitHub
commit
77eaf09e13
71 changed files with 4046 additions and 4164 deletions
44
.github/workflows/ci.yml
vendored
44
.github/workflows/ci.yml
vendored
|
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@ -1,44 +0,0 @@
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name: "Async API tests"
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on:
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push:
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branches:
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- dev
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pull_request:
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branches:
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- dev
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- main
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jobs:
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build-and-test:
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runs-on: ubuntu-latest
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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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- name: Checkout code
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uses: actions/checkout@v4
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- name: Cache Docker layers
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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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restore-keys: |
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${{ runner.os }}-buildx-
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- name: Set up Docker
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run: docker --version && docker-compose --version
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- name: "Create env file"
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working-directory: ./deploy
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run: |
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echo "${{ secrets.ENV_FILE }}" > .env
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- name: Build and start services
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working-directory: ./deploy
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run: docker compose up --exit-code-from tests tests result_backend broker celeryworker db --build
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continue-on-error: true
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# - name: Stop services
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# run: docker compose down
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41
.github/workflows/pre-release-base.yml
vendored
41
.github/workflows/pre-release-base.yml
vendored
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@ -1,26 +1,20 @@
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name: Langflow Base Pre-release
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run-name: Langflow Base Pre-release by @${{ github.actor }}
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on:
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pull_request:
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types:
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- closed
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branches:
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- dev
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paths:
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- "src/backend/base/pyproject.toml"
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workflow_dispatch:
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inputs:
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force_release:
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description: "Force a release"
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required: false
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default: "false"
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release_package:
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description: "Release package"
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required: true
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type: boolean
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default: false
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env:
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POETRY_VERSION: "1.8.2"
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jobs:
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if_release:
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if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'pre-release') }} || ${{ github.event_name == 'workflow_dispatch' && inputs.force_release == 'true' }}
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if: inputs.release_package == true
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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@ -50,22 +44,11 @@ jobs:
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fi
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- name: Build project for distribution
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run: make build base=true
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- name: Publish to PyPI
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env:
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POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
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run: |
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make publish base=true
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- name: Create Release
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uses: ncipollo/release-action@v1
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with:
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artifacts: "dist/*"
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token: ${{ secrets.GITHUB_TOKEN }}
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draft: false
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generateReleaseNotes: true
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prerelease: true
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tag: v${{ steps.check-version.outputs.version }}
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commit: dev
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v3
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- name: Set up Docker Buildx
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@ -83,3 +66,13 @@ jobs:
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file: ./build_and_push_base.Dockerfile
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tags: |
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logspace/langflow:base-${{ steps.check-version.outputs.version }}
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- name: Create Release
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uses: ncipollo/release-action@v1
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with:
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artifacts: "dist/*"
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token: ${{ secrets.GITHUB_TOKEN }}
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draft: false
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generateReleaseNotes: true
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prerelease: true
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tag: v${{ steps.check-version.outputs.version }}
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commit: dev
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|
|
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43
.github/workflows/pre-release-langflow.yml
vendored
43
.github/workflows/pre-release-langflow.yml
vendored
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@ -1,19 +1,13 @@
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name: Langflow Pre-release
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run-name: Langflow Pre-release by @${{ github.actor }}
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on:
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pull_request:
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types:
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- closed
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branches:
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- dev
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paths:
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- "pyproject.toml"
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workflow_dispatch:
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inputs:
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force_release:
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description: "Force a release"
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required: false
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default: "false"
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release_package:
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description: "Release package"
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required: true
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type: boolean
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default: false
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workflow_run:
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workflows: ["pre-release-base"]
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types: [completed]
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@ -24,7 +18,7 @@ env:
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jobs:
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if_release:
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if: ${{ (github.event.pull_request.merged == true) && contains(github.event.pull_request.labels.*.name, 'pre-release') }} || ${{ github.event_name == 'workflow_dispatch' && inputs.force_release == 'true' }}
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if: inputs.release_package == true
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v4
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@ -38,7 +32,7 @@ jobs:
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- name: Check Version
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id: check-version
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run: |
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version=$(cd src/backend/base && poetry version --short)
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version=$(poetry version --short)
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last_released_version=$(curl -s "https://pypi.org/pypi/langflow/json" | jq -r '.releases | keys | .[]' | sort -V | tail -n 1)
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if [ "$version" = "$last_released_version" ]; then
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echo "Version $version is already released. Skipping release."
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@ -55,16 +49,6 @@ jobs:
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POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
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run: |
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make publish main=true
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- name: Create Release
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uses: ncipollo/release-action@v1
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with:
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artifacts: "dist/*"
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token: ${{ secrets.GITHUB_TOKEN }}
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draft: false
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generateReleaseNotes: true
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prerelease: true
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tag: v${{ steps.check-version.outputs.version }}
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commit: dev
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- name: Set up QEMU
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uses: docker/setup-qemu-action@v3
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- name: Set up Docker Buildx
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@ -83,3 +67,14 @@ jobs:
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tags: |
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logspace/langflow:${{ steps.check-version.outputs.version }}
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logspace/langflow:1.0-alpha
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- name: Create Release
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uses: ncipollo/release-action@v1
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with:
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artifacts: "dist/*"
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token: ${{ secrets.GITHUB_TOKEN }}
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draft: false
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generateReleaseNotes: true
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prerelease: true
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tag: v${{ steps.check-version.outputs.version }}
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commit: dev
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|
|
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18
.github/workflows/release.yml
vendored
18
.github/workflows/release.yml
vendored
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@ -31,15 +31,6 @@ jobs:
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id: check-version
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run: |
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echo version=$(poetry version --short) >> $GITHUB_OUTPUT
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- name: Create Release
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uses: ncipollo/release-action@v1
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with:
|
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artifacts: "dist/*"
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token: ${{ secrets.GITHUB_TOKEN }}
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draft: false
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generateReleaseNotes: true
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tag: v${{ steps.check-version.outputs.version }}
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commit: main
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- name: Publish to PyPI
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env:
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POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
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@ -63,3 +54,12 @@ jobs:
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tags: |
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logspace/langflow:${{ steps.check-version.outputs.version }}
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logspace/langflow:latest
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- name: Create Release
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uses: ncipollo/release-action@v1
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with:
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artifacts: "dist/*"
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token: ${{ secrets.GITHUB_TOKEN }}
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draft: false
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generateReleaseNotes: true
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tag: v${{ steps.check-version.outputs.version }}
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commit: main
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|
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@ -16,12 +16,12 @@ The branch structure is as follows:
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## 🚩GitHub Issues
|
||||
|
||||
Our [issues](https://github.com/logspace-ai/langflow/issues) page is kept up to date
|
||||
Our [issues](https://github.com/langflow-ai/langflow/issues) page is kept up to date
|
||||
with bugs, improvements, and feature requests. There is a taxonomy of labels to help
|
||||
with sorting and discovery of issues of interest.
|
||||
|
||||
If you're looking for help with your code, consider posting a question on the
|
||||
[GitHub Discussions board](https://github.com/logspace-ai/langflow/discussions). Please
|
||||
[GitHub Discussions board](https://github.com/langflow-ai/langflow/discussions). Please
|
||||
understand that we won't be able to provide individual support via email. We
|
||||
also believe that help is much more valuable if it's **shared publicly**,
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so that more people can benefit from it.
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@ -40,7 +40,7 @@ so that more people can benefit from it.
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## Issue labels
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[See this page](https://github.com/logspace-ai/langflow/labels) for an overview of
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[See this page](https://github.com/langflow-ai/langflow/labels) for an overview of
|
||||
the system we use to tag our issues and pull requests.
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## Local development
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|
|
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@ -4,25 +4,27 @@ This guide will help you set up a Langflow development VM in a Google Cloud Plat
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> **Note**: When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment.
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## Standard VM
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## Standard VM
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||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md)
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This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment.
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<hr>
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## Spot/Preemptible Instance
|
||||
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||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
|
||||
When running as a [spot (preemptible) instance](https://cloud.google.com/compute/docs/instances/preemptible), the code and VM will behave the same way as in a regular instance, executing the startup script to configure the environment, install necessary dependencies, and run the Langflow application. However, **due to the nature of spot instances, the VM may be terminated at any time if Google Cloud needs to reclaim the resources**. This makes spot instances suitable for fault-tolerant, stateless, or interruptible workloads that can handle unexpected terminations and restarts.
|
||||
|
||||
## Pricing (approximate)
|
||||
> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator)
|
||||
<br>
|
||||
|
||||
| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes |
|
||||
| -------------- | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | ----- |
|
||||
| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs |
|
||||
| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance |
|
||||
| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month |
|
||||
> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator)
|
||||
> <br>
|
||||
|
||||
| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes |
|
||||
| ------------------ | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | -------------------------------------------------------------------------- |
|
||||
| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs |
|
||||
| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance |
|
||||
| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month |
|
||||
|
|
|
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12
README.md
12
README.md
|
|
@ -1,6 +1,6 @@
|
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<!-- markdownlint-disable MD030 -->
|
||||
|
||||
# [](https://www.langflow.org)
|
||||
# [](https://www.langflow.org)
|
||||
|
||||
### [Langflow](https://www.langflow.org) is a new, visual way to build, iterate and deploy AI apps.
|
||||
|
||||
|
|
@ -28,7 +28,7 @@ Then, run Langflow with:
|
|||
python -m langflow run
|
||||
```
|
||||
|
||||
You can also preview Langflow in [HuggingFace Spaces](https://huggingface.co/spaces/Logspace/Langflow-Preview). [Clone the space using this link](https://huggingface.co/spaces/Logspace/Langflow-Preview?duplicate=true), to create your own Langflow workspace in minutes.
|
||||
You can also preview Langflow in [HuggingFace Spaces](https://huggingface.co/spaces/Langflow/Langflow-Preview). [Clone the space using this link](https://huggingface.co/spaces/Langflow/Langflow-Preview?duplicate=true), to create your own Langflow workspace in minutes.
|
||||
|
||||
# 🎨 Creating Flows
|
||||
|
||||
|
|
@ -96,7 +96,7 @@ Follow our step-by-step guide to deploy Langflow on Google Cloud Platform (GCP)
|
|||
|
||||
Alternatively, click the **"Open in Cloud Shell"** button below to launch Google Cloud Shell, clone the Langflow repository, and start an **interactive tutorial** that will guide you through the process of setting up the necessary resources and deploying Langflow on your GCP project.
|
||||
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts/gcp&shellonly=true&tutorial=walkthroughtutorial_spot.md)
|
||||
|
||||
## Deploy on Railway
|
||||
|
||||
|
|
@ -104,7 +104,7 @@ Alternatively, click the **"Open in Cloud Shell"** button below to launch Google
|
|||
|
||||
## Deploy on Render
|
||||
|
||||
<a href="https://render.com/deploy?repo=https://github.com/logspace-ai/langflow/tree/main">
|
||||
<a href="https://render.com/deploy?repo=https://github.com/langflow-ai/langflow/tree/main">
|
||||
<img src="https://render.com/images/deploy-to-render-button.svg" alt="Deploy to Render" />
|
||||
</a>
|
||||
|
||||
|
|
@ -114,11 +114,11 @@ We welcome contributions from developers of all levels to our open-source projec
|
|||
|
||||
---
|
||||
|
||||
[](https://star-history.com/#logspace-ai/langflow&Date)
|
||||
[](https://star-history.com/#langflow-ai/langflow&Date)
|
||||
|
||||
# 🌟 Contributors
|
||||
|
||||
[](https://github.com/logspace-ai/langflow/graphs/contributors)
|
||||
[](https://github.com/langflow-ai/langflow/graphs/contributors)
|
||||
|
||||
# 📄 License
|
||||
|
||||
|
|
|
|||
|
|
@ -37,7 +37,7 @@ The CustomComponent class serves as the foundation for creating custom component
|
|||
| _`langflow.field_typing.Prompt`_ |
|
||||
| _`langchain.chains.base.Chain`_ |
|
||||
| _`langchain.PromptTemplate`_ |
|
||||
| _`langchain.llms.base.BaseLLM`_ |
|
||||
| _`from langchain.schema.language_model import BaseLanguageModel`_ |
|
||||
| _`langchain.Tool`_ |
|
||||
| _`langchain.document_loaders.base.BaseLoader`_ |
|
||||
| _`langchain.schema.Document`_ |
|
||||
|
|
|
|||
|
|
@ -2,11 +2,11 @@
|
|||
|
||||
## 🤖 Join **Langflow** Discord server
|
||||
|
||||
Join us to ask questions and showcase your projects.
|
||||
Join us to ask questions and showcase your projects.
|
||||
|
||||
Let's bring together the building blocks of AI integration!
|
||||
Let's bring together the building blocks of AI integration!
|
||||
|
||||
Langflow [Discord](https://discord.gg/EqksyE2EX9) server.
|
||||
Langflow [Discord](https://discord.gg/EqksyE2EX9) server.
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -15,9 +15,10 @@
|
|||
Follow [@langflow_ai](https://twitter.com/langflow_ai) on **Twitter** to get the latest news about **Langflow**.
|
||||
|
||||
---
|
||||
|
||||
## ⭐️ Star **Langflow** on GitHub
|
||||
|
||||
You can "star" **Langflow** in [GitHub](https://github.com/logspace-ai/langflow).
|
||||
You can "star" **Langflow** in [GitHub](https://github.com/langflow-ai/langflow).
|
||||
|
||||
By adding a star, other users will be able to find it more easily and see that it has been already useful for others.
|
||||
|
||||
|
|
@ -25,14 +26,12 @@ By adding a star, other users will be able to find it more easily and see that i
|
|||
|
||||
## 👀 Watch the GitHub repository for releases
|
||||
|
||||
You can "watch" **Langflow** in [GitHub](https://github.com/logspace-ai/langflow).
|
||||
|
||||
You can "watch" **Langflow** in [GitHub](https://github.com/langflow-ai/langflow).
|
||||
|
||||
If you select "Watching" instead of "Releases only" you will receive notifications when someone creates a new issue or question. You can also specify that you only want to be notified about new issues, discussions, PRs, etc.
|
||||
|
||||
|
||||
Then you can try and help them solve those questions.
|
||||
|
||||
---
|
||||
|
||||
Thanks! 🚀
|
||||
Thanks! 🚀
|
||||
|
|
|
|||
|
|
@ -1,11 +1,11 @@
|
|||
# GitHub Issues
|
||||
|
||||
Our [issues](https://github.com/logspace-ai/langflow/issues) page is kept up to date
|
||||
Our [issues](https://github.com/langflow-ai/langflow/issues) page is kept up to date
|
||||
with bugs, improvements, and feature requests. There is a taxonomy of labels to help
|
||||
with sorting and discovery of issues of interest.
|
||||
|
||||
If you're looking for help with your code, consider posting a question on the
|
||||
[GitHub Discussions board](https://github.com/logspace-ai/langflow/discussions). Please
|
||||
[GitHub Discussions board](https://github.com/langflow-ai/langflow/discussions). Please
|
||||
understand that we won't be able to provide individual support via email. We
|
||||
also believe that help is much more valuable if it's **shared publicly**,
|
||||
so that more people can benefit from it.
|
||||
|
|
@ -21,7 +21,6 @@ so that more people can benefit from it.
|
|||
logs or tracebacks, you can wrap them in `<details>` and `</details>`. This
|
||||
[collapses the content](https://developer.mozilla.org/en/docs/Web/HTML/Element/details) so it only becomes visible on click, making the issue easier to read and follow.
|
||||
|
||||
|
||||
## Issue labels
|
||||
|
||||
[See this page](https://github.com/logspace-ai/langflow/labels) for an overview of the system we use to tag our issues and pull requests.
|
||||
[See this page](https://github.com/langflow-ai/langflow/labels) for an overview of the system we use to tag our issues and pull requests.
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
# How to contribute?
|
||||
|
||||
👋 Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/logspace-ai/langflow). If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.
|
||||
👋 Hello there! We welcome contributions from developers of all levels to our open-source project on [GitHub](https://github.com/langflow-ai/langflow). If you'd like to contribute, please check our contributing guidelines and help make Langflow more accessible.
|
||||
|
||||
As an open-source project in a rapidly developing field, we are extremely open
|
||||
to contributions, whether in the form of a new feature, improved infra, or better documentation.
|
||||
|
|
@ -10,6 +10,7 @@ To contribute to this project, please follow a ["fork and pull request"](https:/
|
|||
Please do not try to push directly to this repo unless you are a maintainer.
|
||||
|
||||
---
|
||||
|
||||
## Local development
|
||||
|
||||
You can develop Langflow using docker compose, or locally.
|
||||
|
|
@ -17,6 +18,7 @@ You can develop Langflow using docker compose, or locally.
|
|||
We provide a .vscode/launch.json file for debugging the backend in VSCode, which is a lot faster than using docker compose.
|
||||
|
||||
Setting up hooks:
|
||||
|
||||
```bash
|
||||
make init
|
||||
```
|
||||
|
|
@ -48,7 +50,6 @@ And the frontend:
|
|||
make frontend
|
||||
```
|
||||
|
||||
|
||||
---
|
||||
|
||||
## Docker compose
|
||||
|
|
|
|||
|
|
@ -6,10 +6,9 @@ This guide will help you set up a Langflow development VM in a Google Cloud Plat
|
|||
|
||||
> Note: When Cloud Shell opens, be sure to select **Trust repo**. Some `gcloud` commands might not run in an ephemeral Cloud Shell environment.
|
||||
|
||||
## Standard VM
|
||||
|
||||
|
||||
## Standard VM
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/logspace-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md)
|
||||
[](https://console.cloud.google.com/cloudshell/open?git_repo=https://github.com/langflow-ai/langflow&working_dir=scripts&shellonly=true&tutorial=walkthroughtutorial.md)
|
||||
|
||||
This script sets up a Debian-based VM with the Langflow package, Nginx, and the necessary configurations to run the Langflow Dev environment.
|
||||
|
||||
|
|
@ -24,11 +23,11 @@ When running as a [spot (preemptible) instance](https://cloud.google.com/compute
|
|||
---
|
||||
|
||||
## Pricing (approximate)
|
||||
|
||||
> For a more accurate breakdown of costs, please use the [**GCP Pricing Calculator**](https://cloud.google.com/products/calculator)
|
||||
|
||||
|
||||
| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes |
|
||||
| -------------- | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | ----- |
|
||||
| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs |
|
||||
| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance |
|
||||
| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month |
|
||||
| Component | Regular Cost (Hourly) | Regular Cost (Monthly) | Spot/Preemptible Cost (Hourly) | Spot/Preemptible Cost (Monthly) | Notes |
|
||||
| ------------------ | --------------------- | ---------------------- | ------------------------------ | ------------------------------- | -------------------------------------------------------------------------- |
|
||||
| 100 GB Disk | - | $10/month | - | $10/month | Disk cost remains the same for both regular and Spot/Preemptible VMs |
|
||||
| VM (n1-standard-4) | $0.15/hr | ~$108/month | ~$0.04/hr | ~$29/month | The VM cost can be significantly reduced using a Spot/Preemptible instance |
|
||||
| **Total** | **$0.15/hr** | **~$118/month** | **~$0.04/hr** | **~$39/month** | Total costs for running the VM and disk 24/7 for an entire month |
|
||||
|
|
|
|||
|
|
@ -78,7 +78,7 @@ The Chat Widget can be embedded into any HTML page, inside a _`<body>`_ tag, as
|
|||
To embed the Chat Widget using React, you'll need to insert this _`<script>`_ tag into the React _index.html_ file, inside the _`<body>`_ tag:
|
||||
|
||||
```html
|
||||
<script src="https://cdn.jsdelivr.net/gh/logspace-ai/langflow-embedded-chat@main/dist/build/static/js/bundle.min.js"></script>
|
||||
<script src="https://cdn.jsdelivr.net/gh/langflow-ai/langflow-embedded-chat@main/dist/build/static/js/bundle.min.js"></script>
|
||||
```
|
||||
|
||||
Then, declare your Web Component and encapsulate it in a React component.
|
||||
|
|
@ -115,7 +115,7 @@ Finally, you can place the component anywhere in your code to display the Chat W
|
|||
To use it in Angular, first add this _`<script>`_ tag into the Angular _index.html_ file, inside the _`<body>`_ tag.
|
||||
|
||||
```html
|
||||
<script src="https://cdn.jsdelivr.net/gh/logspace-ai/langflow-embedded-chat@main/dist/build/static/js/bundle.min.js"></script>
|
||||
<script src="https://cdn.jsdelivr.net/gh/langflow-ai/langflow-embedded-chat@main/dist/build/static/js/bundle.min.js"></script>
|
||||
```
|
||||
|
||||
When you use a custom web component in an Angular template, the Angular compiler might show a warning when it doesn't recognize the custom elements by default. To suppress this warning, add _`CUSTOM_ELEMENTS_SCHEMA`_ to the module's _`@NgModule.schemas`_.
|
||||
|
|
@ -185,7 +185,7 @@ Use the widget API to customize your Chat Widget:
|
|||
</Admonition>
|
||||
|
||||
| Prop | Type | Required | Description |
|
||||
| --------------------- | ------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| --------------------- | ------- | -------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------- |
|
||||
| bot_message_style | JSON | No | Applies custom formatting to bot messages. |
|
||||
| chat_input_field | String | Yes | Defines the type of the input field for chat messages. |
|
||||
| chat_inputs | JSON | Yes | Determines the chat input elements and their respective values. |
|
||||
|
|
@ -208,4 +208,4 @@ Use the widget API to customize your Chat Widget:
|
|||
| tweaks | JSON | No | Applies additional custom adjustments for the associated flow. |
|
||||
| user_message_style | JSON | No | Determines the formatting for user messages in the chat window. |
|
||||
| width | Number | No | Sets the width of the chat window in pixels. |
|
||||
| window_title | String | No | Sets the title displayed in the chat window's header or title bar. |
|
||||
| window_title | String | No | Sets the title displayed in the chat window's header or title bar. | import ThemedImage from "@theme/ThemedImage"; |
|
||||
|
|
|
|||
|
|
@ -131,7 +131,7 @@ class MyComponent(CustomComponent):
|
|||
|
||||
---
|
||||
|
||||
The [Return Type Annotation](https://docs.python.org/3/library/typing.html) of the _`build`_ method defines the component type (e.g., Chain, BaseLLM, or basic Python types). Check out all supported types in the [component reference](../components/custom).
|
||||
The [Return Type Annotation](https://docs.python.org/3/library/typing.html) of the _`build`_ method defines the component type (e.g., Chain, BaseLanguageModel, or basic Python types). Check out all supported types in the [component reference](../components/custom).
|
||||
|
||||
```python
|
||||
from langflow.custom import CustomComponent
|
||||
|
|
@ -366,7 +366,7 @@ For advanced customization, Langflow offers the option to create and load custom
|
|||
|
||||
### Folder Structure
|
||||
|
||||
Create a folder that follows the same structural conventions as the [config.yaml](https://github.com/logspace-ai/langflow/blob/dev/src/backend/base/langflow/config.yaml) file. Inside this main directory, use a `custom_components` subdirectory for your custom components.
|
||||
Create a folder that follows the same structural conventions as the [config.yaml](https://github.com/langflow-ai/langflow/blob/dev/src/backend/base/langflow/config.yaml) file. Inside this main directory, use a `custom_components` subdirectory for your custom components.
|
||||
|
||||
Inside `custom_components`, you can create a Python file for each component. Similarly, any custom agents should be housed in an `agents` subdirectory.
|
||||
|
||||
|
|
@ -406,4 +406,5 @@ Langflow will attempt to load all of the components found in the specified direc
|
|||
|
||||
Once your custom components have been loaded successfully, they will appear in Langflow's sidebar. From there, you can add them to your Langflow canvas for use. However, please note that components with errors will not be available for addition to the canvas. Always ensure your code is error-free before attempting to load components.
|
||||
|
||||
Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding!
|
||||
Remember, creating custom components allows you to extend the functionality of Langflow to better suit your unique needs. Happy coding!import ZoomableImage from "/src/theme/ZoomableImage.js";
|
||||
import Admonition from "@theme/Admonition";
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ import Admonition from "@theme/Admonition";
|
|||
Langflow 1.0 is a significant update that brings many exciting changes and improvements to the platform.
|
||||
This guide will walk you through the key improvements and help you migrate your existing projects to the new version.
|
||||
|
||||
If you have any questions or need assistance during the migration process, please don't hesitate to reach out to in our [Discord](https://discord.gg/wZSWQaukgJ) or [GitHub](https://github.com/logspace-ai/langflow/issues) community.
|
||||
If you have any questions or need assistance during the migration process, please don't hesitate to reach out to in our [Discord](https://discord.gg/wZSWQaukgJ) or [GitHub](https://github.com/langflow-ai/langflow/issues) community.
|
||||
|
||||
We have a special channel in our Discord server dedicated to Langflow 1.0 migration, where you can ask questions, share your experiences, and get help from the community.
|
||||
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ module.exports = {
|
|||
// sidebarPath: 'sidebars.js',
|
||||
},
|
||||
gtag: {
|
||||
trackingID: 'G-XHC7G628ZP',
|
||||
trackingID: "G-XHC7G628ZP",
|
||||
anonymizeIP: true,
|
||||
},
|
||||
theme: {
|
||||
|
|
@ -87,7 +87,7 @@ module.exports = {
|
|||
// right
|
||||
{
|
||||
position: "right",
|
||||
href: "https://github.com/logspace-ai/langflow",
|
||||
href: "https://github.com/langflow-ai/langflow",
|
||||
position: "right",
|
||||
className: "header-github-link",
|
||||
target: "_blank",
|
||||
|
|
@ -124,7 +124,7 @@ module.exports = {
|
|||
},
|
||||
announcementBar: {
|
||||
content:
|
||||
'⭐️ If you like ⛓️Langflow, star it on <a target="_blank" rel="noopener noreferrer" href="https://github.com/logspace-ai/langflow">GitHub</a>! ⭐️',
|
||||
'⭐️ If you like ⛓️Langflow, star it on <a target="_blank" rel="noopener noreferrer" href="https://github.com/langflow-ai/langflow">GitHub</a>! ⭐️',
|
||||
backgroundColor: "#E8EBF1", //Mustard Yellow #D19900 #D4B20B - Salmon #E9967A
|
||||
textColor: "#1C1E21",
|
||||
isCloseable: false,
|
||||
|
|
|
|||
348
poetry.lock
generated
348
poetry.lock
generated
|
|
@ -455,17 +455,17 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "boto3"
|
||||
version = "1.34.79"
|
||||
version = "1.34.80"
|
||||
description = "The AWS SDK for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "boto3-1.34.79-py3-none-any.whl", hash = "sha256:265b0b4865e8c07e27abb32a31d2bd9129bb009b1d89ca0783776ec084886123"},
|
||||
{file = "boto3-1.34.79.tar.gz", hash = "sha256:139dd2d94eaa0e3213ff37ba7cf4cb2e3823269178fe8f3e33c965f680a9ddde"},
|
||||
{file = "boto3-1.34.80-py3-none-any.whl", hash = "sha256:bb8f433c04dcdffbd4a802df56c1c30f2be23b1161fd8fb45e4b76c1487ec122"},
|
||||
{file = "boto3-1.34.80.tar.gz", hash = "sha256:5627f6ecadb46fc7c9f8c368baf948f1b00a3fd2f8eb1275c254469853ad8fdb"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
botocore = ">=1.34.79,<1.35.0"
|
||||
botocore = ">=1.34.80,<1.35.0"
|
||||
jmespath = ">=0.7.1,<2.0.0"
|
||||
s3transfer = ">=0.10.0,<0.11.0"
|
||||
|
||||
|
|
@ -474,13 +474,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
|
|||
|
||||
[[package]]
|
||||
name = "botocore"
|
||||
version = "1.34.79"
|
||||
version = "1.34.80"
|
||||
description = "Low-level, data-driven core of boto 3."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "botocore-1.34.79-py3-none-any.whl", hash = "sha256:a42a014d3dbaa9ef123810592af69f9e55b456c5be3ac9efc037325685519e83"},
|
||||
{file = "botocore-1.34.79.tar.gz", hash = "sha256:6b59b0f7de219d383a2a633f6718c2600642ebcb707749dc6c67a6a436474b7a"},
|
||||
{file = "botocore-1.34.80-py3-none-any.whl", hash = "sha256:354a00f03faba52acc6f1a84fa4f035d48541633be98ccc24b59dc544f679f8b"},
|
||||
{file = "botocore-1.34.80.tar.gz", hash = "sha256:8402262e819f3d46df504bbd781e770858c0130b90f660699f75ef3a63abca5a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1129,13 +1129,13 @@ testing = ["pytest (>=7.2.1)", "pytest-cov (>=4.0.0)", "tox (>=4.4.3)"]
|
|||
|
||||
[[package]]
|
||||
name = "cohere"
|
||||
version = "5.2.2"
|
||||
version = "5.2.4"
|
||||
description = ""
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8"
|
||||
files = [
|
||||
{file = "cohere-5.2.2-py3-none-any.whl", hash = "sha256:dae0c7318fa0d15a2147a044a49c1f40164a6c8135a260cc46214058c7c06085"},
|
||||
{file = "cohere-5.2.2.tar.gz", hash = "sha256:43243b86d4abf3140f9077435580ebceaa431fb7d57a9fef6858eee880f26646"},
|
||||
{file = "cohere-5.2.4-py3-none-any.whl", hash = "sha256:50e8cbd009a6d6f6ce7127a0b62c50d3dfcfdb853f681f0ac315cfa70599fee4"},
|
||||
{file = "cohere-5.2.4.tar.gz", hash = "sha256:2bf6e905773116ad3fff348e054e4ce1a1830092a63cb48fa8180beda4cbb96a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1312,33 +1312,6 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1
|
|||
[package.extras]
|
||||
toml = ["tomli"]
|
||||
|
||||
[[package]]
|
||||
name = "crewai"
|
||||
version = "0.22.5"
|
||||
description = "Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks."
|
||||
optional = false
|
||||
python-versions = ">=3.10,<=3.13"
|
||||
files = [
|
||||
{file = "crewai-0.22.5-py3-none-any.whl", hash = "sha256:9f254d8b2ebd7fae9a43d0ebab116a3f536c6620ca3bc955cb796fc8bb9beb7d"},
|
||||
{file = "crewai-0.22.5.tar.gz", hash = "sha256:03c76a04f46a432a1c3d9e5e0c8e039f983b25019194b0ab2ec594762ac380cf"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
click = ">=8.1.7,<9.0.0"
|
||||
instructor = ">=0.5.2,<0.6.0"
|
||||
langchain = ">=0.1.10,<0.2.0"
|
||||
langchain-openai = ">=0.0.5,<0.0.6"
|
||||
openai = ">=1.13.3,<2.0.0"
|
||||
opentelemetry-api = ">=1.22.0,<2.0.0"
|
||||
opentelemetry-exporter-otlp-proto-http = ">=1.22.0,<2.0.0"
|
||||
opentelemetry-sdk = ">=1.22.0,<2.0.0"
|
||||
pydantic = ">=2.4.2,<3.0.0"
|
||||
python-dotenv = "1.0.0"
|
||||
regex = ">=2023.12.25,<2024.0.0"
|
||||
|
||||
[package.extras]
|
||||
tools = ["crewai-tools (>=0.0.15,<0.0.16)"]
|
||||
|
||||
[[package]]
|
||||
name = "cryptography"
|
||||
version = "42.0.5"
|
||||
|
|
@ -1515,13 +1488,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "deepdiff"
|
||||
version = "7.0.0"
|
||||
version = "7.0.1"
|
||||
description = "Deep Difference and Search of any Python object/data. Recreate objects by adding adding deltas to each other."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "deepdiff-7.0.0-py3-none-any.whl", hash = "sha256:f7bbb845f83ad6b9453a4ab07c579bdc6f1df712edc515740455a9b88c2bc41a"},
|
||||
{file = "deepdiff-7.0.0.tar.gz", hash = "sha256:4e07da4f2a1ae069b7465d264715764f3b36ce181ec89f47050ead61711b1e9a"},
|
||||
{file = "deepdiff-7.0.1-py3-none-any.whl", hash = "sha256:447760081918216aa4fd4ca78a4b6a848b81307b2ea94c810255334b759e1dc3"},
|
||||
{file = "deepdiff-7.0.1.tar.gz", hash = "sha256:260c16f052d4badbf60351b4f77e8390bee03a0b516246f6839bc813fb429ddf"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1674,13 +1647,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "dspy-ai"
|
||||
version = "2.4.0"
|
||||
version = "2.4.5"
|
||||
description = "DSPy"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "dspy-ai-2.4.0.tar.gz", hash = "sha256:d646ec015270b70bd51916346c01680923ccfa386c9e5474caac6d4366148678"},
|
||||
{file = "dspy_ai-2.4.0-py3-none-any.whl", hash = "sha256:edea508dc315702f61ae39568eaf2d8789ba4f8682102e730e016801b9102703"},
|
||||
{file = "dspy-ai-2.4.5.tar.gz", hash = "sha256:6d9f166f9c214a86cfa0ef0e1d1489b4034dbed1cdab61ce152de8c09f64d799"},
|
||||
{file = "dspy_ai-2.4.5-py3-none-any.whl", hash = "sha256:341e239374fba86ad7f9cd1eb3810ccd70bbbdfe8e9b2fa79038da0e0e720d4e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1697,6 +1670,7 @@ tqdm = "*"
|
|||
ujson = "*"
|
||||
|
||||
[package.extras]
|
||||
anthropic = ["anthropic (>=0.18.0,<0.19.0)"]
|
||||
chromadb = ["chromadb (>=0.4.14,<0.5.0)"]
|
||||
dev = ["pytest (>=6.2.5)"]
|
||||
docs = ["autodoc-pydantic", "docutils (<0.17)", "furo (>=2023.3.27)", "m2r2", "myst-nb", "myst-parser", "sphinx (>=4.3.0)", "sphinx-autobuild", "sphinx-automodapi (==0.16.0)", "sphinx-reredirects (>=0.1.2)", "sphinx-rtd-theme"]
|
||||
|
|
@ -1778,13 +1752,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "ecdsa"
|
||||
version = "0.18.0"
|
||||
version = "0.19.0"
|
||||
description = "ECDSA cryptographic signature library (pure python)"
|
||||
optional = false
|
||||
python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.6"
|
||||
files = [
|
||||
{file = "ecdsa-0.18.0-py2.py3-none-any.whl", hash = "sha256:80600258e7ed2f16b9aa1d7c295bd70194109ad5a30fdee0eaeefef1d4c559dd"},
|
||||
{file = "ecdsa-0.18.0.tar.gz", hash = "sha256:190348041559e21b22a1d65cee485282ca11a6f81d503fddb84d5017e9ed1e49"},
|
||||
{file = "ecdsa-0.19.0-py2.py3-none-any.whl", hash = "sha256:2cea9b88407fdac7bbeca0833b189e4c9c53f2ef1e1eaa29f6224dbc809b707a"},
|
||||
{file = "ecdsa-0.19.0.tar.gz", hash = "sha256:60eaad1199659900dd0af521ed462b793bbdf867432b3948e87416ae4caf6bf8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3320,26 +3294,6 @@ files = [
|
|||
{file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "instructor"
|
||||
version = "0.5.2"
|
||||
description = "structured outputs for llm"
|
||||
optional = false
|
||||
python-versions = ">=3.10,<4.0"
|
||||
files = [
|
||||
{file = "instructor-0.5.2-py3-none-any.whl", hash = "sha256:8c7c927f3cbf6cd863eeebceae3f021e27eaca2ceaf9e9f3c8204540a1126160"},
|
||||
{file = "instructor-0.5.2.tar.gz", hash = "sha256:d8d679eb4624254db615794aaab59840e506fa696bc0181d998ae4f9ded2706d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
aiohttp = ">=3.9.1,<4.0.0"
|
||||
docstring-parser = ">=0.15,<0.16"
|
||||
openai = ">=1.1.0,<2.0.0"
|
||||
pydantic = ">=2.0.2,<3.0.0"
|
||||
rich = ">=13.7.0,<14.0.0"
|
||||
tenacity = ">=8.2.3,<9.0.0"
|
||||
typer = ">=0.9.0,<0.10.0"
|
||||
|
||||
[[package]]
|
||||
name = "ipykernel"
|
||||
version = "6.29.4"
|
||||
|
|
@ -3786,37 +3740,6 @@ files = [
|
|||
cohere = ">=5.1.4,<6.0.0"
|
||||
langchain-core = ">=0.1.32,<0.2.0"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-astradb"
|
||||
version = "0.1.0"
|
||||
description = "An integration package connecting Astra DB and LangChain"
|
||||
optional = false
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
files = [
|
||||
{file = "langchain_astradb-0.1.0-py3-none-any.whl", hash = "sha256:c6686089da343fce8c31e36c9162323e88888300b09d56b72347a19449d7361f"},
|
||||
{file = "langchain_astradb-0.1.0.tar.gz", hash = "sha256:c8a3426c9daa2beeec2dc7a718186b0b9c388082e9543e0bc07363712cc3b947"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
astrapy = ">=0.7.7,<0.8.0"
|
||||
langchain-core = ">=0.1.31,<0.2.0"
|
||||
numpy = ">=1,<2"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-cohere"
|
||||
version = "0.1.0"
|
||||
description = "An integration package connecting Cohere and LangChain"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "langchain_cohere-0.1.0-py3-none-any.whl", hash = "sha256:f60e9eb41f7d4ead9659bddb3fae7aa18ddc3fdf2b2867be4bd8a565229f488d"},
|
||||
{file = "langchain_cohere-0.1.0.tar.gz", hash = "sha256:960551293ea58d170fad37d44657d3ae4587f6b2e8f3f58922c53c59b9e9d85c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cohere = ">=5.1.4,<6.0.0"
|
||||
langchain-core = ">=0.1.32,<0.2.0"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-community"
|
||||
version = "0.0.31"
|
||||
|
|
@ -3915,7 +3838,7 @@ files = [
|
|||
[package.dependencies]
|
||||
langchain-core = ">=0.1.33,<0.2.0"
|
||||
openai = ">=1.10.0,<2.0.0"
|
||||
tiktoken = ">=0.5.2,<0.6.0"
|
||||
tiktoken = ">=0.5.2,<1"
|
||||
|
||||
[[package]]
|
||||
name = "langchain-text-splitters"
|
||||
|
|
@ -3950,7 +3873,7 @@ six = "*"
|
|||
|
||||
[[package]]
|
||||
name = "langflow-base"
|
||||
version = "0.0.21"
|
||||
version = "0.0.24"
|
||||
description = "A Python package with a built-in web application"
|
||||
optional = false
|
||||
python-versions = ">=3.10,<3.12"
|
||||
|
|
@ -3974,7 +3897,7 @@ langchain-experimental = "*"
|
|||
loguru = "^0.7.1"
|
||||
multiprocess = "^0.70.14"
|
||||
nest-asyncio = "^1.6.0"
|
||||
orjson = "3.9.15"
|
||||
orjson = "3.10.0"
|
||||
pandas = "2.2.0"
|
||||
passlib = "^1.7.4"
|
||||
pillow = "^10.2.0"
|
||||
|
|
@ -4060,13 +3983,13 @@ regex = ["regex"]
|
|||
|
||||
[[package]]
|
||||
name = "litellm"
|
||||
version = "1.34.34"
|
||||
version = "1.34.36"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
optional = false
|
||||
python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,>=3.8"
|
||||
files = [
|
||||
{file = "litellm-1.34.34-py3-none-any.whl", hash = "sha256:c9eefd4b5adec3c2e6d0ab765a4fcebd475a895c7e417f47f8e677410b607f51"},
|
||||
{file = "litellm-1.34.34.tar.gz", hash = "sha256:d11c9d5296d052a9e5e1187ac7b33683f3a581740abc4de6a9c327d3f3c7187c"},
|
||||
{file = "litellm-1.34.36-py3-none-any.whl", hash = "sha256:e5baa7f4540dd2923472f60d3ffb4f76bb48f5288a7fb26efed2010b57b0d8ad"},
|
||||
{file = "litellm-1.34.36.tar.gz", hash = "sha256:03e59c30ae21b9c1df4064dc20b015233c3d86a07bb4832c40dd73021778f3c8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4338,13 +4261,13 @@ llama-index-program-openai = ">=0.1.1,<0.2.0"
|
|||
|
||||
[[package]]
|
||||
name = "llama-index-readers-file"
|
||||
version = "0.1.13"
|
||||
version = "0.1.15"
|
||||
description = "llama-index readers file integration"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.8.1"
|
||||
files = [
|
||||
{file = "llama_index_readers_file-0.1.13-py3-none-any.whl", hash = "sha256:692988b8c3ca2807d21a171351078b634ada2ff1682ebe4a484f82da384dfc55"},
|
||||
{file = "llama_index_readers_file-0.1.13.tar.gz", hash = "sha256:830f06ec7b34437fc3bb5f268d235c5c7640296adb148d8f92277dceb7f0846d"},
|
||||
{file = "llama_index_readers_file-0.1.15-py3-none-any.whl", hash = "sha256:f0d26a46d4c40334729d4506d9fbdbed8f5e187e36e22159fb48b28d67c7f240"},
|
||||
{file = "llama_index_readers_file-0.1.15.tar.gz", hash = "sha256:98087d983a1f2d26961805217f13d24b0f23cbbe8e32d2327ae86b1ee668d3b2"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -5648,26 +5571,6 @@ opentelemetry-sdk = ">=1.24.0,<1.25.0"
|
|||
[package.extras]
|
||||
test = ["pytest-grpc"]
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-exporter-otlp-proto-http"
|
||||
version = "1.24.0"
|
||||
description = "OpenTelemetry Collector Protobuf over HTTP Exporter"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "opentelemetry_exporter_otlp_proto_http-1.24.0-py3-none-any.whl", hash = "sha256:25af10e46fdf4cd3833175e42f4879a1255fc01655fe14c876183a2903949836"},
|
||||
{file = "opentelemetry_exporter_otlp_proto_http-1.24.0.tar.gz", hash = "sha256:704c066cc96f5131881b75c0eac286cd73fc735c490b054838b4513254bd7850"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
deprecated = ">=1.2.6"
|
||||
googleapis-common-protos = ">=1.52,<2.0"
|
||||
opentelemetry-api = ">=1.15,<2.0"
|
||||
opentelemetry-exporter-otlp-proto-common = "1.24.0"
|
||||
opentelemetry-proto = "1.24.0"
|
||||
opentelemetry-sdk = ">=1.24.0,<1.25.0"
|
||||
requests = ">=2.7,<3.0"
|
||||
|
||||
[[package]]
|
||||
name = "opentelemetry-instrumentation"
|
||||
version = "0.45b0"
|
||||
|
|
@ -5821,61 +5724,62 @@ dev = ["black", "mypy", "pytest"]
|
|||
|
||||
[[package]]
|
||||
name = "orjson"
|
||||
version = "3.9.15"
|
||||
version = "3.10.0"
|
||||
description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "orjson-3.9.15-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:d61f7ce4727a9fa7680cd6f3986b0e2c732639f46a5e0156e550e35258aa313a"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4feeb41882e8aa17634b589533baafdceb387e01e117b1ec65534ec724023d04"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fbbeb3c9b2edb5fd044b2a070f127a0ac456ffd079cb82746fc84af01ef021a4"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b66bcc5670e8a6b78f0313bcb74774c8291f6f8aeef10fe70e910b8040f3ab75"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2973474811db7b35c30248d1129c64fd2bdf40d57d84beed2a9a379a6f57d0ab"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9fe41b6f72f52d3da4db524c8653e46243c8c92df826ab5ffaece2dba9cccd58"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:4228aace81781cc9d05a3ec3a6d2673a1ad0d8725b4e915f1089803e9efd2b99"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:6f7b65bfaf69493c73423ce9db66cfe9138b2f9ef62897486417a8fcb0a92bfe"},
|
||||
{file = "orjson-3.9.15-cp310-none-win32.whl", hash = "sha256:2d99e3c4c13a7b0fb3792cc04c2829c9db07838fb6973e578b85c1745e7d0ce7"},
|
||||
{file = "orjson-3.9.15-cp310-none-win_amd64.whl", hash = "sha256:b725da33e6e58e4a5d27958568484aa766e825e93aa20c26c91168be58e08cbb"},
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||||
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||||
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|
||||
{file = "orjson-3.9.15-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ff0f9913d82e1d1fadbd976424c316fbc4d9c525c81d047bbdd16bd27dd98cfc"},
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||||
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|
||||
{file = "orjson-3.9.15-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d6768a327ea1ba44c9114dba5fdda4a214bdb70129065cd0807eb5f010bfcbb5"},
|
||||
{file = "orjson-3.9.15-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:12365576039b1a5a47df01aadb353b68223da413e2e7f98c02403061aad34bde"},
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||||
{file = "orjson-3.9.15-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:71c6b009d431b3839d7c14c3af86788b3cfac41e969e3e1c22f8a6ea13139404"},
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||||
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||||
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|
||||
{file = "orjson-3.9.15-cp311-none-win_amd64.whl", hash = "sha256:b3d336ed75d17c7b1af233a6561cf421dee41d9204aa3cfcc6c9c65cd5bb69a8"},
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||||
{file = "orjson-3.9.15-cp312-cp312-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:82425dd5c7bd3adfe4e94c78e27e2fa02971750c2b7ffba648b0f5d5cc016a73"},
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||||
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||||
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]
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||||
|
||||
[[package]]
|
||||
|
|
@ -8391,7 +8295,7 @@ name = "shellingham"
|
|||
version = "1.5.4"
|
||||
description = "Tool to Detect Surrounding Shell"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
{file = "shellingham-1.5.4-py2.py3-none-any.whl", hash = "sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686"},
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{file = "shellingham-1.5.4.tar.gz", hash = "sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"},
|
||||
|
|
@ -8730,47 +8634,47 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "tiktoken"
|
||||
version = "0.5.2"
|
||||
version = "0.6.0"
|
||||
description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
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|
||||
{file = "tiktoken-0.5.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42adf7d4fb1ed8de6e0ff2e794a6a15005f056a0d83d22d1d6755a39bffd9e7f"},
|
||||
{file = "tiktoken-0.5.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4c3f894dbe0adb44609f3d532b8ea10820d61fdcb288b325a458dfc60fefb7db"},
|
||||
{file = "tiktoken-0.5.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:58ccfddb4e62f0df974e8f7e34a667981d9bb553a811256e617731bf1d007d19"},
|
||||
{file = "tiktoken-0.5.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:58902a8bad2de4268c2a701f1c844d22bfa3cbcc485b10e8e3e28a050179330b"},
|
||||
{file = "tiktoken-0.5.2-cp312-cp312-win_amd64.whl", hash = "sha256:5e39257826d0647fcac403d8fa0a474b30d02ec8ffc012cfaf13083e9b5e82c5"},
|
||||
{file = "tiktoken-0.5.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:8bde3b0fbf09a23072d39c1ede0e0821f759b4fa254a5f00078909158e90ae1f"},
|
||||
{file = "tiktoken-0.5.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2ddee082dcf1231ccf3a591d234935e6acf3e82ee28521fe99af9630bc8d2a60"},
|
||||
{file = "tiktoken-0.5.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:35c057a6a4e777b5966a7540481a75a31429fc1cb4c9da87b71c8b75b5143037"},
|
||||
{file = "tiktoken-0.5.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4c4a049b87e28f1dc60509f8eb7790bc8d11f9a70d99b9dd18dfdd81a084ffe6"},
|
||||
{file = "tiktoken-0.5.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5bf5ce759089f4f6521ea6ed89d8f988f7b396e9f4afb503b945f5c949c6bec2"},
|
||||
{file = "tiktoken-0.5.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:0c964f554af1a96884e01188f480dad3fc224c4bbcf7af75d4b74c4b74ae0125"},
|
||||
{file = "tiktoken-0.5.2-cp38-cp38-win_amd64.whl", hash = "sha256:368dd5726d2e8788e47ea04f32e20f72a2012a8a67af5b0b003d1e059f1d30a3"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:a2deef9115b8cd55536c0a02c0203512f8deb2447f41585e6d929a0b878a0dd2"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:2ed7d380195affbf886e2f8b92b14edfe13f4768ff5fc8de315adba5b773815e"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c76fce01309c8140ffe15eb34ded2bb94789614b7d1d09e206838fc173776a18"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:60a5654d6a2e2d152637dd9a880b4482267dfc8a86ccf3ab1cec31a8c76bfae8"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:41d4d3228e051b779245a8ddd21d4336f8975563e92375662f42d05a19bdff41"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:a5c1cdec2c92fcde8c17a50814b525ae6a88e8e5b02030dc120b76e11db93f13"},
|
||||
{file = "tiktoken-0.5.2-cp39-cp39-win_amd64.whl", hash = "sha256:84ddb36faedb448a50b246e13d1b6ee3437f60b7169b723a4b2abad75e914f3e"},
|
||||
{file = "tiktoken-0.5.2.tar.gz", hash = "sha256:f54c581f134a8ea96ce2023ab221d4d4d81ab614efa0b2fbce926387deb56c80"},
|
||||
{file = "tiktoken-0.6.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:277de84ccd8fa12730a6b4067456e5cf72fef6300bea61d506c09e45658d41ac"},
|
||||
{file = "tiktoken-0.6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9c44433f658064463650d61387623735641dcc4b6c999ca30bc0f8ba3fccaf5c"},
|
||||
{file = "tiktoken-0.6.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:afb9a2a866ae6eef1995ab656744287a5ac95acc7e0491c33fad54d053288ad3"},
|
||||
{file = "tiktoken-0.6.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c62c05b3109fefca26fedb2820452a050074ad8e5ad9803f4652977778177d9f"},
|
||||
{file = "tiktoken-0.6.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:0ef917fad0bccda07bfbad835525bbed5f3ab97a8a3e66526e48cdc3e7beacf7"},
|
||||
{file = "tiktoken-0.6.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:e095131ab6092d0769a2fda85aa260c7c383072daec599ba9d8b149d2a3f4d8b"},
|
||||
{file = "tiktoken-0.6.0-cp310-cp310-win_amd64.whl", hash = "sha256:05b344c61779f815038292a19a0c6eb7098b63c8f865ff205abb9ea1b656030e"},
|
||||
{file = "tiktoken-0.6.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:cefb9870fb55dca9e450e54dbf61f904aab9180ff6fe568b61f4db9564e78871"},
|
||||
{file = "tiktoken-0.6.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:702950d33d8cabc039845674107d2e6dcabbbb0990ef350f640661368df481bb"},
|
||||
{file = "tiktoken-0.6.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e8d49d076058f23254f2aff9af603863c5c5f9ab095bc896bceed04f8f0b013a"},
|
||||
{file = "tiktoken-0.6.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:430bc4e650a2d23a789dc2cdca3b9e5e7eb3cd3935168d97d43518cbb1f9a911"},
|
||||
{file = "tiktoken-0.6.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:293cb8669757301a3019a12d6770bd55bec38a4d3ee9978ddbe599d68976aca7"},
|
||||
{file = "tiktoken-0.6.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:7bd1a288b7903aadc054b0e16ea78e3171f70b670e7372432298c686ebf9dd47"},
|
||||
{file = "tiktoken-0.6.0-cp311-cp311-win_amd64.whl", hash = "sha256:ac76e000183e3b749634968a45c7169b351e99936ef46f0d2353cd0d46c3118d"},
|
||||
{file = "tiktoken-0.6.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:17cc8a4a3245ab7d935c83a2db6bb71619099d7284b884f4b2aea4c74f2f83e3"},
|
||||
{file = "tiktoken-0.6.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:284aebcccffe1bba0d6571651317df6a5b376ff6cfed5aeb800c55df44c78177"},
|
||||
{file = "tiktoken-0.6.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0c1a3a5d33846f8cd9dd3b7897c1d45722f48625a587f8e6f3d3e85080559be8"},
|
||||
{file = "tiktoken-0.6.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6318b2bb2337f38ee954fd5efa82632c6e5ced1d52a671370fa4b2eff1355e91"},
|
||||
{file = "tiktoken-0.6.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:1f5f0f2ed67ba16373f9a6013b68da298096b27cd4e1cf276d2d3868b5c7efd1"},
|
||||
{file = "tiktoken-0.6.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:75af4c0b16609c2ad02581f3cdcd1fb698c7565091370bf6c0cf8624ffaba6dc"},
|
||||
{file = "tiktoken-0.6.0-cp312-cp312-win_amd64.whl", hash = "sha256:45577faf9a9d383b8fd683e313cf6df88b6076c034f0a16da243bb1c139340c3"},
|
||||
{file = "tiktoken-0.6.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:7c1492ab90c21ca4d11cef3a236ee31a3e279bb21b3fc5b0e2210588c4209e68"},
|
||||
{file = "tiktoken-0.6.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:e2b380c5b7751272015400b26144a2bab4066ebb8daae9c3cd2a92c3b508fe5a"},
|
||||
{file = "tiktoken-0.6.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c9f497598b9f58c99cbc0eb764b4a92272c14d5203fc713dd650b896a03a50ad"},
|
||||
{file = "tiktoken-0.6.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e65e8bd6f3f279d80f1e1fbd5f588f036b9a5fa27690b7f0cc07021f1dfa0839"},
|
||||
{file = "tiktoken-0.6.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5f1495450a54e564d236769d25bfefbf77727e232d7a8a378f97acddee08c1ae"},
|
||||
{file = "tiktoken-0.6.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:6c4e4857d99f6fb4670e928250835b21b68c59250520a1941618b5b4194e20c3"},
|
||||
{file = "tiktoken-0.6.0-cp38-cp38-win_amd64.whl", hash = "sha256:168d718f07a39b013032741867e789971346df8e89983fe3c0ef3fbd5a0b1cb9"},
|
||||
{file = "tiktoken-0.6.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:47fdcfe11bd55376785a6aea8ad1db967db7f66ea81aed5c43fad497521819a4"},
|
||||
{file = "tiktoken-0.6.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:fb7d2ccbf1a7784810aff6b80b4012fb42c6fc37eaa68cb3b553801a5cc2d1fc"},
|
||||
{file = "tiktoken-0.6.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1ccb7a111ee76af5d876a729a347f8747d5ad548e1487eeea90eaf58894b3138"},
|
||||
{file = "tiktoken-0.6.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b2048e1086b48e3c8c6e2ceeac866561374cd57a84622fa49a6b245ffecb7744"},
|
||||
{file = "tiktoken-0.6.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:07f229a5eb250b6403a61200199cecf0aac4aa23c3ecc1c11c1ca002cbb8f159"},
|
||||
{file = "tiktoken-0.6.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:432aa3be8436177b0db5a2b3e7cc28fd6c693f783b2f8722539ba16a867d0c6a"},
|
||||
{file = "tiktoken-0.6.0-cp39-cp39-win_amd64.whl", hash = "sha256:8bfe8a19c8b5c40d121ee7938cd9c6a278e5b97dc035fd61714b4f0399d2f7a1"},
|
||||
{file = "tiktoken-0.6.0.tar.gz", hash = "sha256:ace62a4ede83c75b0374a2ddfa4b76903cf483e9cb06247f566be3bf14e6beed"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -9135,13 +9039,13 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
|
|||
|
||||
[[package]]
|
||||
name = "typer"
|
||||
version = "0.12.1"
|
||||
version = "0.12.2"
|
||||
description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "typer-0.12.1-py3-none-any.whl", hash = "sha256:43ebb23c8a358c3d623e31064359a65f50229d0bf73ae8dfd203f49d9126ae06"},
|
||||
{file = "typer-0.12.1.tar.gz", hash = "sha256:72d218ef3c686aed9c6ff3ca25b238aee0474a1628b29c559b18b634cfdeca88"},
|
||||
{file = "typer-0.12.2-py3-none-any.whl", hash = "sha256:e1accbaa7e2b2350753acec896ac30493ac573211a8d4603e88f8356217e01f7"},
|
||||
{file = "typer-0.12.2.tar.gz", hash = "sha256:977929604fde12aeada011852ad9c64370501be6ac2eac248f3161cdc9eeb7c9"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -9268,13 +9172,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "types-redis"
|
||||
version = "4.6.0.20240311"
|
||||
version = "4.6.0.20240409"
|
||||
description = "Typing stubs for redis"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "types-redis-4.6.0.20240311.tar.gz", hash = "sha256:e049bbdff0e0a1f8e701b64636811291d21bff79bf1e7850850a44055224a85f"},
|
||||
{file = "types_redis-4.6.0.20240311-py3-none-any.whl", hash = "sha256:6b9d68a29aba1ee400c823d8e5fe88675282eb69d7211e72fe65dbe54b33daca"},
|
||||
{file = "types-redis-4.6.0.20240409.tar.gz", hash = "sha256:ce217c279581d769df992c5b76d61c65425b0a679626048e633e643868eb881b"},
|
||||
{file = "types_redis-4.6.0.20240409-py3-none-any.whl", hash = "sha256:a3b92760c49a034827a0c3825206728df4e61e981c1324099d4414335af4f52f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow"
|
||||
version = "1.0.0a9"
|
||||
version = "1.0.0a12"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Logspace <contact@logspace.ai>"]
|
||||
maintainers = [
|
||||
|
|
@ -12,7 +12,7 @@ maintainers = [
|
|||
"Otávio Anovazzi <otavio2204@gmail.com>",
|
||||
"Rodrigo Nader <rodrigo@logspace.ai>",
|
||||
]
|
||||
repository = "https://github.com/logspace-ai/langflow"
|
||||
repository = "https://github.com/langflow-ai/langflow"
|
||||
license = "MIT"
|
||||
readme = "README.md"
|
||||
keywords = ["nlp", "langchain", "openai", "gpt", "gui"]
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ services:
|
|||
name: langflow
|
||||
runtime: docker
|
||||
dockerfilePath: ./Dockerfile
|
||||
repo: https://github.com/logspace-ai/langflow
|
||||
repo: https://github.com/langflow-ai/langflow
|
||||
branch: main
|
||||
healthCheckPath: /health
|
||||
autoDeploy: false
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@
|
|||
**想定時間**: 30 分
|
||||
|
||||
## 説明
|
||||
|
||||
Langflow on AWS では、 [AWS Cloud Development Kit](https://aws.amazon.com/cdk/?nc2=type_a) (CDK) を用いて Langflow を AWS 上にデプロイする方法を学べます。
|
||||
このチュートリアルは、AWS アカウントと AWS に関する基本的な知識を有していることを前提としています。
|
||||
|
||||
|
|
@ -10,44 +11,47 @@ Langflow on AWS では、 [AWS Cloud Development Kit](https://aws.amazon.com/cdk
|
|||
|
||||
AWS CDK によって Langflow のアプリケーションをデプロイします。アプリケーションは [Amazon CloudFront](https://aws.amazon.com/cloudfront/?nc1=h_ls) を介して配信されます。CloudFront は 2 つのオリジンを有しています。1 つ目は静的な Web サイトを配信するための [Amazon Simple Storage Service](https://aws.amazon.com/s3/?nc1=h_ls) (S3)、2 つ目は バックエンドと通信するための [Application Load Balancer](https://aws.amazon.com/elasticloadbalancing/application-load-balancer/?nc1=h_ls) (ALB) です。ALB の背後には FastAPI が動作する [AWS Fargate](https://aws.amazon.com/fargate/?nc2=type_a) 、データベースの [Amazon Aurora](https://aws.amazon.com/rds/aurora/?nc2=type_a) が作成されます。
|
||||
Fargate は [Amazon Elastic Container Registry](https://aws.amazon.com/ecr/?nc1=h_ls) (ECR) に保存された Docker イメージを使用します。
|
||||
Auroraのシークレットは [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/?nc2=type_a) によって管理されます。
|
||||
Aurora のシークレットは [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/?nc2=type_a) によって管理されます。
|
||||
|
||||
# 環境構築とデプロイ方法
|
||||
|
||||
1. [AWS CloudShell](https://us-east-1.console.aws.amazon.com/cloudshell/home?region=us-east-1)を開きます。
|
||||
|
||||
1. 以下のコマンドを実行します。
|
||||
```shell
|
||||
git clone https://github.com/aws-samples/cloud9-setup-for-prototyping
|
||||
cd cloud9-setup-for-prototyping
|
||||
./bin/bootstrap
|
||||
```
|
||||
|
||||
```shell
|
||||
git clone https://github.com/aws-samples/cloud9-setup-for-prototyping
|
||||
cd cloud9-setup-for-prototyping
|
||||
./bin/bootstrap
|
||||
```
|
||||
|
||||
1. `Done!` と表示されたら [AWS Cloud9](https://us-east-1.console.aws.amazon.com/cloud9control/home?region=us-east-1#/) から `cloud9-for-prototyping` を開きます。
|
||||
|
||||
|
||||
|
||||
1. 以下のコマンドを実行します。
|
||||
```shell
|
||||
git clone https://github.com/logspace-ai/langflow.git
|
||||
cd langflow/scripts/aws
|
||||
cp .env.example .env # 環境設定を変える場合はこのファイル(.env)を編集してください。
|
||||
npm ci
|
||||
cdk bootstrap
|
||||
cdk deploy
|
||||
```
|
||||
```shell
|
||||
git clone https://github.com/langflow-ai/langflow.git
|
||||
cd langflow/scripts/aws
|
||||
cp .env.example .env # 環境設定を変える場合はこのファイル(.env)を編集してください。
|
||||
npm ci
|
||||
cdk bootstrap
|
||||
cdk deploy
|
||||
```
|
||||
1. 表示される URL にアクセスします。
|
||||
```shell
|
||||
Outputs:
|
||||
LangflowAppStack.frontendURLXXXXXX = https://XXXXXXXXXXX.cloudfront.net
|
||||
```
|
||||
```shell
|
||||
Outputs:
|
||||
LangflowAppStack.frontendURLXXXXXX = https://XXXXXXXXXXX.cloudfront.net
|
||||
```
|
||||
1. サインイン画面でユーザー名とパスワードを入力します。`.env`ファイルでユーザー名とパスワードを設定していない場合、ユーザー名は`admin`、パスワードは`123456`で設定されます。
|
||||
|
||||
|
||||
|
||||
# 環境の削除
|
||||
1. `Cloud9` で以下のコマンドを実行します。
|
||||
```shell
|
||||
bash delete-resources.sh
|
||||
```
|
||||
|
||||
1. `Cloud9` で以下のコマンドを実行します。
|
||||
|
||||
```shell
|
||||
bash delete-resources.sh
|
||||
```
|
||||
|
||||
1. [AWS CloudFormation](https://us-east-1.console.aws.amazon.com/cloudformation/home?region=us-east-1#/getting-started)を開き、`aws-cloud9-cloud9-for-prototyping-XXXX` を選択して削除します。
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -10,7 +10,7 @@ This tutorial assumes you have an AWS account and basic knowledge of AWS.
|
|||
The architecture of the application to be created:
|
||||
|
||||
Langflow is deployed using AWS CDK. The application is distributed via [Amazon CloudFront](https://aws.amazon.com/cloudfront/?nc1=h_ls), which has two origins: the first is [Amazon Simple Storage Service](https://aws.amazon.com/s3/?nc1=h_ls) (S3) for serving a static website, and the second is an [Application Load Balancer](https://aws.amazon.com/elasticloadbalancing/application-load-balancer/?nc1=h_ls) (ALB) for communicating with the backend. [AWS Fargate](https://aws.amazon.com/fargate/?nc2=type_a), where FastAPI runs and [Amazon Aurora](https://aws.amazon.com/rds/aurora/?nc2=type_a), the database, are created behind the ALB.
|
||||
Fargate uses a Docker image stored in [Amazon Elastic Container Registry](https://aws.amazon.com/ecr/?nc1=h_ls) (ECR).
|
||||
Fargate uses a Docker image stored in [Amazon Elastic Container Registry](https://aws.amazon.com/ecr/?nc1=h_ls) (ECR).
|
||||
Aurora's secret is managed by [AWS Secrets Manager](https://aws.amazon.com/secrets-manager/?nc2=type_a).
|
||||
|
||||
# How to set up your environment and deploy langflow
|
||||
|
|
@ -26,14 +26,14 @@ Aurora's secret is managed by [AWS Secrets Manager](https://aws.amazon.com/secre
|
|||
1. When you see `Done!` in Cloudshell, open `c9-for-langflow` from [AWS Cloud9](https://us-east-1.console.aws.amazon.com/cloud9control/home?region=us-east-1#/).
|
||||
|
||||
1. Run the following command in the Cloud9 terminal.
|
||||
```shell
|
||||
git clone https://github.com/logspace-ai/langflow.git
|
||||
cd langflow/scripts/aws
|
||||
cp .env.example .env # Edit this file if you need environment settings
|
||||
npm ci
|
||||
cdk bootstrap
|
||||
cdk deploy
|
||||
```
|
||||
```shell
|
||||
git clone https://github.com/langflow-ai/langflow.git
|
||||
cd langflow/scripts/aws
|
||||
cp .env.example .env # Edit this file if you need environment settings
|
||||
npm ci
|
||||
cdk bootstrap
|
||||
cdk deploy
|
||||
```
|
||||
1. Access the URL displayed.
|
||||
```shell
|
||||
Outputs:
|
||||
|
|
@ -50,4 +50,4 @@ Aurora's secret is managed by [AWS Secrets Manager](https://aws.amazon.com/secre
|
|||
```
|
||||
1. Open [AWS CloudFormation](https://us-east-1.console.aws.amazon.com/cloudformation/home?region=us-east-1#/getting-started), select `aws-cloud9-c9-for-langflow-XXXX` and delete it.
|
||||
|
||||
s
|
||||
s
|
||||
|
|
|
|||
|
|
@ -9,11 +9,6 @@ import click
|
|||
import httpx
|
||||
import typer
|
||||
from dotenv import load_dotenv
|
||||
from langflow.main import setup_app
|
||||
from langflow.services.database.utils import session_getter
|
||||
from langflow.services.deps import get_db_service, get_settings_service
|
||||
from langflow.services.utils import initialize_services, initialize_settings_service
|
||||
from langflow.utils.logger import configure, logger
|
||||
from multiprocess import Process, cpu_count # type: ignore
|
||||
from packaging import version as pkg_version
|
||||
from rich import box
|
||||
|
|
@ -22,6 +17,12 @@ from rich.console import Console
|
|||
from rich.panel import Panel
|
||||
from rich.table import Table
|
||||
|
||||
from langflow.main import setup_app
|
||||
from langflow.services.database.utils import session_getter
|
||||
from langflow.services.deps import get_db_service, get_settings_service
|
||||
from langflow.services.utils import initialize_services, initialize_settings_service
|
||||
from langflow.utils.logger import configure, logger
|
||||
|
||||
console = Console()
|
||||
|
||||
app = typer.Typer(no_args_is_help=True)
|
||||
|
|
@ -99,12 +100,8 @@ def update_settings(
|
|||
|
||||
@app.command()
|
||||
def run(
|
||||
host: str = typer.Option(
|
||||
"127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"
|
||||
),
|
||||
workers: int = typer.Option(
|
||||
1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"
|
||||
),
|
||||
host: str = typer.Option("127.0.0.1", help="Host to bind the server to.", envvar="LANGFLOW_HOST"),
|
||||
workers: int = typer.Option(1, help="Number of worker processes.", envvar="LANGFLOW_WORKERS"),
|
||||
timeout: int = typer.Option(300, help="Worker timeout in seconds."),
|
||||
port: int = typer.Option(7860, help="Port to listen on.", envvar="LANGFLOW_PORT"),
|
||||
components_path: Optional[Path] = typer.Option(
|
||||
|
|
@ -112,19 +109,11 @@ def run(
|
|||
help="Path to the directory containing custom components.",
|
||||
envvar="LANGFLOW_COMPONENTS_PATH",
|
||||
),
|
||||
config: str = typer.Option(
|
||||
Path(__file__).parent / "config.yaml", help="Path to the configuration file."
|
||||
),
|
||||
config: str = typer.Option(Path(__file__).parent / "config.yaml", help="Path to the configuration file."),
|
||||
# .env file param
|
||||
env_file: Path = typer.Option(
|
||||
None, help="Path to the .env file containing environment variables."
|
||||
),
|
||||
log_level: str = typer.Option(
|
||||
"critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"
|
||||
),
|
||||
log_file: Path = typer.Option(
|
||||
"logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"
|
||||
),
|
||||
env_file: Path = typer.Option(None, help="Path to the .env file containing environment variables."),
|
||||
log_level: str = typer.Option("critical", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"),
|
||||
log_file: Path = typer.Option("logs/langflow.log", help="Path to the log file.", envvar="LANGFLOW_LOG_FILE"),
|
||||
cache: Optional[str] = typer.Option(
|
||||
envvar="LANGFLOW_LANGCHAIN_CACHE",
|
||||
help="Type of cache to use. (InMemoryCache, SQLiteCache)",
|
||||
|
|
@ -200,7 +189,7 @@ def run(
|
|||
run_on_windows(host, port, log_level, options, app)
|
||||
else:
|
||||
# Run using gunicorn on Linux
|
||||
run_on_mac_or_linux(host, port, log_level, options, app, open_browser)
|
||||
run_on_mac_or_linux(host, port, log_level, options, app)
|
||||
if open_browser:
|
||||
click.launch(f"http://{host}:{port}")
|
||||
|
||||
|
|
@ -218,9 +207,7 @@ def wait_for_server_ready(host, port):
|
|||
|
||||
|
||||
def run_on_mac_or_linux(host, port, log_level, options, app):
|
||||
webapp_process = Process(
|
||||
target=run_langflow, args=(host, port, log_level, options, app)
|
||||
)
|
||||
webapp_process = Process(target=run_langflow, args=(host, port, log_level, options, app))
|
||||
webapp_process.start()
|
||||
wait_for_server_ready(host, port)
|
||||
|
||||
|
|
@ -316,9 +303,7 @@ def build_new_version_notice(current_version: str, package_name: str):
|
|||
f"A new pre-release version of {package_name} is available: {latest_version}",
|
||||
)
|
||||
else:
|
||||
latest_version = httpx.get(f"https://pypi.org/pypi/{package_name}/json").json()[
|
||||
"info"
|
||||
]["version"]
|
||||
latest_version = httpx.get(f"https://pypi.org/pypi/{package_name}/json").json()["info"]["version"]
|
||||
if not version_is_prerelease(latest_version):
|
||||
return (
|
||||
False,
|
||||
|
|
@ -342,9 +327,7 @@ def fetch_latest_version(package_name: str, include_prerelease: bool) -> str:
|
|||
|
||||
def build_version_notice(current_version: str, package_name: str) -> str:
|
||||
latest_version = fetch_latest_version(package_name, is_prerelease(current_version))
|
||||
if latest_version and pkg_version.parse(current_version) < pkg_version.parse(
|
||||
latest_version
|
||||
):
|
||||
if latest_version and pkg_version.parse(current_version) < pkg_version.parse(latest_version):
|
||||
release_type = "pre-release" if is_prerelease(latest_version) else "version"
|
||||
return f"A new {release_type} of {package_name} is available: {latest_version}"
|
||||
return ""
|
||||
|
|
@ -393,9 +376,7 @@ def print_banner(host: str, port: int):
|
|||
from importlib import metadata
|
||||
|
||||
langflow_base_version = metadata.version("langflow-base")
|
||||
is_pre_release |= is_prerelease(
|
||||
langflow_base_version
|
||||
) # Update pre-release status
|
||||
is_pre_release |= is_prerelease(langflow_base_version) # Update pre-release status
|
||||
notice = build_version_notice(langflow_base_version, "langflow-base")
|
||||
notice = stylize_text(notice, "langflow-base", is_pre_release)
|
||||
if notice:
|
||||
|
|
@ -414,12 +395,10 @@ def print_banner(host: str, port: int):
|
|||
notices.append(f"Run '{pip_command}' to update.")
|
||||
|
||||
styled_notices = [f"[bold]{notice}[/bold]" for notice in notices if notice]
|
||||
styled_package_name = stylize_text(
|
||||
package_name, package_name, any("pre-release" in notice for notice in notices)
|
||||
)
|
||||
styled_package_name = stylize_text(package_name, package_name, any("pre-release" in notice for notice in notices))
|
||||
|
||||
title = f"[bold]Welcome to :chains: {styled_package_name}[/bold]\n"
|
||||
info_text = "Collaborate, and contribute at our [bold][link=https://github.com/logspace-ai/langflow]GitHub Repo[/link][/bold] :rocket:"
|
||||
info_text = "Collaborate, and contribute at our [bold][link=https://github.com/langflow-ai/langflow]GitHub Repo[/link][/bold] :rocket:"
|
||||
access_link = f"Access [link=http://{host}:{port}]http://{host}:{port}[/link]"
|
||||
|
||||
panel_content = "\n\n".join([title, *styled_notices, info_text, access_link])
|
||||
|
|
@ -459,12 +438,8 @@ def run_langflow(host, port, log_level, options, app):
|
|||
@app.command()
|
||||
def superuser(
|
||||
username: str = typer.Option(..., prompt=True, help="Username for the superuser."),
|
||||
password: str = typer.Option(
|
||||
..., prompt=True, hide_input=True, help="Password for the superuser."
|
||||
),
|
||||
log_level: str = typer.Option(
|
||||
"error", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"
|
||||
),
|
||||
password: str = typer.Option(..., prompt=True, hide_input=True, help="Password for the superuser."),
|
||||
log_level: str = typer.Option("error", help="Logging level.", envvar="LANGFLOW_LOG_LEVEL"),
|
||||
):
|
||||
"""
|
||||
Create a superuser.
|
||||
|
|
@ -491,11 +466,23 @@ def superuser(
|
|||
|
||||
|
||||
@app.command()
|
||||
def migration(test: bool = typer.Option(True, help="Run migrations in test mode.")):
|
||||
def migration(
|
||||
test: bool = typer.Option(True, help="Run migrations in test mode."),
|
||||
fix: bool = typer.Option(
|
||||
False,
|
||||
help="Fix migrations. This is a destructive operation, and should only be used if you know what you are doing.",
|
||||
),
|
||||
):
|
||||
"""
|
||||
Run or test migrations.
|
||||
"""
|
||||
initialize_services()
|
||||
if fix:
|
||||
if not typer.confirm(
|
||||
"This will delete all data necessary to fix migrations. Are you sure you want to continue?"
|
||||
):
|
||||
raise typer.Abort()
|
||||
|
||||
initialize_services(fix_migration=fix)
|
||||
db_service = get_db_service()
|
||||
if not test:
|
||||
db_service.run_migrations()
|
||||
|
|
|
|||
|
|
@ -24,4 +24,5 @@ FIELD_FORMAT_ATTRIBUTES = [
|
|||
"real_time_refresh",
|
||||
"refresh_button",
|
||||
"refresh_button_text",
|
||||
"options",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -10,7 +10,26 @@ from langflow.schema.schema import Record
|
|||
|
||||
# Types of files that can be read simply by file.read()
|
||||
# and have 100% to be completely readable
|
||||
TEXT_FILE_TYPES = ["txt", "md", "mdx", "csv", "json", "yaml", "yml", "xml", "html", "htm", "pdf", "docx"]
|
||||
TEXT_FILE_TYPES = [
|
||||
"txt",
|
||||
"md",
|
||||
"mdx",
|
||||
"csv",
|
||||
"json",
|
||||
"yaml",
|
||||
"yml",
|
||||
"xml",
|
||||
"html",
|
||||
"htm",
|
||||
"pdf",
|
||||
"docx",
|
||||
"py",
|
||||
"sh",
|
||||
"sql",
|
||||
"js",
|
||||
"ts",
|
||||
"tsx",
|
||||
]
|
||||
|
||||
|
||||
def is_hidden(path: Path) -> bool:
|
||||
|
|
|
|||
0
src/backend/base/langflow/base/tools/__init__.py
Normal file
0
src/backend/base/langflow/base/tools/__init__.py
Normal file
23
src/backend/base/langflow/base/tools/base.py
Normal file
23
src/backend/base/langflow/base/tools/base.py
Normal file
|
|
@ -0,0 +1,23 @@
|
|||
from langflow.field_typing import Tool
|
||||
|
||||
|
||||
def build_status_from_tool(tool: Tool):
|
||||
"""
|
||||
Builds a status string representation of a tool.
|
||||
|
||||
Args:
|
||||
tool (Tool): The tool object to build the status for.
|
||||
|
||||
Returns:
|
||||
str: The status string representation of the tool, including its name, description, and arguments (if any).
|
||||
"""
|
||||
description_repr = repr(tool.description).strip("'")
|
||||
args_str = "\n".join(
|
||||
[
|
||||
f"- {arg_name}: {arg_data['description']}"
|
||||
for arg_name, arg_data in tool.args.items()
|
||||
if "description" in arg_data
|
||||
]
|
||||
)
|
||||
status = f"Name: {tool.name}\nDescription: {description_repr}"
|
||||
return status + (f"\nArguments:\n{args_str}" if args_str else "")
|
||||
|
|
@ -4,7 +4,7 @@ from langchain.agents import create_xml_agent
|
|||
from langchain_core.prompts import PromptTemplate
|
||||
|
||||
from langflow.base.agents.agent import LCAgentComponent
|
||||
from langflow.field_typing import BaseLLM, BaseMemory, Text, Tool
|
||||
from langflow.field_typing import BaseLanguageModel, BaseMemory, Text, Tool
|
||||
|
||||
|
||||
class XMLAgentComponent(LCAgentComponent):
|
||||
|
|
@ -66,7 +66,7 @@ class XMLAgentComponent(LCAgentComponent):
|
|||
async def build(
|
||||
self,
|
||||
input_value: str,
|
||||
llm: BaseLLM,
|
||||
llm: BaseLanguageModel,
|
||||
tools: List[Tool],
|
||||
prompt: str,
|
||||
memory: Optional[BaseMemory] = None,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langchain_community.llms.bedrock import Bedrock
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
|
@ -46,7 +45,7 @@ class AmazonBedrockComponent(CustomComponent):
|
|||
endpoint_url: Optional[str] = None,
|
||||
streaming: bool = False,
|
||||
cache: Optional[bool] = None,
|
||||
) -> BaseLLM:
|
||||
) -> BaseLanguageModel:
|
||||
try:
|
||||
output = Bedrock(
|
||||
credentials_profile_name=credentials_profile_name,
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain.llms.base import BaseLanguageModel
|
||||
from langchain_community.chat_models.anthropic import ChatAnthropic
|
||||
from langchain_anthropic import ChatAnthropic
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
||||
|
||||
class AnthropicLLM(CustomComponent):
|
||||
display_name: str = "AnthropicLLM"
|
||||
class ChatAntropicSpecsComponent(CustomComponent):
|
||||
display_name: str = "Anthropic"
|
||||
description: str = "Anthropic Chat&Completion large language models."
|
||||
icon = "Anthropic"
|
||||
|
||||
|
|
|
|||
|
|
@ -1,49 +0,0 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain_community.llms.anthropic import Anthropic
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow.field_typing import BaseLanguageModel, NestedDict
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
||||
|
||||
class AnthropicComponent(CustomComponent):
|
||||
display_name = "Anthropic"
|
||||
description = "Anthropic large language models."
|
||||
icon = "Anthropic"
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"anthropic_api_key": {
|
||||
"display_name": "Anthropic API Key",
|
||||
"type": str,
|
||||
"password": True,
|
||||
},
|
||||
"anthropic_api_url": {
|
||||
"display_name": "Anthropic API URL",
|
||||
"type": str,
|
||||
},
|
||||
"model_kwargs": {
|
||||
"display_name": "Model Kwargs",
|
||||
"field_type": "NestedDict",
|
||||
"advanced": True,
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
anthropic_api_key: str,
|
||||
anthropic_api_url: str,
|
||||
model_kwargs: NestedDict = {},
|
||||
temperature: Optional[float] = None,
|
||||
) -> BaseLanguageModel:
|
||||
return Anthropic(
|
||||
anthropic_api_key=SecretStr(anthropic_api_key),
|
||||
anthropic_api_url=anthropic_api_url,
|
||||
model_kwargs=model_kwargs,
|
||||
temperature=temperature,
|
||||
)
|
||||
|
|
@ -1,9 +1,9 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langchain_community.chat_models.baidu_qianfan_endpoint import QianfanChatEndpoint
|
||||
from pydantic.v1 import SecretStr
|
||||
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
||||
|
||||
|
|
@ -79,7 +79,7 @@ class QianfanChatEndpointComponent(CustomComponent):
|
|||
temperature: Optional[float] = None,
|
||||
penalty_score: Optional[float] = None,
|
||||
endpoint: Optional[str] = None,
|
||||
) -> BaseLLM:
|
||||
) -> BaseLanguageModel:
|
||||
try:
|
||||
output = QianfanChatEndpoint( # type: ignore
|
||||
model=model,
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint
|
||||
from langchain.llms.base import BaseLLM
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
|
||||
|
||||
class QianfanLLMEndpointComponent(CustomComponent):
|
||||
|
|
@ -78,7 +78,7 @@ class QianfanLLMEndpointComponent(CustomComponent):
|
|||
temperature: Optional[float] = None,
|
||||
penalty_score: Optional[float] = None,
|
||||
endpoint: Optional[str] = None,
|
||||
) -> BaseLLM:
|
||||
) -> BaseLanguageModel:
|
||||
try:
|
||||
output = QianfanLLMEndpoint( # type: ignore
|
||||
model=model,
|
||||
|
|
|
|||
|
|
@ -1,67 +1,89 @@
|
|||
from typing import Callable, Optional, Union
|
||||
from typing import Optional
|
||||
|
||||
from langchain_community.chat_models.anthropic import ChatAnthropic
|
||||
from langchain_anthropic import ChatAnthropic
|
||||
from pydantic.v1.types import SecretStr
|
||||
|
||||
|
||||
from langflow.custom import CustomComponent
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
|
||||
|
||||
class ChatAnthropicComponent(CustomComponent):
|
||||
display_name = "ChatAnthropic"
|
||||
description = "`Anthropic` chat large language models."
|
||||
documentation = "https://python.langchain.com/docs/modules/model_io/models/chat/integrations/anthropic"
|
||||
class AnthropicLLM(CustomComponent):
|
||||
display_name: str = "Anthropic"
|
||||
description: str = "Generate text using Anthropic Chat&Completion LLMs."
|
||||
icon = "Anthropic"
|
||||
|
||||
field_order = [
|
||||
"model",
|
||||
"anthropic_api_key",
|
||||
"max_tokens",
|
||||
"temperature",
|
||||
"anthropic_api_url",
|
||||
]
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"model": {
|
||||
"display_name": "Model Name",
|
||||
"options": [
|
||||
"claude-3-opus-20240229",
|
||||
"claude-3-sonnet-20240229",
|
||||
"claude-3-haiku-20240307",
|
||||
"claude-2.1",
|
||||
"claude-2.0",
|
||||
"claude-instant-1.2",
|
||||
"claude-instant-1",
|
||||
],
|
||||
"info": "Name of the model to use.",
|
||||
"required": True,
|
||||
"value": "claude-3-opus-20240229",
|
||||
},
|
||||
"anthropic_api_key": {
|
||||
"display_name": "Anthropic API Key",
|
||||
"field_type": "str",
|
||||
"required": True,
|
||||
"password": True,
|
||||
},
|
||||
"model_kwargs": {
|
||||
"display_name": "Model Kwargs",
|
||||
"field_type": "dict",
|
||||
"advanced": True,
|
||||
},
|
||||
"model_name": {
|
||||
"display_name": "Model Name",
|
||||
"field_type": "str",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"options": ["claude-3-opus-20240229", "claude-3-sonnet-20240229", "claude-3-haiku-20240307"],
|
||||
},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"info": "Your Anthropic API key.",
|
||||
},
|
||||
"max_tokens": {
|
||||
"display_name": "Max Tokens",
|
||||
"field_type": "int",
|
||||
"advanced": False,
|
||||
"required": False,
|
||||
"advanced": True,
|
||||
"value": 256,
|
||||
},
|
||||
"top_k": {"display_name": "Top K", "field_type": "int", "advanced": True},
|
||||
"top_p": {"display_name": "Top P", "field_type": "float", "advanced": True},
|
||||
"temperature": {
|
||||
"display_name": "Temperature",
|
||||
"field_type": "float",
|
||||
"value": 0.1,
|
||||
},
|
||||
"anthropic_api_url": {
|
||||
"display_name": "Anthropic API URL",
|
||||
"advanced": True,
|
||||
"info": "Endpoint of the Anthropic API. Defaults to 'https://api.anthropic.com' if not specified.",
|
||||
},
|
||||
"code": {"show": False},
|
||||
}
|
||||
|
||||
def build(
|
||||
self,
|
||||
anthropic_api_key: str,
|
||||
model_kwargs: dict = {},
|
||||
model_name: str = "claude-3-opus-20240229",
|
||||
model: str,
|
||||
anthropic_api_key: Optional[str] = None,
|
||||
max_tokens: Optional[int] = None,
|
||||
temperature: Optional[float] = None,
|
||||
max_tokens: Optional[int] = 1024,
|
||||
top_k: Optional[int] = None,
|
||||
top_p: Optional[float] = None,
|
||||
) -> Union[BaseLanguageModel, Callable]:
|
||||
return ChatAnthropic(
|
||||
anthropic_api_key=SecretStr(anthropic_api_key),
|
||||
model_kwargs=model_kwargs,
|
||||
model_name=model_name,
|
||||
temperature=temperature,
|
||||
max_tokens=max_tokens, # type: ignore
|
||||
top_k=top_k,
|
||||
top_p=top_p,
|
||||
)
|
||||
anthropic_api_url: Optional[str] = None,
|
||||
) -> BaseLanguageModel:
|
||||
# Set default API endpoint if not provided
|
||||
if not anthropic_api_url:
|
||||
anthropic_api_url = "https://api.anthropic.com"
|
||||
|
||||
try:
|
||||
output = ChatAnthropic(
|
||||
model_name=model,
|
||||
anthropic_api_key=(SecretStr(anthropic_api_key) if anthropic_api_key else None),
|
||||
max_tokens_to_sample=max_tokens, # type: ignore
|
||||
temperature=temperature,
|
||||
anthropic_api_url=anthropic_api_url,
|
||||
)
|
||||
except Exception as e:
|
||||
raise ValueError("Could not connect to Anthropic API.") from e
|
||||
|
||||
return output
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from typing import Any, Callable, Dict, Optional, Union
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
from langchain_community.chat_models.litellm import ChatLiteLLM, ChatLiteLLMException
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
|
|
|
|||
|
|
@ -1,9 +1,9 @@
|
|||
from typing import Optional, Union
|
||||
from typing import Optional
|
||||
|
||||
from langchain.llms import BaseLLM
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langchain_community.chat_models.openai import ChatOpenAI
|
||||
|
||||
from langflow.field_typing import BaseLanguageModel, NestedDict
|
||||
from langflow.field_typing import NestedDict
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
||||
|
||||
|
|
@ -68,7 +68,7 @@ class ChatOpenAIComponent(CustomComponent):
|
|||
openai_api_base: Optional[str] = None,
|
||||
openai_api_key: Optional[str] = None,
|
||||
temperature: float = 0.7,
|
||||
) -> Union[BaseLanguageModel, BaseLLM]:
|
||||
) -> BaseLanguageModel:
|
||||
if not openai_api_base:
|
||||
openai_api_base = "https://api.openai.com/v1"
|
||||
return ChatOpenAI(
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
from typing import List, Optional, Union
|
||||
from typing import List, Optional
|
||||
|
||||
from langchain.llms import BaseLLM
|
||||
from langchain_community.chat_models.vertexai import ChatVertexAI
|
||||
from langchain_core.messages.base import BaseMessage
|
||||
|
||||
|
|
@ -74,7 +73,7 @@ class ChatVertexAIComponent(CustomComponent):
|
|||
top_k: int = 40,
|
||||
top_p: float = 0.95,
|
||||
verbose: bool = False,
|
||||
) -> Union[BaseLanguageModel, BaseLLM]:
|
||||
) -> BaseLanguageModel:
|
||||
return ChatVertexAI(
|
||||
credentials=credentials,
|
||||
examples=examples,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
from typing import Optional
|
||||
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langchain.llms.huggingface_endpoint import HuggingFaceEndpoint
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
|
@ -32,7 +32,7 @@ class HuggingFaceEndpointsComponent(CustomComponent):
|
|||
task: str = "text2text-generation",
|
||||
huggingfacehub_api_token: Optional[str] = None,
|
||||
model_kwargs: Optional[dict] = None,
|
||||
) -> BaseLLM:
|
||||
) -> BaseLanguageModel:
|
||||
try:
|
||||
output = HuggingFaceEndpoint( # type: ignore
|
||||
endpoint_url=endpoint_url,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
from typing import List, Optional
|
||||
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langchain_community.llms.ollama import Ollama
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
|
@ -118,7 +118,7 @@ class OllamaLLM(CustomComponent):
|
|||
tfs_z: Optional[float] = None,
|
||||
top_k: Optional[int] = None,
|
||||
top_p: Optional[int] = None,
|
||||
) -> BaseLLM:
|
||||
) -> BaseLanguageModel:
|
||||
if not base_url:
|
||||
base_url = "http://localhost:11434"
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
from typing import Callable, Dict, Optional, Union
|
||||
from typing import Dict, Optional
|
||||
|
||||
from langchain.llms import BaseLLM
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langchain_community.llms.vertexai import VertexAI
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
|
@ -129,7 +129,7 @@ class VertexAIComponent(CustomComponent):
|
|||
top_p: float = 0.95,
|
||||
tuned_model_name: Optional[str] = None,
|
||||
verbose: bool = False,
|
||||
) -> Union[BaseLLM, Callable]:
|
||||
) -> BaseLanguageModel:
|
||||
return VertexAI(
|
||||
credentials=credentials,
|
||||
location=location,
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
from .AmazonBedrockSpecs import AmazonBedrockComponent
|
||||
from .AnthropicLLMSpecs import AnthropicLLM
|
||||
from .AnthropicSpecs import AnthropicComponent
|
||||
from .AnthropicLLMSpecs import ChatAntropicSpecsComponent
|
||||
|
||||
from .AzureChatOpenAISpecs import AzureChatOpenAISpecsComponent
|
||||
from .BaiduQianfanChatEndpointsSpecs import QianfanChatEndpointComponent
|
||||
from .BaiduQianfanLLMEndpointsSpecs import QianfanLLMEndpointComponent
|
||||
|
|
@ -17,8 +17,7 @@ from .VertexAISpecs import VertexAIComponent
|
|||
|
||||
__all__ = [
|
||||
"AmazonBedrockComponent",
|
||||
"AnthropicLLM",
|
||||
"AnthropicComponent",
|
||||
"ChatAntropicSpecsComponent",
|
||||
"AzureChatOpenAISpecsComponent",
|
||||
"QianfanChatEndpointComponent",
|
||||
"QianfanLLMEndpointComponent",
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
from typing import Callable, Optional, Union
|
||||
from typing import Optional
|
||||
|
||||
from langchain.retrievers import MultiQueryRetriever
|
||||
|
||||
from langflow.field_typing import BaseLLM, BaseRetriever, PromptTemplate
|
||||
from langflow.field_typing import BaseRetriever, PromptTemplate, BaseLanguageModel
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
||||
|
||||
|
|
@ -39,11 +39,11 @@ class MultiQueryRetrieverComponent(CustomComponent):
|
|||
|
||||
def build(
|
||||
self,
|
||||
llm: BaseLLM,
|
||||
llm: BaseLanguageModel,
|
||||
retriever: BaseRetriever,
|
||||
prompt: Optional[PromptTemplate] = None,
|
||||
parser_key: str = "lines",
|
||||
) -> Union[Callable, MultiQueryRetriever]:
|
||||
) -> MultiQueryRetriever:
|
||||
if not prompt:
|
||||
return MultiQueryRetriever.from_llm(llm=llm, retriever=retriever, parser_key=parser_key)
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -1,5 +1,3 @@
|
|||
from typing import Callable, Union
|
||||
|
||||
from langchain.agents.agent_toolkits.vectorstore.toolkit import VectorStoreInfo
|
||||
from langchain_community.vectorstores import VectorStore
|
||||
|
||||
|
|
@ -22,5 +20,5 @@ class VectorStoreInfoComponent(CustomComponent):
|
|||
vectorstore: VectorStore,
|
||||
description: str,
|
||||
name: str,
|
||||
) -> Union[VectorStoreInfo, Callable]:
|
||||
) -> VectorStoreInfo:
|
||||
return VectorStoreInfo(vectorstore=vectorstore, description=description, name=name)
|
||||
|
|
|
|||
68
src/backend/base/langflow/components/tools/PythonREPLTool.py
Normal file
68
src/backend/base/langflow/components/tools/PythonREPLTool.py
Normal file
|
|
@ -0,0 +1,68 @@
|
|||
import importlib
|
||||
|
||||
from langchain.agents import Tool
|
||||
from langchain_experimental.utilities import PythonREPL
|
||||
|
||||
from langflow.base.tools.base import build_status_from_tool
|
||||
from langflow.custom import CustomComponent
|
||||
|
||||
|
||||
class PythonREPLToolComponent(CustomComponent):
|
||||
display_name = "Python REPL Tool"
|
||||
description = "A tool for running Python code in a REPL environment."
|
||||
|
||||
def build_config(self):
|
||||
return {
|
||||
"name": {"display_name": "Name", "info": "The name of the tool."},
|
||||
"description": {"display_name": "Description", "info": "A description of the tool."},
|
||||
"global_imports": {
|
||||
"display_name": "Global Imports",
|
||||
"info": "A list of modules to import globally, e.g. ['math', 'numpy'].",
|
||||
},
|
||||
}
|
||||
|
||||
def get_globals(self, globals: list[str]) -> dict:
|
||||
"""
|
||||
Retrieves the global variables from the specified modules.
|
||||
|
||||
Args:
|
||||
globals (list[str]): A list of module names.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the global variables from the specified modules.
|
||||
"""
|
||||
global_dict = {}
|
||||
for module in globals:
|
||||
try:
|
||||
module = importlib.import_module(module)
|
||||
global_dict[module.__name__] = module
|
||||
except ImportError:
|
||||
print(f"Could not import module {module}")
|
||||
return global_dict
|
||||
|
||||
def build(
|
||||
self,
|
||||
name: str = "python_repl",
|
||||
description: str = "A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.",
|
||||
global_imports: list[str] = ["math"],
|
||||
) -> Tool:
|
||||
"""
|
||||
Builds a Python REPL tool.
|
||||
|
||||
Args:
|
||||
name (str, optional): The name of the tool. Defaults to "python_repl".
|
||||
description (str, optional): The description of the tool. Defaults to "A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`. ".
|
||||
global_imports (list[str], optional): A list of global imports to be available in the Python REPL. Defaults to ["math"].
|
||||
|
||||
Returns:
|
||||
Tool: The built Python REPL tool.
|
||||
"""
|
||||
_globals = self.get_globals(global_imports)
|
||||
python_repl = PythonREPL(_globals=_globals)
|
||||
tool = Tool(
|
||||
name=name,
|
||||
description=description,
|
||||
func=python_repl.run,
|
||||
)
|
||||
self.status = build_status_from_tool(tool)
|
||||
return tool
|
||||
|
|
@ -1,5 +1,7 @@
|
|||
from .PythonREPLTool import PythonREPLToolComponent
|
||||
from .RetrieverTool import RetrieverToolComponent
|
||||
from .SearchAPITool import SearchApiToolComponent
|
||||
from .SearchApi import SearchApi
|
||||
from .SearchAPITool import SearchApiToolComponent
|
||||
|
||||
__all__ = ["RetrieverToolComponent", "SearchApiToolComponent", "SearchApi"]
|
||||
|
||||
__all__ = ["RetrieverToolComponent", "SearchApiToolComponent", "SearchApi", "PythonREPLToolComponent"]
|
||||
|
|
|
|||
|
|
@ -249,7 +249,10 @@ class Graph:
|
|||
vertex.update_raw_params({"session_id": session_id})
|
||||
# Process the graph
|
||||
try:
|
||||
await self.process()
|
||||
start_component_id = next(
|
||||
(vertex_id for vertex_id in self._is_input_vertices if "chat" in vertex_id.lower()), None
|
||||
)
|
||||
await self.process(start_component_id=start_component_id)
|
||||
self.increment_run_count()
|
||||
except Exception as exc:
|
||||
logger.exception(exc)
|
||||
|
|
@ -345,7 +348,7 @@ class Graph:
|
|||
if types is None:
|
||||
types = []
|
||||
for _ in range(len(inputs) - len(types)):
|
||||
types.append("any")
|
||||
types.append("chat") # default to chat
|
||||
for run_inputs, components, input_type in zip(inputs, inputs_components, types):
|
||||
run_outputs = await self._run(
|
||||
inputs=run_inputs,
|
||||
|
|
@ -733,8 +736,10 @@ class Graph:
|
|||
vertices.append(vertex)
|
||||
return vertices
|
||||
|
||||
async def process(self) -> "Graph":
|
||||
async def process(self, start_component_id: Optional[str] = None) -> "Graph":
|
||||
"""Processes the graph with vertices in each layer run in parallel."""
|
||||
|
||||
self.sort_vertices(start_component_id=start_component_id)
|
||||
vertices_layers = self.sorted_vertices_layers
|
||||
vertex_task_run_count: Dict[str, int] = {}
|
||||
for layer_index, layer in enumerate(vertices_layers):
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
import ast
|
||||
import asyncio
|
||||
import inspect
|
||||
import os
|
||||
import types
|
||||
from enum import Enum
|
||||
from typing import TYPE_CHECKING, Any, AsyncIterator, Callable, Dict, Iterator, List, Optional
|
||||
import os
|
||||
|
||||
from loguru import logger
|
||||
|
||||
|
|
@ -315,7 +315,8 @@ class Vertex:
|
|||
raise e
|
||||
params[field_name] = full_path
|
||||
elif field.get("required"):
|
||||
raise ValueError(f"File path not found for {self.display_name}")
|
||||
field_display_name = field.get("display_name")
|
||||
raise ValueError(f"File path not found for {field_display_name} in component {self.display_name}")
|
||||
elif field.get("type") in DIRECT_TYPES and params.get(field_name) is None:
|
||||
val = field.get("value")
|
||||
if field.get("type") == "code":
|
||||
|
|
|
|||
|
|
@ -84,7 +84,7 @@ def log_node_changes(node_changes_log):
|
|||
logger.debug("\n".join(formatted_messages))
|
||||
|
||||
|
||||
def load_starter_projects():
|
||||
def load_starter_projects() -> list[tuple[Path, dict]]:
|
||||
starter_projects = []
|
||||
folder = Path(__file__).parent / "starter_projects"
|
||||
for file in folder.glob("*.json"):
|
||||
|
|
|
|||
|
|
@ -882,7 +882,7 @@
|
|||
}
|
||||
},
|
||||
"description": "This flow will get you experimenting with the basics of the UI, the Chat and the Prompt component. \n\nTry changing the Template in it to see how the model behaves. \nYou can change it to this and a Text Input into the `type_of_person` variable : \"Answer the user as if you were a pirate.\n\nUser: {user_input}\n\nAnswer: \" ",
|
||||
"name": "Basic Prompting (Hello, world!)",
|
||||
"name": "Basic Prompting (Hello, World)",
|
||||
"last_tested_version": "1.0.0a4",
|
||||
"is_component": false
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -245,7 +245,7 @@ def apply_tweaks(node: Dict[str, Any], node_tweaks: Dict[str, Any]) -> None:
|
|||
logger.warning(f"Node {node.get('id')} does not have a tweak named {tweak_name}")
|
||||
continue
|
||||
if tweak_name in template_data:
|
||||
key = tweak_name if tweak_name == "file_path" else "value"
|
||||
key = "file_path" if template_data[tweak_name]["type"] == "file" else "value"
|
||||
template_data[tweak_name][key] = tweak_value
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -35,11 +35,7 @@ class LangflowApplication(BaseApplication):
|
|||
super().__init__()
|
||||
|
||||
def load_config(self):
|
||||
config = {
|
||||
key: value
|
||||
for key, value in self.options.items()
|
||||
if key in self.cfg.settings and value is not None
|
||||
}
|
||||
config = {key: value for key, value in self.options.items() if key in self.cfg.settings and value is not None}
|
||||
for key, value in config.items():
|
||||
self.cfg.set(key.lower(), value)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
from datetime import datetime
|
||||
import time
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
|
|
@ -37,10 +36,7 @@ class DatabaseService(Service):
|
|||
def _create_engine(self) -> "Engine":
|
||||
"""Create the engine for the database."""
|
||||
settings_service = get_settings_service()
|
||||
if (
|
||||
settings_service.settings.DATABASE_URL
|
||||
and settings_service.settings.DATABASE_URL.startswith("sqlite")
|
||||
):
|
||||
if settings_service.settings.DATABASE_URL and settings_service.settings.DATABASE_URL.startswith("sqlite"):
|
||||
connect_args = {"check_same_thread": False}
|
||||
else:
|
||||
connect_args = {}
|
||||
|
|
@ -52,9 +48,7 @@ class DatabaseService(Service):
|
|||
|
||||
def __exit__(self, exc_type, exc_value, traceback):
|
||||
if exc_type is not None: # If an exception has been raised
|
||||
logger.error(
|
||||
f"Session rollback because of exception: {exc_type.__name__} {exc_value}"
|
||||
)
|
||||
logger.error(f"Session rollback because of exception: {exc_type.__name__} {exc_value}")
|
||||
self._session.rollback()
|
||||
else:
|
||||
self._session.commit()
|
||||
|
|
@ -71,9 +65,7 @@ class DatabaseService(Service):
|
|||
settings_service = get_settings_service()
|
||||
if settings_service.auth_settings.AUTO_LOGIN:
|
||||
with Session(self.engine) as session:
|
||||
flows = session.exec(
|
||||
select(models.Flow).where(models.Flow.user_id is None)
|
||||
).all()
|
||||
flows = session.exec(select(models.Flow).where(models.Flow.user_id is None)).all()
|
||||
if flows:
|
||||
logger.debug("Migrating flows to default superuser")
|
||||
username = settings_service.auth_settings.SUPERUSER
|
||||
|
|
@ -103,9 +95,7 @@ class DatabaseService(Service):
|
|||
expected_columns = list(model.model_fields.keys())
|
||||
|
||||
try:
|
||||
available_columns = [
|
||||
col["name"] for col in inspector.get_columns(table)
|
||||
]
|
||||
available_columns = [col["name"] for col in inspector.get_columns(table)]
|
||||
except sa.exc.NoSuchTableError:
|
||||
logger.debug(f"Missing table: {table}")
|
||||
return False
|
||||
|
|
@ -169,9 +159,7 @@ class DatabaseService(Service):
|
|||
buffer.write(f"{datetime.now().isoformat()}: Checking migrations\n")
|
||||
command.check(alembic_cfg)
|
||||
except Exception as exc:
|
||||
if isinstance(
|
||||
exc, (util.exc.CommandError, util.exc.AutogenerateDiffsDetected)
|
||||
):
|
||||
if isinstance(exc, (util.exc.CommandError, util.exc.AutogenerateDiffsDetected)):
|
||||
command.upgrade(alembic_cfg, "head")
|
||||
time.sleep(3)
|
||||
|
||||
|
|
@ -208,10 +196,7 @@ class DatabaseService(Service):
|
|||
# We will check that all models are in the database
|
||||
# and that the database is up to date with all columns
|
||||
sql_models = [models.Flow, models.User, models.ApiKey]
|
||||
return [
|
||||
TableResults(sql_model.__tablename__, self.check_table(sql_model))
|
||||
for sql_model in sql_models
|
||||
]
|
||||
return [TableResults(sql_model.__tablename__, self.check_table(sql_model)) for sql_model in sql_models]
|
||||
|
||||
def check_table(self, model):
|
||||
results = []
|
||||
|
|
@ -220,9 +205,7 @@ class DatabaseService(Service):
|
|||
expected_columns = list(model.__fields__.keys())
|
||||
available_columns = []
|
||||
try:
|
||||
available_columns = [
|
||||
col["name"] for col in inspector.get_columns(table_name)
|
||||
]
|
||||
available_columns = [col["name"] for col in inspector.get_columns(table_name)]
|
||||
results.append(Result(name=table_name, type="table", success=True))
|
||||
except sa.exc.NoSuchTableError:
|
||||
logger.error(f"Missing table: {table_name}")
|
||||
|
|
@ -253,9 +236,7 @@ class DatabaseService(Service):
|
|||
try:
|
||||
table.create(self.engine, checkfirst=True)
|
||||
except OperationalError as oe:
|
||||
logger.warning(
|
||||
f"Table {table} already exists, skipping. Exception: {oe}"
|
||||
)
|
||||
logger.warning(f"Table {table} already exists, skipping. Exception: {oe}")
|
||||
except Exception as exc:
|
||||
logger.error(f"Error creating table {table}: {exc}")
|
||||
raise RuntimeError(f"Error creating table {table}") from exc
|
||||
|
|
@ -267,9 +248,7 @@ class DatabaseService(Service):
|
|||
if table not in table_names:
|
||||
logger.error("Something went wrong creating the database and tables.")
|
||||
logger.error("Please check your database settings.")
|
||||
raise RuntimeError(
|
||||
"Something went wrong creating the database and tables."
|
||||
)
|
||||
raise RuntimeError("Something went wrong creating the database and tables.")
|
||||
|
||||
logger.debug("Database and tables created successfully")
|
||||
|
||||
|
|
|
|||
|
|
@ -63,8 +63,8 @@ class LLMFrontendNode(FrontendNode):
|
|||
field.info = OPENAI_API_BASE_INFO
|
||||
|
||||
def add_extra_base_classes(self) -> None:
|
||||
if "BaseLLM" not in self.base_classes:
|
||||
self.base_classes.append("BaseLLM")
|
||||
if "BaseLanguageModel" not in self.base_classes:
|
||||
self.base_classes.append("BaseLanguageModel")
|
||||
|
||||
@staticmethod
|
||||
def format_azure_field(field: TemplateField):
|
||||
|
|
|
|||
|
|
@ -26,10 +26,7 @@ def patching(record):
|
|||
|
||||
|
||||
def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None):
|
||||
if (
|
||||
os.getenv("LANGFLOW_LOG_LEVEL", "").upper() in VALID_LOG_LEVELS
|
||||
and log_level is None
|
||||
):
|
||||
if os.getenv("LANGFLOW_LOG_LEVEL", "").upper() in VALID_LOG_LEVELS and log_level is None:
|
||||
log_level = os.getenv("LANGFLOW_LOG_LEVEL")
|
||||
if log_level is None:
|
||||
log_level = "ERROR"
|
||||
|
|
@ -77,11 +74,7 @@ def configure(log_level: Optional[str] = None, log_file: Optional[Path] = None):
|
|||
|
||||
|
||||
def setup_uvicorn_logger():
|
||||
loggers = (
|
||||
logging.getLogger(name)
|
||||
for name in logging.root.manager.loggerDict
|
||||
if name.startswith("uvicorn.")
|
||||
)
|
||||
loggers = (logging.getLogger(name) for name in logging.root.manager.loggerDict if name.startswith("uvicorn."))
|
||||
for uvicorn_logger in loggers:
|
||||
uvicorn_logger.handlers = []
|
||||
logging.getLogger("uvicorn").handlers = [InterceptHandler()]
|
||||
|
|
@ -111,6 +104,4 @@ class InterceptHandler(logging.Handler):
|
|||
frame = frame.f_back
|
||||
depth += 1
|
||||
|
||||
logger.opt(depth=depth, exception=record.exc_info).log(
|
||||
level, record.getMessage()
|
||||
)
|
||||
logger.opt(depth=depth, exception=record.exc_info).log(level, record.getMessage())
|
||||
|
|
|
|||
125
src/backend/base/poetry.lock
generated
125
src/backend/base/poetry.lock
generated
|
|
@ -865,13 +865,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "ecdsa"
|
||||
version = "0.18.0"
|
||||
version = "0.19.0"
|
||||
description = "ECDSA cryptographic signature library (pure python)"
|
||||
optional = false
|
||||
python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.6"
|
||||
files = [
|
||||
{file = "ecdsa-0.18.0-py2.py3-none-any.whl", hash = "sha256:80600258e7ed2f16b9aa1d7c295bd70194109ad5a30fdee0eaeefef1d4c559dd"},
|
||||
{file = "ecdsa-0.18.0.tar.gz", hash = "sha256:190348041559e21b22a1d65cee485282ca11a6f81d503fddb84d5017e9ed1e49"},
|
||||
{file = "ecdsa-0.19.0-py2.py3-none-any.whl", hash = "sha256:2cea9b88407fdac7bbeca0833b189e4c9c53f2ef1e1eaa29f6224dbc809b707a"},
|
||||
{file = "ecdsa-0.19.0.tar.gz", hash = "sha256:60eaad1199659900dd0af521ed462b793bbdf867432b3948e87416ae4caf6bf8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2533,61 +2533,62 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "orjson"
|
||||
version = "3.9.15"
|
||||
version = "3.10.0"
|
||||
description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "orjson-3.9.15-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:d61f7ce4727a9fa7680cd6f3986b0e2c732639f46a5e0156e550e35258aa313a"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4feeb41882e8aa17634b589533baafdceb387e01e117b1ec65534ec724023d04"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:fbbeb3c9b2edb5fd044b2a070f127a0ac456ffd079cb82746fc84af01ef021a4"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b66bcc5670e8a6b78f0313bcb74774c8291f6f8aeef10fe70e910b8040f3ab75"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2973474811db7b35c30248d1129c64fd2bdf40d57d84beed2a9a379a6f57d0ab"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9fe41b6f72f52d3da4db524c8653e46243c8c92df826ab5ffaece2dba9cccd58"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:4228aace81781cc9d05a3ec3a6d2673a1ad0d8725b4e915f1089803e9efd2b99"},
|
||||
{file = "orjson-3.9.15-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:6f7b65bfaf69493c73423ce9db66cfe9138b2f9ef62897486417a8fcb0a92bfe"},
|
||||
{file = "orjson-3.9.15-cp310-none-win32.whl", hash = "sha256:2d99e3c4c13a7b0fb3792cc04c2829c9db07838fb6973e578b85c1745e7d0ce7"},
|
||||
{file = "orjson-3.9.15-cp310-none-win_amd64.whl", hash = "sha256:b725da33e6e58e4a5d27958568484aa766e825e93aa20c26c91168be58e08cbb"},
|
||||
{file = "orjson-3.9.15-cp311-cp311-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:c8e8fe01e435005d4421f183038fc70ca85d2c1e490f51fb972db92af6e047c2"},
|
||||
{file = "orjson-3.9.15-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:87f1097acb569dde17f246faa268759a71a2cb8c96dd392cd25c668b104cad2f"},
|
||||
{file = "orjson-3.9.15-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ff0f9913d82e1d1fadbd976424c316fbc4d9c525c81d047bbdd16bd27dd98cfc"},
|
||||
{file = "orjson-3.9.15-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8055ec598605b0077e29652ccfe9372247474375e0e3f5775c91d9434e12d6b1"},
|
||||
{file = "orjson-3.9.15-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d6768a327ea1ba44c9114dba5fdda4a214bdb70129065cd0807eb5f010bfcbb5"},
|
||||
{file = "orjson-3.9.15-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:12365576039b1a5a47df01aadb353b68223da413e2e7f98c02403061aad34bde"},
|
||||
{file = "orjson-3.9.15-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:71c6b009d431b3839d7c14c3af86788b3cfac41e969e3e1c22f8a6ea13139404"},
|
||||
{file = "orjson-3.9.15-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:e18668f1bd39e69b7fed19fa7cd1cd110a121ec25439328b5c89934e6d30d357"},
|
||||
{file = "orjson-3.9.15-cp311-none-win32.whl", hash = "sha256:62482873e0289cf7313461009bf62ac8b2e54bc6f00c6fabcde785709231a5d7"},
|
||||
{file = "orjson-3.9.15-cp311-none-win_amd64.whl", hash = "sha256:b3d336ed75d17c7b1af233a6561cf421dee41d9204aa3cfcc6c9c65cd5bb69a8"},
|
||||
{file = "orjson-3.9.15-cp312-cp312-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:82425dd5c7bd3adfe4e94c78e27e2fa02971750c2b7ffba648b0f5d5cc016a73"},
|
||||
{file = "orjson-3.9.15-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2c51378d4a8255b2e7c1e5cc430644f0939539deddfa77f6fac7b56a9784160a"},
|
||||
{file = "orjson-3.9.15-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6ae4e06be04dc00618247c4ae3f7c3e561d5bc19ab6941427f6d3722a0875ef7"},
|
||||
{file = "orjson-3.9.15-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bcef128f970bb63ecf9a65f7beafd9b55e3aaf0efc271a4154050fc15cdb386e"},
|
||||
{file = "orjson-3.9.15-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b72758f3ffc36ca566ba98a8e7f4f373b6c17c646ff8ad9b21ad10c29186f00d"},
|
||||
{file = "orjson-3.9.15-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10c57bc7b946cf2efa67ac55766e41764b66d40cbd9489041e637c1304400494"},
|
||||
{file = "orjson-3.9.15-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:946c3a1ef25338e78107fba746f299f926db408d34553b4754e90a7de1d44068"},
|
||||
{file = "orjson-3.9.15-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:2f256d03957075fcb5923410058982aea85455d035607486ccb847f095442bda"},
|
||||
{file = "orjson-3.9.15-cp312-none-win_amd64.whl", hash = "sha256:5bb399e1b49db120653a31463b4a7b27cf2fbfe60469546baf681d1b39f4edf2"},
|
||||
{file = "orjson-3.9.15-cp38-cp38-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:b17f0f14a9c0ba55ff6279a922d1932e24b13fc218a3e968ecdbf791b3682b25"},
|
||||
{file = "orjson-3.9.15-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7f6cbd8e6e446fb7e4ed5bac4661a29e43f38aeecbf60c4b900b825a353276a1"},
|
||||
{file = "orjson-3.9.15-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:76bc6356d07c1d9f4b782813094d0caf1703b729d876ab6a676f3aaa9a47e37c"},
|
||||
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||||
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||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -3902,13 +3903,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
|
|||
|
||||
[[package]]
|
||||
name = "typer"
|
||||
version = "0.12.1"
|
||||
version = "0.12.2"
|
||||
description = "Typer, build great CLIs. Easy to code. Based on Python type hints."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "typer-0.12.1-py3-none-any.whl", hash = "sha256:43ebb23c8a358c3d623e31064359a65f50229d0bf73ae8dfd203f49d9126ae06"},
|
||||
{file = "typer-0.12.1.tar.gz", hash = "sha256:72d218ef3c686aed9c6ff3ca25b238aee0474a1628b29c559b18b634cfdeca88"},
|
||||
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|
||||
{file = "typer-0.12.2.tar.gz", hash = "sha256:977929604fde12aeada011852ad9c64370501be6ac2eac248f3161cdc9eeb7c9"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4024,13 +4025,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "types-redis"
|
||||
version = "4.6.0.20240311"
|
||||
version = "4.6.0.20240409"
|
||||
description = "Typing stubs for redis"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "types-redis-4.6.0.20240311.tar.gz", hash = "sha256:e049bbdff0e0a1f8e701b64636811291d21bff79bf1e7850850a44055224a85f"},
|
||||
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|
||||
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|
||||
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|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4443,4 +4444,4 @@ local = []
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10,<3.12"
|
||||
content-hash = "a3de9e754754b1899c6f1755e6e29e170a8286c43e241e8b55be5b060aa464e5"
|
||||
content-hash = "ab99a3fe4d80dff80dfa69347dcb22b85d48d33487a14e01c709b51f7b62699e"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "langflow-base"
|
||||
version = "0.0.21"
|
||||
version = "0.0.24"
|
||||
description = "A Python package with a built-in web application"
|
||||
authors = ["Logspace <contact@logspace.ai>"]
|
||||
maintainers = [
|
||||
|
|
@ -13,7 +13,7 @@ maintainers = [
|
|||
"Otávio Anovazzi <otavio2204@gmail.com>",
|
||||
"Rodrigo Nader <rodrigo@logspace.ai>",
|
||||
]
|
||||
repository = "https://github.com/logspace-ai/langflow"
|
||||
repository = "https://github.com/langflow-ai/langflow"
|
||||
license = "MIT"
|
||||
readme = "README.md"
|
||||
keywords = ["nlp", "langchain", "openai", "gpt", "gui"]
|
||||
|
|
@ -43,7 +43,7 @@ typer = "^0.12.0"
|
|||
cachetools = "^5.3.1"
|
||||
platformdirs = "^4.2.0"
|
||||
python-multipart = "^0.0.7"
|
||||
orjson = "3.9.15"
|
||||
orjson = "3.10.0"
|
||||
alembic = "^1.13.0"
|
||||
passlib = "^1.7.4"
|
||||
bcrypt = "4.0.1"
|
||||
|
|
|
|||
|
|
@ -28,7 +28,7 @@ export default function CrashErrorComponent({
|
|||
<p>
|
||||
Please report errors with detailed tracebacks on the{" "}
|
||||
<a
|
||||
href="https://github.com/logspace-ai/langflow/issues"
|
||||
href="https://github.com/langflow-ai/langflow/issues"
|
||||
target="_blank"
|
||||
rel="noopener noreferrer"
|
||||
className="font-medium hover:underline "
|
||||
|
|
@ -47,7 +47,7 @@ export default function CrashErrorComponent({
|
|||
<Button onClick={resetErrorBoundary}>Restart Langflow</Button>
|
||||
|
||||
<a
|
||||
href="https://github.com/logspace-ai/langflow/issues/new"
|
||||
href="https://github.com/langflow-ai/langflow/issues/new"
|
||||
target="_blank"
|
||||
rel="noopener noreferrer"
|
||||
>
|
||||
|
|
|
|||
|
|
@ -114,7 +114,7 @@ export default function Header(): JSX.Element {
|
|||
<div className="header-end-division lg:w-[30%]">
|
||||
<div className="header-end-display">
|
||||
<a
|
||||
href="https://github.com/logspace-ai/langflow"
|
||||
href="https://github.com/langflow-ai/langflow"
|
||||
target="_blank"
|
||||
rel="noreferrer"
|
||||
className="header-github-link gap-2"
|
||||
|
|
|
|||
|
|
@ -46,9 +46,9 @@ function ApiInterceptor() {
|
|||
|
||||
const isAuthorizedURL = (url) => {
|
||||
const authorizedDomains = [
|
||||
"https://raw.githubusercontent.com/logspace-ai/langflow_examples/main/examples",
|
||||
"https://api.github.com/repos/logspace-ai/langflow_examples/contents/examples",
|
||||
"https://api.github.com/repos/logspace-ai/langflow",
|
||||
"https://raw.githubusercontent.com/langflow-ai/langflow_examples/main/examples",
|
||||
"https://api.github.com/repos/langflow-ai/langflow_examples/contents/examples",
|
||||
"https://api.github.com/repos/langflow-ai/langflow",
|
||||
"auto_login",
|
||||
];
|
||||
|
||||
|
|
|
|||
|
|
@ -90,7 +90,7 @@ export async function postValidatePrompt(
|
|||
*/
|
||||
export async function getExamples(): Promise<FlowType[]> {
|
||||
const url =
|
||||
"https://api.github.com/repos/logspace-ai/langflow_examples/contents/examples?ref=main";
|
||||
"https://api.github.com/repos/langflow-ai/langflow_examples/contents/examples?ref=main";
|
||||
const response = await api.get(url);
|
||||
|
||||
const jsonFiles = response.data.filter((file: any) => {
|
||||
|
|
|
|||
|
|
@ -29,6 +29,7 @@ export const useTypesStore = create<TypesStoreType>((set, get) => ({
|
|||
.catch((error) => {
|
||||
console.error("An error has occurred while fetching types.");
|
||||
console.log(error);
|
||||
setLoading(false);
|
||||
reject();
|
||||
});
|
||||
});
|
||||
|
|
|
|||
|
|
@ -456,7 +456,7 @@ export function getWidgetCode(
|
|||
const inputs = buildInputs();
|
||||
let chat_input_field = getChatInputField(flowState);
|
||||
|
||||
return `<script src="https://cdn.jsdelivr.net/gh/logspace-ai/langflow-embedded-chat@1.0_alpha/dist/build/static/js/bundle.min.js"></script>
|
||||
return `<script src="https://cdn.jsdelivr.net/gh/langflow-ai/langflow-embedded-chat@1.0_alpha/dist/build/static/js/bundle.min.js"></script>
|
||||
|
||||
<langflow-chat
|
||||
window_title="${flowName}"
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
export const custom = `from langflow.custom import CustomComponent
|
||||
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langchain.chains import LLMChain
|
||||
from langchain.prompts import PromptTemplate
|
||||
from langchain_core.documents import Document
|
||||
|
|
@ -15,6 +15,6 @@ class YourComponent(CustomComponent):
|
|||
def build_config(self):
|
||||
return { "file": { "file_type": ["json"], } }
|
||||
|
||||
def build(self, url: str,file:str,integer:int,nested:NestedDict,flt:float,boolean:bool,lisst:list[str],dictionary:dict, llm: BaseLLM, prompt: PromptTemplate) -> Document:
|
||||
def build(self, url: str,file:str,integer:int,nested:NestedDict,flt:float,boolean:bool,lisst:list[str],dictionary:dict, llm: BaseLanguageModel, prompt: PromptTemplate) -> Document:
|
||||
|
||||
return "test"`;
|
||||
|
|
|
|||
|
|
@ -177,7 +177,7 @@ test("dropDownComponent", async ({ page }) => {
|
|||
.click();
|
||||
await page.locator("textarea").press("Control+a");
|
||||
const emptyOptionsCode = `from typing import Optional
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langchain_community.llms.bedrock import Bedrock
|
||||
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
|
@ -212,7 +212,7 @@ class AmazonBedrockComponent(CustomComponent):
|
|||
endpoint_url: Optional[str] = None,
|
||||
streaming: bool = False,
|
||||
cache: Optional[bool] = None,
|
||||
) -> BaseLLM:
|
||||
) -> BaseLanguageModel:
|
||||
try:
|
||||
output = Bedrock(
|
||||
credentials_profile_name=credentials_profile_name,
|
||||
|
|
|
|||
|
|
@ -22,9 +22,6 @@ from langflow.services.database.models.flow.model import Flow, FlowCreate
|
|||
from langflow.services.database.models.user.model import User, UserCreate
|
||||
from langflow.services.database.utils import session_getter
|
||||
from langflow.services.deps import get_db_service
|
||||
from sqlmodel import Session, SQLModel, create_engine, select
|
||||
from sqlmodel.pool import StaticPool
|
||||
from typer.testing import CliRunner
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from langflow.services.database.service import DatabaseService
|
||||
|
|
|
|||
|
|
@ -6,21 +6,15 @@ import pytest
|
|||
from langchain_core.documents import Document
|
||||
|
||||
from langflow.interface.custom.base import CustomComponent
|
||||
from langflow.interface.custom.code_parser.code_parser import (
|
||||
CodeParser,
|
||||
CodeSyntaxError,
|
||||
)
|
||||
from langflow.interface.custom.custom_component.component import (
|
||||
Component,
|
||||
ComponentCodeNullError,
|
||||
)
|
||||
from langflow.interface.custom.code_parser.code_parser import CodeParser, CodeSyntaxError
|
||||
from langflow.interface.custom.custom_component.component import Component, ComponentCodeNullError
|
||||
from langflow.services.database.models.flow import Flow, FlowCreate
|
||||
|
||||
code_default = """
|
||||
from langflow.field_typing import Prompt
|
||||
from langflow.interface.custom.custom_component import CustomComponent
|
||||
|
||||
from langchain.llms.base import BaseLLM
|
||||
from langflow.field_typing import BaseLanguageModel
|
||||
from langchain.chains import LLMChain
|
||||
from langchain.prompts import PromptTemplate
|
||||
from langchain_core.documents import Document
|
||||
|
|
@ -32,7 +26,7 @@ class YourComponent(CustomComponent):
|
|||
description: str = "Your description"
|
||||
field_config = { "url": { "multiline": True, "required": True } }
|
||||
|
||||
def build(self, url: str, llm: BaseLLM, template: Prompt) -> Document:
|
||||
def build(self, url: str, llm: BaseLanguageModel, template: Prompt) -> Document:
|
||||
response = requests.get(url)
|
||||
prompt = PromptTemplate.from_template(template)
|
||||
chain = LLMChain(llm=llm, prompt=prompt)
|
||||
|
|
|
|||
|
|
@ -6,10 +6,9 @@ import httpx
|
|||
import pytest
|
||||
import respx
|
||||
from httpx import Response
|
||||
|
||||
from langflow.components import (
|
||||
data,
|
||||
) # Adjust the import according to your project structure
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
|
|
@ -135,7 +134,7 @@ def test_directory_without_mocks():
|
|||
from langflow.initial_setup import setup
|
||||
from langflow.initial_setup.setup import load_starter_projects
|
||||
|
||||
projects = load_starter_projects()
|
||||
_, projects = zip(*load_starter_projects())
|
||||
# the setup module has a folder where the projects are stored
|
||||
# the contents of that folder are in the projects variable
|
||||
# the directory component can be used to load the projects
|
||||
|
|
|
|||
|
|
@ -472,11 +472,11 @@ def test_successful_run_with_output_type_text(client, starter_project, created_a
|
|||
assert isinstance(outputs_dict.get("outputs"), list)
|
||||
assert len(outputs_dict.get("outputs")) == 1
|
||||
ids = [output.get("component_id") for output in outputs_dict.get("outputs")]
|
||||
assert all(["TextOutput" in _id for _id in ids]), ids
|
||||
assert all(["ChatOutput" in _id for _id in ids]), ids
|
||||
display_names = [output.get("component_display_name") for output in outputs_dict.get("outputs")]
|
||||
assert all([name in display_names for name in ["Prompt Output"]]), display_names
|
||||
assert all([name in display_names for name in ["Chat Output"]]), display_names
|
||||
inner_results = [output.get("results").get("result") for output in outputs_dict.get("outputs")]
|
||||
expected_result = "Langflow"
|
||||
expected_result = ""
|
||||
assert all([expected_result in result for result in inner_results]), inner_results
|
||||
|
||||
|
||||
|
|
@ -501,13 +501,13 @@ def test_successful_run_with_output_type_any(client, starter_project, created_ap
|
|||
assert "outputs" in outputs_dict
|
||||
assert outputs_dict.get("inputs") == {"input_value": ""}
|
||||
assert isinstance(outputs_dict.get("outputs"), list)
|
||||
assert len(outputs_dict.get("outputs")) == 2
|
||||
assert len(outputs_dict.get("outputs")) == 1
|
||||
ids = [output.get("component_id") for output in outputs_dict.get("outputs")]
|
||||
assert all(["ChatOutput" in _id or "TextOutput" in _id for _id in ids]), ids
|
||||
display_names = [output.get("component_display_name") for output in outputs_dict.get("outputs")]
|
||||
assert all([name in display_names for name in ["Chat Output", "Prompt Output"]]), display_names
|
||||
assert all([name in display_names for name in ["Chat Output"]]), display_names
|
||||
inner_results = [output.get("results").get("result") for output in outputs_dict.get("outputs")]
|
||||
expected_result = "Langflow"
|
||||
expected_result = ""
|
||||
assert all([expected_result in result for result in inner_results]), inner_results
|
||||
|
||||
|
||||
|
|
@ -533,7 +533,7 @@ def test_successful_run_with_output_type_debug(client, starter_project, created_
|
|||
assert "outputs" in outputs_dict
|
||||
assert outputs_dict.get("inputs") == {"input_value": ""}
|
||||
assert isinstance(outputs_dict.get("outputs"), list)
|
||||
assert len(outputs_dict.get("outputs")) == 7
|
||||
assert len(outputs_dict.get("outputs")) == 4
|
||||
|
||||
|
||||
# To test input_type wel'l just set it with output_type debug and check if the value is correct
|
||||
|
|
@ -559,10 +559,10 @@ def test_successful_run_with_input_type_text(client, starter_project, created_ap
|
|||
assert "outputs" in outputs_dict
|
||||
assert outputs_dict.get("inputs") == {"input_value": "value1"}
|
||||
assert isinstance(outputs_dict.get("outputs"), list)
|
||||
assert len(outputs_dict.get("outputs")) == 7
|
||||
assert len(outputs_dict.get("outputs")) == 4
|
||||
# Now we get all components that contain TextInput in the component_id
|
||||
text_input_outputs = [output for output in outputs_dict.get("outputs") if "TextInput" in output.get("component_id")]
|
||||
assert len(text_input_outputs) == 2
|
||||
assert len(text_input_outputs) == 0
|
||||
# Now we check if the input_value is correct
|
||||
assert all([output.get("results").get("result") == "value1" for output in text_input_outputs]), text_input_outputs
|
||||
|
||||
|
|
@ -590,7 +590,7 @@ def test_successful_run_with_input_type_chat(client, starter_project, created_ap
|
|||
assert "outputs" in outputs_dict
|
||||
assert outputs_dict.get("inputs") == {"input_value": "value1"}
|
||||
assert isinstance(outputs_dict.get("outputs"), list)
|
||||
assert len(outputs_dict.get("outputs")) == 7
|
||||
assert len(outputs_dict.get("outputs")) == 4
|
||||
# Now we get all components that contain TextInput in the component_id
|
||||
chat_input_outputs = [output for output in outputs_dict.get("outputs") if "ChatInput" in output.get("component_id")]
|
||||
assert len(chat_input_outputs) == 1
|
||||
|
|
@ -620,14 +620,14 @@ def test_successful_run_with_input_type_any(client, starter_project, created_api
|
|||
assert "outputs" in outputs_dict
|
||||
assert outputs_dict.get("inputs") == {"input_value": "value1"}
|
||||
assert isinstance(outputs_dict.get("outputs"), list)
|
||||
assert len(outputs_dict.get("outputs")) == 7
|
||||
assert len(outputs_dict.get("outputs")) == 4
|
||||
# Now we get all components that contain TextInput or ChatInput in the component_id
|
||||
any_input_outputs = [
|
||||
output
|
||||
for output in outputs_dict.get("outputs")
|
||||
if "TextInput" in output.get("component_id") or "ChatInput" in output.get("component_id")
|
||||
]
|
||||
assert len(any_input_outputs) == 3
|
||||
assert len(any_input_outputs) == 1
|
||||
# Now we check if the input_value is correct
|
||||
assert all([output.get("results").get("result") == "value1" for output in any_input_outputs]), any_input_outputs
|
||||
|
||||
|
|
|
|||
|
|
@ -409,7 +409,7 @@ def test_update_source_handle():
|
|||
@pytest.mark.asyncio
|
||||
async def test_pickle_graph(json_vector_store):
|
||||
starter_projects = load_starter_projects()
|
||||
data = starter_projects[0]["data"]
|
||||
data = starter_projects[0][1]["data"]
|
||||
graph = Graph.from_payload(data)
|
||||
assert isinstance(graph, Graph)
|
||||
pickled = pickle.dumps(graph)
|
||||
|
|
@ -421,7 +421,7 @@ async def test_pickle_graph(json_vector_store):
|
|||
@pytest.mark.asyncio
|
||||
async def test_pickle_each_vertex(json_vector_store):
|
||||
starter_projects = load_starter_projects()
|
||||
data = starter_projects[0]["data"]
|
||||
data = starter_projects[0][1]["data"]
|
||||
graph = Graph.from_payload(data)
|
||||
assert isinstance(graph, Graph)
|
||||
for vertex in graph.vertices:
|
||||
|
|
@ -430,15 +430,3 @@ async def test_pickle_each_vertex(json_vector_store):
|
|||
assert pickled is not None
|
||||
unpickled = pickle.loads(pickled)
|
||||
assert unpickled is not None
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_build_ordering(complex_graph_with_groups):
|
||||
sorted_vertices = complex_graph_with_groups.sort_vertices(stop_component_id="ChatInput-Ay8QQ")
|
||||
assert sorted_vertices == [
|
||||
"ChatInput-Ay8QQ",
|
||||
"RecordsAsText-vkx2A",
|
||||
"FileLoader-Vo1Cq",
|
||||
]
|
||||
|
||||
sorted_vertices = complex_graph_with_groups.sort_vertices()
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
|
||||
import pytest
|
||||
from langflow.graph.graph.base import Graph
|
||||
|
|
@ -10,6 +11,7 @@ from langflow.initial_setup.setup import (
|
|||
load_starter_projects,
|
||||
)
|
||||
from langflow.memory import delete_messages
|
||||
from langflow.processing.process import process_tweaks
|
||||
from langflow.services.database.models.flow.model import Flow
|
||||
from langflow.services.deps import session_scope
|
||||
from sqlalchemy import func
|
||||
|
|
@ -19,7 +21,8 @@ from sqlmodel import select
|
|||
def test_load_starter_projects():
|
||||
projects = load_starter_projects()
|
||||
assert isinstance(projects, list)
|
||||
assert all(isinstance(project, dict) for project in projects)
|
||||
assert all(isinstance(project[1], dict) for project in projects)
|
||||
assert all(isinstance(project[0], Path) for project in projects)
|
||||
|
||||
|
||||
def test_get_project_data():
|
||||
|
|
@ -59,7 +62,7 @@ def test_create_or_update_starter_projects(client):
|
|||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_starter_project_can_run_successfully(client):
|
||||
async def test_starter_projects_can_run_successfully(client):
|
||||
with session_scope() as session:
|
||||
# Run the function to create or update projects
|
||||
create_or_update_starter_projects()
|
||||
|
|
@ -75,12 +78,13 @@ async def test_starter_project_can_run_successfully(client):
|
|||
|
||||
# Get all the starter projects
|
||||
projects = session.exec(select(Flow).where(Flow.folder == STARTER_FOLDER_NAME)).all()
|
||||
|
||||
graphs: list[tuple[str, Graph]] = [
|
||||
(project.name, Graph.from_payload(project.data, flow_id=project.id))
|
||||
for project in projects
|
||||
if "Document" not in project.name or "RAG" not in project.name
|
||||
]
|
||||
graphs: list[tuple[str, Graph]] = []
|
||||
for project in projects:
|
||||
# Add tweaks to make file_path work
|
||||
tweaks = {"path": __file__}
|
||||
graph_data = process_tweaks(project.data, tweaks)
|
||||
graph_object = Graph.from_payload(graph_data, flow_id=project.id)
|
||||
graphs.append((project.name, graph_object))
|
||||
assert len(graphs) == len(projects)
|
||||
for name, graph in graphs:
|
||||
outputs = await graph.arun(
|
||||
|
|
|
|||
|
|
@ -1,5 +1,4 @@
|
|||
import pytest
|
||||
|
||||
from langflow.processing.process import process_tweaks
|
||||
from langflow.services.deps import get_session_service
|
||||
|
||||
|
|
@ -284,12 +283,12 @@ async def test_load_langchain_object_with_no_cached_session(client, basic_graph_
|
|||
session_id = session_service.build_key(session_id1, basic_graph_data)
|
||||
graph1, artifacts1 = await session_service.load_session(session_id, data_graph=basic_graph_data, flow_id="flow_id")
|
||||
# Clear the cache
|
||||
session_service.clear_session(session_id)
|
||||
# Use the new session_id to get the langchain_object again
|
||||
await session_service.clear_session(session_id)
|
||||
# Use the new session_id to get the graph again
|
||||
graph2, artifacts2 = await session_service.load_session(session_id, data_graph=basic_graph_data, flow_id="flow_id")
|
||||
|
||||
assert id(graph1) != id(graph2)
|
||||
# Since the cache was cleared, objects should be different
|
||||
assert id(graph1) != id(graph2)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue