Merge branch 'dev' into multi-tenancy
This commit is contained in:
Igor Ilic
GitHub
commit
41bbf5fdd8
43 changed files with 5382 additions and 4568 deletions
|
|
@ -242,13 +242,14 @@ LITELLM_LOG="ERROR"
|
|||
|
||||
########## Local LLM via Ollama ###############################################
|
||||
|
||||
|
||||
#LLM_API_KEY ="ollama"
|
||||
#LLM_MODEL="llama3.1:8b"
|
||||
#LLM_PROVIDER="ollama"
|
||||
#LLM_ENDPOINT="http://localhost:11434/v1"
|
||||
#EMBEDDING_PROVIDER="ollama"
|
||||
#EMBEDDING_MODEL="nomic-embed-text:latest"
|
||||
#EMBEDDING_ENDPOINT="http://localhost:11434/api/embeddings"
|
||||
#EMBEDDING_ENDPOINT="http://localhost:11434/api/embed"
|
||||
#EMBEDDING_DIMENSIONS=768
|
||||
#HUGGINGFACE_TOKENIZER="nomic-ai/nomic-embed-text-v1.5"
|
||||
|
||||
|
|
|
|||
20
.github/workflows/dockerhub-mcp.yml
vendored
20
.github/workflows/dockerhub-mcp.yml
vendored
|
|
@ -7,14 +7,29 @@ on:
|
|||
|
||||
jobs:
|
||||
docker-build-and-push:
|
||||
runs-on: ubuntu-latest
|
||||
runs-on:
|
||||
group: Default
|
||||
labels:
|
||||
- docker_build_runner
|
||||
|
||||
steps:
|
||||
- name: Check and free disk space before build
|
||||
run: |
|
||||
echo "=== Before cleanup ==="
|
||||
df -h
|
||||
echo "Removing unused preinstalled SDKs to free space..."
|
||||
sudo rm -rf /usr/share/dotnet /usr/local/lib/android /opt/ghc || true
|
||||
docker system prune -af || true
|
||||
echo "=== After cleanup ==="
|
||||
df -h
|
||||
|
||||
- name: Checkout repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
with:
|
||||
buildkitd-flags: --root /tmp/buildkit
|
||||
|
||||
- name: Log in to Docker Hub
|
||||
uses: docker/login-action@v3
|
||||
|
|
@ -34,7 +49,7 @@ jobs:
|
|||
|
||||
- name: Build and push
|
||||
id: build
|
||||
uses: docker/build-push-action@v5
|
||||
uses: docker/build-push-action@v6
|
||||
with:
|
||||
context: .
|
||||
platforms: linux/amd64,linux/arm64
|
||||
|
|
@ -45,5 +60,6 @@ jobs:
|
|||
cache-from: type=registry,ref=cognee/cognee-mcp:buildcache
|
||||
cache-to: type=registry,ref=cognee/cognee-mcp:buildcache,mode=max
|
||||
|
||||
|
||||
- name: Image digest
|
||||
run: echo ${{ steps.build.outputs.digest }}
|
||||
|
|
|
|||
132
AGENTS.md
Normal file
132
AGENTS.md
Normal file
|
|
@ -0,0 +1,132 @@
|
|||
## Repository Guidelines
|
||||
|
||||
This document summarizes how to work with the cognee repository: how it’s organized, how to build, test, lint, and contribute. It mirrors our actual tooling and CI while providing quick commands for local development.
|
||||
|
||||
## Project Structure & Module Organization
|
||||
|
||||
- `cognee/`: Core Python library and API.
|
||||
- `api/`: FastAPI application and versioned routers (add, cognify, memify, search, delete, users, datasets, responses, visualize, settings, sync, update, checks).
|
||||
- `cli/`: CLI entry points and subcommands invoked via `cognee` / `cognee-cli`.
|
||||
- `infrastructure/`: Databases, LLM providers, embeddings, loaders, and storage adapters.
|
||||
- `modules/`: Domain logic (graph, retrieval, ontology, users, processing, observability, etc.).
|
||||
- `tasks/`: Reusable tasks (e.g., code graph, web scraping, storage). Extend with new tasks here.
|
||||
- `eval_framework/`: Evaluation utilities and adapters.
|
||||
- `shared/`: Cross-cutting helpers (logging, settings, utils).
|
||||
- `tests/`: Unit, integration, CLI, and end-to-end tests organized by feature.
|
||||
- `__main__.py`: Entrypoint to route to CLI.
|
||||
- `cognee-mcp/`: Model Context Protocol server exposing cognee as MCP tools (SSE/HTTP/stdio). Contains its own README and Dockerfile.
|
||||
- `cognee-frontend/`: Next.js UI for local development and demos.
|
||||
- `distributed/`: Utilities for distributed execution (Modal, workers, queues).
|
||||
- `examples/`: Example scripts demonstrating the public APIs and features (graph, code graph, multimodal, permissions, etc.).
|
||||
- `notebooks/`: Jupyter notebooks for demos and tutorials.
|
||||
- `alembic/`: Database migrations for relational backends.
|
||||
|
||||
Notes:
|
||||
- Co-locate feature-specific helpers under their respective package (`modules/`, `infrastructure/`, or `tasks/`).
|
||||
- Extend the system by adding new tasks, loaders, or retrievers rather than modifying core pipeline mechanisms.
|
||||
|
||||
## Build, Test, and Development Commands
|
||||
|
||||
Python (root) – requires Python >= 3.10 and < 3.14. We recommend `uv` for speed and reproducibility.
|
||||
|
||||
- Create/refresh env and install dev deps:
|
||||
```bash
|
||||
uv sync --dev --all-extras --reinstall
|
||||
```
|
||||
|
||||
- Run the CLI (examples):
|
||||
```bash
|
||||
uv run cognee-cli add "Cognee turns documents into AI memory."
|
||||
uv run cognee-cli cognify
|
||||
uv run cognee-cli search "What does cognee do?"
|
||||
uv run cognee-cli -ui # Launches UI, backend API, and MCP server together
|
||||
```
|
||||
|
||||
- Start the FastAPI server directly:
|
||||
```bash
|
||||
uv run python -m cognee.api.client
|
||||
```
|
||||
|
||||
- Run tests (CI mirrors these commands):
|
||||
```bash
|
||||
uv run pytest cognee/tests/unit/ -v
|
||||
uv run pytest cognee/tests/integration/ -v
|
||||
```
|
||||
|
||||
- Lint and format (ruff):
|
||||
```bash
|
||||
uv run ruff check .
|
||||
uv run ruff format .
|
||||
```
|
||||
|
||||
- Optional static type checks (mypy):
|
||||
```bash
|
||||
uv run mypy cognee/
|
||||
```
|
||||
|
||||
MCP Server (`cognee-mcp/`):
|
||||
|
||||
- Install and run locally:
|
||||
```bash
|
||||
cd cognee-mcp
|
||||
uv sync --dev --all-extras --reinstall
|
||||
uv run python src/server.py # stdio (default)
|
||||
uv run python src/server.py --transport sse
|
||||
uv run python src/server.py --transport http --host 127.0.0.1 --port 8000 --path /mcp
|
||||
```
|
||||
|
||||
- API Mode (connect to a running Cognee API):
|
||||
```bash
|
||||
uv run python src/server.py --transport sse --api-url http://localhost:8000 --api-token YOUR_TOKEN
|
||||
```
|
||||
|
||||
- Docker quickstart (examples): see `cognee-mcp/README.md` for full details
|
||||
```bash
|
||||
docker run -e TRANSPORT_MODE=http --env-file ./.env -p 8000:8000 --rm -it cognee/cognee-mcp:main
|
||||
```
|
||||
|
||||
Frontend (`cognee-frontend/`):
|
||||
```bash
|
||||
cd cognee-frontend
|
||||
npm install
|
||||
npm run dev # Next.js dev server
|
||||
npm run lint # ESLint
|
||||
npm run build && npm start
|
||||
```
|
||||
|
||||
## Coding Style & Naming Conventions
|
||||
|
||||
Python:
|
||||
- 4-space indentation, modules and functions in `snake_case`, classes in `PascalCase`.
|
||||
- Public APIs should be type-annotated where practical.
|
||||
- Use `ruff format` before committing; `ruff check` enforces import hygiene and style (line-length 100 configured in `pyproject.toml`).
|
||||
- Prefer explicit, structured error handling. Use shared logging utilities in `cognee.shared.logging_utils`.
|
||||
|
||||
MCP server and Frontend:
|
||||
- Follow the local `README.md` and ESLint/TypeScript configuration in `cognee-frontend/`.
|
||||
|
||||
## Testing Guidelines
|
||||
|
||||
- Place Python tests under `cognee/tests/`.
|
||||
- Unit tests: `cognee/tests/unit/`
|
||||
- Integration tests: `cognee/tests/integration/`
|
||||
- CLI tests: `cognee/tests/cli_tests/`
|
||||
- Name test files `test_*.py`. Use `pytest.mark.asyncio` for async tests.
|
||||
- Avoid external state; rely on test fixtures and the CI-provided env vars when LLM/embedding providers are required. See CI workflows under `.github/workflows/` for expected environment variables.
|
||||
- When adding public APIs, provide/update targeted examples under `examples/python/`.
|
||||
|
||||
## Commit & Pull Request Guidelines
|
||||
|
||||
- Use clear, imperative subjects (≤ 72 chars) and conventional commit styling in PR titles. Our CI validates semantic PR titles (see `.github/workflows/pr_lint`). Examples:
|
||||
- `feat(graph): add temporal edge weighting`
|
||||
- `fix(api): handle missing auth cookie`
|
||||
- `docs: update installation instructions`
|
||||
- Reference related issues/discussions in the PR body and provide brief context.
|
||||
- PRs should describe scope, list local test commands run, and mention any impacts on MCP server or UI if applicable.
|
||||
- Sign commits and affirm the DCO (see `CONTRIBUTING.md`).
|
||||
|
||||
## CI Mirrors Local Commands
|
||||
|
||||
Our GitHub Actions run the same ruff checks and pytest suites shown above (`.github/workflows/basic_tests.yml` and related workflows). Use the commands in this document locally to minimize CI surprises.
|
||||
|
||||
|
||||
|
|
@ -97,7 +97,7 @@ Hosted platform:
|
|||
|
||||
### 📦 Installation
|
||||
|
||||
You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager.
|
||||
You can install Cognee using either **pip**, **poetry**, **uv** or any other python package manager..
|
||||
|
||||
Cognee supports Python 3.10 to 3.12
|
||||
|
||||
|
|
|
|||
|
|
@ -110,6 +110,47 @@ If you'd rather run cognee-mcp in a container, you have two options:
|
|||
# For stdio transport (default)
|
||||
docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main
|
||||
```
|
||||
|
||||
**Installing optional dependencies at runtime:**
|
||||
|
||||
You can install optional dependencies when running the container by setting the `EXTRAS` environment variable:
|
||||
```bash
|
||||
# Install a single optional dependency group at runtime
|
||||
docker run \
|
||||
-e TRANSPORT_MODE=http \
|
||||
-e EXTRAS=aws \
|
||||
--env-file ./.env \
|
||||
-p 8000:8000 \
|
||||
--rm -it cognee/cognee-mcp:main
|
||||
|
||||
# Install multiple optional dependency groups at runtime (comma-separated)
|
||||
docker run \
|
||||
-e TRANSPORT_MODE=sse \
|
||||
-e EXTRAS=aws,postgres,neo4j \
|
||||
--env-file ./.env \
|
||||
-p 8000:8000 \
|
||||
--rm -it cognee/cognee-mcp:main
|
||||
```
|
||||
|
||||
**Available optional dependency groups:**
|
||||
- `aws` - S3 storage support
|
||||
- `postgres` / `postgres-binary` - PostgreSQL database support
|
||||
- `neo4j` - Neo4j graph database support
|
||||
- `neptune` - AWS Neptune support
|
||||
- `chromadb` - ChromaDB vector store support
|
||||
- `scraping` - Web scraping capabilities
|
||||
- `distributed` - Modal distributed execution
|
||||
- `langchain` - LangChain integration
|
||||
- `llama-index` - LlamaIndex integration
|
||||
- `anthropic` - Anthropic models
|
||||
- `groq` - Groq models
|
||||
- `mistral` - Mistral models
|
||||
- `ollama` / `huggingface` - Local model support
|
||||
- `docs` - Document processing
|
||||
- `codegraph` - Code analysis
|
||||
- `monitoring` - Sentry & Langfuse monitoring
|
||||
- `redis` - Redis support
|
||||
- And more (see [pyproject.toml](https://github.com/topoteretes/cognee/blob/main/pyproject.toml) for full list)
|
||||
2. **Pull from Docker Hub** (no build required):
|
||||
```bash
|
||||
# With HTTP transport (recommended for web deployments)
|
||||
|
|
@ -120,6 +161,17 @@ If you'd rather run cognee-mcp in a container, you have two options:
|
|||
docker run -e TRANSPORT_MODE=stdio --env-file ./.env --rm -it cognee/cognee-mcp:main
|
||||
```
|
||||
|
||||
**With runtime installation of optional dependencies:**
|
||||
```bash
|
||||
# Install optional dependencies from Docker Hub image
|
||||
docker run \
|
||||
-e TRANSPORT_MODE=http \
|
||||
-e EXTRAS=aws,postgres \
|
||||
--env-file ./.env \
|
||||
-p 8000:8000 \
|
||||
--rm -it cognee/cognee-mcp:main
|
||||
```
|
||||
|
||||
### **Important: Docker vs Direct Usage**
|
||||
**Docker uses environment variables**, not command line arguments:
|
||||
- ✅ Docker: `-e TRANSPORT_MODE=http`
|
||||
|
|
|
|||
|
|
@ -4,6 +4,42 @@ set -e # Exit on error
|
|||
echo "Debug mode: $DEBUG"
|
||||
echo "Environment: $ENVIRONMENT"
|
||||
|
||||
# Install optional dependencies if EXTRAS is set
|
||||
if [ -n "$EXTRAS" ]; then
|
||||
echo "Installing optional dependencies: $EXTRAS"
|
||||
|
||||
# Get the cognee version that's currently installed
|
||||
COGNEE_VERSION=$(uv pip show cognee | grep "Version:" | awk '{print $2}')
|
||||
echo "Current cognee version: $COGNEE_VERSION"
|
||||
|
||||
# Build the extras list for cognee
|
||||
IFS=',' read -ra EXTRA_ARRAY <<< "$EXTRAS"
|
||||
# Combine base extras from pyproject.toml with requested extras
|
||||
ALL_EXTRAS=""
|
||||
for extra in "${EXTRA_ARRAY[@]}"; do
|
||||
# Trim whitespace
|
||||
extra=$(echo "$extra" | xargs)
|
||||
# Add to extras list if not already present
|
||||
if [[ ! "$ALL_EXTRAS" =~ (^|,)"$extra"(,|$) ]]; then
|
||||
if [ -z "$ALL_EXTRAS" ]; then
|
||||
ALL_EXTRAS="$extra"
|
||||
else
|
||||
ALL_EXTRAS="$ALL_EXTRAS,$extra"
|
||||
fi
|
||||
fi
|
||||
done
|
||||
|
||||
echo "Installing cognee with extras: $ALL_EXTRAS"
|
||||
echo "Running: uv pip install 'cognee[$ALL_EXTRAS]==$COGNEE_VERSION'"
|
||||
uv pip install "cognee[$ALL_EXTRAS]==$COGNEE_VERSION"
|
||||
|
||||
# Verify installation
|
||||
echo ""
|
||||
echo "✓ Optional dependencies installation completed"
|
||||
else
|
||||
echo "No optional dependencies specified"
|
||||
fi
|
||||
|
||||
# Set default transport mode if not specified
|
||||
TRANSPORT_MODE=${TRANSPORT_MODE:-"stdio"}
|
||||
echo "Transport mode: $TRANSPORT_MODE"
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ dependencies = [
|
|||
# For local cognee repo usage remove comment bellow and add absolute path to cognee. Then run `uv sync --reinstall` in the mcp folder on local cognee changes.
|
||||
#"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users/igorilic/Desktop/cognee",
|
||||
# TODO: Remove gemini from optional dependecnies for new Cognee version after 0.3.4
|
||||
"cognee[postgres,codegraph,huggingface,docs,neo4j]==0.3.7",
|
||||
"cognee[postgres,docs,neo4j]==0.3.7",
|
||||
"fastmcp>=2.10.0,<3.0.0",
|
||||
"mcp>=1.12.0,<2.0.0",
|
||||
"uv>=0.6.3,<1.0.0",
|
||||
|
|
|
|||
|
|
@ -37,12 +37,10 @@ async def run():
|
|||
|
||||
toolResult = await session.call_tool("prune", arguments={})
|
||||
|
||||
toolResult = await session.call_tool(
|
||||
"codify", arguments={"repo_path": "SOME_REPO_PATH"}
|
||||
)
|
||||
toolResult = await session.call_tool("cognify", arguments={})
|
||||
|
||||
toolResult = await session.call_tool(
|
||||
"search", arguments={"search_type": "CODE", "search_query": "exceptions"}
|
||||
"search", arguments={"search_type": "GRAPH_COMPLETION"}
|
||||
)
|
||||
|
||||
print(f"Cognify result: {toolResult.content}")
|
||||
|
|
|
|||
64
cognee-mcp/uv.lock
generated
64
cognee-mcp/uv.lock
generated
|
|
@ -718,19 +718,10 @@ wheels = [
|
|||
]
|
||||
|
||||
[package.optional-dependencies]
|
||||
codegraph = [
|
||||
{ name = "fastembed", marker = "python_full_version < '3.13'" },
|
||||
{ name = "transformers" },
|
||||
{ name = "tree-sitter" },
|
||||
{ name = "tree-sitter-python" },
|
||||
]
|
||||
docs = [
|
||||
{ name = "lxml" },
|
||||
{ name = "unstructured", extra = ["csv", "doc", "docx", "epub", "md", "odt", "org", "pdf", "ppt", "pptx", "rst", "rtf", "tsv", "xlsx"] },
|
||||
]
|
||||
huggingface = [
|
||||
{ name = "transformers" },
|
||||
]
|
||||
neo4j = [
|
||||
{ name = "neo4j" },
|
||||
]
|
||||
|
|
@ -745,7 +736,7 @@ name = "cognee-mcp"
|
|||
version = "0.4.0"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "cognee", extra = ["codegraph", "docs", "huggingface", "neo4j", "postgres"] },
|
||||
{ name = "cognee", extra = ["docs", "neo4j", "postgres"] },
|
||||
{ name = "fastmcp" },
|
||||
{ name = "httpx" },
|
||||
{ name = "mcp" },
|
||||
|
|
@ -759,7 +750,7 @@ dev = [
|
|||
|
||||
[package.metadata]
|
||||
requires-dist = [
|
||||
{ name = "cognee", extras = ["postgres", "codegraph", "huggingface", "docs", "neo4j"], specifier = "==0.3.7" },
|
||||
{ name = "cognee", extras = ["postgres", "docs", "neo4j"], specifier = "==0.3.7" },
|
||||
{ name = "fastmcp", specifier = ">=2.10.0,<3.0.0" },
|
||||
{ name = "httpx", specifier = ">=0.27.0,<1.0.0" },
|
||||
{ name = "mcp", specifier = ">=1.12.0,<2.0.0" },
|
||||
|
|
@ -6038,57 +6029,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/e5/2b/4d2708ac1ff5cd708b6548f4c5812d0ae40d1c28591c4c1c762b6dbdef2d/transformers-4.57.0-py3-none-any.whl", hash = "sha256:9d7c6d098c026e40d897e017ed1f481ab803cbac041021dbc6ae6100e4949b55", size = 11990588 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "tree-sitter"
|
||||
version = "0.24.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a7/a2/698b9d31d08ad5558f8bfbfe3a0781bd4b1f284e89bde3ad18e05101a892/tree-sitter-0.24.0.tar.gz", hash = "sha256:abd95af65ca2f4f7eca356343391ed669e764f37748b5352946f00f7fc78e734", size = 168304 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/08/9a/bd627a02e41671af73222316e1fcf87772c7804dc2fba99405275eb1f3eb/tree_sitter-0.24.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f3f00feff1fc47a8e4863561b8da8f5e023d382dd31ed3e43cd11d4cae445445", size = 140890 },
|
||||
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{ url = "https://files.pythonhosted.org/packages/8c/1c/ff23fa4931b6ef1bbeac461b904ca7e49eaec7e7e5398584e3eef836ec96/tree_sitter-0.24.0-cp312-cp312-win_amd64.whl", hash = "sha256:f9e8b1605ab60ed43803100f067eed71b0b0e6c1fb9860a262727dbfbbb74751", size = 120221 },
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{ url = "https://files.pythonhosted.org/packages/b2/2a/9979c626f303177b7612a802237d0533155bf1e425ff6f73cc40f25453e2/tree_sitter-0.24.0-cp312-cp312-win_arm64.whl", hash = "sha256:f733a83d8355fc95561582b66bbea92ffd365c5d7a665bc9ebd25e049c2b2abb", size = 108234 },
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{ url = "https://files.pythonhosted.org/packages/61/cd/2348339c85803330ce38cee1c6cbbfa78a656b34ff58606ebaf5c9e83bd0/tree_sitter-0.24.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:0d4a6416ed421c4210f0ca405a4834d5ccfbb8ad6692d4d74f7773ef68f92071", size = 140781 },
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{ url = "https://files.pythonhosted.org/packages/8b/a3/1ea9d8b64e8dcfcc0051028a9c84a630301290995cd6e947bf88267ef7b1/tree_sitter-0.24.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:e0992d483677e71d5c5d37f30dfb2e3afec2f932a9c53eec4fca13869b788c6c", size = 133928 },
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{ url = "https://files.pythonhosted.org/packages/fe/ae/55c1055609c9428a4aedf4b164400ab9adb0b1bf1538b51f4b3748a6c983/tree_sitter-0.24.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:57277a12fbcefb1c8b206186068d456c600dbfbc3fd6c76968ee22614c5cd5ad", size = 564497 },
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{ url = "https://files.pythonhosted.org/packages/ce/d0/f2ffcd04882c5aa28d205a787353130cbf84b2b8a977fd211bdc3b399ae3/tree_sitter-0.24.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d25fa22766d63f73716c6fec1a31ee5cf904aa429484256bd5fdf5259051ed74", size = 578917 },
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{ url = "https://files.pythonhosted.org/packages/af/82/aebe78ea23a2b3a79324993d4915f3093ad1af43d7c2208ee90be9273273/tree_sitter-0.24.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:7d5d9537507e1c8c5fa9935b34f320bfec4114d675e028f3ad94f11cf9db37b9", size = 581148 },
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{ url = "https://files.pythonhosted.org/packages/a1/b4/6b0291a590c2b0417cfdb64ccb8ea242f270a46ed429c641fbc2bfab77e0/tree_sitter-0.24.0-cp313-cp313-win_amd64.whl", hash = "sha256:f58bb4956917715ec4d5a28681829a8dad5c342cafd4aea269f9132a83ca9b34", size = 120207 },
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{ url = "https://files.pythonhosted.org/packages/a8/18/542fd844b75272630229c9939b03f7db232c71a9d82aadc59c596319ea6a/tree_sitter-0.24.0-cp313-cp313-win_arm64.whl", hash = "sha256:23641bd25dcd4bb0b6fa91b8fb3f46cc9f1c9f475efe4d536d3f1f688d1b84c8", size = 108232 },
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]
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|
||||
[[package]]
|
||||
name = "tree-sitter-python"
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||||
version = "0.23.6"
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||||
source = { registry = "https://pypi.org/simple" }
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||||
sdist = { url = "https://files.pythonhosted.org/packages/1c/30/6766433b31be476fda6569a3a374c2220e45ffee0bff75460038a57bf23b/tree_sitter_python-0.23.6.tar.gz", hash = "sha256:354bfa0a2f9217431764a631516f85173e9711af2c13dbd796a8815acfe505d9", size = 155868 }
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||||
wheels = [
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||||
{ url = "https://files.pythonhosted.org/packages/ab/67/577a02acae5f776007c924ca86ef14c19c12e71de0aa9d2a036f3c248e7b/tree_sitter_python-0.23.6-cp39-abi3-macosx_10_9_x86_64.whl", hash = "sha256:28fbec8f74eeb2b30292d97715e60fac9ccf8a8091ce19b9d93e9b580ed280fb", size = 74361 },
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{ url = "https://files.pythonhosted.org/packages/d2/a6/194b3625a7245c532ad418130d63077ce6cd241152524152f533e4d6edb0/tree_sitter_python-0.23.6-cp39-abi3-macosx_11_0_arm64.whl", hash = "sha256:680b710051b144fedf61c95197db0094f2245e82551bf7f0c501356333571f7a", size = 76436 },
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||||
{ url = "https://files.pythonhosted.org/packages/d0/62/1da112689d6d282920e62c40e67ab39ea56463b0e7167bfc5e81818a770e/tree_sitter_python-0.23.6-cp39-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8a9dcef55507b6567207e8ee0a6b053d0688019b47ff7f26edc1764b7f4dc0a4", size = 112060 },
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||||
{ url = "https://files.pythonhosted.org/packages/5d/62/c9358584c96e38318d69b6704653684fd8467601f7b74e88aa44f4e6903f/tree_sitter_python-0.23.6-cp39-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:29dacdc0cd2f64e55e61d96c6906533ebb2791972bec988450c46cce60092f5d", size = 112338 },
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{ url = "https://files.pythonhosted.org/packages/1a/58/c5e61add45e34fb8ecbf057c500bae9d96ed7c9ca36edb7985da8ae45526/tree_sitter_python-0.23.6-cp39-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:7e048733c36f564b379831689006801feb267d8194f9e793fbb395ef1723335d", size = 109382 },
|
||||
{ url = "https://files.pythonhosted.org/packages/e9/f3/9b30893cae9b3811fe652dc6f90aaadfda12ae0b2757f5722fc7266f423c/tree_sitter_python-0.23.6-cp39-abi3-win_amd64.whl", hash = "sha256:a24027248399fb41594b696f929f9956828ae7cc85596d9f775e6c239cd0c2be", size = 75904 },
|
||||
{ url = "https://files.pythonhosted.org/packages/87/cb/ce35a65f83a47b510d8a2f1eddf3bdbb0d57aabc87351c8788caf3309f76/tree_sitter_python-0.23.6-cp39-abi3-win_arm64.whl", hash = "sha256:71334371bd73d5fe080aed39fbff49ed8efb9506edebe16795b0c7567ed6a272", size = 73649 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "3.5.0"
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
import os
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
from functools import lru_cache
|
||||
from cognee.root_dir import get_absolute_path, ensure_absolute_path
|
||||
|
|
@ -11,6 +12,9 @@ class BaseConfig(BaseSettings):
|
|||
data_root_directory: str = get_absolute_path(".data_storage")
|
||||
system_root_directory: str = get_absolute_path(".cognee_system")
|
||||
cache_root_directory: str = get_absolute_path(".cognee_cache")
|
||||
logs_root_directory: str = os.getenv(
|
||||
"COGNEE_LOGS_DIR", str(os.path.join(os.path.dirname(os.path.dirname(__file__)), "logs"))
|
||||
)
|
||||
monitoring_tool: object = Observer.NONE
|
||||
|
||||
@pydantic.model_validator(mode="after")
|
||||
|
|
@ -30,6 +34,8 @@ class BaseConfig(BaseSettings):
|
|||
# Require absolute paths for root directories
|
||||
self.data_root_directory = ensure_absolute_path(self.data_root_directory)
|
||||
self.system_root_directory = ensure_absolute_path(self.system_root_directory)
|
||||
self.logs_root_directory = ensure_absolute_path(self.logs_root_directory)
|
||||
|
||||
# Set monitoring tool based on available keys
|
||||
if self.langfuse_public_key and self.langfuse_secret_key:
|
||||
self.monitoring_tool = Observer.LANGFUSE
|
||||
|
|
@ -49,6 +55,7 @@ class BaseConfig(BaseSettings):
|
|||
"system_root_directory": self.system_root_directory,
|
||||
"monitoring_tool": self.monitoring_tool,
|
||||
"cache_root_directory": self.cache_root_directory,
|
||||
"logs_root_directory": self.logs_root_directory,
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -47,7 +47,7 @@ def create_vector_engine(
|
|||
embedding_engine=embedding_engine,
|
||||
)
|
||||
|
||||
if vector_db_provider == "pgvector":
|
||||
if vector_db_provider.lower() == "pgvector":
|
||||
from cognee.infrastructure.databases.relational import get_relational_config
|
||||
|
||||
# Get configuration for postgres database
|
||||
|
|
@ -78,7 +78,7 @@ def create_vector_engine(
|
|||
embedding_engine,
|
||||
)
|
||||
|
||||
elif vector_db_provider == "chromadb":
|
||||
elif vector_db_provider.lower() == "chromadb":
|
||||
try:
|
||||
import chromadb
|
||||
except ImportError:
|
||||
|
|
@ -94,7 +94,7 @@ def create_vector_engine(
|
|||
embedding_engine=embedding_engine,
|
||||
)
|
||||
|
||||
elif vector_db_provider == "neptune_analytics":
|
||||
elif vector_db_provider.lower() == "neptune_analytics":
|
||||
try:
|
||||
from langchain_aws import NeptuneAnalyticsGraph
|
||||
except ImportError:
|
||||
|
|
@ -122,7 +122,7 @@ def create_vector_engine(
|
|||
embedding_engine=embedding_engine,
|
||||
)
|
||||
|
||||
else:
|
||||
elif vector_db_provider.lower() == "lancedb":
|
||||
from .lancedb.LanceDBAdapter import LanceDBAdapter
|
||||
|
||||
return LanceDBAdapter(
|
||||
|
|
@ -130,3 +130,9 @@ def create_vector_engine(
|
|||
api_key=vector_db_key,
|
||||
embedding_engine=embedding_engine,
|
||||
)
|
||||
|
||||
else:
|
||||
raise EnvironmentError(
|
||||
f"Unsupported vector database provider: {vector_db_provider}. "
|
||||
f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['LanceDB', 'PGVector', 'neptune_analytics', 'ChromaDB'])}"
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
from pydantic import BaseModel
|
||||
from pydantic import BaseModel, field_validator
|
||||
from typing import Optional, Any, Dict
|
||||
|
||||
|
||||
|
|
@ -18,9 +18,21 @@ class Edge(BaseModel):
|
|||
|
||||
# Mixed usage
|
||||
has_items: (Edge(weight=0.5, weights={"confidence": 0.9}), list[Item])
|
||||
|
||||
# With edge_text for rich embedding representation
|
||||
contains: (Edge(relationship_type="contains", edge_text="relationship_name: contains; entity_description: Alice"), Entity)
|
||||
"""
|
||||
|
||||
weight: Optional[float] = None
|
||||
weights: Optional[Dict[str, float]] = None
|
||||
relationship_type: Optional[str] = None
|
||||
properties: Optional[Dict[str, Any]] = None
|
||||
edge_text: Optional[str] = None
|
||||
|
||||
@field_validator("edge_text", mode="before")
|
||||
@classmethod
|
||||
def ensure_edge_text(cls, v, info):
|
||||
"""Auto-populate edge_text from relationship_type if not explicitly provided."""
|
||||
if v is None and info.data.get("relationship_type"):
|
||||
return info.data["relationship_type"]
|
||||
return v
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
import io
|
||||
import os.path
|
||||
from typing import BinaryIO, TypedDict
|
||||
from typing import BinaryIO, TypedDict, Optional
|
||||
from pathlib import Path
|
||||
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
|
|
@ -27,7 +27,7 @@ class FileMetadata(TypedDict):
|
|||
file_size: int
|
||||
|
||||
|
||||
async def get_file_metadata(file: BinaryIO) -> FileMetadata:
|
||||
async def get_file_metadata(file: BinaryIO, name: Optional[str] = None) -> FileMetadata:
|
||||
"""
|
||||
Retrieve metadata from a file object.
|
||||
|
||||
|
|
@ -53,7 +53,7 @@ async def get_file_metadata(file: BinaryIO) -> FileMetadata:
|
|||
except io.UnsupportedOperation as error:
|
||||
logger.error(f"Error retrieving content hash for file: {file.name} \n{str(error)}\n\n")
|
||||
|
||||
file_type = guess_file_type(file)
|
||||
file_type = guess_file_type(file, name)
|
||||
|
||||
file_path = getattr(file, "name", None) or getattr(file, "full_name", None)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,9 @@
|
|||
from typing import BinaryIO
|
||||
import io
|
||||
from pathlib import Path
|
||||
from typing import BinaryIO, Optional, Any
|
||||
import filetype
|
||||
from .is_text_content import is_text_content
|
||||
from tempfile import SpooledTemporaryFile
|
||||
from filetype.types.base import Type
|
||||
|
||||
|
||||
class FileTypeException(Exception):
|
||||
|
|
@ -22,90 +25,7 @@ class FileTypeException(Exception):
|
|||
self.message = message
|
||||
|
||||
|
||||
class TxtFileType(filetype.Type):
|
||||
"""
|
||||
Represents a text file type with specific MIME and extension properties.
|
||||
|
||||
Public methods:
|
||||
- match: Determines whether a given buffer matches the text file type.
|
||||
"""
|
||||
|
||||
MIME = "text/plain"
|
||||
EXTENSION = "txt"
|
||||
|
||||
def __init__(self):
|
||||
super(TxtFileType, self).__init__(mime=TxtFileType.MIME, extension=TxtFileType.EXTENSION)
|
||||
|
||||
def match(self, buf):
|
||||
"""
|
||||
Determine if the given buffer contains text content.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- buf: The buffer to check for text content.
|
||||
|
||||
Returns:
|
||||
--------
|
||||
|
||||
Returns True if the buffer is identified as text content, otherwise False.
|
||||
"""
|
||||
return is_text_content(buf)
|
||||
|
||||
|
||||
txt_file_type = TxtFileType()
|
||||
|
||||
filetype.add_type(txt_file_type)
|
||||
|
||||
|
||||
class CustomPdfMatcher(filetype.Type):
|
||||
"""
|
||||
Match PDF file types based on MIME type and extension.
|
||||
|
||||
Public methods:
|
||||
- match
|
||||
|
||||
Instance variables:
|
||||
- MIME: The MIME type of the PDF.
|
||||
- EXTENSION: The file extension of the PDF.
|
||||
"""
|
||||
|
||||
MIME = "application/pdf"
|
||||
EXTENSION = "pdf"
|
||||
|
||||
def __init__(self):
|
||||
super(CustomPdfMatcher, self).__init__(
|
||||
mime=CustomPdfMatcher.MIME, extension=CustomPdfMatcher.EXTENSION
|
||||
)
|
||||
|
||||
def match(self, buf):
|
||||
"""
|
||||
Determine if the provided buffer is a PDF file.
|
||||
|
||||
This method checks for the presence of the PDF signature in the buffer.
|
||||
|
||||
Raises:
|
||||
- TypeError: If the buffer is not of bytes type.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- buf: The buffer containing the data to be checked.
|
||||
|
||||
Returns:
|
||||
--------
|
||||
|
||||
Returns True if the buffer contains a PDF signature, otherwise returns False.
|
||||
"""
|
||||
return b"PDF-" in buf
|
||||
|
||||
|
||||
custom_pdf_matcher = CustomPdfMatcher()
|
||||
|
||||
filetype.add_type(custom_pdf_matcher)
|
||||
|
||||
|
||||
def guess_file_type(file: BinaryIO) -> filetype.Type:
|
||||
def guess_file_type(file: BinaryIO, name: Optional[str] = None) -> filetype.Type:
|
||||
"""
|
||||
Guess the file type from the given binary file stream.
|
||||
|
||||
|
|
@ -122,12 +42,23 @@ def guess_file_type(file: BinaryIO) -> filetype.Type:
|
|||
|
||||
- filetype.Type: The guessed file type, represented as filetype.Type.
|
||||
"""
|
||||
|
||||
# Note: If file has .txt or .text extension, consider it a plain text file as filetype.guess may not detect it properly
|
||||
# as it contains no magic number encoding
|
||||
ext = None
|
||||
if isinstance(file, str):
|
||||
ext = Path(file).suffix
|
||||
elif name is not None:
|
||||
ext = Path(name).suffix
|
||||
|
||||
if ext in [".txt", ".text"]:
|
||||
file_type = Type("text/plain", "txt")
|
||||
return file_type
|
||||
|
||||
file_type = filetype.guess(file)
|
||||
|
||||
# If file type could not be determined consider it a plain text file as they don't have magic number encoding
|
||||
if file_type is None:
|
||||
from filetype.types.base import Type
|
||||
|
||||
file_type = Type("text/plain", "txt")
|
||||
|
||||
if file_type is None:
|
||||
|
|
|
|||
|
|
@ -1,15 +1,13 @@
|
|||
For the purposes of identifying timestamps in a query, you are tasked with extracting relevant timestamps from the query.
|
||||
## Timestamp requirements
|
||||
- If the query contains interval extrack both starts_at and ends_at properties
|
||||
- If the query contains an instantaneous timestamp, starts_at and ends_at should be the same
|
||||
- If the query its open-ended (before 2009 or after 2009), the corresponding non defined end of the time should be none
|
||||
-For example: "before 2009" -- starts_at: None, ends_at: 2009 or "after 2009" -- starts_at: 2009, ends_at: None
|
||||
- Put always the data that comes first in time as starts_at and the timestamps that comes second in time as ends_at
|
||||
- If starts_at or ends_at cannot be extracted both of them has to be None
|
||||
## Output Format
|
||||
Your reply should be a JSON: list of dictionaries with the following structure:
|
||||
```python
|
||||
class QueryInterval(BaseModel):
|
||||
starts_at: Optional[Timestamp] = None
|
||||
ends_at: Optional[Timestamp] = None
|
||||
```
|
||||
You are tasked with identifying relevant time periods where the answer to a given query should be searched.
|
||||
Current date is: `{{ time_now }}`. Determine relevant period(s) and return structured intervals.
|
||||
|
||||
Extraction rules:
|
||||
|
||||
1. Query without specific timestamp: use the time period with starts_at set to None and ends_at set to now.
|
||||
2. Explicit time intervals: If the query specifies a range (e.g., from 2010 to 2020, between January and March 2023), extract both start and end dates. Always assign the earlier date to starts_at and the later date to ends_at.
|
||||
3. Single timestamp: If the query refers to one specific moment (e.g., in 2015, on March 5, 2022), set starts_at and ends_at to that same timestamp.
|
||||
4. Open-ended time references: For phrases such as "before X" or "after X", represent the unspecified side as None. For example: before 2009 → starts_at: None, ends_at: 2009; after 2009 → starts_at: 2009, ends_at: None.
|
||||
5. Current-time references ("now", "current", "today"): If the query explicitly refers to the present, set both starts_at and ends_at to now (the ingestion timestamp).
|
||||
6. "Who is" and "Who was" questions: These imply a general identity or biographical inquiry without a specific temporal scope. Set both starts_at and ends_at to None.
|
||||
7. Ordering rule: Always ensure the earlier date is assigned to starts_at and the later date to ends_at.
|
||||
8. No temporal information: If no valid or inferable time reference is found, set both starts_at and ends_at to None.
|
||||
|
|
@ -1,6 +1,7 @@
|
|||
import filetype
|
||||
from typing import Dict, List, Optional, Any
|
||||
from .LoaderInterface import LoaderInterface
|
||||
from cognee.infrastructure.files.utils.guess_file_type import guess_file_type
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
|
||||
logger = get_logger(__name__)
|
||||
|
|
@ -80,7 +81,7 @@ class LoaderEngine:
|
|||
"""
|
||||
from pathlib import Path
|
||||
|
||||
file_info = filetype.guess(file_path)
|
||||
file_info = guess_file_type(file_path)
|
||||
|
||||
path_extension = Path(file_path).suffix.lstrip(".")
|
||||
|
||||
|
|
|
|||
|
|
@ -42,6 +42,7 @@ class AudioLoader(LoaderInterface):
|
|||
"audio/wav",
|
||||
"audio/amr",
|
||||
"audio/aiff",
|
||||
"audio/x-wav",
|
||||
]
|
||||
|
||||
@property
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
from typing import List, Union
|
||||
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.infrastructure.engine.models.Edge import Edge
|
||||
from cognee.modules.data.processing.document_types import Document
|
||||
from cognee.modules.engine.models import Entity
|
||||
from cognee.tasks.temporal_graph.models import Event
|
||||
|
|
@ -31,6 +32,6 @@ class DocumentChunk(DataPoint):
|
|||
chunk_index: int
|
||||
cut_type: str
|
||||
is_part_of: Document
|
||||
contains: List[Union[Entity, Event]] = None
|
||||
contains: List[Union[Entity, Event, tuple[Edge, Entity]]] = None
|
||||
|
||||
metadata: dict = {"index_fields": ["text"]}
|
||||
|
|
|
|||
|
|
@ -171,8 +171,10 @@ class CogneeGraph(CogneeAbstractGraph):
|
|||
embedding_map = {result.payload["text"]: result.score for result in edge_distances}
|
||||
|
||||
for edge in self.edges:
|
||||
relationship_type = edge.attributes.get("relationship_type")
|
||||
distance = embedding_map.get(relationship_type, None)
|
||||
edge_key = edge.attributes.get("edge_text") or edge.attributes.get(
|
||||
"relationship_type"
|
||||
)
|
||||
distance = embedding_map.get(edge_key, None)
|
||||
if distance is not None:
|
||||
edge.attributes["vector_distance"] = distance
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
from typing import Optional
|
||||
|
||||
from cognee.infrastructure.engine.models.Edge import Edge
|
||||
from cognee.modules.chunking.models import DocumentChunk
|
||||
from cognee.modules.engine.models import Entity, EntityType
|
||||
from cognee.modules.engine.utils import (
|
||||
|
|
@ -243,10 +244,26 @@ def _process_graph_nodes(
|
|||
ontology_relationships,
|
||||
)
|
||||
|
||||
# Add entity to data chunk
|
||||
if data_chunk.contains is None:
|
||||
data_chunk.contains = []
|
||||
data_chunk.contains.append(entity_node)
|
||||
|
||||
edge_text = "; ".join(
|
||||
[
|
||||
"relationship_name: contains",
|
||||
f"entity_name: {entity_node.name}",
|
||||
f"entity_description: {entity_node.description}",
|
||||
]
|
||||
)
|
||||
|
||||
data_chunk.contains.append(
|
||||
(
|
||||
Edge(
|
||||
relationship_type="contains",
|
||||
edge_text=edge_text,
|
||||
),
|
||||
entity_node,
|
||||
)
|
||||
)
|
||||
|
||||
|
||||
def _process_graph_edges(
|
||||
|
|
|
|||
|
|
@ -1,71 +1,70 @@
|
|||
from typing import List
|
||||
from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge
|
||||
|
||||
|
||||
async def resolve_edges_to_text(retrieved_edges: List[Edge]) -> str:
|
||||
"""
|
||||
Converts retrieved graph edges into a human-readable string format.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- retrieved_edges (list): A list of edges retrieved from the graph.
|
||||
|
||||
Returns:
|
||||
--------
|
||||
|
||||
- str: A formatted string representation of the nodes and their connections.
|
||||
"""
|
||||
|
||||
def _get_nodes(retrieved_edges: List[Edge]) -> dict:
|
||||
def _get_title(text: str, first_n_words: int = 7, top_n_words: int = 3) -> str:
|
||||
def _top_n_words(text, stop_words=None, top_n=3, separator=", "):
|
||||
"""Concatenates the top N frequent words in text."""
|
||||
if stop_words is None:
|
||||
from cognee.modules.retrieval.utils.stop_words import DEFAULT_STOP_WORDS
|
||||
|
||||
stop_words = DEFAULT_STOP_WORDS
|
||||
|
||||
import string
|
||||
|
||||
words = [word.lower().strip(string.punctuation) for word in text.split()]
|
||||
|
||||
if stop_words:
|
||||
words = [word for word in words if word and word not in stop_words]
|
||||
|
||||
from typing import List
|
||||
from collections import Counter
|
||||
|
||||
top_words = [word for word, freq in Counter(words).most_common(top_n)]
|
||||
from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge
|
||||
from cognee.modules.retrieval.utils.stop_words import DEFAULT_STOP_WORDS
|
||||
|
||||
|
||||
def _get_top_n_frequent_words(
|
||||
text: str, stop_words: set = None, top_n: int = 3, separator: str = ", "
|
||||
) -> str:
|
||||
"""Concatenates the top N frequent words in text."""
|
||||
if stop_words is None:
|
||||
stop_words = DEFAULT_STOP_WORDS
|
||||
|
||||
words = [word.lower().strip(string.punctuation) for word in text.split()]
|
||||
words = [word for word in words if word and word not in stop_words]
|
||||
|
||||
top_words = [word for word, freq in Counter(words).most_common(top_n)]
|
||||
return separator.join(top_words)
|
||||
|
||||
"""Creates a title, by combining first words with most frequent words from the text."""
|
||||
|
||||
def _create_title_from_text(text: str, first_n_words: int = 7, top_n_words: int = 3) -> str:
|
||||
"""Creates a title by combining first words with most frequent words from the text."""
|
||||
first_words = text.split()[:first_n_words]
|
||||
top_words = _top_n_words(text, top_n=first_n_words)
|
||||
top_words = _get_top_n_frequent_words(text, top_n=top_n_words)
|
||||
return f"{' '.join(first_words)}... [{top_words}]"
|
||||
|
||||
|
||||
def _extract_nodes_from_edges(retrieved_edges: List[Edge]) -> dict:
|
||||
"""Creates a dictionary of nodes with their names and content."""
|
||||
nodes = {}
|
||||
|
||||
for edge in retrieved_edges:
|
||||
for node in (edge.node1, edge.node2):
|
||||
if node.id not in nodes:
|
||||
if node.id in nodes:
|
||||
continue
|
||||
|
||||
text = node.attributes.get("text")
|
||||
if text:
|
||||
name = _get_title(text)
|
||||
name = _create_title_from_text(text)
|
||||
content = text
|
||||
else:
|
||||
name = node.attributes.get("name", "Unnamed Node")
|
||||
content = node.attributes.get("description", name)
|
||||
|
||||
nodes[node.id] = {"node": node, "name": name, "content": content}
|
||||
|
||||
return nodes
|
||||
|
||||
nodes = _get_nodes(retrieved_edges)
|
||||
|
||||
async def resolve_edges_to_text(retrieved_edges: List[Edge]) -> str:
|
||||
"""Converts retrieved graph edges into a human-readable string format."""
|
||||
nodes = _extract_nodes_from_edges(retrieved_edges)
|
||||
|
||||
node_section = "\n".join(
|
||||
f"Node: {info['name']}\n__node_content_start__\n{info['content']}\n__node_content_end__\n"
|
||||
for info in nodes.values()
|
||||
)
|
||||
connection_section = "\n".join(
|
||||
f"{nodes[edge.node1.id]['name']} --[{edge.attributes['relationship_type']}]--> {nodes[edge.node2.id]['name']}"
|
||||
for edge in retrieved_edges
|
||||
)
|
||||
|
||||
connections = []
|
||||
for edge in retrieved_edges:
|
||||
source_name = nodes[edge.node1.id]["name"]
|
||||
target_name = nodes[edge.node2.id]["name"]
|
||||
edge_label = edge.attributes.get("edge_text") or edge.attributes.get("relationship_type")
|
||||
connections.append(f"{source_name} --[{edge_label}]--> {target_name}")
|
||||
|
||||
connection_section = "\n".join(connections)
|
||||
|
||||
return f"Nodes:\n{node_section}\n\nConnections:\n{connection_section}"
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ class BinaryData(IngestionData):
|
|||
|
||||
async def ensure_metadata(self):
|
||||
if self.metadata is None:
|
||||
self.metadata = await get_file_metadata(self.data)
|
||||
self.metadata = await get_file_metadata(self.data, name=self.name)
|
||||
|
||||
if self.metadata["name"] is None:
|
||||
self.metadata["name"] = self.name
|
||||
|
|
|
|||
|
|
@ -21,7 +21,8 @@ def get_ontology_resolver_from_env(
|
|||
Supported value: "rdflib".
|
||||
matching_strategy (str): The matching strategy to apply.
|
||||
Supported value: "fuzzy".
|
||||
ontology_file_path (str): Path to the ontology file required for the resolver.
|
||||
ontology_file_path (str): Path to the ontology file(s) required for the resolver.
|
||||
Can be a single path or comma-separated paths for multiple files.
|
||||
|
||||
Returns:
|
||||
BaseOntologyResolver: An instance of the requested ontology resolver.
|
||||
|
|
@ -31,8 +32,13 @@ def get_ontology_resolver_from_env(
|
|||
or if required parameters are missing.
|
||||
"""
|
||||
if ontology_resolver == "rdflib" and matching_strategy == "fuzzy" and ontology_file_path:
|
||||
if "," in ontology_file_path:
|
||||
file_paths = [path.strip() for path in ontology_file_path.split(",")]
|
||||
else:
|
||||
file_paths = ontology_file_path
|
||||
|
||||
return RDFLibOntologyResolver(
|
||||
matching_strategy=FuzzyMatchingStrategy(), ontology_file=ontology_file_path
|
||||
matching_strategy=FuzzyMatchingStrategy(), ontology_file=file_paths
|
||||
)
|
||||
else:
|
||||
raise EnvironmentError(
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import os
|
|||
import difflib
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from collections import deque
|
||||
from typing import List, Tuple, Dict, Optional, Any
|
||||
from typing import List, Tuple, Dict, Optional, Any, Union
|
||||
from rdflib import Graph, URIRef, RDF, RDFS, OWL
|
||||
|
||||
from cognee.modules.ontology.exceptions import (
|
||||
|
|
@ -26,22 +26,50 @@ class RDFLibOntologyResolver(BaseOntologyResolver):
|
|||
|
||||
def __init__(
|
||||
self,
|
||||
ontology_file: Optional[str] = None,
|
||||
ontology_file: Optional[Union[str, List[str]]] = None,
|
||||
matching_strategy: Optional[MatchingStrategy] = None,
|
||||
) -> None:
|
||||
super().__init__(matching_strategy)
|
||||
self.ontology_file = ontology_file
|
||||
try:
|
||||
if ontology_file and os.path.exists(ontology_file):
|
||||
self.graph = Graph()
|
||||
self.graph.parse(ontology_file)
|
||||
logger.info("Ontology loaded successfully from file: %s", ontology_file)
|
||||
files_to_load = []
|
||||
if ontology_file is not None:
|
||||
if isinstance(ontology_file, str):
|
||||
files_to_load = [ontology_file]
|
||||
elif isinstance(ontology_file, list):
|
||||
files_to_load = ontology_file
|
||||
else:
|
||||
raise ValueError(
|
||||
f"ontology_file must be a string, list of strings, or None. Got: {type(ontology_file)}"
|
||||
)
|
||||
|
||||
if files_to_load:
|
||||
self.graph = Graph()
|
||||
loaded_files = []
|
||||
for file_path in files_to_load:
|
||||
if os.path.exists(file_path):
|
||||
self.graph.parse(file_path)
|
||||
loaded_files.append(file_path)
|
||||
logger.info("Ontology loaded successfully from file: %s", file_path)
|
||||
else:
|
||||
logger.warning(
|
||||
"Ontology file '%s' not found. Skipping this file.",
|
||||
file_path,
|
||||
)
|
||||
|
||||
if not loaded_files:
|
||||
logger.info(
|
||||
"Ontology file '%s' not found. No owl ontology will be attached to the graph.",
|
||||
ontology_file,
|
||||
"No valid ontology files found. No owl ontology will be attached to the graph."
|
||||
)
|
||||
self.graph = None
|
||||
else:
|
||||
logger.info("Total ontology files loaded: %d", len(loaded_files))
|
||||
else:
|
||||
logger.info(
|
||||
"No ontology file provided. No owl ontology will be attached to the graph."
|
||||
)
|
||||
self.graph = None
|
||||
|
||||
self.build_lookup()
|
||||
except Exception as e:
|
||||
logger.error("Failed to load ontology", exc_info=e)
|
||||
|
|
|
|||
|
|
@ -27,6 +27,7 @@ async def handle_task(
|
|||
additional_properties={
|
||||
"task_name": running_task.executable.__name__,
|
||||
"cognee_version": cognee_version,
|
||||
"tenant_id": str(user.tenant_id) if user.tenant_id else "Single User Tenant",
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -49,6 +50,7 @@ async def handle_task(
|
|||
additional_properties={
|
||||
"task_name": running_task.executable.__name__,
|
||||
"cognee_version": cognee_version,
|
||||
"tenant_id": str(user.tenant_id) if user.tenant_id else "Single User Tenant",
|
||||
},
|
||||
)
|
||||
except Exception as error:
|
||||
|
|
@ -62,6 +64,7 @@ async def handle_task(
|
|||
additional_properties={
|
||||
"task_name": running_task.executable.__name__,
|
||||
"cognee_version": cognee_version,
|
||||
"tenant_id": str(user.tenant_id) if user.tenant_id else "Single User Tenant",
|
||||
},
|
||||
)
|
||||
raise error
|
||||
|
|
|
|||
|
|
@ -28,6 +28,7 @@ async def run_tasks_with_telemetry(
|
|||
additional_properties={
|
||||
"pipeline_name": str(pipeline_name),
|
||||
"cognee_version": cognee_version,
|
||||
"tenant_id": str(user.tenant_id) if user.tenant_id else "Single User Tenant",
|
||||
}
|
||||
| config,
|
||||
)
|
||||
|
|
@ -42,6 +43,7 @@ async def run_tasks_with_telemetry(
|
|||
additional_properties={
|
||||
"pipeline_name": str(pipeline_name),
|
||||
"cognee_version": cognee_version,
|
||||
"tenant_id": str(user.tenant_id) if user.tenant_id else "Single User Tenant",
|
||||
}
|
||||
| config,
|
||||
)
|
||||
|
|
@ -58,6 +60,7 @@ async def run_tasks_with_telemetry(
|
|||
additional_properties={
|
||||
"pipeline_name": str(pipeline_name),
|
||||
"cognee_version": cognee_version,
|
||||
"tenant_id": str(user.tenant_id) if user.tenant_id else "Single User Tenant",
|
||||
}
|
||||
| config,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import os
|
||||
import asyncio
|
||||
from typing import Any, Optional, List, Type
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
from operator import itemgetter
|
||||
from cognee.infrastructure.databases.vector import get_vector_engine
|
||||
|
|
@ -79,7 +79,11 @@ class TemporalRetriever(GraphCompletionRetriever):
|
|||
else:
|
||||
base_directory = None
|
||||
|
||||
system_prompt = render_prompt(prompt_path, {}, base_directory=base_directory)
|
||||
time_now = datetime.now().strftime("%d-%m-%Y")
|
||||
|
||||
system_prompt = render_prompt(
|
||||
prompt_path, {"time_now": time_now}, base_directory=base_directory
|
||||
)
|
||||
|
||||
interval = await LLMGateway.acreate_structured_output(query, system_prompt, QueryInterval)
|
||||
|
||||
|
|
@ -108,8 +112,6 @@ class TemporalRetriever(GraphCompletionRetriever):
|
|||
|
||||
graph_engine = await get_graph_engine()
|
||||
|
||||
triplets = []
|
||||
|
||||
if time_from and time_to:
|
||||
ids = await graph_engine.collect_time_ids(time_from=time_from, time_to=time_to)
|
||||
elif time_from:
|
||||
|
|
|
|||
|
|
@ -71,7 +71,7 @@ async def get_memory_fragment(
|
|||
await memory_fragment.project_graph_from_db(
|
||||
graph_engine,
|
||||
node_properties_to_project=properties_to_project,
|
||||
edge_properties_to_project=["relationship_name"],
|
||||
edge_properties_to_project=["relationship_name", "edge_text"],
|
||||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -67,7 +67,10 @@ async def search(
|
|||
send_telemetry(
|
||||
"cognee.search EXECUTION STARTED",
|
||||
user.id,
|
||||
additional_properties={"cognee_version": cognee_version},
|
||||
additional_properties={
|
||||
"cognee_version": cognee_version,
|
||||
"tenant_id": str(user.tenant_id) if user.tenant_id else "Single User Tenant",
|
||||
},
|
||||
)
|
||||
|
||||
# Use search function filtered by permissions if access control is enabled
|
||||
|
|
@ -108,7 +111,10 @@ async def search(
|
|||
send_telemetry(
|
||||
"cognee.search EXECUTION COMPLETED",
|
||||
user.id,
|
||||
additional_properties={"cognee_version": cognee_version},
|
||||
additional_properties={
|
||||
"cognee_version": cognee_version,
|
||||
"tenant_id": str(user.tenant_id) if user.tenant_id else "Single User Tenant",
|
||||
},
|
||||
)
|
||||
|
||||
await log_result(
|
||||
|
|
|
|||
|
|
@ -16,17 +16,17 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
|
||||
nodes_list = []
|
||||
color_map = {
|
||||
"Entity": "#f47710",
|
||||
"EntityType": "#6510f4",
|
||||
"DocumentChunk": "#801212",
|
||||
"TextSummary": "#1077f4",
|
||||
"TableRow": "#f47710",
|
||||
"TableType": "#6510f4",
|
||||
"ColumnValue": "#13613a",
|
||||
"SchemaTable": "#f47710",
|
||||
"DatabaseSchema": "#6510f4",
|
||||
"SchemaRelationship": "#13613a",
|
||||
"default": "#D3D3D3",
|
||||
"Entity": "#5C10F4",
|
||||
"EntityType": "#A550FF",
|
||||
"DocumentChunk": "#0DFF00",
|
||||
"TextSummary": "#5C10F4",
|
||||
"TableRow": "#A550FF",
|
||||
"TableType": "#5C10F4",
|
||||
"ColumnValue": "#757470",
|
||||
"SchemaTable": "#A550FF",
|
||||
"DatabaseSchema": "#5C10F4",
|
||||
"SchemaRelationship": "#323332",
|
||||
"default": "#D8D8D8",
|
||||
}
|
||||
|
||||
for node_id, node_info in nodes_data:
|
||||
|
|
@ -98,16 +98,19 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
<head>
|
||||
<meta charset="utf-8">
|
||||
<script src="https://d3js.org/d3.v5.min.js"></script>
|
||||
<script src="https://d3js.org/d3-contour.v1.min.js"></script>
|
||||
<style>
|
||||
body, html { margin: 0; padding: 0; width: 100%; height: 100%; overflow: hidden; background: linear-gradient(90deg, #101010, #1a1a2e); color: white; font-family: 'Inter', sans-serif; }
|
||||
|
||||
svg { width: 100vw; height: 100vh; display: block; }
|
||||
.links line { stroke: rgba(255, 255, 255, 0.4); stroke-width: 2px; }
|
||||
.links line.weighted { stroke: rgba(255, 215, 0, 0.7); }
|
||||
.links line.multi-weighted { stroke: rgba(0, 255, 127, 0.8); }
|
||||
.nodes circle { stroke: white; stroke-width: 0.5px; filter: drop-shadow(0 0 5px rgba(255,255,255,0.3)); }
|
||||
.node-label { font-size: 5px; font-weight: bold; fill: white; text-anchor: middle; dominant-baseline: middle; font-family: 'Inter', sans-serif; pointer-events: none; }
|
||||
.edge-label { font-size: 3px; fill: rgba(255, 255, 255, 0.7); text-anchor: middle; dominant-baseline: middle; font-family: 'Inter', sans-serif; pointer-events: none; }
|
||||
.links line { stroke: rgba(160, 160, 160, 0.25); stroke-width: 1.5px; stroke-linecap: round; }
|
||||
.links line.weighted { stroke: rgba(255, 215, 0, 0.4); }
|
||||
.links line.multi-weighted { stroke: rgba(0, 255, 127, 0.45); }
|
||||
.nodes circle { stroke: white; stroke-width: 0.5px; }
|
||||
.node-label { font-size: 5px; font-weight: bold; fill: #F4F4F4; text-anchor: middle; dominant-baseline: middle; font-family: 'Inter', sans-serif; pointer-events: none; }
|
||||
.edge-label { font-size: 3px; fill: #F4F4F4; text-anchor: middle; dominant-baseline: middle; font-family: 'Inter', sans-serif; pointer-events: none; paint-order: stroke; stroke: rgba(50,51,50,0.75); stroke-width: 1px; }
|
||||
|
||||
.density path { mix-blend-mode: screen; }
|
||||
|
||||
.tooltip {
|
||||
position: absolute;
|
||||
|
|
@ -125,11 +128,32 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
max-width: 300px;
|
||||
word-wrap: break-word;
|
||||
}
|
||||
#info-panel {
|
||||
position: fixed;
|
||||
left: 12px;
|
||||
top: 12px;
|
||||
width: 340px;
|
||||
max-height: calc(100vh - 24px);
|
||||
overflow: auto;
|
||||
background: rgba(50, 51, 50, 0.7);
|
||||
backdrop-filter: blur(6px);
|
||||
border: 1px solid rgba(216, 216, 216, 0.35);
|
||||
border-radius: 8px;
|
||||
color: #F4F4F4;
|
||||
padding: 12px 14px;
|
||||
z-index: 1100;
|
||||
}
|
||||
#info-panel h3 { margin: 0 0 8px 0; font-size: 14px; color: #F4F4F4; }
|
||||
#info-panel .kv { font-size: 12px; line-height: 1.4; }
|
||||
#info-panel .kv .k { color: #D8D8D8; }
|
||||
#info-panel .kv .v { color: #F4F4F4; }
|
||||
#info-panel .placeholder { opacity: 0.7; font-size: 12px; }
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<svg></svg>
|
||||
<div class="tooltip" id="tooltip"></div>
|
||||
<div id="info-panel"><div class="placeholder">Hover a node or edge to inspect details</div></div>
|
||||
<script>
|
||||
var nodes = {nodes};
|
||||
var links = {links};
|
||||
|
|
@ -140,19 +164,141 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
|
||||
var container = svg.append("g");
|
||||
var tooltip = d3.select("#tooltip");
|
||||
var infoPanel = d3.select('#info-panel');
|
||||
|
||||
function renderInfo(title, entries){
|
||||
function esc(s){ return String(s).replace(/&/g,'&').replace(/</g,'<').replace(/>/g,'>'); }
|
||||
var html = '<h3>' + esc(title) + '</h3>';
|
||||
html += '<div class="kv">';
|
||||
entries.forEach(function(e){
|
||||
html += '<div><span class="k">' + esc(e.k) + ':</span> <span class="v">' + esc(e.v) + '</span></div>';
|
||||
});
|
||||
html += '</div>';
|
||||
infoPanel.html(html);
|
||||
}
|
||||
function pickDescription(obj){
|
||||
if (!obj) return null;
|
||||
var keys = ['description','summary','text','content'];
|
||||
for (var i=0; i<keys.length; i++){
|
||||
var v = obj[keys[i]];
|
||||
if (typeof v === 'string' && v.trim()) return v.trim();
|
||||
}
|
||||
return null;
|
||||
}
|
||||
function truncate(s, n){ if (!s) return s; return s.length > n ? (s.slice(0, n) + '…') : s; }
|
||||
function renderNodeInfo(n){
|
||||
var entries = [];
|
||||
if (n.name) entries.push({k:'Name', v: n.name});
|
||||
if (n.type) entries.push({k:'Type', v: n.type});
|
||||
if (n.id) entries.push({k:'ID', v: n.id});
|
||||
var desc = pickDescription(n) || pickDescription(n.properties);
|
||||
if (desc) entries.push({k:'Description', v: truncate(desc.replace(/\s+/g,' ').trim(), 280)});
|
||||
if (n.properties) {
|
||||
Object.keys(n.properties).slice(0, 12).forEach(function(key){
|
||||
var v = n.properties[key];
|
||||
if (v !== undefined && v !== null && typeof v !== 'object') entries.push({k: key, v: String(v)});
|
||||
});
|
||||
}
|
||||
renderInfo(n.name || 'Node', entries);
|
||||
}
|
||||
function renderEdgeInfo(e){
|
||||
var entries = [];
|
||||
if (e.relation) entries.push({k:'Relation', v: e.relation});
|
||||
if (e.weight !== undefined && e.weight !== null) entries.push({k:'Weight', v: e.weight});
|
||||
if (e.all_weights && Object.keys(e.all_weights).length){
|
||||
Object.keys(e.all_weights).slice(0, 8).forEach(function(k){ entries.push({k: 'w.'+k, v: e.all_weights[k]}); });
|
||||
}
|
||||
if (e.relationship_type) entries.push({k:'Type', v: e.relationship_type});
|
||||
var edesc = pickDescription(e.edge_info);
|
||||
if (edesc) entries.push({k:'Description', v: truncate(edesc.replace(/\s+/g,' ').trim(), 280)});
|
||||
renderInfo('Edge', entries);
|
||||
}
|
||||
|
||||
// Basic runtime diagnostics
|
||||
console.log('[Cognee Visualization] nodes:', nodes ? nodes.length : 0, 'links:', links ? links.length : 0);
|
||||
window.addEventListener('error', function(e){
|
||||
try {
|
||||
tooltip.html('<strong>Error:</strong> ' + e.message)
|
||||
.style('left', '12px')
|
||||
.style('top', '12px')
|
||||
.style('opacity', 1);
|
||||
} catch(_) {}
|
||||
});
|
||||
|
||||
// Normalize node IDs and link endpoints for robustness
|
||||
function resolveId(d){ return (d && (d.id || d.node_id || d.uuid || d.external_id || d.name)) || undefined; }
|
||||
if (Array.isArray(nodes)) {
|
||||
nodes.forEach(function(n){ var id = resolveId(n); if (id !== undefined) n.id = id; });
|
||||
}
|
||||
if (Array.isArray(links)) {
|
||||
links.forEach(function(l){
|
||||
if (typeof l.source === 'object') l.source = resolveId(l.source);
|
||||
if (typeof l.target === 'object') l.target = resolveId(l.target);
|
||||
});
|
||||
}
|
||||
|
||||
if (!nodes || nodes.length === 0) {
|
||||
container.append('text')
|
||||
.attr('x', width / 2)
|
||||
.attr('y', height / 2)
|
||||
.attr('fill', '#fff')
|
||||
.attr('font-size', 14)
|
||||
.attr('text-anchor', 'middle')
|
||||
.text('No graph data available');
|
||||
}
|
||||
|
||||
// Visual defs - reusable glow
|
||||
var defs = svg.append("defs");
|
||||
var glow = defs.append("filter").attr("id", "glow")
|
||||
.attr("x", "-30%")
|
||||
.attr("y", "-30%")
|
||||
.attr("width", "160%")
|
||||
.attr("height", "160%");
|
||||
glow.append("feGaussianBlur").attr("stdDeviation", 8).attr("result", "coloredBlur");
|
||||
var feMerge = glow.append("feMerge");
|
||||
feMerge.append("feMergeNode").attr("in", "coloredBlur");
|
||||
feMerge.append("feMergeNode").attr("in", "SourceGraphic");
|
||||
|
||||
// Stronger glow for hovered adjacency
|
||||
var glowStrong = defs.append("filter").attr("id", "glow-strong")
|
||||
.attr("x", "-40%")
|
||||
.attr("y", "-40%")
|
||||
.attr("width", "180%")
|
||||
.attr("height", "180%");
|
||||
glowStrong.append("feGaussianBlur").attr("stdDeviation", 14).attr("result", "coloredBlur");
|
||||
var feMerge2 = glowStrong.append("feMerge");
|
||||
feMerge2.append("feMergeNode").attr("in", "coloredBlur");
|
||||
feMerge2.append("feMergeNode").attr("in", "SourceGraphic");
|
||||
|
||||
var currentTransform = d3.zoomIdentity;
|
||||
var densityZoomTimer = null;
|
||||
var isInteracting = false;
|
||||
var labelBaseSize = 10;
|
||||
function getGroupKey(d){ return d && (d.type || d.category || d.group || d.color) || 'default'; }
|
||||
|
||||
var simulation = d3.forceSimulation(nodes)
|
||||
.force("link", d3.forceLink(links).id(d => d.id).strength(0.1))
|
||||
.force("charge", d3.forceManyBody().strength(-275))
|
||||
.force("link", d3.forceLink(links).id(function(d){ return d.id; }).distance(100).strength(0.2))
|
||||
.force("charge", d3.forceManyBody().strength(-180))
|
||||
.force("collide", d3.forceCollide().radius(16).iterations(2))
|
||||
.force("center", d3.forceCenter(width / 2, height / 2))
|
||||
.force("x", d3.forceX().strength(0.1).x(width / 2))
|
||||
.force("y", d3.forceY().strength(0.1).y(height / 2));
|
||||
.force("x", d3.forceX().strength(0.06).x(width / 2))
|
||||
.force("y", d3.forceY().strength(0.06).y(height / 2))
|
||||
.alphaDecay(0.06)
|
||||
.velocityDecay(0.6);
|
||||
|
||||
// Density layer (sibling of container to avoid double transforms)
|
||||
var densityLayer = svg.append("g")
|
||||
.attr("class", "density")
|
||||
.style("pointer-events", "none");
|
||||
if (densityLayer.lower) densityLayer.lower();
|
||||
|
||||
var link = container.append("g")
|
||||
.attr("class", "links")
|
||||
.selectAll("line")
|
||||
.data(links)
|
||||
.enter().append("line")
|
||||
.style("opacity", 0)
|
||||
.style("pointer-events", "none")
|
||||
.attr("stroke-width", d => {
|
||||
if (d.weight) return Math.max(2, d.weight * 5);
|
||||
if (d.all_weights && Object.keys(d.all_weights).length > 0) {
|
||||
|
|
@ -168,6 +314,7 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
})
|
||||
.on("mouseover", function(d) {
|
||||
// Create tooltip content for edge
|
||||
renderEdgeInfo(d);
|
||||
var content = "<strong>Edge Information</strong><br/>";
|
||||
content += "Relationship: " + d.relation + "<br/>";
|
||||
|
||||
|
|
@ -212,6 +359,7 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
.data(links)
|
||||
.enter().append("text")
|
||||
.attr("class", "edge-label")
|
||||
.style("opacity", 0)
|
||||
.text(d => {
|
||||
var label = d.relation;
|
||||
if (d.all_weights && Object.keys(d.all_weights).length > 1) {
|
||||
|
|
@ -232,21 +380,225 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
.data(nodes)
|
||||
.enter().append("g");
|
||||
|
||||
// Color fallback by type when d.color is missing
|
||||
var colorByType = {
|
||||
"Entity": "#5C10F4",
|
||||
"EntityType": "#A550FF",
|
||||
"DocumentChunk": "#0DFF00",
|
||||
"TextSummary": "#5C10F4",
|
||||
"TableRow": "#A550FF",
|
||||
"TableType": "#5C10F4",
|
||||
"ColumnValue": "#757470",
|
||||
"SchemaTable": "#A550FF",
|
||||
"DatabaseSchema": "#5C10F4",
|
||||
"SchemaRelationship": "#323332"
|
||||
};
|
||||
|
||||
var node = nodeGroup.append("circle")
|
||||
.attr("r", 13)
|
||||
.attr("fill", d => d.color)
|
||||
.attr("fill", function(d){ return d.color || colorByType[d.type] || "#D3D3D3"; })
|
||||
.style("filter", "url(#glow)")
|
||||
.attr("shape-rendering", "geometricPrecision")
|
||||
.call(d3.drag()
|
||||
.on("start", dragstarted)
|
||||
.on("drag", dragged)
|
||||
.on("drag", function(d){ dragged(d); updateDensity(); showAdjacency(d); })
|
||||
.on("end", dragended));
|
||||
|
||||
nodeGroup.append("text")
|
||||
// Show links only for hovered node adjacency
|
||||
function isAdjacent(linkDatum, nodeId) {
|
||||
var sid = linkDatum && linkDatum.source && (linkDatum.source.id || linkDatum.source);
|
||||
var tid = linkDatum && linkDatum.target && (linkDatum.target.id || linkDatum.target);
|
||||
return sid === nodeId || tid === nodeId;
|
||||
}
|
||||
|
||||
function showAdjacency(d) {
|
||||
var nodeId = d && (d.id || d.node_id || d.uuid || d.external_id || d.name);
|
||||
if (!nodeId) return;
|
||||
// Build neighbor set
|
||||
var neighborIds = {};
|
||||
neighborIds[nodeId] = true;
|
||||
for (var i = 0; i < links.length; i++) {
|
||||
var l = links[i];
|
||||
var sid = l && l.source && (l.source.id || l.source);
|
||||
var tid = l && l.target && (l.target.id || l.target);
|
||||
if (sid === nodeId) neighborIds[tid] = true;
|
||||
if (tid === nodeId) neighborIds[sid] = true;
|
||||
}
|
||||
|
||||
link
|
||||
.style("opacity", function(l){ return isAdjacent(l, nodeId) ? 0.95 : 0; })
|
||||
.style("stroke", function(l){ return isAdjacent(l, nodeId) ? "rgba(255,255,255,0.95)" : null; })
|
||||
.style("stroke-width", function(l){ return isAdjacent(l, nodeId) ? 2.5 : 1.5; });
|
||||
edgeLabels.style("opacity", function(l){ return isAdjacent(l, nodeId) ? 1 : 0; });
|
||||
densityLayer.style("opacity", 0.35);
|
||||
|
||||
// Highlight neighbor nodes and dim others
|
||||
node
|
||||
.style("opacity", function(n){ return neighborIds[n.id] ? 1 : 0.25; })
|
||||
.style("filter", function(n){ return neighborIds[n.id] ? "url(#glow-strong)" : "url(#glow)"; })
|
||||
.attr("r", function(n){ return neighborIds[n.id] ? 15 : 13; });
|
||||
// Raise highlighted nodes
|
||||
node.filter(function(n){ return neighborIds[n.id]; }).raise();
|
||||
// Neighbor labels brighter
|
||||
nodeGroup.select("text")
|
||||
.style("opacity", function(n){ return neighborIds[n.id] ? 1 : 0.2; })
|
||||
.style("font-size", function(n){
|
||||
var size = neighborIds[n.id] ? Math.min(22, labelBaseSize * 1.25) : labelBaseSize;
|
||||
return size + "px";
|
||||
});
|
||||
}
|
||||
|
||||
function clearAdjacency() {
|
||||
link.style("opacity", 0)
|
||||
.style("stroke", null)
|
||||
.style("stroke-width", 1.5);
|
||||
edgeLabels.style("opacity", 0);
|
||||
densityLayer.style("opacity", 1);
|
||||
node
|
||||
.style("opacity", 1)
|
||||
.style("filter", "url(#glow)")
|
||||
.attr("r", 13);
|
||||
nodeGroup.select("text")
|
||||
.style("opacity", 1)
|
||||
.style("font-size", labelBaseSize + "px");
|
||||
}
|
||||
|
||||
node.on("mouseover", function(d){ showAdjacency(d); })
|
||||
.on("mouseout", function(){ clearAdjacency(); });
|
||||
node.on("mouseover", function(d){ renderNodeInfo(d); tooltip.style('opacity', 0); });
|
||||
// Also bind on the group so labels trigger adjacency too
|
||||
nodeGroup.on("mouseover", function(d){ showAdjacency(d); })
|
||||
.on("mouseout", function(){ clearAdjacency(); });
|
||||
|
||||
// Density always on; no hover gating
|
||||
|
||||
// Add labels sparsely to reduce clutter (every ~50th node), and truncate long text
|
||||
nodeGroup
|
||||
.filter(function(d, i){ return i % 14 === 0; })
|
||||
.append("text")
|
||||
.attr("class", "node-label")
|
||||
.attr("dy", 4)
|
||||
.attr("text-anchor", "middle")
|
||||
.text(d => d.name);
|
||||
.text(function(d){
|
||||
var s = d && d.name ? String(d.name) : '';
|
||||
return s.length > 40 ? (s.slice(0, 40) + "…") : s;
|
||||
})
|
||||
.style("font-size", labelBaseSize + "px");
|
||||
|
||||
node.append("title").text(d => JSON.stringify(d));
|
||||
function applyLabelSize() {
|
||||
var k = (currentTransform && currentTransform.k) || 1;
|
||||
// Keep labels readable across zoom levels and hide when too small
|
||||
labelBaseSize = Math.max(7, Math.min(18, 10 / Math.sqrt(k)));
|
||||
nodeGroup.select("text")
|
||||
.style("font-size", labelBaseSize + "px")
|
||||
.style("display", (k < 0.35 ? "none" : null));
|
||||
}
|
||||
|
||||
|
||||
|
||||
// Density cloud computation (throttled)
|
||||
var densityTick = 0;
|
||||
var geoPath = d3.geoPath().projection(null);
|
||||
var MAX_POINTS_PER_GROUP = 400;
|
||||
function updateDensity() {
|
||||
try {
|
||||
if (isInteracting) return; // skip during interaction for smoother UX
|
||||
if (typeof d3 === 'undefined' || typeof d3.contourDensity !== 'function') {
|
||||
return; // d3-contour not available; skip gracefully
|
||||
}
|
||||
if (!nodes || nodes.length === 0) return;
|
||||
var usable = nodes.filter(function(d){ return d && typeof d.x === 'number' && isFinite(d.x) && typeof d.y === 'number' && isFinite(d.y); });
|
||||
if (usable.length < 3) return; // not enough positioned points yet
|
||||
|
||||
var t = currentTransform || d3.zoomIdentity;
|
||||
if (t.k && t.k < 0.08) {
|
||||
// Skip density at extreme zoom-out to avoid numerical instability/perf issues
|
||||
densityLayer.selectAll('*').remove();
|
||||
return;
|
||||
}
|
||||
|
||||
function hexToRgb(hex){
|
||||
if (!hex) return {r: 0, g: 200, b: 255};
|
||||
var c = hex.replace('#','');
|
||||
if (c.length === 3) c = c.split('').map(function(x){ return x+x; }).join('');
|
||||
var num = parseInt(c, 16);
|
||||
return { r: (num >> 16) & 255, g: (num >> 8) & 255, b: num & 255 };
|
||||
}
|
||||
|
||||
// Build groups across all nodes
|
||||
var groups = {};
|
||||
for (var i = 0; i < usable.length; i++) {
|
||||
var k = getGroupKey(usable[i]);
|
||||
if (!groups[k]) groups[k] = [];
|
||||
groups[k].push(usable[i]);
|
||||
}
|
||||
|
||||
densityLayer.selectAll('*').remove();
|
||||
|
||||
Object.keys(groups).forEach(function(key){
|
||||
var arr = groups[key];
|
||||
if (!arr || arr.length < 3) return;
|
||||
|
||||
// Transform positions into screen space and sample to cap cost
|
||||
var arrT = [];
|
||||
var step = Math.max(1, Math.floor(arr.length / MAX_POINTS_PER_GROUP));
|
||||
for (var j = 0; j < arr.length; j += step) {
|
||||
var nx = t.applyX(arr[j].x);
|
||||
var ny = t.applyY(arr[j].y);
|
||||
if (isFinite(nx) && isFinite(ny)) {
|
||||
arrT.push({ x: nx, y: ny, type: arr[j].type, color: arr[j].color });
|
||||
}
|
||||
}
|
||||
if (arrT.length < 3) return;
|
||||
|
||||
// Compute adaptive bandwidth based on group spread
|
||||
var cx = 0, cy = 0;
|
||||
for (var k = 0; k < arrT.length; k++){ cx += arrT[k].x; cy += arrT[k].y; }
|
||||
cx /= arrT.length; cy /= arrT.length;
|
||||
var sumR = 0;
|
||||
for (var k2 = 0; k2 < arrT.length; k2++){
|
||||
var dx = arrT[k2].x - cx, dy = arrT[k2].y - cy;
|
||||
sumR += Math.sqrt(dx*dx + dy*dy);
|
||||
}
|
||||
var avgR = sumR / arrT.length;
|
||||
var dynamicBandwidth = Math.max(12, Math.min(80, avgR));
|
||||
var densityBandwidth = dynamicBandwidth / (t.k || 1);
|
||||
|
||||
var contours = d3.contourDensity()
|
||||
.x(function(d){ return d.x; })
|
||||
.y(function(d){ return d.y; })
|
||||
.size([width, height])
|
||||
.bandwidth(densityBandwidth)
|
||||
.thresholds(8)
|
||||
(arrT);
|
||||
|
||||
if (!contours || contours.length === 0) return;
|
||||
var maxVal = d3.max(contours, function(d){ return d.value; }) || 1;
|
||||
|
||||
// Use the first node color in the group or fallback neon palette
|
||||
var baseColor = (arr.find(function(d){ return d && d.color; }) || {}).color || '#00c8ff';
|
||||
var rgb = hexToRgb(baseColor);
|
||||
|
||||
var g = densityLayer.append('g').attr('data-group', key);
|
||||
g.selectAll('path')
|
||||
.data(contours)
|
||||
.enter()
|
||||
.append('path')
|
||||
.attr('d', geoPath)
|
||||
.attr('fill', 'rgb(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ')')
|
||||
.attr('stroke', 'none')
|
||||
.style('opacity', function(d){
|
||||
var v = maxVal ? (d.value / maxVal) : 0;
|
||||
var alpha = Math.pow(Math.max(0, Math.min(1, v)), 1.6); // accentuate clusters
|
||||
return 0.65 * alpha; // up to 0.65 opacity at peak density
|
||||
})
|
||||
.style('filter', 'blur(2px)');
|
||||
});
|
||||
} catch (e) {
|
||||
// Reduce impact of any runtime errors during zoom
|
||||
console.warn('Density update failed:', e);
|
||||
}
|
||||
}
|
||||
|
||||
simulation.on("tick", function() {
|
||||
link.attr("x1", d => d.source.x)
|
||||
|
|
@ -266,16 +618,29 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
.attr("y", d => d.y)
|
||||
.attr("dy", 4)
|
||||
.attr("text-anchor", "middle");
|
||||
|
||||
densityTick += 1;
|
||||
if (densityTick % 24 === 0) updateDensity();
|
||||
});
|
||||
|
||||
svg.call(d3.zoom().on("zoom", function() {
|
||||
container.attr("transform", d3.event.transform);
|
||||
}));
|
||||
var zoomBehavior = d3.zoom()
|
||||
.on("start", function(){ isInteracting = true; densityLayer.style("opacity", 0.2); })
|
||||
.on("zoom", function(){
|
||||
currentTransform = d3.event.transform;
|
||||
container.attr("transform", currentTransform);
|
||||
})
|
||||
.on("end", function(){
|
||||
if (densityZoomTimer) clearTimeout(densityZoomTimer);
|
||||
densityZoomTimer = setTimeout(function(){ isInteracting = false; densityLayer.style("opacity", 1); updateDensity(); }, 140);
|
||||
});
|
||||
svg.call(zoomBehavior);
|
||||
|
||||
function dragstarted(d) {
|
||||
if (!d3.event.active) simulation.alphaTarget(0.3).restart();
|
||||
d.fx = d.x;
|
||||
d.fy = d.y;
|
||||
isInteracting = true;
|
||||
densityLayer.style("opacity", 0.2);
|
||||
}
|
||||
|
||||
function dragged(d) {
|
||||
|
|
@ -287,6 +652,8 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
if (!d3.event.active) simulation.alphaTarget(0);
|
||||
d.fx = null;
|
||||
d.fy = null;
|
||||
if (densityZoomTimer) clearTimeout(densityZoomTimer);
|
||||
densityZoomTimer = setTimeout(function(){ isInteracting = false; densityLayer.style("opacity", 1); updateDensity(); }, 140);
|
||||
}
|
||||
|
||||
window.addEventListener("resize", function() {
|
||||
|
|
@ -295,7 +662,13 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
svg.attr("width", width).attr("height", height);
|
||||
simulation.force("center", d3.forceCenter(width / 2, height / 2));
|
||||
simulation.alpha(1).restart();
|
||||
updateDensity();
|
||||
applyLabelSize();
|
||||
});
|
||||
|
||||
// Initial density draw
|
||||
updateDensity();
|
||||
applyLabelSize();
|
||||
</script>
|
||||
|
||||
<svg style="position: fixed; bottom: 10px; right: 10px; width: 150px; height: auto; z-index: 9999;" viewBox="0 0 158 44" fill="none" xmlns="http://www.w3.org/2000/svg">
|
||||
|
|
@ -305,8 +678,12 @@ async def cognee_network_visualization(graph_data, destination_file_path: str =
|
|||
</html>
|
||||
"""
|
||||
|
||||
html_content = html_template.replace("{nodes}", json.dumps(nodes_list))
|
||||
html_content = html_content.replace("{links}", json.dumps(links_list))
|
||||
# Safely embed JSON inside <script> by escaping </ to avoid prematurely closing the tag
|
||||
def _safe_json_embed(obj):
|
||||
return json.dumps(obj).replace("</", "<\\/")
|
||||
|
||||
html_content = html_template.replace("{nodes}", _safe_json_embed(nodes_list))
|
||||
html_content = html_content.replace("{links}", _safe_json_embed(links_list))
|
||||
|
||||
if not destination_file_path:
|
||||
home_dir = os.path.expanduser("~")
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import os
|
||||
import sys
|
||||
import logging
|
||||
import tempfile
|
||||
import structlog
|
||||
import traceback
|
||||
import platform
|
||||
|
|
@ -76,9 +77,38 @@ log_levels = {
|
|||
# Track if structlog logging has been configured
|
||||
_is_structlog_configured = False
|
||||
|
||||
# Path to logs directory
|
||||
LOGS_DIR = Path(os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "logs"))
|
||||
LOGS_DIR.mkdir(exist_ok=True) # Create logs dir if it doesn't exist
|
||||
|
||||
def resolve_logs_dir():
|
||||
"""Resolve a writable logs directory.
|
||||
|
||||
Priority:
|
||||
1) BaseConfig.logs_root_directory (respects COGNEE_LOGS_DIR)
|
||||
2) /tmp/cognee_logs (default, best-effort create)
|
||||
|
||||
Returns a Path or None if none are writable/creatable.
|
||||
"""
|
||||
from cognee.base_config import get_base_config
|
||||
|
||||
base_config = get_base_config()
|
||||
logs_root_directory = Path(base_config.logs_root_directory)
|
||||
|
||||
try:
|
||||
logs_root_directory.mkdir(parents=True, exist_ok=True)
|
||||
if os.access(logs_root_directory, os.W_OK):
|
||||
return logs_root_directory
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
try:
|
||||
tmp_log_path = Path(os.path.join("/tmp", "cognee_logs"))
|
||||
tmp_log_path.mkdir(parents=True, exist_ok=True)
|
||||
if os.access(tmp_log_path, os.W_OK):
|
||||
return tmp_log_path
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
return None
|
||||
|
||||
|
||||
# Maximum number of log files to keep
|
||||
MAX_LOG_FILES = 10
|
||||
|
|
@ -430,28 +460,38 @@ def setup_logging(log_level=None, name=None):
|
|||
stream_handler.setFormatter(console_formatter)
|
||||
stream_handler.setLevel(log_level)
|
||||
|
||||
root_logger = logging.getLogger()
|
||||
if root_logger.hasHandlers():
|
||||
root_logger.handlers.clear()
|
||||
root_logger.addHandler(stream_handler)
|
||||
|
||||
# Note: root logger needs to be set at NOTSET to allow all messages through and specific stream and file handlers
|
||||
# can define their own levels.
|
||||
root_logger.setLevel(logging.NOTSET)
|
||||
|
||||
# Resolve logs directory with env and safe fallbacks
|
||||
logs_dir = resolve_logs_dir()
|
||||
|
||||
# Check if we already have a log file path from the environment
|
||||
# NOTE: environment variable must be used here as it allows us to
|
||||
# log to a single file with a name based on a timestamp in a multiprocess setting.
|
||||
# Without it, we would have a separate log file for every process.
|
||||
log_file_path = os.environ.get("LOG_FILE_NAME")
|
||||
if not log_file_path:
|
||||
if not log_file_path and logs_dir is not None:
|
||||
# Create a new log file name with the cognee start time
|
||||
start_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
|
||||
log_file_path = os.path.join(LOGS_DIR, f"{start_time}.log")
|
||||
log_file_path = str((logs_dir / f"{start_time}.log").resolve())
|
||||
os.environ["LOG_FILE_NAME"] = log_file_path
|
||||
|
||||
try:
|
||||
# Create a file handler that uses our custom PlainFileHandler
|
||||
file_handler = PlainFileHandler(log_file_path, encoding="utf-8")
|
||||
file_handler.setLevel(DEBUG)
|
||||
|
||||
# Configure root logger
|
||||
root_logger = logging.getLogger()
|
||||
if root_logger.hasHandlers():
|
||||
root_logger.handlers.clear()
|
||||
root_logger.addHandler(stream_handler)
|
||||
root_logger.addHandler(file_handler)
|
||||
root_logger.setLevel(log_level)
|
||||
except Exception as e:
|
||||
# Note: Exceptions happen in case of read only file systems or log file path poiting to location where it does
|
||||
# not have write permission. Logging to file is not mandatory so we just log a warning to console.
|
||||
root_logger.warning(f"Warning: Could not create log file handler at {log_file_path}: {e}")
|
||||
|
||||
if log_level > logging.DEBUG:
|
||||
import warnings
|
||||
|
|
@ -466,7 +506,8 @@ def setup_logging(log_level=None, name=None):
|
|||
)
|
||||
|
||||
# Clean up old log files, keeping only the most recent ones
|
||||
cleanup_old_logs(LOGS_DIR, MAX_LOG_FILES)
|
||||
if logs_dir is not None:
|
||||
cleanup_old_logs(logs_dir, MAX_LOG_FILES)
|
||||
|
||||
# Mark logging as configured
|
||||
_is_structlog_configured = True
|
||||
|
|
@ -490,6 +531,10 @@ def setup_logging(log_level=None, name=None):
|
|||
|
||||
# Get a configured logger and log system information
|
||||
logger = structlog.get_logger(name if name else __name__)
|
||||
|
||||
if logs_dir is not None:
|
||||
logger.info(f"Log file created at: {log_file_path}", log_file=log_file_path)
|
||||
|
||||
# Detailed initialization for regular usage
|
||||
logger.info(
|
||||
"Logging initialized",
|
||||
|
|
|
|||
|
|
@ -11,8 +11,10 @@ import pathlib
|
|||
from uuid import uuid4, uuid5, NAMESPACE_OID
|
||||
|
||||
from cognee.base_config import get_base_config
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.infrastructure.databases.graph import get_graph_engine
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
# Analytics Proxy Url, currently hosted by Vercel
|
||||
proxy_url = "https://test.prometh.ai"
|
||||
|
|
@ -38,6 +40,7 @@ def get_anonymous_id():
|
|||
|
||||
home_dir = str(pathlib.Path(pathlib.Path(__file__).parent.parent.parent.resolve()))
|
||||
|
||||
try:
|
||||
if not os.path.isdir(home_dir):
|
||||
os.makedirs(home_dir, exist_ok=True)
|
||||
anonymous_id_file = os.path.join(home_dir, ".anon_id")
|
||||
|
|
@ -48,6 +51,10 @@ def get_anonymous_id():
|
|||
else:
|
||||
with open(anonymous_id_file, "r", encoding="utf-8") as f:
|
||||
anonymous_id = f.read()
|
||||
except Exception as e:
|
||||
# In case of read-only filesystem or other issues
|
||||
logger.warning("Could not create or read anonymous id file: %s", e)
|
||||
return "unknown-anonymous-id"
|
||||
return anonymous_id
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -239,7 +239,7 @@ async def complete_database_ingestion(schema, migrate_column_data):
|
|||
id=uuid5(NAMESPACE_OID, name=column_node_id),
|
||||
name=column_node_id,
|
||||
properties=f"{key} {value} {table_name}",
|
||||
description=f"column from relational database table={table_name}. Column name={key} and value={value}. The value of the column is related to the following row with this id: {row_node.id}. This column has the following ID: {column_node_id}",
|
||||
description=f"column from relational database table={table_name}. Column name={key} and value={value}. This column has the following ID: {column_node_id}",
|
||||
)
|
||||
node_mapping[column_node_id] = column_node
|
||||
|
||||
|
|
|
|||
|
|
@ -8,47 +8,58 @@ logger = get_logger("index_data_points")
|
|||
|
||||
|
||||
async def index_data_points(data_points: list[DataPoint]):
|
||||
created_indexes = {}
|
||||
index_points = {}
|
||||
"""Index data points in the vector engine by creating embeddings for specified fields.
|
||||
|
||||
Process:
|
||||
1. Groups data points into a nested dict: {type_name: {field_name: [points]}}
|
||||
2. Creates vector indexes for each (type, field) combination on first encounter
|
||||
3. Batches points per (type, field) and creates async indexing tasks
|
||||
4. Executes all indexing tasks in parallel for efficient embedding generation
|
||||
|
||||
Args:
|
||||
data_points: List of DataPoint objects to index. Each DataPoint's metadata must
|
||||
contain an 'index_fields' list specifying which fields to embed.
|
||||
|
||||
Returns:
|
||||
The original data_points list.
|
||||
"""
|
||||
data_points_by_type = {}
|
||||
|
||||
vector_engine = get_vector_engine()
|
||||
|
||||
for data_point in data_points:
|
||||
data_point_type = type(data_point)
|
||||
type_name = data_point_type.__name__
|
||||
|
||||
for field_name in data_point.metadata["index_fields"]:
|
||||
if getattr(data_point, field_name, None) is None:
|
||||
continue
|
||||
|
||||
index_name = f"{data_point_type.__name__}_{field_name}"
|
||||
if type_name not in data_points_by_type:
|
||||
data_points_by_type[type_name] = {}
|
||||
|
||||
if index_name not in created_indexes:
|
||||
await vector_engine.create_vector_index(data_point_type.__name__, field_name)
|
||||
created_indexes[index_name] = True
|
||||
|
||||
if index_name not in index_points:
|
||||
index_points[index_name] = []
|
||||
if field_name not in data_points_by_type[type_name]:
|
||||
await vector_engine.create_vector_index(type_name, field_name)
|
||||
data_points_by_type[type_name][field_name] = []
|
||||
|
||||
indexed_data_point = data_point.model_copy()
|
||||
indexed_data_point.metadata["index_fields"] = [field_name]
|
||||
index_points[index_name].append(indexed_data_point)
|
||||
data_points_by_type[type_name][field_name].append(indexed_data_point)
|
||||
|
||||
tasks: list[asyncio.Task] = []
|
||||
batch_size = vector_engine.embedding_engine.get_batch_size()
|
||||
|
||||
for index_name_and_field, points in index_points.items():
|
||||
first = index_name_and_field.index("_")
|
||||
index_name = index_name_and_field[:first]
|
||||
field_name = index_name_and_field[first + 1 :]
|
||||
|
||||
# Create embedding requests per batch to run in parallel later
|
||||
for i in range(0, len(points), batch_size):
|
||||
batch = points[i : i + batch_size]
|
||||
tasks.append(
|
||||
asyncio.create_task(vector_engine.index_data_points(index_name, field_name, batch))
|
||||
batches = (
|
||||
(type_name, field_name, points[i : i + batch_size])
|
||||
for type_name, fields in data_points_by_type.items()
|
||||
for field_name, points in fields.items()
|
||||
for i in range(0, len(points), batch_size)
|
||||
)
|
||||
|
||||
# Run all embedding requests in parallel
|
||||
tasks = [
|
||||
asyncio.create_task(vector_engine.index_data_points(type_name, field_name, batch_points))
|
||||
for type_name, field_name, batch_points in batches
|
||||
]
|
||||
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
return data_points
|
||||
|
|
|
|||
|
|
@ -1,17 +1,44 @@
|
|||
import asyncio
|
||||
from collections import Counter
|
||||
from typing import Optional, Dict, Any, List, Tuple, Union
|
||||
|
||||
from cognee.modules.engine.utils.generate_edge_id import generate_edge_id
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from collections import Counter
|
||||
from typing import Optional, Dict, Any, List, Tuple, Union
|
||||
from cognee.infrastructure.databases.vector import get_vector_engine
|
||||
from cognee.infrastructure.databases.graph import get_graph_engine
|
||||
from cognee.modules.graph.models.EdgeType import EdgeType
|
||||
from cognee.infrastructure.databases.graph.graph_db_interface import EdgeData
|
||||
from cognee.tasks.storage.index_data_points import index_data_points
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
def _get_edge_text(item: dict) -> str:
|
||||
"""Extract edge text for embedding - prefers edge_text field with fallback."""
|
||||
if "edge_text" in item:
|
||||
return item["edge_text"]
|
||||
|
||||
if "relationship_name" in item:
|
||||
return item["relationship_name"]
|
||||
|
||||
return ""
|
||||
|
||||
|
||||
def create_edge_type_datapoints(edges_data) -> list[EdgeType]:
|
||||
"""Transform raw edge data into EdgeType datapoints."""
|
||||
edge_texts = [
|
||||
_get_edge_text(item)
|
||||
for edge in edges_data
|
||||
for item in edge
|
||||
if isinstance(item, dict) and "relationship_name" in item
|
||||
]
|
||||
|
||||
edge_types = Counter(edge_texts)
|
||||
|
||||
return [
|
||||
EdgeType(id=generate_edge_id(edge_id=text), relationship_name=text, number_of_edges=count)
|
||||
for text, count in edge_types.items()
|
||||
]
|
||||
|
||||
|
||||
async def index_graph_edges(
|
||||
edges_data: Union[List[EdgeData], List[Tuple[str, str, str, Optional[Dict[str, Any]]]]] = None,
|
||||
):
|
||||
|
|
@ -23,24 +50,17 @@ async def index_graph_edges(
|
|||
the `relationship_name` field.
|
||||
|
||||
Steps:
|
||||
1. Initialize the vector engine and graph engine.
|
||||
2. Retrieve graph edge data and count relationship types (`relationship_name`).
|
||||
3. Create vector indexes for `relationship_name` if they don't exist.
|
||||
4. Transform the counted relationships into `EdgeType` objects.
|
||||
5. Index the transformed data points in the vector engine.
|
||||
1. Initialize the graph engine if needed and retrieve edge data.
|
||||
2. Transform edge data into EdgeType datapoints.
|
||||
3. Index the EdgeType datapoints using the standard indexing function.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If initialization of the vector engine or graph engine fails.
|
||||
RuntimeError: If initialization of the graph engine fails.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
try:
|
||||
created_indexes = {}
|
||||
index_points = {}
|
||||
|
||||
vector_engine = get_vector_engine()
|
||||
|
||||
if edges_data is None:
|
||||
graph_engine = await get_graph_engine()
|
||||
_, edges_data = await graph_engine.get_graph_data()
|
||||
|
|
@ -51,47 +71,7 @@ async def index_graph_edges(
|
|||
logger.error("Failed to initialize engines: %s", e)
|
||||
raise RuntimeError("Initialization error") from e
|
||||
|
||||
edge_types = Counter(
|
||||
item.get("relationship_name")
|
||||
for edge in edges_data
|
||||
for item in edge
|
||||
if isinstance(item, dict) and "relationship_name" in item
|
||||
)
|
||||
|
||||
for text, count in edge_types.items():
|
||||
edge = EdgeType(
|
||||
id=generate_edge_id(edge_id=text), relationship_name=text, number_of_edges=count
|
||||
)
|
||||
data_point_type = type(edge)
|
||||
|
||||
for field_name in edge.metadata["index_fields"]:
|
||||
index_name = f"{data_point_type.__name__}.{field_name}"
|
||||
|
||||
if index_name not in created_indexes:
|
||||
await vector_engine.create_vector_index(data_point_type.__name__, field_name)
|
||||
created_indexes[index_name] = True
|
||||
|
||||
if index_name not in index_points:
|
||||
index_points[index_name] = []
|
||||
|
||||
indexed_data_point = edge.model_copy()
|
||||
indexed_data_point.metadata["index_fields"] = [field_name]
|
||||
index_points[index_name].append(indexed_data_point)
|
||||
|
||||
# Get maximum batch size for embedding model
|
||||
batch_size = vector_engine.embedding_engine.get_batch_size()
|
||||
tasks: list[asyncio.Task] = []
|
||||
|
||||
for index_name, indexable_points in index_points.items():
|
||||
index_name, field_name = index_name.split(".")
|
||||
|
||||
# Create embedding tasks to run in parallel later
|
||||
for start in range(0, len(indexable_points), batch_size):
|
||||
batch = indexable_points[start : start + batch_size]
|
||||
|
||||
tasks.append(vector_engine.index_data_points(index_name, field_name, batch))
|
||||
|
||||
# Start all embedding tasks and wait for completion
|
||||
await asyncio.gather(*tasks)
|
||||
edge_type_datapoints = create_edge_type_datapoints(edges_data)
|
||||
await index_data_points(edge_type_datapoints)
|
||||
|
||||
return None
|
||||
|
|
|
|||
|
|
@ -52,6 +52,33 @@ async def test_edge_ingestion():
|
|||
|
||||
edge_type_counts = Counter(edge_type[2] for edge_type in graph[1])
|
||||
|
||||
"Tests edge_text presence and format"
|
||||
contains_edges = [edge for edge in graph[1] if edge[2] == "contains"]
|
||||
assert len(contains_edges) > 0, "Expected at least one contains edge for edge_text verification"
|
||||
|
||||
edge_properties = contains_edges[0][3]
|
||||
assert "edge_text" in edge_properties, "Expected edge_text in edge properties"
|
||||
|
||||
edge_text = edge_properties["edge_text"]
|
||||
assert "relationship_name: contains" in edge_text, (
|
||||
f"Expected 'relationship_name: contains' in edge_text, got: {edge_text}"
|
||||
)
|
||||
assert "entity_name:" in edge_text, f"Expected 'entity_name:' in edge_text, got: {edge_text}"
|
||||
assert "entity_description:" in edge_text, (
|
||||
f"Expected 'entity_description:' in edge_text, got: {edge_text}"
|
||||
)
|
||||
|
||||
all_edge_texts = [
|
||||
edge[3].get("edge_text", "") for edge in contains_edges if "edge_text" in edge[3]
|
||||
]
|
||||
expected_entities = ["dave", "ana", "bob", "dexter", "apples", "cognee"]
|
||||
found_entity = any(
|
||||
any(entity in text.lower() for entity in expected_entities) for text in all_edge_texts
|
||||
)
|
||||
assert found_entity, (
|
||||
f"Expected to find at least one entity name in edge_text: {all_edge_texts[:3]}"
|
||||
)
|
||||
|
||||
"Tests the presence of basic nested edges"
|
||||
for basic_nested_edge in basic_nested_edges:
|
||||
assert edge_type_counts.get(basic_nested_edge, 0) >= 1, (
|
||||
|
|
|
|||
|
|
@ -0,0 +1,27 @@
|
|||
import pytest
|
||||
from unittest.mock import AsyncMock, patch, MagicMock
|
||||
from cognee.tasks.storage.index_data_points import index_data_points
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
|
||||
|
||||
class TestDataPoint(DataPoint):
|
||||
name: str
|
||||
metadata: dict = {"index_fields": ["name"]}
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_index_data_points_calls_vector_engine():
|
||||
"""Test that index_data_points creates vector index and indexes data."""
|
||||
data_points = [TestDataPoint(name="test1")]
|
||||
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_vector_engine.embedding_engine.get_batch_size = MagicMock(return_value=100)
|
||||
|
||||
with patch.dict(
|
||||
index_data_points.__globals__,
|
||||
{"get_vector_engine": lambda: mock_vector_engine},
|
||||
):
|
||||
await index_data_points(data_points)
|
||||
|
||||
assert mock_vector_engine.create_vector_index.await_count >= 1
|
||||
assert mock_vector_engine.index_data_points.await_count >= 1
|
||||
|
|
@ -5,8 +5,7 @@ from cognee.tasks.storage.index_graph_edges import index_graph_edges
|
|||
|
||||
@pytest.mark.asyncio
|
||||
async def test_index_graph_edges_success():
|
||||
"""Test that index_graph_edges uses the index datapoints and creates vector index."""
|
||||
# Create the mocks for the graph and vector engines.
|
||||
"""Test that index_graph_edges retrieves edges and delegates to index_data_points."""
|
||||
mock_graph_engine = AsyncMock()
|
||||
mock_graph_engine.get_graph_data.return_value = (
|
||||
None,
|
||||
|
|
@ -15,26 +14,23 @@ async def test_index_graph_edges_success():
|
|||
[{"relationship_name": "rel2"}],
|
||||
],
|
||||
)
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_vector_engine.embedding_engine.get_batch_size = MagicMock(return_value=100)
|
||||
mock_index_data_points = AsyncMock()
|
||||
|
||||
# Patch the globals of the function so that when it does:
|
||||
# vector_engine = get_vector_engine()
|
||||
# graph_engine = await get_graph_engine()
|
||||
# it uses the mocked versions.
|
||||
with patch.dict(
|
||||
index_graph_edges.__globals__,
|
||||
{
|
||||
"get_graph_engine": AsyncMock(return_value=mock_graph_engine),
|
||||
"get_vector_engine": lambda: mock_vector_engine,
|
||||
"index_data_points": mock_index_data_points,
|
||||
},
|
||||
):
|
||||
await index_graph_edges()
|
||||
|
||||
# Assertions on the mock calls.
|
||||
mock_graph_engine.get_graph_data.assert_awaited_once()
|
||||
assert mock_vector_engine.create_vector_index.await_count == 1
|
||||
assert mock_vector_engine.index_data_points.await_count == 1
|
||||
mock_index_data_points.assert_awaited_once()
|
||||
|
||||
call_args = mock_index_data_points.call_args[0][0]
|
||||
assert len(call_args) == 2
|
||||
assert all(hasattr(item, "relationship_name") for item in call_args)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
|
|
@ -42,20 +38,22 @@ async def test_index_graph_edges_no_relationships():
|
|||
"""Test that index_graph_edges handles empty relationships correctly."""
|
||||
mock_graph_engine = AsyncMock()
|
||||
mock_graph_engine.get_graph_data.return_value = (None, [])
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_index_data_points = AsyncMock()
|
||||
|
||||
with patch.dict(
|
||||
index_graph_edges.__globals__,
|
||||
{
|
||||
"get_graph_engine": AsyncMock(return_value=mock_graph_engine),
|
||||
"get_vector_engine": lambda: mock_vector_engine,
|
||||
"index_data_points": mock_index_data_points,
|
||||
},
|
||||
):
|
||||
await index_graph_edges()
|
||||
|
||||
mock_graph_engine.get_graph_data.assert_awaited_once()
|
||||
mock_vector_engine.create_vector_index.assert_not_awaited()
|
||||
mock_vector_engine.index_data_points.assert_not_awaited()
|
||||
mock_index_data_points.assert_awaited_once()
|
||||
|
||||
call_args = mock_index_data_points.call_args[0][0]
|
||||
assert len(call_args) == 0
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
|
|
|
|||
|
|
@ -489,3 +489,154 @@ def test_get_ontology_resolver_from_env_resolver_functionality():
|
|||
assert nodes == []
|
||||
assert relationships == []
|
||||
assert start_node is None
|
||||
|
||||
|
||||
def test_multifile_ontology_loading_success():
|
||||
"""Test successful loading of multiple ontology files."""
|
||||
ns1 = Namespace("http://example.org/cars#")
|
||||
ns2 = Namespace("http://example.org/tech#")
|
||||
|
||||
g1 = Graph()
|
||||
g1.add((ns1.Vehicle, RDF.type, OWL.Class))
|
||||
g1.add((ns1.Car, RDF.type, OWL.Class))
|
||||
g1.add((ns1.Car, RDFS.subClassOf, ns1.Vehicle))
|
||||
g1.add((ns1.Audi, RDF.type, ns1.Car))
|
||||
g1.add((ns1.BMW, RDF.type, ns1.Car))
|
||||
|
||||
g2 = Graph()
|
||||
g2.add((ns2.Company, RDF.type, OWL.Class))
|
||||
g2.add((ns2.TechCompany, RDF.type, OWL.Class))
|
||||
g2.add((ns2.TechCompany, RDFS.subClassOf, ns2.Company))
|
||||
g2.add((ns2.Apple, RDF.type, ns2.TechCompany))
|
||||
g2.add((ns2.Google, RDF.type, ns2.TechCompany))
|
||||
|
||||
import tempfile
|
||||
|
||||
with tempfile.NamedTemporaryFile(mode="w", suffix=".owl", delete=False) as f1:
|
||||
g1.serialize(f1.name, format="xml")
|
||||
file1_path = f1.name
|
||||
|
||||
with tempfile.NamedTemporaryFile(mode="w", suffix=".owl", delete=False) as f2:
|
||||
g2.serialize(f2.name, format="xml")
|
||||
file2_path = f2.name
|
||||
|
||||
try:
|
||||
resolver = RDFLibOntologyResolver(ontology_file=[file1_path, file2_path])
|
||||
|
||||
assert resolver.graph is not None
|
||||
|
||||
assert "car" in resolver.lookup["classes"]
|
||||
assert "vehicle" in resolver.lookup["classes"]
|
||||
assert "company" in resolver.lookup["classes"]
|
||||
assert "techcompany" in resolver.lookup["classes"]
|
||||
|
||||
assert "audi" in resolver.lookup["individuals"]
|
||||
assert "bmw" in resolver.lookup["individuals"]
|
||||
assert "apple" in resolver.lookup["individuals"]
|
||||
assert "google" in resolver.lookup["individuals"]
|
||||
|
||||
car_match = resolver.find_closest_match("Audi", "individuals")
|
||||
assert car_match == "audi"
|
||||
|
||||
tech_match = resolver.find_closest_match("Google", "individuals")
|
||||
assert tech_match == "google"
|
||||
|
||||
finally:
|
||||
import os
|
||||
|
||||
os.unlink(file1_path)
|
||||
os.unlink(file2_path)
|
||||
|
||||
|
||||
def test_multifile_ontology_with_missing_files():
|
||||
"""Test loading multiple ontology files where some don't exist."""
|
||||
ns = Namespace("http://example.org/test#")
|
||||
g = Graph()
|
||||
g.add((ns.Car, RDF.type, OWL.Class))
|
||||
g.add((ns.Audi, RDF.type, ns.Car))
|
||||
|
||||
import tempfile
|
||||
|
||||
with tempfile.NamedTemporaryFile(mode="w", suffix=".owl", delete=False) as f:
|
||||
g.serialize(f.name, format="xml")
|
||||
valid_file = f.name
|
||||
|
||||
try:
|
||||
resolver = RDFLibOntologyResolver(
|
||||
ontology_file=["nonexistent_file_1.owl", valid_file, "nonexistent_file_2.owl"]
|
||||
)
|
||||
|
||||
assert resolver.graph is not None
|
||||
|
||||
assert "car" in resolver.lookup["classes"]
|
||||
assert "audi" in resolver.lookup["individuals"]
|
||||
|
||||
match = resolver.find_closest_match("Audi", "individuals")
|
||||
assert match == "audi"
|
||||
|
||||
finally:
|
||||
import os
|
||||
|
||||
os.unlink(valid_file)
|
||||
|
||||
|
||||
def test_multifile_ontology_all_files_missing():
|
||||
"""Test loading multiple ontology files where all files are missing."""
|
||||
resolver = RDFLibOntologyResolver(
|
||||
ontology_file=["nonexistent_file_1.owl", "nonexistent_file_2.owl", "nonexistent_file_3.owl"]
|
||||
)
|
||||
|
||||
assert resolver.graph is None
|
||||
|
||||
assert resolver.lookup["classes"] == {}
|
||||
assert resolver.lookup["individuals"] == {}
|
||||
|
||||
|
||||
def test_multifile_ontology_with_overlapping_entities():
|
||||
"""Test loading multiple ontology files with overlapping/related entities."""
|
||||
ns = Namespace("http://example.org/automotive#")
|
||||
|
||||
g1 = Graph()
|
||||
g1.add((ns.Vehicle, RDF.type, OWL.Class))
|
||||
g1.add((ns.Car, RDF.type, OWL.Class))
|
||||
g1.add((ns.Car, RDFS.subClassOf, ns.Vehicle))
|
||||
|
||||
g2 = Graph()
|
||||
g2.add((ns.LuxuryCar, RDF.type, OWL.Class))
|
||||
g2.add((ns.LuxuryCar, RDFS.subClassOf, ns.Car))
|
||||
g2.add((ns.Mercedes, RDF.type, ns.LuxuryCar))
|
||||
g2.add((ns.BMW, RDF.type, ns.LuxuryCar))
|
||||
|
||||
import tempfile
|
||||
|
||||
with tempfile.NamedTemporaryFile(mode="w", suffix=".owl", delete=False) as f1:
|
||||
g1.serialize(f1.name, format="xml")
|
||||
file1_path = f1.name
|
||||
|
||||
with tempfile.NamedTemporaryFile(mode="w", suffix=".owl", delete=False) as f2:
|
||||
g2.serialize(f2.name, format="xml")
|
||||
file2_path = f2.name
|
||||
|
||||
try:
|
||||
resolver = RDFLibOntologyResolver(ontology_file=[file1_path, file2_path])
|
||||
|
||||
assert "vehicle" in resolver.lookup["classes"]
|
||||
assert "car" in resolver.lookup["classes"]
|
||||
assert "luxurycar" in resolver.lookup["classes"]
|
||||
|
||||
assert "mercedes" in resolver.lookup["individuals"]
|
||||
assert "bmw" in resolver.lookup["individuals"]
|
||||
|
||||
nodes, relationships, start_node = resolver.get_subgraph("Mercedes", "individuals")
|
||||
|
||||
uri_labels = {resolver._uri_to_key(n.uri) for n in nodes}
|
||||
assert "mercedes" in uri_labels
|
||||
assert "luxurycar" in uri_labels
|
||||
assert "car" in uri_labels
|
||||
assert "vehicle" in uri_labels
|
||||
|
||||
finally:
|
||||
import os
|
||||
|
||||
os.unlink(file1_path)
|
||||
os.unlink(file2_path)
|
||||
|
|
|
|||
|
|
@ -77,6 +77,7 @@ async def main():
|
|||
"What happened between 2000 and 2006?",
|
||||
"What happened between 1903 and 1995, I am interested in the Selected Works of Arnulf Øverland Ole Peter Arnulf Øverland?",
|
||||
"Who is Attaphol Buspakom Attaphol Buspakom?",
|
||||
"Who was Arnulf Øverland?",
|
||||
]
|
||||
|
||||
for query_text in queries:
|
||||
|
|
|
|||
6
poetry.lock
generated
6
poetry.lock
generated
|
|
@ -1,4 +1,4 @@
|
|||
# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "accelerate"
|
||||
|
|
@ -2543,7 +2543,6 @@ files = [
|
|||
{file = "fastuuid-0.12.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b9b31dd488d0778c36f8279b306dc92a42f16904cba54acca71e107d65b60b0c"},
|
||||
{file = "fastuuid-0.12.0-cp313-cp313-manylinux_2_34_x86_64.whl", hash = "sha256:b19361ee649365eefc717ec08005972d3d1eb9ee39908022d98e3bfa9da59e37"},
|
||||
{file = "fastuuid-0.12.0-cp313-cp313-win_amd64.whl", hash = "sha256:8fc66b11423e6f3e1937385f655bedd67aebe56a3dcec0cb835351cfe7d358c9"},
|
||||
{file = "fastuuid-0.12.0-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:2925f67b88d47cb16aa3eb1ab20fdcf21b94d74490e0818c91ea41434b987493"},
|
||||
{file = "fastuuid-0.12.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7b15c54d300279ab20a9cc0579ada9c9f80d1bc92997fc61fb7bf3103d7cb26b"},
|
||||
{file = "fastuuid-0.12.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:458f1bc3ebbd76fdb89ad83e6b81ccd3b2a99fa6707cd3650b27606745cfb170"},
|
||||
{file = "fastuuid-0.12.0-cp38-cp38-manylinux_2_34_x86_64.whl", hash = "sha256:a8f0f83fbba6dc44271a11b22e15838641b8c45612cdf541b4822a5930f6893c"},
|
||||
|
|
@ -4170,8 +4169,6 @@ groups = ["main"]
|
|||
markers = "extra == \"dlt\""
|
||||
files = [
|
||||
{file = "jsonpath-ng-1.7.0.tar.gz", hash = "sha256:f6f5f7fd4e5ff79c785f1573b394043b39849fb2bb47bcead935d12b00beab3c"},
|
||||
{file = "jsonpath_ng-1.7.0-py2-none-any.whl", hash = "sha256:898c93fc173f0c336784a3fa63d7434297544b7198124a68f9a3ef9597b0ae6e"},
|
||||
{file = "jsonpath_ng-1.7.0-py3-none-any.whl", hash = "sha256:f3d7f9e848cba1b6da28c55b1c26ff915dc9e0b1ba7e752a53d6da8d5cbd00b6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8593,7 +8590,6 @@ files = [
|
|||
{file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:bb89f0a835bcfc1d42ccd5f41f04870c1b936d8507c6df12b7737febc40f0909"},
|
||||
{file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f0c2d907a1e102526dd2986df638343388b94c33860ff3bbe1384130828714b1"},
|
||||
{file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f8157bed2f51db683f31306aa497311b560f2265998122abe1dce6428bd86567"},
|
||||
{file = "psycopg2_binary-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:27422aa5f11fbcd9b18da48373eb67081243662f9b46e6fd07c3eb46e4535142"},
|
||||
{file = "psycopg2_binary-2.9.10-cp38-cp38-macosx_12_0_x86_64.whl", hash = "sha256:eb09aa7f9cecb45027683bb55aebaaf45a0df8bf6de68801a6afdc7947bb09d4"},
|
||||
{file = "psycopg2_binary-2.9.10-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b73d6d7f0ccdad7bc43e6d34273f70d587ef62f824d7261c4ae9b8b1b6af90e8"},
|
||||
{file = "psycopg2_binary-2.9.10-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ce5ab4bf46a211a8e924d307c1b1fcda82368586a19d0a24f8ae166f5c784864"},
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
[project]
|
||||
name = "cognee"
|
||||
|
||||
version = "0.3.7"
|
||||
version = "0.3.9"
|
||||
description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning."
|
||||
authors = [
|
||||
{ name = "Vasilije Markovic" },
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue