test: remove docs tests. add trigger to docs repo
This commit is contained in:
Andrej Milicevic
parent
b3f9795d2e
commit
cd60ae3174
12 changed files with 16 additions and 651 deletions
280
.github/workflows/docs_tests.yml
vendored
280
.github/workflows/docs_tests.yml
vendored
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@ -1,280 +0,0 @@
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name: Docs Tests
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permissions:
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contents: read
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on:
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workflow_dispatch:
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workflow_call:
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secrets:
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LLM_PROVIDER:
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required: true
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LLM_MODEL:
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required: true
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LLM_ENDPOINT:
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required: true
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LLM_API_KEY:
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required: true
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LLM_API_VERSION:
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required: true
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EMBEDDING_PROVIDER:
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required: true
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EMBEDDING_MODEL:
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required: true
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EMBEDDING_ENDPOINT:
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required: true
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EMBEDDING_API_KEY:
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required: true
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EMBEDDING_API_VERSION:
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required: true
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env:
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ENV: 'dev'
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jobs:
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test-search-basics:
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name: Test Search Basics
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runs-on: ubuntu-22.04
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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- name: Run Search Basics Test
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env:
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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run: uv run python ./cognee/tests/docs/guides/search_basics.py
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test-temporal-cognify:
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name: Test Temporal Cognify
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runs-on: ubuntu-22.04
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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- name: Run Temporal Cognify Test
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env:
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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run: uv run python ./cognee/tests/docs/guides/temporal_cognify.py
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test-ontology-quickstart:
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name: Test Temporal Cognify
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runs-on: ubuntu-22.04
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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- name: Run Temporal Cognify Test
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env:
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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run: uv run python ./cognee/tests/docs/guides/temporal_cognify.py
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test-s3-storage:
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name: Test S3 Docs Guide
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runs-on: ubuntu-22.04
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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extra-dependencies: "aws"
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- name: Run S3 Docs Guide Test
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env:
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ENABLE_BACKEND_ACCESS_CONTROL: True
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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STORAGE_BACKEND: s3
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AWS_REGION: eu-west-1
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AWS_ENDPOINT_URL: https://s3-eu-west-1.amazonaws.com
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AWS_ACCESS_KEY_ID: ${{ secrets.AWS_S3_DEV_USER_KEY_ID }}
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AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_S3_DEV_USER_SECRET_KEY }}
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run: uv run python ./cognee/tests/docs/guides/s3_storage.py
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test-graph-visualization:
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name: Test Graph Visualization
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runs-on: ubuntu-22.04
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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- name: Run Graph Visualization Test
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env:
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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run: uv run python ./cognee/tests/docs/guides/graph_visualization.py
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test-low-level-llm:
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name: Test Low Level LLM
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runs-on: ubuntu-22.04
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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- name: Run Low Level LLM Test
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env:
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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run: uv run python ./cognee/tests/docs/guides/low_level_llm.py
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test-memify-quickstart:
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name: Test Memify Quickstart
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runs-on: ubuntu-22.04
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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- name: Run Memify Quickstart Test
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env:
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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run: uv run python ./cognee/tests/docs/guides/memify_quickstart.py
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test-custom-data-models:
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name: Test Custom Data Models
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runs-on: ubuntu-22.04
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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- name: Run Custom Data Models Test
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env:
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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run: uv run python ./cognee/tests/docs/guides/custom_data_models.py
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test-custom-tasks-and-pipelines:
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name: Test Custom Tasks and Pipelines
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runs-on: ubuntu-22.04
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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- name: Run Custom Tasks and Pipelines Test
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env:
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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run: uv run python ./cognee/tests/docs/guides/custom_tasks_and_pipelines.py
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test-custom-prompts:
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name: Test Custom Prompts
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runs-on: ubuntu-22.04
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steps:
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- name: Check out repository
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uses: actions/checkout@v4
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- name: Cognee Setup
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uses: ./.github/actions/cognee_setup
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with:
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python-version: '3.11.x'
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- name: Run Custom Prompts Test
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env:
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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run: uv run python ./cognee/tests/docs/guides/custom_prompts.py
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23
.github/workflows/release_test.yml
vendored
23
.github/workflows/release_test.yml
vendored
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@ -5,18 +5,27 @@ permissions:
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contents: read
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on:
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push:
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branches:
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- feature/cog-3213-docs-set-up-guide-script-tests
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workflow_dispatch:
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pull_request:
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branches:
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- main
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jobs:
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load-tests:
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name: Load Tests
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uses: ./.github/workflows/load_tests.yml
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secrets: inherit
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# load-tests:
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# name: Load Tests
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# uses: ./.github/workflows/load_tests.yml
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# secrets: inherit
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docs-tests:
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name: Docs Tests
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uses: ./.github/workflows/docs_tests.yml
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secrets: inherit
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runs-on: ubuntu-22.04
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steps:
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- name: Trigger docs tests
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run: |
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curl -sS -X POST \
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-H "Accept: application/vnd.github+json" \
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-H "Authorization: Bearer ${{ secrets.DOCS_REPO_PAT_TOKEN }}" \
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https://api.github.com/repos/your-org/repo-b/dispatches \
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-d '{"event_type":"new-main-release","client_payload":{"caller_repo":"'"${GITHUB_REPOSITORY}"'"}}'
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@ -1,38 +0,0 @@
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import asyncio
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from typing import Any
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from pydantic import SkipValidation
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import cognee
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from cognee.infrastructure.engine import DataPoint
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from cognee.infrastructure.engine.models.Edge import Edge
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from cognee.tasks.storage import add_data_points
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class Person(DataPoint):
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name: str
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# Keep it simple for forward refs / mixed values
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knows: SkipValidation[Any] = None # single Person or list[Person]
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# Recommended: specify which fields to index for search
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metadata: dict = {"index_fields": ["name"]}
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async def main():
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# Start clean (optional in your app)
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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alice = Person(name="Alice")
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bob = Person(name="Bob")
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charlie = Person(name="Charlie")
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# Create relationships - field name becomes edge label
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alice.knows = bob
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# You can also do lists: alice.knows = [bob, charlie]
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# Optional: add weights and custom relationship types
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bob.knows = (Edge(weight=0.9, relationship_type="friend_of"), charlie)
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await add_data_points([alice, bob, charlie])
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asyncio.run(main())
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import asyncio
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import cognee
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from cognee.api.v1.search import SearchType
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custom_prompt = """
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Extract only people and cities as entities.
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Connect people to cities with the relationship "lives_in".
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Ignore all other entities.
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"""
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async def main():
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await cognee.add(
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[
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"Alice moved to Paris in 2010, while Bob has always lived in New York.",
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"Andreas was born in Venice, but later settled in Lisbon.",
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"Diana and Tom were born and raised in Helsingy. Diana currently resides in Berlin, while Tom never moved.",
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]
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)
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await cognee.cognify(custom_prompt=custom_prompt)
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res = await cognee.search(
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query_type=SearchType.GRAPH_COMPLETION,
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query_text="Where does Alice live?",
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)
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print(res)
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if __name__ == "__main__":
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asyncio.run(main())
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@ -1,53 +0,0 @@
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import asyncio
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from typing import Any, Dict, List
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from pydantic import BaseModel, SkipValidation
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import cognee
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from cognee.modules.engine.operations.setup import setup
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from cognee.infrastructure.llm.LLMGateway import LLMGateway
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from cognee.infrastructure.engine import DataPoint
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from cognee.tasks.storage import add_data_points
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from cognee.modules.pipelines import Task, run_pipeline
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class Person(DataPoint):
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name: str
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# Optional relationships (we'll let the LLM populate this)
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knows: List["Person"] = []
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# Make names searchable in the vector store
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metadata: Dict[str, Any] = {"index_fields": ["name"]}
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class People(BaseModel):
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persons: List[Person]
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async def extract_people(text: str) -> List[Person]:
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system_prompt = (
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"Extract people mentioned in the text. "
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"Return as `persons: Person[]` with each Person having `name` and optional `knows` relations. "
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"If the text says someone knows someone set `knows` accordingly. "
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"Only include facts explicitly stated."
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)
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people = await LLMGateway.acreate_structured_output(text, system_prompt, People)
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return people.persons
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async def main():
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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await setup()
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text = "Alice knows Bob."
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tasks = [
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Task(extract_people), # input: text -> output: list[Person]
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Task(add_data_points), # input: list[Person] -> output: list[Person]
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]
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async for _ in run_pipeline(tasks=tasks, data=text, datasets=["people_demo"]):
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pass
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if __name__ == "__main__":
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asyncio.run(main())
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@ -1,13 +0,0 @@
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import asyncio
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import cognee
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||||
from cognee.api.v1.visualize.visualize import visualize_graph
|
||||
|
||||
|
||||
async def main():
|
||||
await cognee.add(["Alice knows Bob.", "NLP is a subfield of CS."])
|
||||
await cognee.cognify()
|
||||
|
||||
await visualize_graph("./graph_after_cognify.html")
|
||||
|
||||
|
||||
asyncio.run(main())
|
||||
|
|
@ -1,31 +0,0 @@
|
|||
import asyncio
|
||||
|
||||
from pydantic import BaseModel
|
||||
from typing import List
|
||||
from cognee.infrastructure.llm.LLMGateway import LLMGateway
|
||||
|
||||
|
||||
class MiniEntity(BaseModel):
|
||||
name: str
|
||||
type: str
|
||||
|
||||
|
||||
class MiniGraph(BaseModel):
|
||||
nodes: List[MiniEntity]
|
||||
|
||||
|
||||
async def main():
|
||||
system_prompt = (
|
||||
"Extract entities as nodes with name and type. "
|
||||
"Use concise, literal values present in the text."
|
||||
)
|
||||
|
||||
text = "Apple develops iPhone; Audi produces the R8."
|
||||
|
||||
result = await LLMGateway.acreate_structured_output(text, system_prompt, MiniGraph)
|
||||
print(result)
|
||||
# MiniGraph(nodes=[MiniEntity(name='Apple', type='Organization'), ...])
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
|
@ -1,29 +0,0 @@
|
|||
import asyncio
|
||||
import cognee
|
||||
from cognee import SearchType
|
||||
|
||||
|
||||
async def main():
|
||||
# 1) Add two short chats and build a graph
|
||||
await cognee.add(
|
||||
[
|
||||
"We follow PEP8. Add type hints and docstrings.",
|
||||
"Releases should not be on Friday. Susan must review PRs.",
|
||||
],
|
||||
dataset_name="rules_demo",
|
||||
)
|
||||
await cognee.cognify(datasets=["rules_demo"]) # builds graph
|
||||
|
||||
# 2) Enrich the graph (uses default memify tasks)
|
||||
await cognee.memify(dataset="rules_demo")
|
||||
|
||||
# 3) Query the new coding rules
|
||||
rules = await cognee.search(
|
||||
query_type=SearchType.CODING_RULES,
|
||||
query_text="List coding rules",
|
||||
node_name=["coding_agent_rules"],
|
||||
)
|
||||
print("Rules:", rules)
|
||||
|
||||
|
||||
asyncio.run(main())
|
||||
|
|
@ -1,30 +0,0 @@
|
|||
import asyncio
|
||||
import cognee
|
||||
|
||||
|
||||
async def main():
|
||||
texts = ["Audi produces the R8 and e-tron.", "Apple develops iPhone and MacBook."]
|
||||
|
||||
await cognee.add(texts)
|
||||
# or: await cognee.add("/path/to/folder/of/files")
|
||||
|
||||
import os
|
||||
from cognee.modules.ontology.ontology_config import Config
|
||||
from cognee.modules.ontology.rdf_xml.RDFLibOntologyResolver import RDFLibOntologyResolver
|
||||
|
||||
ontology_path = os.path.join(
|
||||
os.path.dirname(os.path.abspath(__file__)), "ontology_input_example/basic_ontology.owl"
|
||||
)
|
||||
|
||||
# Create full config structure manually
|
||||
config: Config = {
|
||||
"ontology_config": {
|
||||
"ontology_resolver": RDFLibOntologyResolver(ontology_file=ontology_path)
|
||||
}
|
||||
}
|
||||
|
||||
await cognee.cognify(config=config)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
|
@ -1,25 +0,0 @@
|
|||
import asyncio
|
||||
import cognee
|
||||
|
||||
|
||||
async def main():
|
||||
# Single file
|
||||
await cognee.add("s3://cognee-temp/2024-11-04.md")
|
||||
|
||||
# Folder/prefix (recursively expands)
|
||||
await cognee.add("s3://cognee-temp")
|
||||
|
||||
# Mixed list
|
||||
await cognee.add(
|
||||
[
|
||||
"s3://cognee-temp/2024-11-04.md",
|
||||
"Some inline text to ingest",
|
||||
]
|
||||
)
|
||||
|
||||
# Process the data
|
||||
await cognee.cognify()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
|
|
@ -1,58 +0,0 @@
|
|||
import asyncio
|
||||
import cognee
|
||||
|
||||
from cognee.modules.search.types import SearchType, CombinedSearchResult
|
||||
|
||||
|
||||
async def main():
|
||||
await cognee.prune.prune_data()
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
|
||||
text = """
|
||||
Natural language processing (NLP) is an interdisciplinary
|
||||
subfield of computer science and information retrieval.
|
||||
First rule of coding: Do not talk about coding.
|
||||
"""
|
||||
|
||||
text2 = """
|
||||
Sandwiches are best served toasted with cheese, ham, mayo,
|
||||
lettuce, mustard, and salt & pepper.
|
||||
"""
|
||||
|
||||
await cognee.add(text, dataset_name="NLP_coding")
|
||||
await cognee.add(text2, dataset_name="Sandwiches")
|
||||
await cognee.add(text2)
|
||||
|
||||
await cognee.cognify()
|
||||
|
||||
# Make sure you've already run cognee.cognify(...) so the graph has content
|
||||
answers = await cognee.search(query_text="What are the main themes in my data?")
|
||||
assert len(answers) > 0
|
||||
|
||||
answers = await cognee.search(
|
||||
query_text="List coding guidelines",
|
||||
query_type=SearchType.CODING_RULES,
|
||||
)
|
||||
assert len(answers) > 0
|
||||
|
||||
answers = await cognee.search(
|
||||
query_text="Give me a confident answer: What is NLP?",
|
||||
system_prompt="Answer succinctly and state confidence at the end.",
|
||||
)
|
||||
assert len(answers) > 0
|
||||
|
||||
answers = await cognee.search(
|
||||
query_text="Tell me about NLP",
|
||||
only_context=True,
|
||||
)
|
||||
assert len(answers) > 0
|
||||
|
||||
answers = await cognee.search(
|
||||
query_text="Quarterly financial highlights",
|
||||
datasets=["NLP_coding", "Sandwiches"],
|
||||
use_combined_context=True,
|
||||
)
|
||||
assert isinstance(answers, CombinedSearchResult)
|
||||
|
||||
|
||||
asyncio.run(main())
|
||||
|
|
@ -1,57 +0,0 @@
|
|||
import asyncio
|
||||
import cognee
|
||||
|
||||
|
||||
async def main():
|
||||
text = """
|
||||
In 1998 the project launched. In 2001 version 1.0 shipped. In 2004 the team merged
|
||||
with another group. In 2010 support for v1 ended.
|
||||
"""
|
||||
|
||||
await cognee.add(text, dataset_name="timeline_demo")
|
||||
|
||||
await cognee.cognify(datasets=["timeline_demo"], temporal_cognify=True)
|
||||
|
||||
from cognee.api.v1.search import SearchType
|
||||
|
||||
# Before / after queries
|
||||
result = await cognee.search(
|
||||
query_type=SearchType.TEMPORAL, query_text="What happened before 2000?", top_k=10
|
||||
)
|
||||
|
||||
assert result != []
|
||||
|
||||
result = await cognee.search(
|
||||
query_type=SearchType.TEMPORAL, query_text="What happened after 2010?", top_k=10
|
||||
)
|
||||
|
||||
assert result != []
|
||||
|
||||
# Between queries
|
||||
result = await cognee.search(
|
||||
query_type=SearchType.TEMPORAL, query_text="Events between 2001 and 2004", top_k=10
|
||||
)
|
||||
|
||||
assert result != []
|
||||
|
||||
# Scoped descriptions
|
||||
result = await cognee.search(
|
||||
query_type=SearchType.TEMPORAL,
|
||||
query_text="Key project milestones between 1998 and 2010",
|
||||
top_k=10,
|
||||
)
|
||||
|
||||
assert result != []
|
||||
|
||||
result = await cognee.search(
|
||||
query_type=SearchType.TEMPORAL,
|
||||
query_text="What happened after 2004?",
|
||||
datasets=["timeline_demo"],
|
||||
top_k=10,
|
||||
)
|
||||
|
||||
assert result != []
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Loading…
Add table
Reference in a new issue