Merge remote-tracking branch 'origin/dev' into feat/modal-parallelization
This commit is contained in:
Boris Arzentar
commit
44cc74994f
55 changed files with 12965 additions and 4198 deletions
2
.github/actions/cognee_setup/action.yml
vendored
2
.github/actions/cognee_setup/action.yml
vendored
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@ -24,4 +24,4 @@ runs:
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|||
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- name: Install dependencies
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shell: bash
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run: poetry install --no-interaction -E api -E docs -E evals -E gemini -E codegraph -E ollama
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run: poetry install --no-interaction -E api -E docs -E evals -E gemini -E codegraph -E ollama -E dev
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19
.github/workflows/python_version_tests.yml
vendored
19
.github/workflows/python_version_tests.yml
vendored
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@ -58,8 +58,10 @@ jobs:
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python-version: ${{ inputs.python-version }}
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- name: Run unit tests
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shell: bash
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run: poetry run pytest cognee/tests/unit/
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env:
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PYTHONUTF8: 1
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LLM_PROVIDER: openai
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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@ -74,10 +76,26 @@ jobs:
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- name: Run integration tests
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if: ${{ !contains(matrix.os, 'windows') }}
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shell: bash
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run: poetry run pytest cognee/tests/integration/
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env:
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PYTHONUTF8: 1
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LLM_PROVIDER: openai
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LLM_MODEL: ${{ secrets.LLM_MODEL }}
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LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
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LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
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LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
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EMBEDDING_PROVIDER: openai
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EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
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EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
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EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
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EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
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- name: Run default basic pipeline
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shell: bash
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env:
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PYTHONUTF8: 1
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GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
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GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
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@ -95,6 +113,7 @@ jobs:
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run: poetry run python ./cognee/tests/test_library.py
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- name: Build with Poetry
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shell: bash
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run: poetry build
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- name: Install Package
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@ -29,6 +29,7 @@ RUN apt-get update
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RUN apt-get install -y \
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gcc \
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build-essential \
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libpq-dev
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WORKDIR /app
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@ -40,7 +41,7 @@ RUN pip install poetry
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RUN poetry config virtualenvs.create false
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# Install the dependencies using the defined extras
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RUN poetry install --extras "${POETRY_EXTRAS}" --no-root --without dev
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RUN poetry install --extras "${POETRY_EXTRAS}" --no-root
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# Set the PYTHONPATH environment variable to include the /app directory
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ENV PYTHONPATH=/app
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20
README.md
20
README.md
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@ -32,6 +32,14 @@ Build dynamic Agent memory using scalable, modular ECL (Extract, Cognify, Load)
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More on [use-cases](https://docs.cognee.ai/use-cases) and [evals](https://github.com/topoteretes/cognee/tree/main/evals)
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<p align="center">
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🌐 Available Languages
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:
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<a href="community/README.pt.md">🇵🇹 Português</a>
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·
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<a href="community/README.zh.md">🇨🇳 [中文]</a>
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</p>
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<div style="text-align: center">
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<img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/main/assets/cognee_benefits.png" alt="Why cognee?" width="50%" />
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</div>
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@ -50,7 +58,7 @@ More on [use-cases](https://docs.cognee.ai/use-cases) and [evals](https://github
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## Get Started
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Get started quickly with a Google Colab <a href="https://colab.research.google.com/drive/1g-Qnx6l_ecHZi0IOw23rg0qC4TYvEvWZ?usp=sharing">notebook</a> or <a href="https://github.com/topoteretes/cognee-starter">starter repo</a>
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Get started quickly with a Google Colab <a href="https://colab.research.google.com/drive/1jHbWVypDgCLwjE71GSXhRL3YxYhCZzG1?usp=sharing">notebook</a> or <a href="https://github.com/topoteretes/cognee-starter">starter repo</a>
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## Contributing
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Your contributions are at the core of making this a true open source project. Any contributions you make are **greatly appreciated**. See [`CONTRIBUTING.md`](CONTRIBUTING.md) for more information.
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@ -116,12 +124,14 @@ Example output:
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|||
Natural Language Processing (NLP) is a cross-disciplinary and interdisciplinary field that involves computer science and information retrieval. It focuses on the interaction between computers and human language, enabling machines to understand and process natural language.
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```
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Graph visualization:
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<a href="https://rawcdn.githack.com/topoteretes/cognee/refs/heads/add-visualization-readme/assets/graph_visualization.html"><img src="assets/graph_visualization.png" width="100%" alt="Graph Visualization"></a>
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||||
Open in [browser](https://rawcdn.githack.com/topoteretes/cognee/refs/heads/add-visualization-readme/assets/graph_visualization.html).
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For more advanced usage, have a look at our <a href="https://docs.cognee.ai"> documentation</a>.
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### cognee UI
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You can also cognify your files and query using cognee UI.
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<img src="assets/cognee-ui-2.webp" width="100%" alt="Cognee UI 2"></a>
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Try cognee UI out locally [here](https://docs.cognee.ai/how-to-guides/cognee-ui).
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## Understand our architecture
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||||
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||||
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|||
BIN
assets/cognee-ui-1.webp
Normal file
BIN
assets/cognee-ui-1.webp
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BIN
assets/cognee-ui-2.webp
Normal file
BIN
assets/cognee-ui-2.webp
Normal file
Binary file not shown.
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After Width: | Height: | Size: 936 KiB |
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@ -1,128 +0,0 @@
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|||
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||||
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<head>
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</head>
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<body>
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<svg></svg>
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||||
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|
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</svg>
|
||||
</body>
|
||||
</html>
|
||||
|
||||
Binary file not shown.
|
Before Width: | Height: | Size: 365 KiB |
|
|
@ -7,6 +7,7 @@ requires-python = ">=3.10"
|
|||
|
||||
dependencies = [
|
||||
"cognee[postgres,codegraph,gemini,huggingface]==0.1.39",
|
||||
"fastmcp>=1.0",
|
||||
"mcp==1.5.0",
|
||||
"uv>=0.6.3",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -1,253 +1,141 @@
|
|||
import asyncio
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
import argparse
|
||||
import cognee
|
||||
import asyncio
|
||||
from cognee.shared.logging_utils import get_logger, get_log_file_location
|
||||
import importlib.util
|
||||
from contextlib import redirect_stdout
|
||||
|
||||
# from PIL import Image as PILImage
|
||||
import mcp.types as types
|
||||
from mcp.server import Server, NotificationOptions
|
||||
from mcp.server.models import InitializationOptions
|
||||
from mcp.server import FastMCP
|
||||
from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline
|
||||
from cognee.modules.search.types import SearchType
|
||||
from cognee.shared.data_models import KnowledgeGraph
|
||||
from cognee.modules.storage.utils import JSONEncoder
|
||||
|
||||
mcp = Server("cognee")
|
||||
mcp = FastMCP("Cognee")
|
||||
|
||||
logger = get_logger()
|
||||
log_file = get_log_file_location()
|
||||
|
||||
|
||||
@mcp.list_tools()
|
||||
async def list_tools() -> list[types.Tool]:
|
||||
@mcp.tool()
|
||||
async def cognify(text: str, graph_model_file: str = None, graph_model_name: str = None) -> list:
|
||||
async def cognify_task(
|
||||
text: str, graph_model_file: str = None, graph_model_name: str = None
|
||||
) -> str:
|
||||
"""Build knowledge graph from the input text"""
|
||||
# NOTE: MCP uses stdout to communicate, we must redirect all output
|
||||
# going to stdout ( like the print function ) to stderr.
|
||||
# As cognify is an async background job the output had to be redirected again.
|
||||
with redirect_stdout(sys.stderr):
|
||||
logger.info("Cognify process starting.")
|
||||
if graph_model_file and graph_model_name:
|
||||
graph_model = load_class(graph_model_file, graph_model_name)
|
||||
else:
|
||||
graph_model = KnowledgeGraph
|
||||
|
||||
await cognee.add(text)
|
||||
|
||||
try:
|
||||
await cognee.cognify(graph_model=graph_model)
|
||||
logger.info("Cognify process finished.")
|
||||
except Exception as e:
|
||||
logger.error("Cognify process failed.")
|
||||
raise ValueError(f"Failed to cognify: {str(e)}")
|
||||
|
||||
asyncio.create_task(
|
||||
cognify_task(
|
||||
text=text,
|
||||
graph_model_file=graph_model_file,
|
||||
graph_model_name=graph_model_name,
|
||||
)
|
||||
)
|
||||
|
||||
text = (
|
||||
f"Background process launched due to MCP timeout limitations.\n"
|
||||
f"Average completion time is around 4 minutes.\n"
|
||||
f"For current cognify status you can check the log file at: {log_file}"
|
||||
)
|
||||
|
||||
return [
|
||||
types.Tool(
|
||||
name="cognify",
|
||||
description="Cognifies text into knowledge graph",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"text": {
|
||||
"type": "string",
|
||||
"description": "The text to cognify",
|
||||
},
|
||||
"graph_model_file": {
|
||||
"type": "string",
|
||||
"description": "The path to the graph model file (Optional)",
|
||||
},
|
||||
"graph_model_name": {
|
||||
"type": "string",
|
||||
"description": "The name of the graph model (Optional)",
|
||||
},
|
||||
},
|
||||
"required": ["text"],
|
||||
},
|
||||
),
|
||||
types.Tool(
|
||||
name="codify",
|
||||
description="Transforms codebase into knowledge graph",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"repo_path": {
|
||||
"type": "string",
|
||||
},
|
||||
},
|
||||
"required": ["repo_path"],
|
||||
},
|
||||
),
|
||||
types.Tool(
|
||||
name="search",
|
||||
description="Searches for information in knowledge graph",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"search_query": {
|
||||
"type": "string",
|
||||
"description": "The query to search for",
|
||||
},
|
||||
"search_type": {
|
||||
"type": "string",
|
||||
"description": "The type of search to perform (e.g., INSIGHTS, CODE)",
|
||||
},
|
||||
},
|
||||
"required": ["search_query"],
|
||||
},
|
||||
),
|
||||
types.Tool(
|
||||
name="prune",
|
||||
description="Prunes knowledge graph",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
},
|
||||
),
|
||||
types.TextContent(
|
||||
type="text",
|
||||
text=text,
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
@mcp.call_tool()
|
||||
async def call_tools(name: str, arguments: dict) -> list[types.TextContent]:
|
||||
try:
|
||||
@mcp.tool()
|
||||
async def codify(repo_path: str) -> list:
|
||||
async def codify_task(repo_path: str):
|
||||
# NOTE: MCP uses stdout to communicate, we must redirect all output
|
||||
# going to stdout ( like the print function ) to stderr.
|
||||
# As codify is an async background job the output had to be redirected again.
|
||||
with redirect_stdout(sys.stderr):
|
||||
logger.info("Codify process starting.")
|
||||
results = []
|
||||
async for result in run_code_graph_pipeline(repo_path, False):
|
||||
results.append(result)
|
||||
logger.info(result)
|
||||
if all(results):
|
||||
logger.info("Codify process finished succesfully.")
|
||||
else:
|
||||
logger.info("Codify process failed.")
|
||||
|
||||
asyncio.create_task(codify_task(repo_path))
|
||||
|
||||
text = (
|
||||
f"Background process launched due to MCP timeout limitations.\n"
|
||||
f"Average completion time is around 4 minutes.\n"
|
||||
f"For current codify status you can check the log file at: {log_file}"
|
||||
)
|
||||
|
||||
return [
|
||||
types.TextContent(
|
||||
type="text",
|
||||
text=text,
|
||||
)
|
||||
]
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def search(search_query: str, search_type: str) -> list:
|
||||
async def search_task(search_query: str, search_type: str) -> str:
|
||||
"""Search the knowledge graph"""
|
||||
# NOTE: MCP uses stdout to communicate, we must redirect all output
|
||||
# going to stdout ( like the print function ) to stderr.
|
||||
with redirect_stdout(sys.stderr):
|
||||
log_file = get_log_file_location()
|
||||
|
||||
if name == "cognify":
|
||||
asyncio.create_task(
|
||||
cognify(
|
||||
text=arguments["text"],
|
||||
graph_model_file=arguments.get("graph_model_file"),
|
||||
graph_model_name=arguments.get("graph_model_name"),
|
||||
)
|
||||
)
|
||||
|
||||
text = (
|
||||
f"Background process launched due to MCP timeout limitations.\n"
|
||||
f"Average completion time is around 4 minutes.\n"
|
||||
f"For current cognify status you can check the log file at: {log_file}"
|
||||
)
|
||||
|
||||
return [
|
||||
types.TextContent(
|
||||
type="text",
|
||||
text=text,
|
||||
)
|
||||
]
|
||||
if name == "codify":
|
||||
asyncio.create_task(codify(arguments.get("repo_path")))
|
||||
|
||||
text = (
|
||||
f"Background process launched due to MCP timeout limitations.\n"
|
||||
f"Average completion time is around 4 minutes.\n"
|
||||
f"For current codify status you can check the log file at: {log_file}"
|
||||
)
|
||||
|
||||
return [
|
||||
types.TextContent(
|
||||
type="text",
|
||||
text=text,
|
||||
)
|
||||
]
|
||||
elif name == "search":
|
||||
search_results = await search(arguments["search_query"], arguments["search_type"])
|
||||
|
||||
return [types.TextContent(type="text", text=search_results)]
|
||||
elif name == "prune":
|
||||
await prune()
|
||||
|
||||
return [types.TextContent(type="text", text="Pruned")]
|
||||
except Exception as e:
|
||||
logger.error(f"Error calling tool '{name}': {str(e)}")
|
||||
return [types.TextContent(type="text", text=f"Error calling tool '{name}': {str(e)}")]
|
||||
|
||||
|
||||
async def cognify(text: str, graph_model_file: str = None, graph_model_name: str = None) -> str:
|
||||
"""Build knowledge graph from the input text"""
|
||||
# NOTE: MCP uses stdout to communicate, we must redirect all output
|
||||
# going to stdout ( like the print function ) to stderr.
|
||||
# As cognify is an async background job the output had to be redirected again.
|
||||
with redirect_stdout(sys.stderr):
|
||||
logger.info("Cognify process starting.")
|
||||
if graph_model_file and graph_model_name:
|
||||
graph_model = load_class(graph_model_file, graph_model_name)
|
||||
else:
|
||||
graph_model = KnowledgeGraph
|
||||
|
||||
await cognee.add(text)
|
||||
|
||||
try:
|
||||
await cognee.cognify(graph_model=graph_model)
|
||||
logger.info("Cognify process finished.")
|
||||
except Exception as e:
|
||||
logger.error("Cognify process failed.")
|
||||
raise ValueError(f"Failed to cognify: {str(e)}")
|
||||
|
||||
|
||||
async def codify(repo_path: str):
|
||||
# NOTE: MCP uses stdout to communicate, we must redirect all output
|
||||
# going to stdout ( like the print function ) to stderr.
|
||||
# As codify is an async background job the output had to be redirected again.
|
||||
with redirect_stdout(sys.stderr):
|
||||
logger.info("Codify process starting.")
|
||||
results = []
|
||||
async for result in run_code_graph_pipeline(repo_path, False):
|
||||
results.append(result)
|
||||
logger.info(result)
|
||||
if all(results):
|
||||
logger.info("Codify process finished succesfully.")
|
||||
else:
|
||||
logger.info("Codify process failed.")
|
||||
|
||||
|
||||
async def search(search_query: str, search_type: str) -> str:
|
||||
"""Search the knowledge graph"""
|
||||
# NOTE: MCP uses stdout to communicate, we must redirect all output
|
||||
# going to stdout ( like the print function ) to stderr.
|
||||
with redirect_stdout(sys.stderr):
|
||||
search_results = await cognee.search(
|
||||
query_type=SearchType[search_type.upper()], query_text=search_query
|
||||
)
|
||||
|
||||
if search_type.upper() == "CODE":
|
||||
return json.dumps(search_results, cls=JSONEncoder)
|
||||
elif search_type.upper() == "GRAPH_COMPLETION" or search_type.upper() == "RAG_COMPLETION":
|
||||
return search_results[0]
|
||||
elif search_type.upper() == "CHUNKS":
|
||||
return str(search_results)
|
||||
elif search_type.upper() == "INSIGHTS":
|
||||
results = retrieved_edges_to_string(search_results)
|
||||
return results
|
||||
else:
|
||||
return str(search_results)
|
||||
|
||||
|
||||
async def prune():
|
||||
"""Reset the knowledge graph"""
|
||||
await cognee.prune.prune_data()
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
|
||||
|
||||
async def main():
|
||||
try:
|
||||
from mcp.server.stdio import stdio_server
|
||||
|
||||
logger.info("Cognee MCP server started...")
|
||||
|
||||
async with stdio_server() as (read_stream, write_stream):
|
||||
await mcp.run(
|
||||
read_stream=read_stream,
|
||||
write_stream=write_stream,
|
||||
initialization_options=InitializationOptions(
|
||||
server_name="cognee",
|
||||
server_version="0.1.0",
|
||||
capabilities=mcp.get_capabilities(
|
||||
notification_options=NotificationOptions(),
|
||||
experimental_capabilities={},
|
||||
),
|
||||
),
|
||||
raise_exceptions=True,
|
||||
search_results = await cognee.search(
|
||||
query_type=SearchType[search_type.upper()], query_text=search_query
|
||||
)
|
||||
|
||||
logger.info("Cognee MCP server closed.")
|
||||
if search_type.upper() == "CODE":
|
||||
return json.dumps(search_results, cls=JSONEncoder)
|
||||
elif (
|
||||
search_type.upper() == "GRAPH_COMPLETION" or search_type.upper() == "RAG_COMPLETION"
|
||||
):
|
||||
return search_results[0]
|
||||
elif search_type.upper() == "CHUNKS":
|
||||
return str(search_results)
|
||||
elif search_type.upper() == "INSIGHTS":
|
||||
results = retrieved_edges_to_string(search_results)
|
||||
return results
|
||||
else:
|
||||
return str(search_results)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Server failed to start: {str(e)}", exc_info=True)
|
||||
raise
|
||||
search_results = await search_task(search_query, search_type)
|
||||
return [types.TextContent(type="text", text=search_results)]
|
||||
|
||||
|
||||
# async def visualize() -> Image:
|
||||
# """Visualize the knowledge graph"""
|
||||
# try:
|
||||
# image_path = await cognee.visualize_graph()
|
||||
|
||||
# img = PILImage.open(image_path)
|
||||
# return Image(data=img.tobytes(), format="png")
|
||||
# except (FileNotFoundError, IOError, ValueError) as e:
|
||||
# raise ValueError(f"Failed to create visualization: {str(e)}")
|
||||
@mcp.tool()
|
||||
async def prune():
|
||||
"""Reset the knowledge graph"""
|
||||
with redirect_stdout(sys.stderr):
|
||||
await cognee.prune.prune_data()
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
return [types.TextContent(type="text", text="Pruned")]
|
||||
|
||||
|
||||
def node_to_string(node):
|
||||
|
|
@ -265,6 +153,7 @@ def retrieved_edges_to_string(search_results):
|
|||
relationship_type = edge["relationship_name"]
|
||||
edge_str = f"{node_to_string(node1)} {relationship_type} {node_to_string(node2)}"
|
||||
edge_strings.append(edge_str)
|
||||
|
||||
return "\n".join(edge_strings)
|
||||
|
||||
|
||||
|
|
@ -279,32 +168,31 @@ def load_class(model_file, model_name):
|
|||
return model_class
|
||||
|
||||
|
||||
# def get_freshest_png(directory: str) -> Image:
|
||||
# if not os.path.exists(directory):
|
||||
# raise FileNotFoundError(f"Directory {directory} does not exist")
|
||||
async def main():
|
||||
parser = argparse.ArgumentParser()
|
||||
|
||||
# # List all files in 'directory' that end with .png
|
||||
# files = [f for f in os.listdir(directory) if f.endswith(".png")]
|
||||
# if not files:
|
||||
# raise FileNotFoundError("No PNG files found in the given directory.")
|
||||
parser.add_argument(
|
||||
"--transport",
|
||||
choices=["sse", "stdio"],
|
||||
default="stdio",
|
||||
help="Transport to use for communication with the client. (default: stdio)",
|
||||
)
|
||||
|
||||
# # Sort by integer value of the filename (minus the '.png')
|
||||
# # Example filename: 1673185134.png -> integer 1673185134
|
||||
# try:
|
||||
# files_sorted = sorted(files, key=lambda x: int(x.replace(".png", "")))
|
||||
# except ValueError as e:
|
||||
# raise ValueError("Invalid PNG filename format. Expected timestamp format.") from e
|
||||
args = parser.parse_args()
|
||||
|
||||
# # The "freshest" file has the largest timestamp
|
||||
# freshest_filename = files_sorted[-1]
|
||||
# freshest_path = os.path.join(directory, freshest_filename)
|
||||
logger.info(f"Starting MCP server with transport: {args.transport}")
|
||||
if args.transport == "stdio":
|
||||
await mcp.run_stdio_async()
|
||||
elif args.transport == "sse":
|
||||
logger.info(
|
||||
f"Running MCP server with SSE transport on {mcp.settings.host}:{mcp.settings.port}"
|
||||
)
|
||||
await mcp.run_sse_async()
|
||||
|
||||
# # Open the image with PIL and return the PIL Image object
|
||||
# try:
|
||||
# return PILImage.open(freshest_path)
|
||||
# except (IOError, OSError) as e:
|
||||
# raise IOError(f"Failed to open PNG file {freshest_path}") from e
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Initialize and run the server
|
||||
asyncio.run(main())
|
||||
try:
|
||||
asyncio.run(main())
|
||||
except Exception as e:
|
||||
logger.error(f"Error initializing Cognee MCP server: {str(e)}")
|
||||
raise
|
||||
|
|
|
|||
4946
cognee-mcp/uv.lock
generated
4946
cognee-mcp/uv.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -1,18 +1,17 @@
|
|||
import os
|
||||
import pathlib
|
||||
import asyncio
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from uuid import NAMESPACE_OID, uuid5
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.modules.observability.get_observe import get_observe
|
||||
|
||||
from cognee.api.v1.search import SearchType, search
|
||||
from cognee.api.v1.visualize.visualize import visualize_graph
|
||||
from cognee.base_config import get_base_config
|
||||
from cognee.modules.cognify.config import get_cognify_config
|
||||
from cognee.modules.pipelines import run_tasks
|
||||
from cognee.modules.pipelines.tasks.task import Task
|
||||
from cognee.modules.users.methods import get_default_user
|
||||
from cognee.shared.data_models import KnowledgeGraph, MonitoringTool
|
||||
from cognee.shared.utils import render_graph
|
||||
from cognee.shared.data_models import KnowledgeGraph
|
||||
from cognee.tasks.documents import classify_documents, extract_chunks_from_documents
|
||||
from cognee.tasks.graph import extract_graph_from_data
|
||||
from cognee.tasks.ingestion import ingest_data
|
||||
|
|
@ -22,11 +21,7 @@ from cognee.tasks.storage import add_data_points
|
|||
from cognee.tasks.summarization import summarize_text
|
||||
from cognee.infrastructure.llm import get_max_chunk_tokens
|
||||
|
||||
monitoring = get_base_config().monitoring_tool
|
||||
|
||||
if monitoring == MonitoringTool.LANGFUSE:
|
||||
from langfuse.decorators import observe
|
||||
|
||||
observe = get_observe()
|
||||
|
||||
logger = get_logger("code_graph_pipeline")
|
||||
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
import os
|
||||
from typing import Optional
|
||||
from functools import lru_cache
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
from cognee.root_dir import get_absolute_path
|
||||
from cognee.shared.data_models import MonitoringTool
|
||||
from cognee.modules.observability.observers import Observer
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
|
||||
|
||||
class BaseConfig(BaseSettings):
|
||||
data_root_directory: str = get_absolute_path(".data_storage")
|
||||
monitoring_tool: object = MonitoringTool.LANGFUSE
|
||||
monitoring_tool: object = Observer.LANGFUSE
|
||||
graphistry_username: Optional[str] = os.getenv("GRAPHISTRY_USERNAME")
|
||||
graphistry_password: Optional[str] = os.getenv("GRAPHISTRY_PASSWORD")
|
||||
langfuse_public_key: Optional[str] = os.getenv("LANGFUSE_PUBLIC_KEY")
|
||||
|
|
|
|||
|
|
@ -12,13 +12,22 @@ class CogneeApiError(Exception):
|
|||
message: str = "Service is unavailable.",
|
||||
name: str = "Cognee",
|
||||
status_code=status.HTTP_418_IM_A_TEAPOT,
|
||||
log=True,
|
||||
log_level="ERROR",
|
||||
):
|
||||
self.message = message
|
||||
self.name = name
|
||||
self.status_code = status_code
|
||||
|
||||
# Automatically log the exception details
|
||||
logger.error(f"{self.name}: {self.message} (Status code: {self.status_code})")
|
||||
if log and (log_level == "ERROR"):
|
||||
logger.error(f"{self.name}: {self.message} (Status code: {self.status_code})")
|
||||
elif log and (log_level == "WARNING"):
|
||||
logger.warning(f"{self.name}: {self.message} (Status code: {self.status_code})")
|
||||
elif log and (log_level == "INFO"):
|
||||
logger.info(f"{self.name}: {self.message} (Status code: {self.status_code})")
|
||||
elif log and (log_level == "DEBUG"):
|
||||
logger.debug(f"{self.name}: {self.message} (Status code: {self.status_code})")
|
||||
|
||||
super().__init__(self.message, self.name)
|
||||
|
||||
|
|
|
|||
|
|
@ -58,7 +58,7 @@ def record_graph_changes(func):
|
|||
session.add(relationship)
|
||||
await session.flush()
|
||||
except Exception as e:
|
||||
logger.error(f"Error adding relationship: {e}")
|
||||
logger.debug(f"Error adding relationship: {e}")
|
||||
await session.rollback()
|
||||
continue
|
||||
|
||||
|
|
@ -78,14 +78,14 @@ def record_graph_changes(func):
|
|||
session.add(relationship)
|
||||
await session.flush()
|
||||
except Exception as e:
|
||||
logger.error(f"Error adding relationship: {e}")
|
||||
logger.debug(f"Error adding relationship: {e}")
|
||||
await session.rollback()
|
||||
continue
|
||||
|
||||
try:
|
||||
await session.commit()
|
||||
except Exception as e:
|
||||
logger.error(f"Error committing session: {e}")
|
||||
logger.debug(f"Error committing session: {e}")
|
||||
|
||||
return result
|
||||
|
||||
|
|
|
|||
|
|
@ -42,7 +42,7 @@ class NetworkXAdapter(GraphDBInterface):
|
|||
async def query(self, query: str, params: dict):
|
||||
pass
|
||||
|
||||
async def has_node(self, node_id: str) -> bool:
|
||||
async def has_node(self, node_id: UUID) -> bool:
|
||||
return self.graph.has_node(node_id)
|
||||
|
||||
async def add_node(self, node: DataPoint) -> None:
|
||||
|
|
@ -136,7 +136,7 @@ class NetworkXAdapter(GraphDBInterface):
|
|||
logger.error(f"Failed to add edges: {e}")
|
||||
raise
|
||||
|
||||
async def get_edges(self, node_id: str):
|
||||
async def get_edges(self, node_id: UUID):
|
||||
return list(self.graph.in_edges(node_id, data=True)) + list(
|
||||
self.graph.out_edges(node_id, data=True)
|
||||
)
|
||||
|
|
@ -174,13 +174,13 @@ class NetworkXAdapter(GraphDBInterface):
|
|||
|
||||
return disconnected_nodes
|
||||
|
||||
async def extract_node(self, node_id: str) -> dict:
|
||||
async def extract_node(self, node_id: UUID) -> dict:
|
||||
if self.graph.has_node(node_id):
|
||||
return self.graph.nodes[node_id]
|
||||
|
||||
return None
|
||||
|
||||
async def extract_nodes(self, node_ids: List[str]) -> List[dict]:
|
||||
async def extract_nodes(self, node_ids: List[UUID]) -> List[dict]:
|
||||
return [self.graph.nodes[node_id] for node_id in node_ids if self.graph.has_node(node_id)]
|
||||
|
||||
async def get_predecessors(self, node_id: UUID, edge_label: str = None) -> list:
|
||||
|
|
@ -215,7 +215,7 @@ class NetworkXAdapter(GraphDBInterface):
|
|||
|
||||
return nodes
|
||||
|
||||
async def get_neighbors(self, node_id: str) -> list:
|
||||
async def get_neighbors(self, node_id: UUID) -> list:
|
||||
if not self.graph.has_node(node_id):
|
||||
return []
|
||||
|
||||
|
|
@ -264,7 +264,7 @@ class NetworkXAdapter(GraphDBInterface):
|
|||
return connections
|
||||
|
||||
async def remove_connection_to_predecessors_of(
|
||||
self, node_ids: list[str], edge_label: str
|
||||
self, node_ids: list[UUID], edge_label: str
|
||||
) -> None:
|
||||
for node_id in node_ids:
|
||||
if self.graph.has_node(node_id):
|
||||
|
|
@ -275,7 +275,7 @@ class NetworkXAdapter(GraphDBInterface):
|
|||
await self.save_graph_to_file(self.filename)
|
||||
|
||||
async def remove_connection_to_successors_of(
|
||||
self, node_ids: list[str], edge_label: str
|
||||
self, node_ids: list[UUID], edge_label: str
|
||||
) -> None:
|
||||
for node_id in node_ids:
|
||||
if self.graph.has_node(node_id):
|
||||
|
|
@ -621,12 +621,12 @@ class NetworkXAdapter(GraphDBInterface):
|
|||
nodes.append(node_data)
|
||||
return nodes
|
||||
|
||||
async def get_node(self, node_id: str) -> dict:
|
||||
async def get_node(self, node_id: UUID) -> dict:
|
||||
if self.graph.has_node(node_id):
|
||||
return self.graph.nodes[node_id]
|
||||
return None
|
||||
|
||||
async def get_nodes(self, node_ids: List[str] = None) -> List[dict]:
|
||||
async def get_nodes(self, node_ids: List[UUID] = None) -> List[dict]:
|
||||
if node_ids is None:
|
||||
return [{"id": node_id, **data} for node_id, data in self.graph.nodes(data=True)]
|
||||
return [
|
||||
|
|
|
|||
|
|
@ -69,7 +69,7 @@ class SQLAlchemyAdapter:
|
|||
async def delete_table(self, table_name: str, schema_name: Optional[str] = "public"):
|
||||
async with self.engine.begin() as connection:
|
||||
if self.engine.dialect.name == "sqlite":
|
||||
# SQLite doesn’t support schema namespaces and the CASCADE keyword.
|
||||
# SQLite doesn't support schema namespaces and the CASCADE keyword.
|
||||
# However, foreign key constraint can be defined with ON DELETE CASCADE during table creation.
|
||||
await connection.execute(text(f'DROP TABLE IF EXISTS "{table_name}";'))
|
||||
else:
|
||||
|
|
@ -327,10 +327,10 @@ class SQLAlchemyAdapter:
|
|||
file.write("")
|
||||
else:
|
||||
async with self.engine.begin() as connection:
|
||||
schema_list = await self.get_schema_list()
|
||||
# Create a MetaData instance to load table information
|
||||
metadata = MetaData()
|
||||
# Drop all tables from all schemas
|
||||
# Drop all tables from the public schema
|
||||
schema_list = ["public", "public_staging"]
|
||||
for schema_name in schema_list:
|
||||
# Load the schema information into the MetaData object
|
||||
await connection.run_sync(metadata.reflect, schema=schema_name)
|
||||
|
|
|
|||
|
|
@ -6,8 +6,9 @@ from chromadb import AsyncHttpClient, Settings
|
|||
from cognee.exceptions import InvalidValueError
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.modules.storage.utils import get_own_properties
|
||||
from cognee.infrastructure.engine.utils import parse_id
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.infrastructure.engine.utils import parse_id
|
||||
from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
|
||||
from cognee.infrastructure.databases.vector.models.ScoredResult import ScoredResult
|
||||
|
||||
from ..embeddings.EmbeddingEngine import EmbeddingEngine
|
||||
|
|
@ -108,9 +109,7 @@ class ChromaDBAdapter(VectorDBInterface):
|
|||
return await self.embedding_engine.embed_text(data)
|
||||
|
||||
async def has_collection(self, collection_name: str) -> bool:
|
||||
client = await self.get_connection()
|
||||
collections = await client.list_collections()
|
||||
# In ChromaDB v0.6.0, list_collections returns collection names directly
|
||||
collections = await self.get_collection_names()
|
||||
return collection_name in collections
|
||||
|
||||
async def create_collection(self, collection_name: str, payload_schema=None):
|
||||
|
|
@ -119,13 +118,17 @@ class ChromaDBAdapter(VectorDBInterface):
|
|||
if not await self.has_collection(collection_name):
|
||||
await client.create_collection(name=collection_name, metadata={"hnsw:space": "cosine"})
|
||||
|
||||
async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
|
||||
client = await self.get_connection()
|
||||
|
||||
async def get_collection(self, collection_name: str) -> AsyncHttpClient:
|
||||
if not await self.has_collection(collection_name):
|
||||
await self.create_collection(collection_name)
|
||||
raise CollectionNotFoundError(f"Collection '{collection_name}' not found!")
|
||||
|
||||
collection = await client.get_collection(collection_name)
|
||||
client = await self.get_connection()
|
||||
return await client.get_collection(collection_name)
|
||||
|
||||
async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
|
||||
await self.create_collection(collection_name)
|
||||
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
texts = [DataPoint.get_embeddable_data(data_point) for data_point in data_points]
|
||||
embeddings = await self.embed_data(texts)
|
||||
|
|
@ -161,8 +164,7 @@ class ChromaDBAdapter(VectorDBInterface):
|
|||
|
||||
async def retrieve(self, collection_name: str, data_point_ids: list[str]):
|
||||
"""Retrieve data points by their IDs from a collection."""
|
||||
client = await self.get_connection()
|
||||
collection = await client.get_collection(collection_name)
|
||||
collection = await self.get_collection(collection_name)
|
||||
results = await collection.get(ids=data_point_ids, include=["metadatas"])
|
||||
|
||||
return [
|
||||
|
|
@ -174,62 +176,12 @@ class ChromaDBAdapter(VectorDBInterface):
|
|||
for id, metadata in zip(results["ids"], results["metadatas"])
|
||||
]
|
||||
|
||||
async def get_distance_from_collection_elements(
|
||||
self, collection_name: str, query_text: str = None, query_vector: List[float] = None
|
||||
):
|
||||
"""Calculate distance between query and all elements in a collection."""
|
||||
if query_text is None and query_vector is None:
|
||||
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
|
||||
|
||||
if query_text and not query_vector:
|
||||
query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
|
||||
|
||||
client = await self.get_connection()
|
||||
try:
|
||||
collection = await client.get_collection(collection_name)
|
||||
|
||||
collection_count = await collection.count()
|
||||
|
||||
results = await collection.query(
|
||||
query_embeddings=[query_vector],
|
||||
include=["metadatas", "distances"],
|
||||
n_results=collection_count,
|
||||
)
|
||||
|
||||
result_values = []
|
||||
for i, (id, metadata, distance) in enumerate(
|
||||
zip(results["ids"][0], results["metadatas"][0], results["distances"][0])
|
||||
):
|
||||
result_values.append(
|
||||
{
|
||||
"id": parse_id(id),
|
||||
"payload": restore_data_from_chroma(metadata),
|
||||
"_distance": distance,
|
||||
}
|
||||
)
|
||||
|
||||
normalized_values = normalize_distances(result_values)
|
||||
|
||||
scored_results = []
|
||||
for i, result in enumerate(result_values):
|
||||
scored_results.append(
|
||||
ScoredResult(
|
||||
id=result["id"],
|
||||
payload=result["payload"],
|
||||
score=normalized_values[i],
|
||||
)
|
||||
)
|
||||
|
||||
return scored_results
|
||||
except Exception:
|
||||
return []
|
||||
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: str = None,
|
||||
query_vector: List[float] = None,
|
||||
limit: int = 5,
|
||||
limit: int = 15,
|
||||
with_vector: bool = False,
|
||||
normalized: bool = True,
|
||||
):
|
||||
|
|
@ -241,8 +193,10 @@ class ChromaDBAdapter(VectorDBInterface):
|
|||
query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
|
||||
|
||||
try:
|
||||
client = await self.get_connection()
|
||||
collection = await client.get_collection(collection_name)
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
if limit == 0:
|
||||
limit = await collection.count()
|
||||
|
||||
results = await collection.query(
|
||||
query_embeddings=[query_vector],
|
||||
|
|
@ -296,8 +250,7 @@ class ChromaDBAdapter(VectorDBInterface):
|
|||
"""Perform multiple searches in a single request for efficiency."""
|
||||
query_vectors = await self.embed_data(query_texts)
|
||||
|
||||
client = await self.get_connection()
|
||||
collection = await client.get_collection(collection_name)
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
results = await collection.query(
|
||||
query_embeddings=query_vectors,
|
||||
|
|
@ -346,15 +299,14 @@ class ChromaDBAdapter(VectorDBInterface):
|
|||
|
||||
async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
|
||||
"""Remove data points from a collection by their IDs."""
|
||||
client = await self.get_connection()
|
||||
collection = await client.get_collection(collection_name)
|
||||
collection = await self.get_collection(collection_name)
|
||||
await collection.delete(ids=data_point_ids)
|
||||
return True
|
||||
|
||||
async def prune(self):
|
||||
"""Delete all collections in the ChromaDB database."""
|
||||
client = await self.get_connection()
|
||||
collections = await client.list_collections()
|
||||
collections = await self.list_collections()
|
||||
for collection_name in collections:
|
||||
await client.delete_collection(collection_name)
|
||||
return True
|
||||
|
|
@ -362,4 +314,8 @@ class ChromaDBAdapter(VectorDBInterface):
|
|||
async def get_collection_names(self):
|
||||
"""Get a list of all collection names in the database."""
|
||||
client = await self.get_connection()
|
||||
return await client.list_collections()
|
||||
collections = await client.list_collections()
|
||||
return [
|
||||
collection.name if hasattr(collection, "name") else collection["name"]
|
||||
for collection in collections
|
||||
]
|
||||
|
|
|
|||
|
|
@ -6,7 +6,9 @@ class CollectionNotFoundError(CriticalError):
|
|||
def __init__(
|
||||
self,
|
||||
message,
|
||||
name: str = "DatabaseNotCreatedError",
|
||||
name: str = "CollectionNotFoundError",
|
||||
status_code: int = status.HTTP_422_UNPROCESSABLE_ENTITY,
|
||||
log=True,
|
||||
log_level="ERROR",
|
||||
):
|
||||
super().__init__(message, name, status_code)
|
||||
super().__init__(message, name, status_code, log, log_level)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,5 @@
|
|||
import asyncio
|
||||
from typing import Generic, List, Optional, TypeVar, Union, get_args, get_origin, get_type_hints
|
||||
|
||||
import lancedb
|
||||
from lancedb.pydantic import LanceModel, Vector
|
||||
from pydantic import BaseModel
|
||||
|
|
@ -76,9 +75,14 @@ class LanceDBAdapter(VectorDBInterface):
|
|||
exist_ok=True,
|
||||
)
|
||||
|
||||
async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
|
||||
connection = await self.get_connection()
|
||||
async def get_collection(self, collection_name: str):
|
||||
if not await self.has_collection(collection_name):
|
||||
raise CollectionNotFoundError(f"Collection '{collection_name}' not found!")
|
||||
|
||||
connection = await self.get_connection()
|
||||
return await connection.open_table(collection_name)
|
||||
|
||||
async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
|
||||
payload_schema = type(data_points[0])
|
||||
|
||||
if not await self.has_collection(collection_name):
|
||||
|
|
@ -87,7 +91,7 @@ class LanceDBAdapter(VectorDBInterface):
|
|||
payload_schema,
|
||||
)
|
||||
|
||||
collection = await connection.open_table(collection_name)
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
data_vectors = await self.embed_data(
|
||||
[DataPoint.get_embeddable_data(data_point) for data_point in data_points]
|
||||
|
|
@ -125,8 +129,7 @@ class LanceDBAdapter(VectorDBInterface):
|
|||
)
|
||||
|
||||
async def retrieve(self, collection_name: str, data_point_ids: list[str]):
|
||||
connection = await self.get_connection()
|
||||
collection = await connection.open_table(collection_name)
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
if len(data_point_ids) == 1:
|
||||
results = await collection.query().where(f"id = '{data_point_ids[0]}'").to_pandas()
|
||||
|
|
@ -142,48 +145,12 @@ class LanceDBAdapter(VectorDBInterface):
|
|||
for result in results.to_dict("index").values()
|
||||
]
|
||||
|
||||
async def get_distance_from_collection_elements(
|
||||
self, collection_name: str, query_text: str = None, query_vector: List[float] = None
|
||||
):
|
||||
if query_text is None and query_vector is None:
|
||||
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
|
||||
|
||||
if query_text and not query_vector:
|
||||
query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
|
||||
|
||||
connection = await self.get_connection()
|
||||
|
||||
try:
|
||||
collection = await connection.open_table(collection_name)
|
||||
|
||||
collection_size = await collection.count_rows()
|
||||
|
||||
results = (
|
||||
await collection.vector_search(query_vector).limit(collection_size).to_pandas()
|
||||
)
|
||||
|
||||
result_values = list(results.to_dict("index").values())
|
||||
|
||||
normalized_values = normalize_distances(result_values)
|
||||
|
||||
return [
|
||||
ScoredResult(
|
||||
id=parse_id(result["id"]),
|
||||
payload=result["payload"],
|
||||
score=normalized_values[value_index],
|
||||
)
|
||||
for value_index, result in enumerate(result_values)
|
||||
]
|
||||
except ValueError:
|
||||
# Ignore if collection doesn't exist
|
||||
return []
|
||||
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: str = None,
|
||||
query_vector: List[float] = None,
|
||||
limit: int = 5,
|
||||
limit: int = 15,
|
||||
with_vector: bool = False,
|
||||
normalized: bool = True,
|
||||
):
|
||||
|
|
@ -193,12 +160,10 @@ class LanceDBAdapter(VectorDBInterface):
|
|||
if query_text and not query_vector:
|
||||
query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
|
||||
|
||||
connection = await self.get_connection()
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
try:
|
||||
collection = await connection.open_table(collection_name)
|
||||
except ValueError:
|
||||
raise CollectionNotFoundError(f"Collection '{collection_name}' not found!")
|
||||
if limit == 0:
|
||||
limit = await collection.count_rows()
|
||||
|
||||
results = await collection.vector_search(query_vector).limit(limit).to_pandas()
|
||||
|
||||
|
|
@ -242,8 +207,7 @@ class LanceDBAdapter(VectorDBInterface):
|
|||
def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
|
||||
@retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
|
||||
async def _delete_data_points():
|
||||
connection = await self.get_connection()
|
||||
collection = await connection.open_table(collection_name)
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
# Delete one at a time to avoid commit conflicts
|
||||
for data_point_id in data_point_ids:
|
||||
|
|
@ -288,7 +252,7 @@ class LanceDBAdapter(VectorDBInterface):
|
|||
collection_names = await connection.table_names()
|
||||
|
||||
for collection_name in collection_names:
|
||||
collection = await connection.open_table(collection_name)
|
||||
collection = await self.get_collection(collection_name)
|
||||
await collection.delete("id IS NOT NULL")
|
||||
await connection.drop_table(collection_name)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,11 +1,12 @@
|
|||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from uuid import UUID
|
||||
from typing import List, Optional
|
||||
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.infrastructure.engine.utils import parse_id
|
||||
from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
|
||||
|
||||
from ..embeddings.EmbeddingEngine import EmbeddingEngine
|
||||
from ..models.ScoredResult import ScoredResult
|
||||
|
|
@ -96,7 +97,7 @@ class MilvusAdapter(VectorDBInterface):
|
|||
raise e
|
||||
|
||||
async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
|
||||
from pymilvus import MilvusException
|
||||
from pymilvus import MilvusException, exceptions
|
||||
|
||||
client = self.get_milvus_client()
|
||||
data_vectors = await self.embed_data(
|
||||
|
|
@ -118,6 +119,10 @@ class MilvusAdapter(VectorDBInterface):
|
|||
f"Inserted {result.get('insert_count', 0)} data points into collection '{collection_name}'."
|
||||
)
|
||||
return result
|
||||
except exceptions.CollectionNotExistException as error:
|
||||
raise CollectionNotFoundError(
|
||||
f"Collection '{collection_name}' does not exist!"
|
||||
) from error
|
||||
except MilvusException as e:
|
||||
logger.error(
|
||||
f"Error inserting data points into collection '{collection_name}': {str(e)}"
|
||||
|
|
@ -140,8 +145,8 @@ class MilvusAdapter(VectorDBInterface):
|
|||
collection_name = f"{index_name}_{index_property_name}"
|
||||
await self.create_data_points(collection_name, formatted_data_points)
|
||||
|
||||
async def retrieve(self, collection_name: str, data_point_ids: list[str]):
|
||||
from pymilvus import MilvusException
|
||||
async def retrieve(self, collection_name: str, data_point_ids: list[UUID]):
|
||||
from pymilvus import MilvusException, exceptions
|
||||
|
||||
client = self.get_milvus_client()
|
||||
try:
|
||||
|
|
@ -153,6 +158,10 @@ class MilvusAdapter(VectorDBInterface):
|
|||
output_fields=["*"],
|
||||
)
|
||||
return results
|
||||
except exceptions.CollectionNotExistException as error:
|
||||
raise CollectionNotFoundError(
|
||||
f"Collection '{collection_name}' does not exist!"
|
||||
) from error
|
||||
except MilvusException as e:
|
||||
logger.error(
|
||||
f"Error retrieving data points from collection '{collection_name}': {str(e)}"
|
||||
|
|
@ -164,10 +173,10 @@ class MilvusAdapter(VectorDBInterface):
|
|||
collection_name: str,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = 5,
|
||||
limit: int = 15,
|
||||
with_vector: bool = False,
|
||||
):
|
||||
from pymilvus import MilvusException
|
||||
from pymilvus import MilvusException, exceptions
|
||||
|
||||
client = self.get_milvus_client()
|
||||
if query_text is None and query_vector is None:
|
||||
|
|
@ -184,7 +193,7 @@ class MilvusAdapter(VectorDBInterface):
|
|||
collection_name=collection_name,
|
||||
data=[query_vector],
|
||||
anns_field="vector",
|
||||
limit=limit,
|
||||
limit=limit if limit > 0 else None,
|
||||
output_fields=output_fields,
|
||||
search_params={
|
||||
"metric_type": "COSINE",
|
||||
|
|
@ -199,6 +208,10 @@ class MilvusAdapter(VectorDBInterface):
|
|||
)
|
||||
for result in results[0]
|
||||
]
|
||||
except exceptions.CollectionNotExistException as error:
|
||||
raise CollectionNotFoundError(
|
||||
f"Collection '{collection_name}' does not exist!"
|
||||
) from error
|
||||
except MilvusException as e:
|
||||
logger.error(f"Error during search in collection '{collection_name}': {str(e)}")
|
||||
raise e
|
||||
|
|
@ -220,7 +233,7 @@ class MilvusAdapter(VectorDBInterface):
|
|||
]
|
||||
)
|
||||
|
||||
async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
|
||||
async def delete_data_points(self, collection_name: str, data_point_ids: list[UUID]):
|
||||
from pymilvus import MilvusException
|
||||
|
||||
client = self.get_milvus_client()
|
||||
|
|
|
|||
|
|
@ -8,19 +8,18 @@ from sqlalchemy.ext.asyncio import create_async_engine, async_sessionmaker
|
|||
|
||||
from cognee.exceptions import InvalidValueError
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.infrastructure.databases.exceptions import EntityNotFoundError
|
||||
from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.infrastructure.engine.utils import parse_id
|
||||
from cognee.infrastructure.databases.relational import get_relational_engine
|
||||
|
||||
from ...relational.ModelBase import Base
|
||||
from ...relational.sqlalchemy.SqlAlchemyAdapter import SQLAlchemyAdapter
|
||||
from ..embeddings.EmbeddingEngine import EmbeddingEngine
|
||||
from ..models.ScoredResult import ScoredResult
|
||||
from ..vector_db_interface import VectorDBInterface
|
||||
from .serialize_data import serialize_data
|
||||
from ..utils import normalize_distances
|
||||
from ..models.ScoredResult import ScoredResult
|
||||
from ..exceptions import CollectionNotFoundError
|
||||
from ..vector_db_interface import VectorDBInterface
|
||||
from ..embeddings.EmbeddingEngine import EmbeddingEngine
|
||||
from .serialize_data import serialize_data
|
||||
|
||||
logger = get_logger("PGVectorAdapter")
|
||||
|
||||
|
|
@ -180,7 +179,9 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
|
|||
if collection_name in metadata.tables:
|
||||
return metadata.tables[collection_name]
|
||||
else:
|
||||
raise CollectionNotFoundError(f"Collection '{collection_name}' not found!")
|
||||
raise CollectionNotFoundError(
|
||||
f"Collection '{collection_name}' not found!", log_level="DEBUG"
|
||||
)
|
||||
|
||||
async def retrieve(self, collection_name: str, data_point_ids: List[str]):
|
||||
# Get PGVectorDataPoint Table from database
|
||||
|
|
@ -197,60 +198,12 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
|
|||
for result in results
|
||||
]
|
||||
|
||||
async def get_distance_from_collection_elements(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: str = None,
|
||||
query_vector: List[float] = None,
|
||||
with_vector: bool = False,
|
||||
) -> List[ScoredResult]:
|
||||
if query_text is None and query_vector is None:
|
||||
raise ValueError("One of query_text or query_vector must be provided!")
|
||||
|
||||
if query_text and not query_vector:
|
||||
query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
|
||||
|
||||
try:
|
||||
# Get PGVectorDataPoint Table from database
|
||||
PGVectorDataPoint = await self.get_table(collection_name)
|
||||
|
||||
# Use async session to connect to the database
|
||||
async with self.get_async_session() as session:
|
||||
# Find closest vectors to query_vector
|
||||
closest_items = await session.execute(
|
||||
select(
|
||||
PGVectorDataPoint,
|
||||
PGVectorDataPoint.c.vector.cosine_distance(query_vector).label(
|
||||
"similarity"
|
||||
),
|
||||
).order_by("similarity")
|
||||
)
|
||||
|
||||
vector_list = []
|
||||
|
||||
# Extract distances and find min/max for normalization
|
||||
for vector in closest_items:
|
||||
# TODO: Add normalization of similarity score
|
||||
vector_list.append(vector)
|
||||
|
||||
# Create and return ScoredResult objects
|
||||
return [
|
||||
ScoredResult(id=parse_id(str(row.id)), payload=row.payload, score=row.similarity)
|
||||
for row in vector_list
|
||||
]
|
||||
except EntityNotFoundError:
|
||||
# Ignore if collection does not exist
|
||||
return []
|
||||
except CollectionNotFoundError:
|
||||
# Ignore if collection does not exist
|
||||
return []
|
||||
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = 5,
|
||||
limit: int = 15,
|
||||
with_vector: bool = False,
|
||||
) -> List[ScoredResult]:
|
||||
if query_text is None and query_vector is None:
|
||||
|
|
@ -262,24 +215,26 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
|
|||
# Get PGVectorDataPoint Table from database
|
||||
PGVectorDataPoint = await self.get_table(collection_name)
|
||||
|
||||
# NOTE: This needs to be initialized in case search doesn't return a value
|
||||
closest_items = []
|
||||
|
||||
# Use async session to connect to the database
|
||||
async with self.get_async_session() as session:
|
||||
query = select(
|
||||
PGVectorDataPoint,
|
||||
PGVectorDataPoint.c.vector.cosine_distance(query_vector).label("similarity"),
|
||||
).order_by("similarity")
|
||||
|
||||
if limit > 0:
|
||||
query = query.limit(limit)
|
||||
|
||||
# Find closest vectors to query_vector
|
||||
closest_items = await session.execute(
|
||||
select(
|
||||
PGVectorDataPoint,
|
||||
PGVectorDataPoint.c.vector.cosine_distance(query_vector).label("similarity"),
|
||||
)
|
||||
.order_by("similarity")
|
||||
.limit(limit)
|
||||
)
|
||||
closest_items = await session.execute(query)
|
||||
|
||||
vector_list = []
|
||||
|
||||
# Extract distances and find min/max for normalization
|
||||
for vector in closest_items:
|
||||
for vector in closest_items.all():
|
||||
vector_list.append(
|
||||
{
|
||||
"id": parse_id(str(vector.id)),
|
||||
|
|
@ -288,6 +243,9 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
|
|||
}
|
||||
)
|
||||
|
||||
if len(vector_list) == 0:
|
||||
return []
|
||||
|
||||
# Normalize vector distance and add this as score information to vector_list
|
||||
normalized_values = normalize_distances(vector_list)
|
||||
for i in range(0, len(normalized_values)):
|
||||
|
|
|
|||
|
|
@ -1,12 +1,12 @@
|
|||
from cognee.shared.logging_utils import get_logger
|
||||
from typing import Dict, List, Optional
|
||||
|
||||
from cognee.infrastructure.engine.utils import parse_id
|
||||
from qdrant_client import AsyncQdrantClient, models
|
||||
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.infrastructure.engine.utils import parse_id
|
||||
from cognee.exceptions import InvalidValueError
|
||||
from cognee.infrastructure.databases.vector.models.ScoredResult import ScoredResult
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
|
||||
from cognee.infrastructure.databases.vector.models.ScoredResult import ScoredResult
|
||||
|
||||
from ..embeddings.EmbeddingEngine import EmbeddingEngine
|
||||
from ..vector_db_interface import VectorDBInterface
|
||||
|
|
@ -97,6 +97,8 @@ class QDrantAdapter(VectorDBInterface):
|
|||
await client.close()
|
||||
|
||||
async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
|
||||
from qdrant_client.http.exceptions import UnexpectedResponse
|
||||
|
||||
client = self.get_qdrant_client()
|
||||
|
||||
data_vectors = await self.embed_data(
|
||||
|
|
@ -114,6 +116,13 @@ class QDrantAdapter(VectorDBInterface):
|
|||
|
||||
try:
|
||||
client.upload_points(collection_name=collection_name, points=points)
|
||||
except UnexpectedResponse as error:
|
||||
if "Collection not found" in str(error):
|
||||
raise CollectionNotFoundError(
|
||||
message=f"Collection {collection_name} not found!"
|
||||
) from error
|
||||
else:
|
||||
raise error
|
||||
except Exception as error:
|
||||
logger.error("Error uploading data points to Qdrant: %s", str(error))
|
||||
raise error
|
||||
|
|
@ -143,19 +152,22 @@ class QDrantAdapter(VectorDBInterface):
|
|||
await client.close()
|
||||
return results
|
||||
|
||||
async def get_distance_from_collection_elements(
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: str = None,
|
||||
query_vector: List[float] = None,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = 15,
|
||||
with_vector: bool = False,
|
||||
) -> List[ScoredResult]:
|
||||
if query_text is None and query_vector is None:
|
||||
raise ValueError("One of query_text or query_vector must be provided!")
|
||||
):
|
||||
from qdrant_client.http.exceptions import UnexpectedResponse
|
||||
|
||||
client = self.get_qdrant_client()
|
||||
if query_text is None and query_vector is None:
|
||||
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
|
||||
|
||||
try:
|
||||
client = self.get_qdrant_client()
|
||||
|
||||
results = await client.search(
|
||||
collection_name=collection_name,
|
||||
query_vector=models.NamedVector(
|
||||
|
|
@ -164,9 +176,12 @@ class QDrantAdapter(VectorDBInterface):
|
|||
if query_vector is not None
|
||||
else (await self.embed_data([query_text]))[0],
|
||||
),
|
||||
limit=limit if limit > 0 else None,
|
||||
with_vectors=with_vector,
|
||||
)
|
||||
|
||||
await client.close()
|
||||
|
||||
return [
|
||||
ScoredResult(
|
||||
id=parse_id(result.id),
|
||||
|
|
@ -178,51 +193,16 @@ class QDrantAdapter(VectorDBInterface):
|
|||
)
|
||||
for result in results
|
||||
]
|
||||
except ValueError:
|
||||
# Ignore if the collection doesn't exist
|
||||
return []
|
||||
except UnexpectedResponse as error:
|
||||
if "Collection not found" in str(error):
|
||||
raise CollectionNotFoundError(
|
||||
message=f"Collection {collection_name} not found!"
|
||||
) from error
|
||||
else:
|
||||
raise error
|
||||
finally:
|
||||
await client.close()
|
||||
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = 5,
|
||||
with_vector: bool = False,
|
||||
):
|
||||
if query_text is None and query_vector is None:
|
||||
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
|
||||
|
||||
client = self.get_qdrant_client()
|
||||
|
||||
results = await client.search(
|
||||
collection_name=collection_name,
|
||||
query_vector=models.NamedVector(
|
||||
name="text",
|
||||
vector=query_vector
|
||||
if query_vector is not None
|
||||
else (await self.embed_data([query_text]))[0],
|
||||
),
|
||||
limit=limit,
|
||||
with_vectors=with_vector,
|
||||
)
|
||||
|
||||
await client.close()
|
||||
|
||||
return [
|
||||
ScoredResult(
|
||||
id=parse_id(result.id),
|
||||
payload={
|
||||
**result.payload,
|
||||
"id": parse_id(result.id),
|
||||
},
|
||||
score=1 - result.score,
|
||||
)
|
||||
for result in results
|
||||
]
|
||||
|
||||
async def batch_search(
|
||||
self,
|
||||
collection_name: str,
|
||||
|
|
|
|||
|
|
@ -1,10 +1,10 @@
|
|||
import asyncio
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from typing import List, Optional
|
||||
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.exceptions import InvalidValueError
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.infrastructure.engine.utils import parse_id
|
||||
from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
|
||||
|
||||
from ..embeddings.EmbeddingEngine import EmbeddingEngine
|
||||
from ..models.ScoredResult import ScoredResult
|
||||
|
|
@ -34,21 +34,23 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
|
||||
self.embedding_engine = embedding_engine
|
||||
|
||||
self.client = weaviate.connect_to_wcs(
|
||||
self.client = weaviate.use_async_with_weaviate_cloud(
|
||||
cluster_url=url,
|
||||
auth_credentials=weaviate.auth.AuthApiKey(api_key),
|
||||
additional_config=wvc.init.AdditionalConfig(timeout=wvc.init.Timeout(init=30)),
|
||||
)
|
||||
|
||||
async def get_client(self):
|
||||
await self.client.connect()
|
||||
|
||||
return self.client
|
||||
|
||||
async def embed_data(self, data: List[str]) -> List[float]:
|
||||
return await self.embedding_engine.embed_text(data)
|
||||
|
||||
async def has_collection(self, collection_name: str) -> bool:
|
||||
future = asyncio.Future()
|
||||
|
||||
future.set_result(self.client.collections.exists(collection_name))
|
||||
|
||||
return await future
|
||||
client = await self.get_client()
|
||||
return await client.collections.exists(collection_name)
|
||||
|
||||
async def create_collection(
|
||||
self,
|
||||
|
|
@ -57,26 +59,25 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
):
|
||||
import weaviate.classes.config as wvcc
|
||||
|
||||
future = asyncio.Future()
|
||||
|
||||
if not self.client.collections.exists(collection_name):
|
||||
future.set_result(
|
||||
self.client.collections.create(
|
||||
name=collection_name,
|
||||
properties=[
|
||||
wvcc.Property(
|
||||
name="text", data_type=wvcc.DataType.TEXT, skip_vectorization=True
|
||||
)
|
||||
],
|
||||
)
|
||||
if not await self.has_collection(collection_name):
|
||||
client = await self.get_client()
|
||||
return await client.collections.create(
|
||||
name=collection_name,
|
||||
properties=[
|
||||
wvcc.Property(
|
||||
name="text", data_type=wvcc.DataType.TEXT, skip_vectorization=True
|
||||
)
|
||||
],
|
||||
)
|
||||
else:
|
||||
future.set_result(self.get_collection(collection_name))
|
||||
return await self.get_collection(collection_name)
|
||||
|
||||
return await future
|
||||
async def get_collection(self, collection_name: str):
|
||||
if not await self.has_collection(collection_name):
|
||||
raise CollectionNotFoundError(f"Collection '{collection_name}' not found.")
|
||||
|
||||
def get_collection(self, collection_name: str):
|
||||
return self.client.collections.get(collection_name)
|
||||
client = await self.get_client()
|
||||
return client.collections.get(collection_name)
|
||||
|
||||
async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
|
||||
from weaviate.classes.data import DataObject
|
||||
|
|
@ -97,29 +98,30 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
|
||||
data_points = [convert_to_weaviate_data_points(data_point) for data_point in data_points]
|
||||
|
||||
collection = self.get_collection(collection_name)
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
try:
|
||||
if len(data_points) > 1:
|
||||
with collection.batch.dynamic() as batch:
|
||||
for data_point in data_points:
|
||||
batch.add_object(
|
||||
uuid=data_point.uuid,
|
||||
vector=data_point.vector,
|
||||
properties=data_point.properties,
|
||||
references=data_point.references,
|
||||
)
|
||||
return await collection.data.insert_many(data_points)
|
||||
# with collection.batch.dynamic() as batch:
|
||||
# for data_point in data_points:
|
||||
# batch.add_object(
|
||||
# uuid=data_point.uuid,
|
||||
# vector=data_point.vector,
|
||||
# properties=data_point.properties,
|
||||
# references=data_point.references,
|
||||
# )
|
||||
else:
|
||||
data_point: DataObject = data_points[0]
|
||||
if collection.data.exists(data_point.uuid):
|
||||
return collection.data.update(
|
||||
return await collection.data.update(
|
||||
uuid=data_point.uuid,
|
||||
vector=data_point.vector,
|
||||
properties=data_point.properties,
|
||||
references=data_point.references,
|
||||
)
|
||||
else:
|
||||
return collection.data.insert(
|
||||
return await collection.data.insert(
|
||||
uuid=data_point.uuid,
|
||||
vector=data_point.vector,
|
||||
properties=data_point.properties,
|
||||
|
|
@ -130,12 +132,12 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
raise error
|
||||
|
||||
async def create_vector_index(self, index_name: str, index_property_name: str):
|
||||
await self.create_collection(f"{index_name}_{index_property_name}")
|
||||
return await self.create_collection(f"{index_name}_{index_property_name}")
|
||||
|
||||
async def index_data_points(
|
||||
self, index_name: str, index_property_name: str, data_points: list[DataPoint]
|
||||
):
|
||||
await self.create_data_points(
|
||||
return await self.create_data_points(
|
||||
f"{index_name}_{index_property_name}",
|
||||
[
|
||||
IndexSchema(
|
||||
|
|
@ -149,9 +151,8 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
async def retrieve(self, collection_name: str, data_point_ids: list[str]):
|
||||
from weaviate.classes.query import Filter
|
||||
|
||||
future = asyncio.Future()
|
||||
|
||||
data_points = self.get_collection(collection_name).query.fetch_objects(
|
||||
collection = await self.get_collection(collection_name)
|
||||
data_points = await collection.query.fetch_objects(
|
||||
filters=Filter.by_id().contains_any(data_point_ids)
|
||||
)
|
||||
|
||||
|
|
@ -160,30 +161,32 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
data_point.id = data_point.uuid
|
||||
del data_point.properties
|
||||
|
||||
future.set_result(data_points.objects)
|
||||
return data_points.objects
|
||||
|
||||
return await future
|
||||
|
||||
async def get_distance_from_collection_elements(
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: str = None,
|
||||
query_vector: List[float] = None,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = 15,
|
||||
with_vector: bool = False,
|
||||
) -> List[ScoredResult]:
|
||||
):
|
||||
import weaviate.classes as wvc
|
||||
import weaviate.exceptions
|
||||
|
||||
if query_text is None and query_vector is None:
|
||||
raise ValueError("One of query_text or query_vector must be provided!")
|
||||
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
|
||||
|
||||
if query_vector is None:
|
||||
query_vector = (await self.embed_data([query_text]))[0]
|
||||
|
||||
collection = await self.get_collection(collection_name)
|
||||
|
||||
try:
|
||||
search_result = self.get_collection(collection_name).query.hybrid(
|
||||
search_result = await collection.query.hybrid(
|
||||
query=None,
|
||||
vector=query_vector,
|
||||
limit=limit if limit > 0 else None,
|
||||
include_vector=with_vector,
|
||||
return_metadata=wvc.query.MetadataQuery(score=True),
|
||||
)
|
||||
|
|
@ -196,43 +199,10 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
)
|
||||
for result in search_result.objects
|
||||
]
|
||||
except weaviate.exceptions.UnexpectedStatusCodeError:
|
||||
except weaviate.exceptions.WeaviateInvalidInputError:
|
||||
# Ignore if the collection doesn't exist
|
||||
return []
|
||||
|
||||
async def search(
|
||||
self,
|
||||
collection_name: str,
|
||||
query_text: Optional[str] = None,
|
||||
query_vector: Optional[List[float]] = None,
|
||||
limit: int = None,
|
||||
with_vector: bool = False,
|
||||
):
|
||||
import weaviate.classes as wvc
|
||||
|
||||
if query_text is None and query_vector is None:
|
||||
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
|
||||
|
||||
if query_vector is None:
|
||||
query_vector = (await self.embed_data([query_text]))[0]
|
||||
|
||||
search_result = self.get_collection(collection_name).query.hybrid(
|
||||
query=None,
|
||||
vector=query_vector,
|
||||
limit=limit,
|
||||
include_vector=with_vector,
|
||||
return_metadata=wvc.query.MetadataQuery(score=True),
|
||||
)
|
||||
|
||||
return [
|
||||
ScoredResult(
|
||||
id=parse_id(str(result.uuid)),
|
||||
payload=result.properties,
|
||||
score=1 - float(result.metadata.score),
|
||||
)
|
||||
for result in search_result.objects
|
||||
]
|
||||
|
||||
async def batch_search(
|
||||
self, collection_name: str, query_texts: List[str], limit: int, with_vectors: bool = False
|
||||
):
|
||||
|
|
@ -248,14 +218,13 @@ class WeaviateAdapter(VectorDBInterface):
|
|||
async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
|
||||
from weaviate.classes.query import Filter
|
||||
|
||||
future = asyncio.Future()
|
||||
|
||||
result = self.get_collection(collection_name).data.delete_many(
|
||||
collection = await self.get_collection(collection_name)
|
||||
result = await collection.data.delete_many(
|
||||
filters=Filter.by_id().contains_any(data_point_ids)
|
||||
)
|
||||
future.set_result(result)
|
||||
|
||||
return await future
|
||||
return result
|
||||
|
||||
async def prune(self):
|
||||
self.client.collections.delete_all()
|
||||
client = await self.get_client()
|
||||
await client.collections.delete_all()
|
||||
|
|
|
|||
|
|
@ -1,9 +1,10 @@
|
|||
from typing import Type, Optional
|
||||
from pydantic import BaseModel
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
import litellm
|
||||
from pydantic import BaseModel
|
||||
from typing import Type, Optional
|
||||
from litellm import acompletion, JSONSchemaValidationError
|
||||
from cognee.shared.data_models import MonitoringTool
|
||||
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.modules.observability.get_observe import get_observe
|
||||
from cognee.exceptions import InvalidValueError
|
||||
from cognee.infrastructure.llm.llm_interface import LLMInterface
|
||||
from cognee.infrastructure.llm.prompts import read_query_prompt
|
||||
|
|
@ -11,14 +12,9 @@ from cognee.infrastructure.llm.rate_limiter import (
|
|||
rate_limit_async,
|
||||
sleep_and_retry_async,
|
||||
)
|
||||
from cognee.base_config import get_base_config
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
monitoring = get_base_config().monitoring_tool
|
||||
|
||||
if monitoring == MonitoringTool.LANGFUSE:
|
||||
from langfuse.decorators import observe
|
||||
observe = get_observe()
|
||||
|
||||
|
||||
class GeminiAdapter(LLMInterface):
|
||||
|
|
|
|||
|
|
@ -1,14 +1,12 @@
|
|||
import os
|
||||
import base64
|
||||
from typing import Type
|
||||
|
||||
import litellm
|
||||
import instructor
|
||||
from typing import Type
|
||||
from pydantic import BaseModel
|
||||
from openai import ContentFilterFinishReasonError
|
||||
|
||||
from cognee.exceptions import InvalidValueError
|
||||
from cognee.shared.data_models import MonitoringTool
|
||||
from cognee.infrastructure.llm.prompts import read_query_prompt
|
||||
from cognee.infrastructure.llm.llm_interface import LLMInterface
|
||||
from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError
|
||||
|
|
@ -19,12 +17,9 @@ from cognee.infrastructure.llm.rate_limiter import (
|
|||
sleep_and_retry_async,
|
||||
sleep_and_retry_sync,
|
||||
)
|
||||
from cognee.base_config import get_base_config
|
||||
from cognee.modules.observability.get_observe import get_observe
|
||||
|
||||
monitoring = get_base_config().monitoring_tool
|
||||
|
||||
if monitoring == MonitoringTool.LANGFUSE:
|
||||
from langfuse.decorators import observe
|
||||
observe = get_observe()
|
||||
|
||||
|
||||
class OpenAIAdapter(LLMInterface):
|
||||
|
|
|
|||
|
|
@ -128,8 +128,10 @@ class CogneeGraph(CogneeAbstractGraph):
|
|||
if query_vector is None or len(query_vector) == 0:
|
||||
raise ValueError("Failed to generate query embedding.")
|
||||
|
||||
edge_distances = await vector_engine.get_distance_from_collection_elements(
|
||||
"EdgeType_relationship_name", query_text=query
|
||||
edge_distances = await vector_engine.search(
|
||||
collection_name="EdgeType_relationship_name",
|
||||
query_text=query,
|
||||
limit=0,
|
||||
)
|
||||
|
||||
embedding_map = {result.payload["text"]: result.score for result in edge_distances}
|
||||
|
|
|
|||
11
cognee/modules/observability/get_observe.py
Normal file
11
cognee/modules/observability/get_observe.py
Normal file
|
|
@ -0,0 +1,11 @@
|
|||
from cognee.base_config import get_base_config
|
||||
from .observers import Observer
|
||||
|
||||
|
||||
def get_observe():
|
||||
monitoring = get_base_config().monitoring_tool
|
||||
|
||||
if monitoring == Observer.LANGFUSE:
|
||||
from langfuse.decorators import observe
|
||||
|
||||
return observe
|
||||
9
cognee/modules/observability/observers.py
Normal file
9
cognee/modules/observability/observers.py
Normal file
|
|
@ -0,0 +1,9 @@
|
|||
from enum import Enum
|
||||
|
||||
|
||||
class Observer(str, Enum):
|
||||
"""Monitoring tools"""
|
||||
|
||||
LANGFUSE = "langfuse"
|
||||
LLMLITE = "llmlite"
|
||||
LANGSMITH = "langsmith"
|
||||
|
|
@ -20,7 +20,9 @@ from ..tasks.task import Task
|
|||
logger = get_logger("run_tasks(tasks: [Task], data)")
|
||||
|
||||
|
||||
async def run_tasks_with_telemetry(tasks: list[Task], data, user: User, pipeline_name: str):
|
||||
async def run_tasks_with_telemetry(
|
||||
tasks: list[Task], data, user: User, pipeline_name: str, context: dict = None
|
||||
):
|
||||
config = get_current_settings()
|
||||
|
||||
logger.debug("\nRunning pipeline with configuration:\n%s\n", json.dumps(config, indent=1))
|
||||
|
|
@ -36,7 +38,7 @@ async def run_tasks_with_telemetry(tasks: list[Task], data, user: User, pipeline
|
|||
| config,
|
||||
)
|
||||
|
||||
async for result in run_tasks_base(tasks, data, user):
|
||||
async for result in run_tasks_base(tasks, data, user, context):
|
||||
yield result
|
||||
|
||||
logger.info("Pipeline run completed: `%s`", pipeline_name)
|
||||
|
|
@ -72,6 +74,7 @@ async def run_tasks(
|
|||
data: Any = None,
|
||||
user: User = None,
|
||||
pipeline_name: str = "unknown_pipeline",
|
||||
context: dict = None,
|
||||
):
|
||||
pipeline_id = uuid5(NAMESPACE_OID, pipeline_name)
|
||||
|
||||
|
|
@ -82,7 +85,11 @@ async def run_tasks(
|
|||
|
||||
try:
|
||||
async for _ in run_tasks_with_telemetry(
|
||||
tasks=tasks, data=data, user=user, pipeline_name=pipeline_id
|
||||
tasks=tasks,
|
||||
data=data,
|
||||
user=user,
|
||||
pipeline_name=pipeline_id,
|
||||
context=context,
|
||||
):
|
||||
pass
|
||||
|
||||
|
|
|
|||
|
|
@ -14,6 +14,7 @@ async def handle_task(
|
|||
leftover_tasks: list[Task],
|
||||
next_task_batch_size: int,
|
||||
user: User,
|
||||
context: dict = None,
|
||||
):
|
||||
"""Handle common task workflow with logging, telemetry, and error handling around the core execution logic."""
|
||||
task_type = running_task.task_type
|
||||
|
|
@ -27,9 +28,16 @@ async def handle_task(
|
|||
},
|
||||
)
|
||||
|
||||
has_context = any(
|
||||
[key == "context" for key in inspect.signature(running_task.executable).parameters.keys()]
|
||||
)
|
||||
|
||||
if has_context:
|
||||
args.append(context)
|
||||
|
||||
try:
|
||||
async for result_data in running_task.execute(args, next_task_batch_size):
|
||||
async for result in run_tasks_base(leftover_tasks, result_data, user):
|
||||
async for result in run_tasks_base(leftover_tasks, result_data, user, context):
|
||||
yield result
|
||||
|
||||
logger.info(f"{task_type} task completed: `{running_task.executable.__name__}`")
|
||||
|
|
@ -55,7 +63,7 @@ async def handle_task(
|
|||
raise error
|
||||
|
||||
|
||||
async def run_tasks_base(tasks: list[Task], data=None, user: User = None):
|
||||
async def run_tasks_base(tasks: list[Task], data=None, user: User = None, context: dict = None):
|
||||
"""Base function to execute tasks in a pipeline, handling task type detection and execution."""
|
||||
if len(tasks) == 0:
|
||||
yield data
|
||||
|
|
@ -68,5 +76,7 @@ async def run_tasks_base(tasks: list[Task], data=None, user: User = None):
|
|||
next_task = leftover_tasks[0] if len(leftover_tasks) > 0 else None
|
||||
next_task_batch_size = next_task.task_config["batch_size"] if next_task else 1
|
||||
|
||||
async for result in handle_task(running_task, args, leftover_tasks, next_task_batch_size, user):
|
||||
async for result in handle_task(
|
||||
running_task, args, leftover_tasks, next_task_batch_size, user, context
|
||||
):
|
||||
yield result
|
||||
|
|
|
|||
|
|
@ -4,4 +4,4 @@ Custom exceptions for the Cognee API.
|
|||
This module defines a set of exceptions for handling various data errors
|
||||
"""
|
||||
|
||||
from .exceptions import SearchTypeNotSupported, CypherSearchError, CollectionDistancesNotFoundError
|
||||
from .exceptions import SearchTypeNotSupported, CypherSearchError
|
||||
|
|
|
|||
|
|
@ -2,16 +2,6 @@ from fastapi import status
|
|||
from cognee.exceptions import CogneeApiError, CriticalError
|
||||
|
||||
|
||||
class CollectionDistancesNotFoundError(CogneeApiError):
|
||||
def __init__(
|
||||
self,
|
||||
message: str = "No distances found between the query and collections. It is possible that the given collection names don't exist.",
|
||||
name: str = "CollectionDistancesNotFoundError",
|
||||
status_code: int = status.HTTP_404_NOT_FOUND,
|
||||
):
|
||||
super().__init__(message, name, status_code)
|
||||
|
||||
|
||||
class SearchTypeNotSupported(CogneeApiError):
|
||||
def __init__(
|
||||
self,
|
||||
|
|
|
|||
|
|
@ -3,7 +3,6 @@ from collections import Counter
|
|||
import string
|
||||
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.modules.graph.exceptions.exceptions import EntityNotFoundError
|
||||
from cognee.modules.graph.utils.convert_node_to_data_point import get_all_subclasses
|
||||
from cognee.modules.retrieval.base_retriever import BaseRetriever
|
||||
from cognee.modules.retrieval.utils.brute_force_triplet_search import brute_force_triplet_search
|
||||
|
|
@ -76,10 +75,7 @@ class GraphCompletionRetriever(BaseRetriever):
|
|||
|
||||
async def get_context(self, query: str) -> str:
|
||||
"""Retrieves and resolves graph triplets into context."""
|
||||
try:
|
||||
triplets = await self.get_triplets(query)
|
||||
except EntityNotFoundError:
|
||||
return ""
|
||||
triplets = await self.get_triplets(query)
|
||||
|
||||
if len(triplets) == 0:
|
||||
return ""
|
||||
|
|
|
|||
|
|
@ -1,14 +1,15 @@
|
|||
import asyncio
|
||||
from cognee.shared.logging_utils import get_logger, ERROR
|
||||
from typing import List, Optional
|
||||
|
||||
from cognee.shared.logging_utils import get_logger, ERROR
|
||||
from cognee.modules.graph.exceptions.exceptions import EntityNotFoundError
|
||||
from cognee.infrastructure.databases.vector.exceptions import CollectionNotFoundError
|
||||
from cognee.infrastructure.databases.graph import get_graph_engine
|
||||
from cognee.infrastructure.databases.vector import get_vector_engine
|
||||
from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
|
||||
from cognee.modules.users.methods import get_default_user
|
||||
from cognee.modules.users.models import User
|
||||
from cognee.shared.utils import send_telemetry
|
||||
from cognee.modules.retrieval.exceptions import CollectionDistancesNotFoundError
|
||||
|
||||
logger = get_logger(level=ERROR)
|
||||
|
||||
|
|
@ -62,11 +63,14 @@ async def get_memory_fragment(
|
|||
if properties_to_project is None:
|
||||
properties_to_project = ["id", "description", "name", "type", "text"]
|
||||
|
||||
await memory_fragment.project_graph_from_db(
|
||||
graph_engine,
|
||||
node_properties_to_project=properties_to_project,
|
||||
edge_properties_to_project=["relationship_name"],
|
||||
)
|
||||
try:
|
||||
await memory_fragment.project_graph_from_db(
|
||||
graph_engine,
|
||||
node_properties_to_project=properties_to_project,
|
||||
edge_properties_to_project=["relationship_name"],
|
||||
)
|
||||
except EntityNotFoundError:
|
||||
pass
|
||||
|
||||
return memory_fragment
|
||||
|
||||
|
|
@ -139,16 +143,21 @@ async def brute_force_search(
|
|||
|
||||
send_telemetry("cognee.brute_force_triplet_search EXECUTION STARTED", user.id)
|
||||
|
||||
async def search_in_collection(collection_name: str):
|
||||
try:
|
||||
return await vector_engine.search(
|
||||
collection_name=collection_name, query_text=query, limit=top_k
|
||||
)
|
||||
except CollectionNotFoundError:
|
||||
return []
|
||||
|
||||
try:
|
||||
results = await asyncio.gather(
|
||||
*[
|
||||
vector_engine.get_distance_from_collection_elements(collection, query_text=query)
|
||||
for collection in collections
|
||||
]
|
||||
*[search_in_collection(collection_name) for collection_name in collections]
|
||||
)
|
||||
|
||||
if all(not item for item in results):
|
||||
raise CollectionDistancesNotFoundError()
|
||||
return []
|
||||
|
||||
node_distances = {collection: result for collection, result in zip(collections, results)}
|
||||
|
||||
|
|
@ -161,6 +170,8 @@ async def brute_force_search(
|
|||
|
||||
return results
|
||||
|
||||
except CollectionNotFoundError:
|
||||
return []
|
||||
except Exception as error:
|
||||
logger.error(
|
||||
"Error during brute force search for user: %s, query: %s. Error: %s",
|
||||
|
|
|
|||
|
|
@ -350,11 +350,3 @@ class ChunkSummaries(BaseModel):
|
|||
"""Relevant summary and chunk id"""
|
||||
|
||||
summaries: List[ChunkSummary]
|
||||
|
||||
|
||||
class MonitoringTool(str, Enum):
|
||||
"""Monitoring tools"""
|
||||
|
||||
LANGFUSE = "langfuse"
|
||||
LLMLITE = "llmlite"
|
||||
LANGSMITH = "langsmith"
|
||||
|
|
|
|||
|
|
@ -312,7 +312,7 @@ def setup_logging(log_level=None, name=None):
|
|||
root_logger.addHandler(file_handler)
|
||||
root_logger.setLevel(log_level)
|
||||
|
||||
if log_level > logging.WARNING:
|
||||
if log_level > logging.DEBUG:
|
||||
import warnings
|
||||
from sqlalchemy.exc import SAWarning
|
||||
|
||||
|
|
|
|||
|
|
@ -1,11 +0,0 @@
|
|||
import os
|
||||
|
||||
import pytest
|
||||
|
||||
|
||||
@pytest.fixture(autouse=True, scope="session")
|
||||
def copy_cognee_db_to_target_location():
|
||||
os.makedirs("cognee/.cognee_system/databases/", exist_ok=True)
|
||||
os.system(
|
||||
"cp cognee/tests/integration/run_toy_tasks/data/cognee_db cognee/.cognee_system/databases/cognee_db"
|
||||
)
|
||||
Binary file not shown.
|
|
@ -74,19 +74,20 @@ async def main():
|
|||
for result in search_results:
|
||||
print(f"{result}\n")
|
||||
|
||||
search_results = await cognee.search(
|
||||
query_type=SearchType.NATURAL_LANGUAGE,
|
||||
query_text=f"Find nodes connected to node with name {random_node_name}",
|
||||
)
|
||||
assert len(search_results) != 0, "Query related natural language don't exist."
|
||||
print("\nExtracted results are:\n")
|
||||
for result in search_results:
|
||||
print(f"{result}\n")
|
||||
# NOTE: Due to the test failing often on weak LLM models we've removed this test for now
|
||||
# search_results = await cognee.search(
|
||||
# query_type=SearchType.NATURAL_LANGUAGE,
|
||||
# query_text=f"Find nodes connected to node with name {random_node_name}",
|
||||
# )
|
||||
# assert len(search_results) != 0, "Query related natural language don't exist."
|
||||
# print("\nExtracted results are:\n")
|
||||
# for result in search_results:
|
||||
# print(f"{result}\n")
|
||||
|
||||
user = await get_default_user()
|
||||
history = await get_history(user.id)
|
||||
|
||||
assert len(history) == 8, "Search history is not correct."
|
||||
assert len(history) == 6, "Search history is not correct."
|
||||
|
||||
await cognee.prune.prune_data()
|
||||
assert not os.path.isdir(data_directory_path), "Local data files are not deleted"
|
||||
|
|
|
|||
|
|
@ -82,7 +82,7 @@ async def main():
|
|||
assert not os.path.isdir(data_directory_path), "Local data files are not deleted"
|
||||
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
collections = get_vector_engine().client.collections.list_all()
|
||||
collections = await get_vector_engine().client.collections.list_all()
|
||||
assert len(collections) == 0, "Weaviate vector database is not empty"
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -48,3 +48,7 @@ async def run_and_check_tasks():
|
|||
|
||||
def test_run_tasks():
|
||||
asyncio.run(run_and_check_tasks())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_run_tasks()
|
||||
|
|
@ -0,0 +1,47 @@
|
|||
import asyncio
|
||||
|
||||
import cognee
|
||||
from cognee.modules.pipelines.tasks.task import Task
|
||||
from cognee.modules.users.methods import get_default_user
|
||||
from cognee.modules.pipelines.operations.run_tasks import run_tasks_base
|
||||
from cognee.infrastructure.databases.relational import create_db_and_tables
|
||||
|
||||
|
||||
async def run_and_check_tasks():
|
||||
await cognee.prune.prune_data()
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
|
||||
def task_1(num, context):
|
||||
return num + context
|
||||
|
||||
def task_2(num):
|
||||
return num * 2
|
||||
|
||||
def task_3(num, context):
|
||||
return num**context
|
||||
|
||||
await create_db_and_tables()
|
||||
user = await get_default_user()
|
||||
|
||||
pipeline = run_tasks_base(
|
||||
[
|
||||
Task(task_1),
|
||||
Task(task_2),
|
||||
Task(task_3),
|
||||
],
|
||||
data=5,
|
||||
user=user,
|
||||
context=7,
|
||||
)
|
||||
|
||||
final_result = 4586471424
|
||||
async for result in pipeline:
|
||||
assert result == final_result
|
||||
|
||||
|
||||
def test_run_tasks():
|
||||
asyncio.run(run_and_check_tasks())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
test_run_tasks()
|
||||
|
|
@ -16,11 +16,11 @@ class TestChunksRetriever:
|
|||
@pytest.mark.asyncio
|
||||
async def test_chunk_context_simple(self):
|
||||
system_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_rag_context"
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_chunks_context_simple"
|
||||
)
|
||||
cognee.config.system_root_directory(system_directory_path)
|
||||
data_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_rag_context"
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_chunks_context_simple"
|
||||
)
|
||||
cognee.config.data_root_directory(data_directory_path)
|
||||
|
||||
|
|
@ -73,11 +73,11 @@ class TestChunksRetriever:
|
|||
@pytest.mark.asyncio
|
||||
async def test_chunk_context_complex(self):
|
||||
system_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_chunk_context"
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_chunk_context_complex"
|
||||
)
|
||||
cognee.config.system_root_directory(system_directory_path)
|
||||
data_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_chunk_context"
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_chunk_context_complex"
|
||||
)
|
||||
cognee.config.data_root_directory(data_directory_path)
|
||||
|
||||
|
|
@ -162,11 +162,11 @@ class TestChunksRetriever:
|
|||
@pytest.mark.asyncio
|
||||
async def test_chunk_context_on_empty_graph(self):
|
||||
system_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_chunk_context"
|
||||
pathlib.Path(__file__).parent, ".cognee_system/test_chunk_context_empty"
|
||||
)
|
||||
cognee.config.system_root_directory(system_directory_path)
|
||||
data_directory_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_chunk_context"
|
||||
pathlib.Path(__file__).parent, ".data_storage/test_chunk_context_empty"
|
||||
)
|
||||
cognee.config.data_root_directory(data_directory_path)
|
||||
|
||||
|
|
@ -190,6 +190,9 @@ if __name__ == "__main__":
|
|||
|
||||
test = TestChunksRetriever()
|
||||
|
||||
run(test.test_chunk_context_simple())
|
||||
run(test.test_chunk_context_complex())
|
||||
run(test.test_chunk_context_on_empty_graph())
|
||||
async def main():
|
||||
await test.test_chunk_context_simple()
|
||||
await test.test_chunk_context_complex()
|
||||
await test.test_chunk_context_on_empty_graph()
|
||||
|
||||
run(main())
|
||||
|
|
|
|||
|
|
@ -154,6 +154,9 @@ if __name__ == "__main__":
|
|||
|
||||
test = TestGraphCompletionRetriever()
|
||||
|
||||
run(test.test_graph_completion_context_simple())
|
||||
run(test.test_graph_completion_context_complex())
|
||||
run(test.test_get_graph_completion_context_on_empty_graph())
|
||||
async def main():
|
||||
await test.test_graph_completion_context_simple()
|
||||
await test.test_graph_completion_context_complex()
|
||||
await test.test_get_graph_completion_context_on_empty_graph()
|
||||
|
||||
run(main())
|
||||
|
|
|
|||
|
|
@ -127,7 +127,7 @@ class TextSummariesRetriever:
|
|||
|
||||
await add_data_points(entities)
|
||||
|
||||
retriever = SummariesRetriever(limit=20)
|
||||
retriever = SummariesRetriever(top_k=20)
|
||||
|
||||
context = await retriever.get_context("Christina")
|
||||
|
||||
|
|
|
|||
|
|
@ -1,44 +0,0 @@
|
|||
import pytest
|
||||
from unittest.mock import AsyncMock, patch
|
||||
from cognee.modules.users.models import User
|
||||
from cognee.modules.retrieval.exceptions import CollectionDistancesNotFoundError
|
||||
from cognee.modules.retrieval.utils.brute_force_triplet_search import (
|
||||
brute_force_search,
|
||||
brute_force_triplet_search,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@patch("cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine")
|
||||
async def test_brute_force_search_collection_not_found(mock_get_vector_engine):
|
||||
user = User(id="test_user")
|
||||
query = "test query"
|
||||
collections = ["nonexistent_collection"]
|
||||
top_k = 5
|
||||
mock_memory_fragment = AsyncMock()
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_vector_engine.get_distance_from_collection_elements.return_value = []
|
||||
mock_get_vector_engine.return_value = mock_vector_engine
|
||||
|
||||
with pytest.raises(CollectionDistancesNotFoundError):
|
||||
await brute_force_search(
|
||||
query, user, top_k, collections=collections, memory_fragment=mock_memory_fragment
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@patch("cognee.modules.retrieval.utils.brute_force_triplet_search.get_vector_engine")
|
||||
async def test_brute_force_triplet_search_collection_not_found(mock_get_vector_engine):
|
||||
user = User(id="test_user")
|
||||
query = "test query"
|
||||
collections = ["nonexistent_collection"]
|
||||
top_k = 5
|
||||
mock_memory_fragment = AsyncMock()
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_vector_engine.get_distance_from_collection_elements.return_value = []
|
||||
mock_get_vector_engine.return_value = mock_vector_engine
|
||||
|
||||
with pytest.raises(CollectionDistancesNotFoundError):
|
||||
await brute_force_triplet_search(
|
||||
query, user, top_k, collections=collections, memory_fragment=mock_memory_fragment
|
||||
)
|
||||
162
community/README.zh.md
Normal file
162
community/README.zh.md
Normal file
|
|
@ -0,0 +1,162 @@
|
|||
<div align="center">
|
||||
<a href="https://github.com/topoteretes/cognee">
|
||||
<img src="https://raw.githubusercontent.com/topoteretes/cognee/refs/heads/dev/assets/cognee-logo-transparent.png" alt="Cognee Logo" height="60">
|
||||
</a>
|
||||
|
||||
<br />
|
||||
|
||||
cognee - AI应用和智能体的记忆层
|
||||
|
||||
<p align="center">
|
||||
<a href="https://www.youtube.com/watch?v=1bezuvLwJmw&t=2s">演示</a>
|
||||
.
|
||||
<a href="https://cognee.ai">了解更多</a>
|
||||
·
|
||||
<a href="https://discord.gg/NQPKmU5CCg">加入Discord</a>
|
||||
</p>
|
||||
|
||||
|
||||
[](https://GitHub.com/topoteretes/cognee/network/)
|
||||
[](https://GitHub.com/topoteretes/cognee/stargazers/)
|
||||
[](https://GitHub.com/topoteretes/cognee/commit/)
|
||||
[](https://github.com/topoteretes/cognee/tags/)
|
||||
[](https://pepy.tech/project/cognee)
|
||||
[](https://github.com/topoteretes/cognee/blob/main/LICENSE)
|
||||
[](https://github.com/topoteretes/cognee/graphs/contributors)
|
||||
|
||||
可靠的AI智能体响应。
|
||||
|
||||
|
||||
|
||||
使用可扩展、模块化的ECL（提取、认知、加载）管道构建动态智能体记忆。
|
||||
|
||||
更多[使用场景](https://docs.cognee.ai/use_cases)。
|
||||
|
||||
<div style="text-align: center">
|
||||
<img src="cognee_benefits_zh.JPG" alt="为什么选择cognee？" width="100%" />
|
||||
</div>
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
## 功能特性
|
||||
|
||||
- 互联并检索您的历史对话、文档、图像和音频转录
|
||||
- 减少幻觉、开发人员工作量和成本
|
||||
- 仅使用Pydantic将数据加载到图形和向量数据库
|
||||
- 从30多个数据源摄取数据时进行数据操作
|
||||
|
||||
## 开始使用
|
||||
|
||||
通过Google Colab <a href="https://colab.research.google.com/drive/1g-Qnx6l_ecHZi0IOw23rg0qC4TYvEvWZ?usp=sharing">笔记本</a>或<a href="https://github.com/topoteretes/cognee-starter">入门项目</a>快速上手
|
||||
|
||||
## 贡献
|
||||
您的贡献是使这成为真正开源项目的核心。我们**非常感谢**任何贡献。更多信息请参阅[`CONTRIBUTING.md`](CONTRIBUTING.md)。
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## 📦 安装
|
||||
|
||||
您可以使用**pip**、**poetry**、**uv**或任何其他Python包管理器安装Cognee。
|
||||
|
||||
### 使用pip
|
||||
|
||||
```bash
|
||||
pip install cognee
|
||||
```
|
||||
|
||||
## 💻 基本用法
|
||||
|
||||
### 设置
|
||||
|
||||
```
|
||||
import os
|
||||
os.environ["LLM_API_KEY"] = "YOUR OPENAI_API_KEY"
|
||||
|
||||
```
|
||||
|
||||
您也可以通过创建.env文件设置变量，使用我们的<a href="https://github.com/topoteretes/cognee/blob/main/.env.template">模板</a>。
|
||||
要使用不同的LLM提供商，请查看我们的<a href="https://docs.cognee.ai">文档</a>获取更多信息。
|
||||
|
||||
|
||||
### 简单示例
|
||||
|
||||
此脚本将运行默认管道：
|
||||
|
||||
```python
|
||||
import cognee
|
||||
import asyncio
|
||||
|
||||
|
||||
async def main():
|
||||
# Add text to cognee
|
||||
await cognee.add("自然语言处理（NLP）是计算机科学和信息检索的跨学科领域。")
|
||||
|
||||
# Generate the knowledge graph
|
||||
await cognee.cognify()
|
||||
|
||||
# Query the knowledge graph
|
||||
results = await cognee.search("告诉我关于NLP")
|
||||
|
||||
# Display the results
|
||||
for result in results:
|
||||
print(result)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
asyncio.run(main())
|
||||
|
||||
```
|
||||
示例输出：
|
||||
```
|
||||
自然语言处理（NLP）是计算机科学和信息检索的跨学科领域。它关注计算机和人类语言之间的交互，使机器能够理解和处理自然语言。
|
||||
|
||||
```
|
||||
图形可视化：
|
||||
<a href="https://rawcdn.githack.com/topoteretes/cognee/refs/heads/main/assets/graph_visualization.html"><img src="https://rawcdn.githack.com/topoteretes/cognee/refs/heads/main/assets/graph_visualization.png" width="100%" alt="图形可视化"></a>
|
||||
在[浏览器](https://rawcdn.githack.com/topoteretes/cognee/refs/heads/main/assets/graph_visualization.html)中打开。
|
||||
|
||||
有关更高级的用法，请查看我们的<a href="https://docs.cognee.ai">文档</a>。
|
||||
|
||||
|
||||
## 了解我们的架构
|
||||
|
||||
<div style="text-align: center">
|
||||
<img src="cognee_diagram_zh.JPG" alt="cognee概念图" width="100%" />
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
## 演示
|
||||
|
||||
1. 什么是AI记忆：
|
||||
|
||||
[了解cognee](https://github.com/user-attachments/assets/8b2a0050-5ec4-424c-b417-8269971503f0)
|
||||
|
||||
2. 简单GraphRAG演示
|
||||
|
||||
[简单GraphRAG演示](https://github.com/user-attachments/assets/f57fd9ea-1dc0-4904-86eb-de78519fdc32)
|
||||
|
||||
3. cognee与Ollama
|
||||
|
||||
[cognee与本地模型](https://github.com/user-attachments/assets/834baf9a-c371-4ecf-92dd-e144bd0eb3f6)
|
||||
|
||||
|
||||
## 行为准则
|
||||
|
||||
我们致力于为我们的社区提供愉快和尊重的开源体验。有关更多信息，请参阅<a href="https://github.com/topoteretes/cognee/blob/main/CODE_OF_CONDUCT.md"><code>CODE_OF_CONDUCT</code></a>。
|
||||
|
||||
## 💫 贡献者
|
||||
|
||||
<a href="https://github.com/topoteretes/cognee/graphs/contributors">
|
||||
<img alt="contributors" src="https://contrib.rocks/image?repo=topoteretes/cognee"/>
|
||||
</a>
|
||||
|
||||
|
||||
## Star历史
|
||||
|
||||
[](https://star-history.com/#topoteretes/cognee&Date)
|
||||
BIN
community/cognee_benefits_zh.JPG
Normal file
BIN
community/cognee_benefits_zh.JPG
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 262 KiB |
BIN
community/cognee_diagram_zh.JPG
Normal file
BIN
community/cognee_diagram_zh.JPG
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 181 KiB |
37
examples/data/car_and_tech_companies.txt
Normal file
37
examples/data/car_and_tech_companies.txt
Normal file
|
|
@ -0,0 +1,37 @@
|
|||
text_1 = """
|
||||
1. Audi
|
||||
Audi is known for its modern designs and advanced technology. Founded in the early 1900s, the brand has earned a reputation for precision engineering and innovation. With features like the Quattro all-wheel-drive system, Audi offers a range of vehicles from stylish sedans to high-performance sports cars.
|
||||
|
||||
2. BMW
|
||||
BMW, short for Bayerische Motoren Werke, is celebrated for its focus on performance and driving pleasure. The company's vehicles are designed to provide a dynamic and engaging driving experience, and their slogan, "The Ultimate Driving Machine," reflects that commitment. BMW produces a variety of cars that combine luxury with sporty performance.
|
||||
|
||||
3. Mercedes-Benz
|
||||
Mercedes-Benz is synonymous with luxury and quality. With a history dating back to the early 20th century, the brand is known for its elegant designs, innovative safety features, and high-quality engineering. Mercedes-Benz manufactures not only luxury sedans but also SUVs, sports cars, and commercial vehicles, catering to a wide range of needs.
|
||||
|
||||
4. Porsche
|
||||
Porsche is a name that stands for high-performance sports cars. Founded in 1931, the brand has become famous for models like the iconic Porsche 911. Porsche cars are celebrated for their speed, precision, and distinctive design, appealing to car enthusiasts who value both performance and style.
|
||||
|
||||
5. Volkswagen
|
||||
Volkswagen, which means "people's car" in German, was established with the idea of making affordable and reliable vehicles accessible to everyone. Over the years, Volkswagen has produced several iconic models, such as the Beetle and the Golf. Today, it remains one of the largest car manufacturers in the world, offering a wide range of vehicles that balance practicality with quality.
|
||||
|
||||
Each of these car manufacturer contributes to Germany's reputation as a leader in the global automotive industry, showcasing a blend of innovation, performance, and design excellence.
|
||||
"""
|
||||
|
||||
text_2 = """
|
||||
1. Apple
|
||||
Apple is renowned for its innovative consumer electronics and software. Its product lineup includes the iPhone, iPad, Mac computers, and wearables like the Apple Watch. Known for its emphasis on sleek design and user-friendly interfaces, Apple has built a loyal customer base and created a seamless ecosystem that integrates hardware, software, and services.
|
||||
|
||||
2. Google
|
||||
Founded in 1998, Google started as a search engine and quickly became the go-to resource for finding information online. Over the years, the company has diversified its offerings to include digital advertising, cloud computing, mobile operating systems (Android), and various web services like Gmail and Google Maps. Google's innovations have played a major role in shaping the internet landscape.
|
||||
|
||||
3. Microsoft
|
||||
Microsoft Corporation has been a dominant force in software for decades. Its Windows operating system and Microsoft Office suite are staples in both business and personal computing. In recent years, Microsoft has expanded into cloud computing with Azure, gaming with the Xbox platform, and even hardware through products like the Surface line. This evolution has helped the company maintain its relevance in a rapidly changing tech world.
|
||||
|
||||
4. Amazon
|
||||
What began as an online bookstore has grown into one of the largest e-commerce platforms globally. Amazon is known for its vast online marketplace, but its influence extends far beyond retail. With Amazon Web Services (AWS), the company has become a leader in cloud computing, offering robust solutions that power websites, applications, and businesses around the world. Amazon's constant drive for innovation continues to reshape both retail and technology sectors.
|
||||
|
||||
5. Meta
|
||||
Meta, originally known as Facebook, revolutionized social media by connecting billions of people worldwide. Beyond its core social networking service, Meta is investing in the next generation of digital experiences through virtual and augmented reality technologies, with projects like Oculus. The company's efforts signal a commitment to evolving digital interaction and building the metaverse—a shared virtual space where users can connect and collaborate.
|
||||
|
||||
Each of these companies has significantly impacted the technology landscape, driving innovation and transforming everyday life through their groundbreaking products and services.
|
||||
"""
|
||||
1725
poetry.lock
generated
1725
poetry.lock
generated
File diff suppressed because it is too large
Load diff
253
pyproject.toml
253
pyproject.toml
|
|
@ -1,12 +1,14 @@
|
|||
[tool.poetry]
|
||||
[project]
|
||||
name = "cognee"
|
||||
version = "0.1.39"
|
||||
description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning."
|
||||
authors = ["Vasilije Markovic", "Boris Arzentar"]
|
||||
authors = [
|
||||
{ name = "Vasilije Markovic" },
|
||||
{ name = "Boris Arzentar" },
|
||||
]
|
||||
requires-python = ">=3.10,<=3.13"
|
||||
readme = "README.md"
|
||||
license = "Apache-2.0"
|
||||
homepage = "https://www.cognee.ai"
|
||||
repository = "https://github.com/topoteretes/cognee"
|
||||
classifiers = [
|
||||
"Development Status :: 4 - Beta",
|
||||
"Intended Audience :: Developers",
|
||||
|
|
@ -14,130 +16,131 @@ classifiers = [
|
|||
"Topic :: Software Development :: Libraries",
|
||||
"Operating System :: MacOS :: MacOS X",
|
||||
"Operating System :: POSIX :: Linux",
|
||||
"Operating System :: Microsoft :: Windows"
|
||||
"Operating System :: Microsoft :: Windows",
|
||||
]
|
||||
dependencies = [
|
||||
"openai>=1.59.4,<2",
|
||||
"python-dotenv==1.0.1",
|
||||
"pydantic==2.10.5",
|
||||
"pydantic-settings>=2.2.1,<3",
|
||||
"typing_extensions==4.12.2",
|
||||
"nltk==3.9.1",
|
||||
"numpy>=1.26.4, <=2.1",
|
||||
"pandas==2.2.3",
|
||||
# Note: New s3fs and boto3 versions don't work well together
|
||||
# Always use comaptible fixed versions of these two dependencies
|
||||
"s3fs==2025.3.2",
|
||||
"boto3==1.37.1",
|
||||
"botocore>=1.35.54,<2",
|
||||
"sqlalchemy==2.0.39",
|
||||
"aiosqlite>=0.20.0,<0.21",
|
||||
"tiktoken<=0.9.0",
|
||||
"litellm>=1.57.4",
|
||||
"instructor==1.7.2",
|
||||
"langfuse>=2.32.0,<3",
|
||||
"filetype>=1.2.0,<2",
|
||||
"aiohttp>=3.11.14,<4",
|
||||
"aiofiles>=23.2.1,<24",
|
||||
"owlready2>=0.47,<0.48",
|
||||
"graphistry>=0.33.5,<0.34",
|
||||
"pypdf>=4.1.0,<6.0.0",
|
||||
"jinja2>=3.1.3,<4",
|
||||
"matplotlib>=3.8.3,<4",
|
||||
"networkx>=3.2.1,<4",
|
||||
"lancedb==0.16.0",
|
||||
"alembic>=1.13.3,<2",
|
||||
"pre-commit>=4.0.1,<5",
|
||||
"scikit-learn>=1.6.1,<2",
|
||||
"limits>=4.4.1,<5",
|
||||
"fastapi==0.115.7",
|
||||
"python-multipart==0.0.20",
|
||||
"fastapi-users[sqlalchemy]==14.0.1",
|
||||
"dlt[sqlalchemy]>=1.9.0,<2",
|
||||
"sentry-sdk[fastapi]>=2.9.0,<3",
|
||||
"structlog>=25.2.0,<26",
|
||||
]
|
||||
|
||||
[tool.poetry.dependencies]
|
||||
python = ">=3.10,<=3.13"
|
||||
openai = "^1.59.4"
|
||||
python-dotenv = "1.0.1"
|
||||
pydantic = "2.10.5"
|
||||
pydantic-settings = "^2.2.1"
|
||||
typing_extensions = "4.12.2"
|
||||
nltk = "3.9.1"
|
||||
numpy = ">=1.26.4, <=2.1"
|
||||
pandas = "2.2.3"
|
||||
boto3 = "^1.26.125"
|
||||
botocore="^1.35.54"
|
||||
sqlalchemy = "2.0.39"
|
||||
aiosqlite = "^0.20.0"
|
||||
tiktoken = "<=0.9.0"
|
||||
litellm = ">=1.57.4"
|
||||
instructor = "1.7.2"
|
||||
langfuse = "^2.32.0"
|
||||
filetype = "^1.2.0"
|
||||
aiohttp = "^3.11.14"
|
||||
aiofiles = "^23.2.1"
|
||||
owlready2 = "^0.47"
|
||||
graphistry = "^0.33.5"
|
||||
pypdf = ">=4.1.0,<6.0.0"
|
||||
jinja2 = "^3.1.3"
|
||||
matplotlib = "^3.8.3"
|
||||
networkx = "^3.2.1"
|
||||
lancedb = "0.16.0"
|
||||
alembic = "^1.13.3"
|
||||
pre-commit = "^4.0.1"
|
||||
scikit-learn = "^1.6.1"
|
||||
limits = "^4.4.1"
|
||||
fastapi = {version = "0.115.7"}
|
||||
python-multipart = "0.0.20"
|
||||
fastapi-users = {version = "14.0.1", extras = ["sqlalchemy"]}
|
||||
uvicorn = {version = "0.34.0", optional = true}
|
||||
gunicorn = {version = "^20.1.0", optional = true}
|
||||
dlt = {extras = ["sqlalchemy"], version = "^1.9.0"}
|
||||
qdrant-client = {version = "^1.9.0", optional = true}
|
||||
weaviate-client = {version = "4.9.6", optional = true}
|
||||
neo4j = {version = "^5.20.0", optional = true}
|
||||
falkordb = {version = "1.0.9", optional = true}
|
||||
kuzu = {version = "0.8.2", optional = true}
|
||||
chromadb = {version = "^0.6.0", optional = true}
|
||||
langchain_text_splitters = {version = "0.3.2", optional = true}
|
||||
langsmith = {version = "0.2.3", optional = true}
|
||||
posthog = {version = "^3.5.0", optional = true}
|
||||
groq = {version = "0.8.0", optional = true}
|
||||
anthropic = {version = "^0.26.1", optional = true}
|
||||
sentry-sdk = {extras = ["fastapi"], version = "^2.9.0"}
|
||||
asyncpg = {version = "0.30.0", optional = true}
|
||||
pgvector = {version = "^0.3.5", optional = true}
|
||||
psycopg2 = {version = "^2.9.10", optional = true}
|
||||
llama-index-core = {version = "^0.12.11", optional = true}
|
||||
deepeval = {version = "^2.0.1", optional = true}
|
||||
transformers = {version = "^4.46.3", optional = true}
|
||||
pymilvus = {version = "^2.5.0", optional = true}
|
||||
unstructured = { extras = ["csv", "doc", "docx", "epub", "md", "odt", "org", "ppt", "pptx", "rst", "rtf", "tsv", "xlsx"], version = "^0.16.13", optional = true }
|
||||
mistral-common = {version = "^1.5.2", optional = true}
|
||||
fastembed = {version = "<=0.6.0", optional = true, markers = "python_version < '3.13'"}
|
||||
tree-sitter = {version = "^0.24.0", optional = true}
|
||||
tree-sitter-python = {version = "^0.23.6", optional = true}
|
||||
plotly = {version = "^6.0.0", optional = true}
|
||||
gdown = {version = "^5.2.0", optional = true}
|
||||
qasync = {version = "^0.27.1", optional = true}
|
||||
graphiti-core = {version = "^0.7.0", optional = true}
|
||||
structlog = "^25.2.0"
|
||||
pyside6 = {version = "^6.8.3", optional = true}
|
||||
google-generativeai = {version = "^0.8.4", optional = true}
|
||||
notebook = {version = "^7.1.0", optional = true}
|
||||
s3fs = "^2025.3.2"
|
||||
modal = "^0.74.15"
|
||||
[project.optional-dependencies]
|
||||
api = [
|
||||
"uvicorn==0.34.0",
|
||||
"gunicorn>=20.1.0,<21",
|
||||
]
|
||||
distributed = [
|
||||
"modal==0.74.15",
|
||||
]
|
||||
weaviate = ["weaviate-client==4.9.6"]
|
||||
qdrant = ["qdrant-client>=1.9.0,<2"]
|
||||
neo4j = ["neo4j>=5.20.0,<6"]
|
||||
postgres = [
|
||||
"psycopg2>=2.9.10,<3",
|
||||
"pgvector>=0.3.5,<0.4",
|
||||
"asyncpg==0.30.0",
|
||||
]
|
||||
notebook = ["notebook>=7.1.0,<8"]
|
||||
langchain = [
|
||||
"langsmith==0.2.3",
|
||||
"langchain_text_splitters==0.3.2",
|
||||
]
|
||||
llama-index = ["llama-index-core>=0.12.11,<0.13"]
|
||||
gemini = ["google-generativeai>=0.8.4,<0.9"]
|
||||
huggingface = ["transformers>=4.46.3,<5"]
|
||||
ollama = ["transformers>=4.46.3,<5"]
|
||||
mistral = ["mistral-common>=1.5.2,<2"]
|
||||
anthropic = ["anthropic>=0.26.1,<0.27"]
|
||||
deepeval = ["deepeval>=2.0.1,<3"]
|
||||
posthog = ["posthog>=3.5.0,<4"]
|
||||
falkordb = ["falkordb==1.0.9"]
|
||||
kuzu = ["kuzu==0.8.2"]
|
||||
groq = ["groq==0.8.0"]
|
||||
milvus = ["pymilvus>=2.5.0,<3"]
|
||||
chromadb = [
|
||||
"chromadb>=0.3.0,<0.7",
|
||||
"pypika==0.48.8",
|
||||
]
|
||||
docs = ["unstructured[csv, doc, docx, epub, md, odt, org, ppt, pptx, rst, rtf, tsv, xlsx]>=0.16.13,<0.17"]
|
||||
codegraph = [
|
||||
"fastembed<=0.6.0 ; python_version < '3.13'",
|
||||
"transformers>=4.46.3,<5",
|
||||
"tree-sitter>=0.24.0,<0.25",
|
||||
"tree-sitter-python>=0.23.6,<0.24",
|
||||
]
|
||||
evals = [
|
||||
"plotly>=6.0.0,<7",
|
||||
"gdown>=5.2.0,<6",
|
||||
]
|
||||
gui = [
|
||||
"pyside6>=6.8.3,<7",
|
||||
"qasync>=0.27.1,<0.28",
|
||||
]
|
||||
graphiti = ["graphiti-core>=0.7.0,<0.8"]
|
||||
dev = [
|
||||
"pytest>=7.4.0,<8",
|
||||
"pytest-cov>=6.1.1",
|
||||
"pytest-asyncio>=0.21.1,<0.22",
|
||||
"coverage>=7.3.2,<8",
|
||||
"mypy>=1.7.1,<2",
|
||||
"notebook>=7.1.0,<8",
|
||||
"deptry>=0.20.0,<0.21",
|
||||
"debugpy==1.8.9",
|
||||
"pylint>=3.0.3,<4",
|
||||
"ruff>=0.9.2,<1.0.0",
|
||||
"tweepy==4.14.0",
|
||||
"gitpython>=3.1.43,<4",
|
||||
"pylance==0.19.2",
|
||||
"mkdocs-material>=9.5.42,<10",
|
||||
"mkdocs-minify-plugin>=0.8.0,<0.9",
|
||||
"mkdocstrings[python]>=0.26.2,<0.27",
|
||||
]
|
||||
|
||||
[project.urls]
|
||||
Homepage = "https://www.cognee.ai"
|
||||
Repository = "https://github.com/topoteretes/cognee"
|
||||
|
||||
[tool.poetry.extras]
|
||||
api = ["uvicorn", "gunicorn"]
|
||||
weaviate = ["weaviate-client"]
|
||||
qdrant = ["qdrant-client"]
|
||||
neo4j = ["neo4j"]
|
||||
postgres = ["psycopg2", "pgvector", "asyncpg"]
|
||||
notebook = ["notebook", "ipykernel", "overrides", "ipywidgets", "jupyterlab", "jupyterlab_widgets", "jupyterlab-server", "jupyterlab-git"]
|
||||
langchain = ["langsmith", "langchain_text_splitters"]
|
||||
llama-index = ["llama-index-core"]
|
||||
gemini = ["google-generativeai"]
|
||||
huggingface = ["transformers"]
|
||||
ollama = ["transformers"]
|
||||
mistral = ["mistral-common"]
|
||||
anthropic = ["anthropic"]
|
||||
deepeval = ["deepeval"]
|
||||
posthog = ["posthog"]
|
||||
falkordb = ["falkordb"]
|
||||
kuzu = ["kuzu"]
|
||||
groq = ["groq"]
|
||||
milvus = ["pymilvus"]
|
||||
chromadb = ["chromadb"]
|
||||
docs = ["unstructured"]
|
||||
codegraph = ["fastembed", "transformers", "tree-sitter", "tree-sitter-python"]
|
||||
evals = ["plotly", "gdown"]
|
||||
gui = ["pyside6", "qasync"]
|
||||
graphiti = ["graphiti-core"]
|
||||
[build-system]
|
||||
requires = ["hatchling"]
|
||||
build-backend = "hatchling.build"
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest = "^7.4.0"
|
||||
pytest-asyncio = "^0.21.1"
|
||||
coverage = "^7.3.2"
|
||||
mypy = "^1.7.1"
|
||||
notebook = {version = "^7.1.0", optional = true}
|
||||
deptry = "^0.20.0"
|
||||
debugpy = "1.8.9"
|
||||
pylint = "^3.0.3"
|
||||
ruff = ">=0.9.2,<1.0.0"
|
||||
tweepy = "4.14.0"
|
||||
gitpython = "^3.1.43"
|
||||
pylance = "0.19.2"
|
||||
|
||||
[tool.poetry.group.docs.dependencies]
|
||||
mkdocs-material = "^9.5.42"
|
||||
mkdocs-minify-plugin = "^0.8.0"
|
||||
mkdocstrings = {extras = ["python"], version = "^0.26.2"}
|
||||
|
||||
[tool.ruff] # https://beta.ruff.rs/docs/
|
||||
[tool.ruff]
|
||||
line-length = 100
|
||||
exclude = [
|
||||
"migrations/", # Ignore migrations directory
|
||||
|
|
@ -152,7 +155,3 @@ exclude = [
|
|||
|
||||
[tool.ruff.lint]
|
||||
ignore = ["F401"]
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
build-backend = "poetry.core.masonry.api"
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue