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Merge branch 'dev' into COG-970-refactor-tokenizing

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This commit is contained in:
Igor Ilic 2025-01-23 18:14:49 +01:00
commit 6d5679f9d2
38 changed files with 1368 additions and 3114 deletions
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42
.github/workflows/test_dynamic_steps_example_windows.yml vendored Normal file
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@ -0,0 +1,42 @@
name: test
on:
workflow_dispatch:
pull_request:
types: [labeled, synchronize]
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
jobs:
run_notebook_test_windows:
name: windows-latest
runs-on: windows-latest
defaults:
run:
shell: bash
steps:
- name: Check out
uses: actions/checkout@master
- name: Setup Python
uses: actions/setup-python@v5
with:
python-version: '3.11.x'
- name: Install Poetry
run: |
python -m pip install --upgrade pip
pip install poetry
- name: Install dependencies
run: |
poetry install --no-interaction --all-extras
- name: Execute Python Example
env:
ENV: 'dev'
PYTHONFAULTHANDLER: 1
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
run: poetry run python ./examples/python/dynamic_steps_example.py

45
cognee-mcp/README.md
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@ -1,31 +1,19 @@
# cognee MCP server
### Installing Manually
A MCP server project
=======
1. Clone the [cognee](https://github.com/topoteretes/cognee) repo
2. Install dependencies
```
pip install uv
```
```
brew install postgresql
```
```
brew install rust
brew install uv
```
```jsx
cd cognee-mcp
uv sync --dev --all-extras
uv sync --dev --all-extras --reinstall
```
3. Activate the venv with
@ -48,8 +36,6 @@ nano claude_desktop_config.json
```
```
{
"mcpServers": {
"cognee": {
@ -65,16 +51,7 @@ nano claude_desktop_config.json
"TOKENIZERS_PARALLELISM": "false",
"LLM_API_KEY": "sk-"
}
},
"filesystem": {
"command": "npx",
"args": [
"-y",
"@modelcontextprotocol/server-filesystem",
"/Users/{user}/Desktop",
"/Users/{user}/Projects"
]
}
}
}
}
```
@ -95,13 +72,15 @@ Restart your Claude desktop.
To use debugger, run:
```bash
npx @modelcontextprotocol/inspector uv --directory /Users/name/folder run cognee
mcp dev src/server.py
```
Open inspector with timeout passed:
```
http://localhost:5173?timeout=120000
```
To apply new changes while development you do:
1. Poetry lock in cognee folder
2. uv sync --dev --all-extras --reinstall
3. npx @modelcontextprotocol/inspector uv --directory /Users/vasilije/cognee/cognee-mcp run cognee
To apply new changes while developing cognee you need to do:
1. `poetry lock` in cognee folder
2. `uv sync --dev --all-extras --reinstall `
3. `mcp dev src/server.py`

15
cognee-mcp/cognee_mcp/__init__.py
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@ -1,15 +0,0 @@
import asyncio
from . import server
def main():
"""Main entry point for the package."""
asyncio.run(server.main())
# Optionally expose other important items at package level
__all__ = ["main", "server"]
if __name__ == "__main__":
main()

235
cognee-mcp/cognee_mcp/server.py
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@ -1,235 +0,0 @@
import importlib.util
import os
import asyncio
from contextlib import redirect_stderr, redirect_stdout
from sqlalchemy.testing.plugin.plugin_base import logging
import cognee
import mcp.server.stdio
import mcp.types as types
from cognee.api.v1.search import SearchType
from cognee.shared.data_models import KnowledgeGraph
from mcp.server import NotificationOptions, Server
from mcp.server.models import InitializationOptions
from PIL import Image
server = Server("cognee-mcp")
def node_to_string(node):
# keys_to_keep = ["chunk_index", "topological_rank", "cut_type", "id", "text"]
# keyset = set(keys_to_keep) & node.keys()
# return "Node(" + " ".join([key + ": " + str(node[key]) + "," for key in keyset]) + ")"
node_data = ", ".join(
[f'{key}: "{value}"' for key, value in node.items() if key in ["id", "name"]]
)
return f"Node({node_data})"
def retrieved_edges_to_string(search_results):
edge_strings = []
for triplet in search_results:
node1, edge, node2 = triplet
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)
def load_class(model_file, model_name):
model_file = os.path.abspath(model_file)
spec = importlib.util.spec_from_file_location("graph_model", model_file)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
model_class = getattr(module, model_name)
return model_class
@server.list_tools()
async def handle_list_tools() -> list[types.Tool]:
"""
List available tools.
Each tool specifies its arguments using JSON Schema validation.
"""
return [
types.Tool(
name="cognify",
description="Build knowledge graph from the input text.",
inputSchema={
"type": "object",
"properties": {
"text": {"type": "string"},
"graph_model_file": {"type": "string"},
"graph_model_name": {"type": "string"},
},
"required": ["text"],
},
),
types.Tool(
name="search",
description="Search the knowledge graph.",
inputSchema={
"type": "object",
"properties": {
"query": {"type": "string"},
},
"required": ["query"],
},
),
types.Tool(
name="prune",
description="Reset the knowledge graph.",
inputSchema={
"type": "object",
"properties": {
"query": {"type": "string"},
},
},
),
types.Tool(
name="visualize",
description="Visualize the knowledge graph.",
inputSchema={
"type": "object",
"properties": {
"query": {"type": "string"},
},
},
),
]
def get_freshest_png(directory: str) -> Image.Image:
if not os.path.exists(directory):
raise FileNotFoundError(f"Directory {directory} does not exist")
# 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.")
# 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
# The "freshest" file has the largest timestamp
freshest_filename = files_sorted[-1]
freshest_path = os.path.join(directory, freshest_filename)
# Open the image with PIL and return the PIL Image object
try:
return Image.open(freshest_path)
except (IOError, OSError) as e:
raise IOError(f"Failed to open PNG file {freshest_path}") from e
@server.call_tool()
async def handle_call_tool(
name: str, arguments: dict | None
) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
"""
Handle tool execution requests.
Tools can modify server state and notify clients of changes.
"""
if name == "cognify":
with open(os.devnull, "w") as fnull:
with redirect_stdout(fnull), redirect_stderr(fnull):
if not arguments:
raise ValueError("Missing arguments")
text = arguments.get("text")
if ("graph_model_file" in arguments) and ("graph_model_name" in arguments):
model_file = arguments.get("graph_model_file")
model_name = arguments.get("graph_model_name")
graph_model = load_class(model_file, model_name)
else:
graph_model = KnowledgeGraph
await cognee.add(text)
await cognee.cognify(graph_model=graph_model)
return [
types.TextContent(
type="text",
text="Ingested",
)
]
elif name == "search":
with open(os.devnull, "w") as fnull:
with redirect_stdout(fnull), redirect_stderr(fnull):
if not arguments:
raise ValueError("Missing arguments")
search_query = arguments.get("query")
search_results = await cognee.search(SearchType.INSIGHTS, query_text=search_query)
results = retrieved_edges_to_string(search_results)
return [
types.TextContent(
type="text",
text=results,
)
]
elif name == "prune":
with open(os.devnull, "w") as fnull:
with redirect_stdout(fnull), redirect_stderr(fnull):
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
return [
types.TextContent(
type="text",
text="Pruned",
)
]
elif name == "visualize":
with open(os.devnull, "w") as fnull:
with redirect_stdout(fnull), redirect_stderr(fnull):
try:
results = await cognee.visualize_graph()
return [
types.TextContent(
type="text",
text=results,
)
]
except (FileNotFoundError, IOError, ValueError) as e:
raise ValueError(f"Failed to create visualization: {str(e)}")
else:
raise ValueError(f"Unknown tool: {name}")
async def main():
# Run the server using stdin/stdout streams
async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
await server.run(
read_stream,
write_stream,
InitializationOptions(
server_name="cognee-mcp",
server_version="0.1.0",
capabilities=server.get_capabilities(
notification_options=NotificationOptions(),
experimental_capabilities={},
),
),
)
# This is needed if you'd like to connect to a custom client
if __name__ == "__main__":
asyncio.run(main())

79
cognee-mcp/pyproject.toml
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@ -6,73 +6,8 @@ readme = "README.md"
requires-python = ">=3.10"
dependencies = [
"mcp>=1.1.1",
"openai==1.59.4",
"pydantic==2.8.2",
"python-dotenv==1.0.1",
"fastapi>=0.109.2,<0.110.0",
"uvicorn==0.22.0",
"requests==2.32.3",
"aiohttp==3.10.10",
"typing_extensions==4.12.2",
"nest_asyncio==1.6.0",
"numpy==1.26.4",
"datasets==3.1.0",
"falkordb==1.0.9", # Optional
"boto3>=1.26.125,<2.0.0",
"botocore>=1.35.54,<2.0.0",
"gunicorn>=20.1.0,<21.0.0",
"sqlalchemy==2.0.36",
"instructor==1.7.2",
"networkx>=3.2.1,<4.0.0",
"aiosqlite>=0.20.0,<0.21.0",
"pandas==2.2.3",
"filetype>=1.2.0,<2.0.0",
"nltk>=3.8.1,<4.0.0",
"dlt[sqlalchemy]>=1.4.1,<2.0.0",
"aiofiles>=23.2.1,<24.0.0",
"qdrant-client>=1.9.0,<2.0.0", # Optional
"graphistry>=0.33.5,<0.34.0",
"tenacity>=9.0.0",
"weaviate-client==4.6.7", # Optional
"scikit-learn>=1.5.0,<2.0.0",
"pypdf>=4.1.0,<5.0.0",
"neo4j>=5.20.0,<6.0.0", # Optional
"jinja2>=3.1.3,<4.0.0",
"matplotlib>=3.8.3,<4.0.0",
"tiktoken==0.7.0",
"langchain_text_splitters==0.3.2", # Optional
"langsmith==0.1.139", # Optional
"langdetect==1.0.9",
"posthog>=3.5.0,<4.0.0", # Optional
"lancedb==0.16.0",
"litellm==1.57.2",
"groq==0.8.0", # Optional
"langfuse>=2.32.0,<3.0.0", # Optional
"pydantic-settings>=2.2.1,<3.0.0",
"anthropic>=0.26.1,<1.0.0",
"sentry-sdk[fastapi]>=2.9.0,<3.0.0",
"fastapi-users[sqlalchemy]>=14.0.0", # Optional
"alembic>=1.13.3,<2.0.0",
"asyncpg==0.30.0", # Optional
"pgvector>=0.3.5,<0.4.0", # Optional
"psycopg2>=2.9.10,<3.0.0", # Optional
"llama-index-core>=0.12.0", # Optional
"deepeval>=2.0.1,<3.0.0", # Optional
"transformers>=4.46.3,<5.0.0",
"pymilvus>=2.5.0,<3.0.0", # Optional
"unstructured[csv,doc,docx,epub,md,odt,org,ppt,pptx,rst,rtf,tsv,xlsx]>=0.16.10,<1.0.0", # Optional
"pytest>=7.4.0,<8.0.0",
"pytest-asyncio>=0.21.1,<0.22.0",
"coverage>=7.3.2,<8.0.0",
"mypy>=1.7.1,<2.0.0",
"deptry>=0.20.0,<0.21.0",
"debugpy==1.8.2",
"pylint>=3.0.3,<4.0.0",
"ruff>=0.2.2,<0.3.0",
"tweepy==4.14.0",
"gitpython>=3.1.43,<4.0.0",
"cognee",
"mcp==1.2.0",
]
[[project.authors]]
@ -80,16 +15,14 @@ name = "Rita Aleksziev"
email = "rita@topoteretes.com"
[build-system]
requires = [ "hatchling",]
requires = [ "hatchling", ]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src"]
[tool.uv.sources]
cognee = { path = "../../cognee" }
[dependency-groups]
dev = [
"cognee",
]
[project.scripts]
cognee = "cognee_mcp:main"
cognee = "src:main"

5
cognee-mcp/src/__init__.py Normal file
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@ -0,0 +1,5 @@
from .server import mcp
def main():
"""Main entry point for the package."""
mcp.run(transport="stdio")

42
cognee-mcp/src/client.py Normal file
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@ -0,0 +1,42 @@
from datetime import timedelta
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client
# Create server parameters for stdio connection
server_params = StdioServerParameters(
command="mcp", # Executable
args=["run", "src/server.py"], # Optional command line arguments
env=None # Optional environment variables
)
text = """
Artificial intelligence, or AI, is technology that enables computers
and machines to simulate human intelligence and problem-solving
capabilities.
On its own or combined with other technologies (e.g., sensors,
geolocation, robotics) AI can perform tasks that would otherwise
require human intelligence or intervention. Digital assistants, GPS
guidance, autonomous vehicles, and generative AI tools (like Open
AI's Chat GPT) are just a few examples of AI in the daily news and
our daily lives.
As a field of computer science, artificial intelligence encompasses
(and is often mentioned together with) machine learning and deep
learning. These disciplines involve the development of AI
algorithms, modeled after the decision-making processes of the human
brain, that can ‘learn’ from available data and make increasingly
more accurate classifications or predictions over time.
"""
async def run():
async with stdio_client(server_params) as (read, write):
async with ClientSession(read, write, timedelta(minutes=3)) as session:
await session.initialize()
toolResult = await session.call_tool("cognify", arguments={"text": text})
# toolResult = await session.call_tool("search", arguments={"search_query": "AI"})
print(f"Cognify result: {toolResult}")
if __name__ == "__main__":
import asyncio
asyncio.run(run())

118
cognee-mcp/src/server.py Normal file
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@ -0,0 +1,118 @@
import os
import cognee
import importlib.util
# from PIL import Image as PILImage
from mcp.server.fastmcp import FastMCP
from cognee.api.v1.search import SearchType
from cognee.shared.data_models import KnowledgeGraph
mcp = FastMCP("cognee", timeout=120000)
@mcp.tool()
async def cognify(text: str, graph_model_file: str = None, graph_model_name: str = None) -> str:
"""Build knowledge graph from the input text"""
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)
except Exception as e:
raise ValueError(f"Failed to cognify: {str(e)}")
return "Ingested"
@mcp.tool()
async def search(search_query: str) -> str:
"""Search the knowledge graph"""
search_results = await cognee.search(SearchType.INSIGHTS, query_text=search_query)
results = retrieved_edges_to_string(search_results)
return results
@mcp.tool()
async def prune() -> str:
"""Reset the knowledge graph"""
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
return "Pruned"
# @mcp.tool()
# 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)}")
def node_to_string(node):
node_data = ", ".join(
[f'{key}: "{value}"' for key, value in node.items() if key in ["id", "name"]]
)
return f"Node({node_data})"
def retrieved_edges_to_string(search_results):
edge_strings = []
for triplet in search_results:
node1, edge, node2 = triplet
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)
def load_class(model_file, model_name):
model_file = os.path.abspath(model_file)
spec = importlib.util.spec_from_file_location("graph_model", model_file)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
model_class = getattr(module, 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")
# # 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.")
# # 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
# # The "freshest" file has the largest timestamp
# freshest_filename = files_sorted[-1]
# freshest_path = os.path.join(directory, freshest_filename)
# # 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
mcp.run(transport="stdio")

3057
cognee-mcp/uv.lock generated
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File diff suppressed because it is too large Load diff

34
cognee/infrastructure/databases/graph/networkx/adapter.py
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@ -247,10 +247,11 @@ class NetworkXAdapter(GraphDBInterface):
if not file_path:
file_path = self.filename
graph_data = nx.readwrite.json_graph.node_link_data(self.graph)
graph_data = nx.readwrite.json_graph.node_link_data(self.graph, edges="links")
async with aiofiles.open(file_path, "w") as file:
await file.write(json.dumps(graph_data, cls=JSONEncoder))
json_data = json.dumps(graph_data, cls=JSONEncoder)
await file.write(json_data)
async def load_graph_from_file(self, file_path: str = None):
"""Asynchronously load the graph from a file in JSON format."""
@ -265,19 +266,32 @@ class NetworkXAdapter(GraphDBInterface):
graph_data = json.loads(await file.read())
for node in graph_data["nodes"]:
try:
node["id"] = UUID(node["id"])
if not isinstance(node["id"], UUID):
node["id"] = UUID(node["id"])
except Exception as e:
print(e)
pass
if "updated_at" in node:
if isinstance(node.get("updated_at"), int):
node["updated_at"] = datetime.fromtimestamp(
node["updated_at"] / 1000, tz=timezone.utc
)
elif isinstance(node.get("updated_at"), str):
node["updated_at"] = datetime.strptime(
node["updated_at"], "%Y-%m-%dT%H:%M:%S.%f%z"
)
for edge in graph_data["links"]:
try:
source_id = UUID(edge["source"])
target_id = UUID(edge["target"])
if not isinstance(edge["source"], UUID):
source_id = UUID(edge["source"])
else:
source_id = edge["source"]
if not isinstance(edge["target"], UUID):
target_id = UUID(edge["target"])
else:
target_id = edge["target"]
edge["source"] = source_id
edge["target"] = target_id
@ -287,12 +301,16 @@ class NetworkXAdapter(GraphDBInterface):
print(e)
pass
if "updated_at" in edge:
if isinstance(edge["updated_at"], int): # Handle timestamp in milliseconds
edge["updated_at"] = datetime.fromtimestamp(
edge["updated_at"] / 1000, tz=timezone.utc
)
elif isinstance(edge["updated_at"], str):
edge["updated_at"] = datetime.strptime(
edge["updated_at"], "%Y-%m-%dT%H:%M:%S.%f%z"
)
self.graph = nx.readwrite.json_graph.node_link_graph(graph_data)
self.graph = nx.readwrite.json_graph.node_link_graph(graph_data, edges="links")
for node_id, node_data in self.graph.nodes(data=True):
node_data["id"] = node_id

2
cognee/modules/data/models/Data.py
View file

@ -19,7 +19,7 @@ class Data(Base):
raw_data_location = Column(String)
owner_id = Column(UUID, index=True)
content_hash = Column(String)
foreign_metadata = Column(JSON)
external_metadata = Column(JSON)
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
updated_at = Column(DateTime(timezone=True), onupdate=lambda: datetime.now(timezone.utc))

2
cognee/modules/data/processing/document_types/Document.py
View file

@ -7,7 +7,7 @@ from cognee.infrastructure.engine import DataPoint
class Document(DataPoint):
name: str
raw_data_location: str
foreign_metadata: Optional[str]
external_metadata: Optional[str]
mime_type: str
_metadata: dict = {"index_fields": ["name"], "type": "Document"}

24
cognee/tasks/chunk_naive_llm_classifier/chunk_naive_llm_classifier.py
View file

@ -10,7 +10,29 @@ from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
async def chunk_naive_llm_classifier(
data_chunks: list[DocumentChunk], classification_model: Type[BaseModel]
):
) -> list[DocumentChunk]:
"""
Classifies a list of document chunks using a specified classification model and updates vector and graph databases with the classification results.
Vector Database Structure:
- Collection Name: `classification`
- Payload Schema:
- uuid (str): Unique identifier for the classification.
- text (str): Text label of the classification.
- chunk_id (str): Identifier of the chunk associated with this classification.
- document_id (str): Identifier of the document associated with this classification.
Graph Database Structure:
- Nodes:
- Represent document chunks, classification types, and classification subtypes.
- Edges:
- `is_media_type`: Links document chunks to their classification type.
- `is_subtype_of`: Links classification subtypes to their parent type.
- `is_classified_as`: Links document chunks to their classification subtypes.
Notes:
- The function assumes that vector and graph database engines (`get_vector_engine` and `get_graph_engine`) are properly initialized and accessible.
- Classification labels are processed to ensure uniqueness using UUIDs based on their values.
"""
if len(data_chunks) == 0:
return data_chunks

6
cognee/tasks/chunks/chunk_by_paragraph.py
View file

@ -17,6 +17,12 @@ def chunk_by_paragraph(
"""
Chunks text by paragraph while preserving exact text reconstruction capability.
When chunks are joined with empty string "", they reproduce the original text exactly.
Notes:
- Tokenization is handled using the `tiktoken` library, ensuring compatibility with the vector engine's embedding model.
- If `batch_paragraphs` is False, each paragraph will be yielded as a separate chunk.
- Handles cases where paragraphs exceed the specified token or word limits by splitting them as needed.
- Remaining text at the end of the input will be yielded as a final chunk.
"""
current_chunk = ""
current_word_count = 0

16
cognee/tasks/chunks/chunk_by_sentence.py
View file

@ -1,9 +1,19 @@
from uuid import uuid4
from typing import Optional
from uuid import uuid4, UUID
from typing import Optional, Iterator, Tuple
from .chunk_by_word import chunk_by_word
def chunk_by_sentence(data: str, maximum_length: Optional[int] = None):
def chunk_by_sentence(
data: str, maximum_length: Optional[int] = None
) -> Iterator[Tuple[UUID, str, int, Optional[str]]]:
"""
Splits the input text into sentences based on word-level processing, with optional sentence length constraints.
Notes:
- Relies on the `chunk_by_word` function for word-level tokenization and classification.
- Ensures sentences within paragraphs are uniquely identifiable using UUIDs.
- Handles cases where the text ends mid-sentence by appending a special "sentence_cut" type.
"""
sentence = ""
paragraph_id = uuid4()
word_count = 0

4
cognee/tasks/chunks/chunk_by_word.py
View file

@ -1,4 +1,6 @@
import re
from typing import Iterator, Tuple
SENTENCE_ENDINGS = r"[.;!?…]"
PARAGRAPH_ENDINGS = r"[\n\r]"
@ -34,7 +36,7 @@ def is_real_paragraph_end(last_char: str, current_pos: int, text: str) -> bool:
return False
def chunk_by_word(data: str):
def chunk_by_word(data: str) -> Iterator[Tuple[str, str]]:
"""
Chunks text into words and endings while preserving whitespace.
Whitespace is included with the preceding word.

8
cognee/tasks/chunks/query_chunks.py
View file

@ -3,11 +3,19 @@ from cognee.infrastructure.databases.vector import get_vector_engine
async def query_chunks(query: str) -> list[dict]:
"""
Queries the vector database to retrieve chunks related to the given query string.
Parameters:
- query (str): The query string to filter nodes by.
Returns:
- list(dict): A list of objects providing information about the chunks related to query.
Notes:
- The function uses the `search` method of the vector engine to find matches.
- Limits the results to the top 5 matching chunks to balance performance and relevance.
- Ensure that the vector database is properly initialized and contains the "document_chunk_text" collection.
"""
vector_engine = get_vector_engine()

8
cognee/tasks/chunks/remove_disconnected_chunks.py
View file

@ -3,6 +3,14 @@ from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
async def remove_disconnected_chunks(data_chunks: list[DocumentChunk]) -> list[DocumentChunk]:
"""
Removes disconnected or obsolete chunks from the graph database.
Notes:
- Obsolete chunks are defined as chunks with no "next_chunk" predecessor.
- Fully disconnected nodes are identified and deleted separately.
- This function assumes that the graph database is properly initialized and accessible.
"""
graph_engine = await get_graph_engine()
document_ids = set((data_chunk.document_id for data_chunk in data_chunks))

12
cognee/tasks/completion/graph_query_completion.py
View file

@ -6,6 +6,10 @@ from cognee.modules.retrieval.brute_force_triplet_search import brute_force_trip
def retrieved_edges_to_string(retrieved_edges: list) -> str:
"""
Converts a list of retrieved graph edges into a human-readable string format.
"""
edge_strings = []
for edge in retrieved_edges:
node1_string = edge.node1.attributes.get("text") or edge.node1.attributes.get("name")
@ -18,11 +22,19 @@ def retrieved_edges_to_string(retrieved_edges: list) -> str:
async def graph_query_completion(query: str) -> list:
"""
Executes a query on the graph database and retrieves a relevant completion based on the found data.
Parameters:
- query (str): The query string to compute.
Returns:
- list: Answer to the query.
Notes:
- The `brute_force_triplet_search` is used to retrieve relevant graph data.
- Prompts are dynamically rendered and provided to the LLM for contextual understanding.
- Ensure that the LLM client and graph database are properly configured and accessible.
"""
found_triplets = await brute_force_triplet_search(query, top_k=5)

9
cognee/tasks/completion/query_completion.py
View file

@ -6,11 +6,20 @@ from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt
async def query_completion(query: str) -> list:
"""
Executes a query against a vector database and computes a relevant response using an LLM.
Parameters:
- query (str): The query string to compute.
Returns:
- list: Answer to the query.
Notes:
- Limits the search to the top 1 matching chunk for simplicity and relevance.
- Ensure that the vector database and LLM client are properly configured and accessible.
- The response model used for the LLM output is expected to be a string.
"""
vector_engine = get_vector_engine()

13
cognee/tasks/documents/check_permissions_on_documents.py
View file

@ -1,8 +1,19 @@
from cognee.modules.data.processing.document_types import Document
from cognee.modules.users.permissions.methods import check_permission_on_documents
from typing import List
async def check_permissions_on_documents(documents: list[Document], user, permissions):
async def check_permissions_on_documents(
documents: list[Document], user, permissions
) -> List[Document]:
"""
Validates a user's permissions on a list of documents.
Notes:
- This function assumes that `check_permission_on_documents` raises an exception if the permission check fails.
- It is designed to validate multiple permissions in a sequential manner for the same set of documents.
- Ensure that the `Document` and `user` objects conform to the expected structure and interfaces.
"""
document_ids = [document.id for document in documents]
for permission in permissions:

9
cognee/tasks/documents/classify_documents.py
View file

@ -50,6 +50,13 @@ EXTENSION_TO_DOCUMENT_CLASS = {
async def classify_documents(data_documents: list[Data]) -> list[Document]:
"""
Classifies a list of data items into specific document types based on file extensions.
Notes:
- The function relies on `get_metadata` to retrieve metadata information for each data item.
- Ensure the `Data` objects and their attributes (e.g., `extension`, `id`) are valid before calling this function.
"""
documents = []
for data_item in data_documents:
document = EXTENSION_TO_DOCUMENT_CLASS[data_item.extension](
@ -58,7 +65,7 @@ async def classify_documents(data_documents: list[Data]) -> list[Document]:
raw_data_location=data_item.raw_data_location,
name=data_item.name,
mime_type=data_item.mime_type,
foreign_metadata=json.dumps(data_item.foreign_metadata, indent=4),
external_metadata=json.dumps(data_item.external_metadata, indent=4),
)
documents.append(document)

11
cognee/tasks/documents/extract_chunks_from_documents.py
View file

@ -1,4 +1,4 @@
from typing import Optional
from typing import Optional, AsyncGenerator
from cognee.modules.data.processing.document_types.Document import Document
@ -7,7 +7,14 @@ async def extract_chunks_from_documents(
documents: list[Document],
chunk_size: int = 1024,
chunker="text_chunker",
):
) -> AsyncGenerator:
"""
Extracts chunks of data from a list of documents based on the specified chunking parameters.
Notes:
- The `read` method of the `Document` class must be implemented to support the chunking operation.
- The `chunker` parameter determines the chunking logic and should align with the document type.
"""
for document in documents:
for document_chunk in document.read(chunk_size=chunk_size, chunker=chunker):
yield document_chunk

13
cognee/tasks/graph/extract_graph_from_code.py
View file

@ -1,12 +1,21 @@
import asyncio
from typing import Type
from typing import Type, List
from pydantic import BaseModel
from cognee.modules.data.extraction.knowledge_graph import extract_content_graph
from cognee.modules.chunking.models.DocumentChunk import DocumentChunk
from cognee.tasks.storage import add_data_points
async def extract_graph_from_code(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel]):
async def extract_graph_from_code(
data_chunks: list[DocumentChunk], graph_model: Type[BaseModel]
) -> List[DocumentChunk]:
"""
Extracts a knowledge graph from the text content of document chunks using a specified graph model.
Notes:
- The `extract_content_graph` function processes each chunk's text to extract graph information.
- Graph nodes are stored using the `add_data_points` function for later retrieval or analysis.
"""
chunk_graphs = await asyncio.gather(
*[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
)

11
cognee/tasks/graph/extract_graph_from_data.py
View file

@ -1,5 +1,5 @@
import asyncio
from typing import Type
from typing import Type, List
from pydantic import BaseModel
@ -13,7 +13,14 @@ from cognee.modules.graph.utils import (
from cognee.tasks.storage import add_data_points
async def extract_graph_from_data(data_chunks: list[DocumentChunk], graph_model: Type[BaseModel]):
async def extract_graph_from_data(
data_chunks: list[DocumentChunk], graph_model: Type[BaseModel]
) -> List[DocumentChunk]:
"""
Extracts and integrates a knowledge graph from the text content of document chunks using a specified graph model.
"""
chunk_graphs = await asyncio.gather(
*[extract_content_graph(chunk.text, graph_model) for chunk in data_chunks]
)

6
cognee/tasks/ingestion/ingest_data.py
View file

@ -26,7 +26,7 @@ async def ingest_data(data: Any, dataset_name: str, user: User):
destination=destination,
)
def get_foreign_metadata_dict(data_item: Union[BinaryIO, str, Any]) -> dict[str, Any]:
def get_external_metadata_dict(data_item: Union[BinaryIO, str, Any]) -> dict[str, Any]:
if hasattr(data_item, "dict") and inspect.ismethod(getattr(data_item, "dict")):
return {"metadata": data_item.dict(), "origin": str(type(data_item))}
else:
@ -95,7 +95,7 @@ async def ingest_data(data: Any, dataset_name: str, user: User):
data_point.mime_type = file_metadata["mime_type"]
data_point.owner_id = user.id
data_point.content_hash = file_metadata["content_hash"]
data_point.foreign_metadata = (get_foreign_metadata_dict(data_item),)
data_point.external_metadata = (get_external_metadata_dict(data_item),)
await session.merge(data_point)
else:
data_point = Data(
@ -106,7 +106,7 @@ async def ingest_data(data: Any, dataset_name: str, user: User):
mime_type=file_metadata["mime_type"],
owner_id=user.id,
content_hash=file_metadata["content_hash"],
foreign_metadata=get_foreign_metadata_dict(data_item),
external_metadata=get_external_metadata_dict(data_item),
)
# Check if data is already in dataset

2
cognee/tests/integration/documents/AudioDocument_test.py
View file

@ -29,7 +29,7 @@ def test_AudioDocument():
id=uuid.uuid4(),
name="audio-dummy-test",
raw_data_location="",
foreign_metadata="",
external_metadata="",
mime_type="",
)
with patch.object(AudioDocument, "create_transcript", return_value=TEST_TEXT):

2
cognee/tests/integration/documents/ImageDocument_test.py
View file

@ -18,7 +18,7 @@ def test_ImageDocument():
id=uuid.uuid4(),
name="image-dummy-test",
raw_data_location="",
foreign_metadata="",
external_metadata="",
mime_type="",
)
with patch.object(ImageDocument, "transcribe_image", return_value=TEST_TEXT):

2
cognee/tests/integration/documents/PdfDocument_test.py
View file

@ -20,7 +20,7 @@ def test_PdfDocument():
id=uuid.uuid4(),
name="Test document.pdf",
raw_data_location=test_file_path,
foreign_metadata="",
external_metadata="",
mime_type="",
)

2
cognee/tests/integration/documents/TextDocument_test.py
View file

@ -32,7 +32,7 @@ def test_TextDocument(input_file, chunk_size):
id=uuid.uuid4(),
name=input_file,
raw_data_location=test_file_path,
foreign_metadata="",
external_metadata="",
mime_type="",
)

8
cognee/tests/integration/documents/UnstructuredDocument_test.py
View file

@ -39,7 +39,7 @@ def test_UnstructuredDocument():
id=uuid.uuid4(),
name="example.pptx",
raw_data_location=pptx_file_path,
foreign_metadata="",
external_metadata="",
mime_type="application/vnd.openxmlformats-officedocument.presentationml.presentation",
)
@ -47,7 +47,7 @@ def test_UnstructuredDocument():
id=uuid.uuid4(),
name="example.docx",
raw_data_location=docx_file_path,
foreign_metadata="",
external_metadata="",
mime_type="application/vnd.openxmlformats-officedocument.wordprocessingml.document",
)
@ -55,7 +55,7 @@ def test_UnstructuredDocument():
id=uuid.uuid4(),
name="example.csv",
raw_data_location=csv_file_path,
foreign_metadata="",
external_metadata="",
mime_type="text/csv",
)
@ -63,7 +63,7 @@ def test_UnstructuredDocument():
id=uuid.uuid4(),
name="example.xlsx",
raw_data_location=xlsx_file_path,
foreign_metadata="",
external_metadata="",
mime_type="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
)

69
evals/eval_on_hotpot.py
View file

@ -10,12 +10,29 @@ from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt
from evals.qa_dataset_utils import load_qa_dataset
from evals.qa_metrics_utils import get_metrics
from evals.qa_context_provider_utils import qa_context_providers, valid_pipeline_slices
import random
import os
import json
from pathlib import Path
logger = logging.getLogger(__name__)
async def answer_qa_instance(instance, context_provider):
context = await context_provider(instance)
async def answer_qa_instance(instance, context_provider, contexts_filename):
if os.path.exists(contexts_filename):
with open(contexts_filename, "r") as file:
preloaded_contexts = json.load(file)
else:
preloaded_contexts = {}
if instance["_id"] in preloaded_contexts:
context = preloaded_contexts[instance["_id"]]
else:
context = await context_provider(instance)
preloaded_contexts[instance["_id"]] = context
with open(contexts_filename, "w") as file:
json.dump(preloaded_contexts, file)
args = {
"question": instance["question"],
@ -49,12 +66,27 @@ async def deepeval_answers(instances, answers, eval_metrics):
return eval_results
async def deepeval_on_instances(instances, context_provider, eval_metrics):
async def deepeval_on_instances(
instances, context_provider, eval_metrics, answers_filename, contexts_filename
):
if os.path.exists(answers_filename):
with open(answers_filename, "r") as file:
preloaded_answers = json.load(file)
else:
preloaded_answers = {}
answers = []
for instance in tqdm(instances, desc="Getting answers"):
answer = await answer_qa_instance(instance, context_provider)
if instance["_id"] in preloaded_answers:
answer = preloaded_answers[instance["_id"]]
else:
answer = await answer_qa_instance(instance, context_provider, contexts_filename)
preloaded_answers[instance["_id"]] = answer
answers.append(answer)
with open(answers_filename, "w") as file:
json.dump(preloaded_answers, file)
eval_results = await deepeval_answers(instances, answers, eval_metrics)
score_lists_dict = {}
for instance_result in eval_results.test_results:
@ -72,21 +104,38 @@ async def deepeval_on_instances(instances, context_provider, eval_metrics):
async def eval_on_QA_dataset(
dataset_name_or_filename: str, context_provider_name, num_samples, metric_name_list
dataset_name_or_filename: str, context_provider_name, num_samples, metric_name_list, out_path
):
dataset = load_qa_dataset(dataset_name_or_filename)
context_provider = qa_context_providers[context_provider_name]
eval_metrics = get_metrics(metric_name_list)
instances = dataset if not num_samples else dataset[:num_samples]
out_path = Path(out_path)
if not out_path.exists():
out_path.mkdir(parents=True, exist_ok=True)
random.seed(42)
instances = dataset if not num_samples else random.sample(dataset, num_samples)
contexts_filename = out_path / Path(
f"contexts_{dataset_name_or_filename.split('.')[0]}_{context_provider_name}.json"
)
if "promptfoo_metrics" in eval_metrics:
promptfoo_results = await eval_metrics["promptfoo_metrics"].measure(
instances, context_provider
instances, context_provider, contexts_filename
)
else:
promptfoo_results = {}
answers_filename = out_path / Path(
f"answers_{dataset_name_or_filename.split('.')[0]}_{context_provider_name}.json"
)
deepeval_results = await deepeval_on_instances(
instances, context_provider, eval_metrics["deepeval_metrics"]
instances,
context_provider,
eval_metrics["deepeval_metrics"],
answers_filename,
contexts_filename,
)
results = promptfoo_results | deepeval_results
@ -95,14 +144,14 @@ async def eval_on_QA_dataset(
async def incremental_eval_on_QA_dataset(
dataset_name_or_filename: str, num_samples, metric_name_list
dataset_name_or_filename: str, num_samples, metric_name_list, out_path
):
pipeline_slice_names = valid_pipeline_slices.keys()
incremental_results = {}
for pipeline_slice_name in pipeline_slice_names:
results = await eval_on_QA_dataset(
dataset_name_or_filename, pipeline_slice_name, num_samples, metric_name_list
dataset_name_or_filename, pipeline_slice_name, num_samples, metric_name_list, out_path
)
incremental_results[pipeline_slice_name] = results

19
evals/promptfoo_metrics.py
View file

@ -29,7 +29,7 @@ class PromptfooMetric:
else:
raise Exception(f"{metric_name} is not a valid promptfoo metric")
async def measure(self, instances, context_provider):
async def measure(self, instances, context_provider, contexts_filename):
with open(os.path.join(os.getcwd(), "evals/promptfoo_config_template.yaml"), "r") as file:
config = yaml.safe_load(file)
@ -40,10 +40,20 @@ class PromptfooMetric:
]
}
# Fill config file with test cases
tests = []
if os.path.exists(contexts_filename):
with open(contexts_filename, "r") as file:
preloaded_contexts = json.load(file)
else:
preloaded_contexts = {}
for instance in instances:
context = await context_provider(instance)
if instance["_id"] in preloaded_contexts:
context = preloaded_contexts[instance["_id"]]
else:
context = await context_provider(instance)
preloaded_contexts[instance["_id"]] = context
test = {
"vars": {
"name": instance["question"][:15],
@ -52,7 +62,10 @@ class PromptfooMetric:
}
}
tests.append(test)
config["tests"] = tests
with open(contexts_filename, "w") as file:
json.dump(preloaded_contexts, file)
# Write the updated YAML back, preserving formatting and structure
updated_yaml_file_path = os.path.join(os.getcwd(), "config_with_context.yaml")

18
evals/qa_context_provider_utils.py
View file

@ -39,10 +39,22 @@ def _insight_to_string(triplet: tuple) -> str:
return ""
node1_name = node1["name"] if "name" in node1 else "N/A"
node1_description = node1["description"] if "description" in node1 else node1["text"]
node1_description = (
node1["description"]
if "description" in node1
else node1["text"]
if "text" in node1
else "N/A"
)
node1_string = f"name: {node1_name}, description: {node1_description}"
node2_name = node2["name"] if "name" in node2 else "N/A"
node2_description = node2["description"] if "description" in node2 else node2["text"]
node2_description = (
node2["description"]
if "description" in node2
else node2["text"]
if "text" in node2
else "N/A"
)
node2_string = f"name: {node2_name}, description: {node2_description}"
edge_string = edge.get("relationship_name", "")
@ -58,7 +70,7 @@ def _insight_to_string(triplet: tuple) -> str:
async def get_context_with_cognee(
instance: dict,
task_indices: list[int] = None,
search_types: list[SearchType] = [SearchType.SUMMARIES, SearchType.CHUNKS],
search_types: list[SearchType] = [SearchType.INSIGHTS, SearchType.SUMMARIES, SearchType.CHUNKS],
) -> str:
await cognify_instance(instance, task_indices)

6
evals/qa_eval_parameters.json
View file

@ -14,6 +14,10 @@
],
"metric_names": [
"Correctness",
"Comprehensiveness"
"Comprehensiveness",
"Directness",
"Diversity",
"Empowerment",
"promptfoo.directness"
]
}

9
evals/run_qa_eval.py
View file

@ -22,17 +22,12 @@ async def run_evals_on_paramset(paramset: dict, out_path: str):
if rag_option == "cognee_incremental":
result = await incremental_eval_on_QA_dataset(
dataset,
num_samples,
paramset["metric_names"],
dataset, num_samples, paramset["metric_names"], out_path
)
results[dataset][num_samples] |= result
else:
result = await eval_on_QA_dataset(
dataset,
rag_option,
num_samples,
paramset["metric_names"],
dataset, rag_option, num_samples, paramset["metric_names"], out_path
)
results[dataset][num_samples][rag_option] = result

515
poetry.lock generated
View file

@ -168,13 +168,13 @@ docs = ["sphinx (==7.2.6)", "sphinx-mdinclude (==0.5.3)"]
[[package]]
name = "alembic"
version = "1.14.0"
version = "1.14.1"
description = "A database migration tool for SQLAlchemy."
optional = false
python-versions = ">=3.8"
files = [
{file = "alembic-1.14.0-py3-none-any.whl", hash = "sha256:99bd884ca390466db5e27ffccff1d179ec5c05c965cfefc0607e69f9e411cb25"},
{file = "alembic-1.14.0.tar.gz", hash = "sha256:b00892b53b3642d0b8dbedba234dbf1924b69be83a9a769d5a624b01094e304b"},
{file = "alembic-1.14.1-py3-none-any.whl", hash = "sha256:1acdd7a3a478e208b0503cd73614d5e4c6efafa4e73518bb60e4f2846a37b1c5"},
{file = "alembic-1.14.1.tar.gz", hash = "sha256:496e888245a53adf1498fcab31713a469c65836f8de76e01399aa1c3e90dd213"},
]
[package.dependencies]
@ -183,7 +183,7 @@ SQLAlchemy = ">=1.3.0"
typing-extensions = ">=4"
[package.extras]
tz = ["backports.zoneinfo"]
tz = ["backports.zoneinfo", "tzdata"]
[[package]]
name = "annotated-types"
@ -609,17 +609,17 @@ xyzservices = ">=2021.09.1"
[[package]]
name = "boto3"
version = "1.36.0"
version = "1.36.2"
description = "The AWS SDK for Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "boto3-1.36.0-py3-none-any.whl", hash = "sha256:d0ca7a58ce25701a52232cc8df9d87854824f1f2964b929305722ebc7959d5a9"},
{file = "boto3-1.36.0.tar.gz", hash = "sha256:159898f51c2997a12541c0e02d6e5a8fe2993ddb307b9478fd9a339f98b57e00"},
{file = "boto3-1.36.2-py3-none-any.whl", hash = "sha256:76cfc9a705be46e8d22607efacc8d688c064f923d785a01c00b28e9a96425d1a"},
{file = "boto3-1.36.2.tar.gz", hash = "sha256:fde1c29996b77274a60b7bc9f741525afa6267bb1716eb644a764fb7c124a0d2"},
]
[package.dependencies]
botocore = ">=1.36.0,<1.37.0"
botocore = ">=1.36.2,<1.37.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.11.0,<0.12.0"
@ -628,13 +628,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
version = "1.36.0"
version = "1.36.2"
description = "Low-level, data-driven core of boto 3."
optional = false
python-versions = ">=3.8"
files = [
{file = "botocore-1.36.0-py3-none-any.whl", hash = "sha256:b54b11f0cfc47fc1243ada0f7f461266c279968487616720fa8ebb02183917d7"},
{file = "botocore-1.36.0.tar.gz", hash = "sha256:0232029ff9ae3f5b50cdb25cbd257c16f87402b6d31a05bd6483638ee6434c4b"},
{file = "botocore-1.36.2-py3-none-any.whl", hash = "sha256:bc3b7e3b573a48af2bd7116b80fe24f9a335b0b67314dcb2697a327d009abf29"},
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[[package]]
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fastembed = ["fastembed (==0.5.0)"]
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fastembed = ["fastembed (==0.5.1)"]
fastembed-gpu = ["fastembed-gpu (==0.5.1)"]
[[package]]
name = "rapidfuzz"
@ -6822,19 +6838,19 @@ ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==23.2.1)", "requests (>=2.31.0)"
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version = "0.11.1"
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crt = ["botocore[crt] (>=1.36.0,<2.0a.0)"]
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[[package]]
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name = "validators"
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version = "20.29.1"
description = "Virtual Python Environment builder"
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[metadata]
lock-version = "2.0"
python-versions = ">=3.10.0,<3.13"
content-hash = "b74880407c173a0b15631d3f2197e2f66cc72fbe1d80f93b20140c8779e2bcc2"
content-hash = "585d4ecc16fcc18370d9729046baef7b3b02f92a4860b7f7f7be2d1a26654127"

4
pyproject.toml
View file

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pydantic = "2.8.2"
pydantic = "2.10.5"
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requests = "2.32.3"
aiohttp = "3.10.10"
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