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Merge branch 'dev' into cog-1069-update-notebooks-evals

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alekszievr 2025-01-23 20:32:30 +01:00 • committed by GitHub
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45
cognee-mcp/README.md
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@ -1,31 +1,19 @@
# cognee MCP server # cognee MCP server
### Installing Manually ### Installing Manually
A MCP server project A MCP server project
======= =======
1. Clone the [cognee](https://github.com/topoteretes/cognee) repo 1. Clone the [cognee](https://github.com/topoteretes/cognee) repo
2. Install dependencies 2. Install dependencies
``` ```
pip install uv brew install uv
```
```
brew install postgresql
```
```
brew install rust
``` ```
```jsx ```jsx
cd cognee-mcp cd cognee-mcp
uv sync --dev --all-extras uv sync --dev --all-extras --reinstall
``` ```
3. Activate the venv with 3. Activate the venv with
@ -48,8 +36,6 @@ nano claude_desktop_config.json
``` ```
``` ```
{ {
"mcpServers": { "mcpServers": {
"cognee": { "cognee": {
@ -65,16 +51,7 @@ nano claude_desktop_config.json
"TOKENIZERS_PARALLELISM": "false", "TOKENIZERS_PARALLELISM": "false",
"LLM_API_KEY": "sk-" "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: To use debugger, run:
```bash ```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: 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. npx @modelcontextprotocol/inspector uv --directory /Users/vasilije/cognee/cognee-mcp run cognee
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" requires-python = ">=3.10"
dependencies = [ 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", "cognee",
"mcp==1.2.0",
] ]
[[project.authors]] [[project.authors]]
@ -80,16 +15,14 @@ name = "Rita Aleksziev"
email = "rita@topoteretes.com" email = "rita@topoteretes.com"
[build-system] [build-system]
requires = [ "hatchling",] requires = [ "hatchling", ]
build-backend = "hatchling.build" build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["src"]
[tool.uv.sources] [tool.uv.sources]
cognee = { path = "../../cognee" } cognee = { path = "../../cognee" }
[dependency-groups]
dev = [
"cognee",
]
[project.scripts] [project.scripts]
cognee = "cognee_mcp:main" cognee = "src:main"

6
cognee-mcp/src/__init__.py Normal file
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@ -0,0 +1,6 @@
from .server import mcp
def main():
"""Main entry point for the package."""
mcp.run(transport="stdio")

45
cognee-mcp/src/client.py Normal file
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@ -0,0 +1,45 @@
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())

119
cognee-mcp/src/server.py Normal file
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@ -0,0 +1,119 @@
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

4
cognee/api/v1/add/add_v2.py
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@ -2,7 +2,7 @@ from typing import Union, BinaryIO
from cognee.modules.users.models import User from cognee.modules.users.models import User
from cognee.modules.users.methods import get_default_user from cognee.modules.users.methods import get_default_user
from cognee.modules.pipelines import run_tasks, Task from cognee.modules.pipelines import run_tasks, Task
from cognee.tasks.ingestion import ingest_data_with_metadata, resolve_data_directories from cognee.tasks.ingestion import ingest_data, resolve_data_directories
from cognee.infrastructure.databases.relational import ( from cognee.infrastructure.databases.relational import (
create_db_and_tables as create_relational_db_and_tables, create_db_and_tables as create_relational_db_and_tables,
) )
@ -22,7 +22,7 @@ async def add(
if user is None: if user is None:
user = await get_default_user() user = await get_default_user()
tasks = [Task(resolve_data_directories), Task(ingest_data_with_metadata, dataset_name, user)] tasks = [Task(resolve_data_directories), Task(ingest_data, dataset_name, user)]
pipeline = run_tasks(tasks, data, "add_pipeline") pipeline = run_tasks(tasks, data, "add_pipeline")

4
cognee/api/v1/cognify/code_graph_pipeline.py
View file

@ -10,7 +10,7 @@ from cognee.modules.users.methods import get_default_user
from cognee.shared.data_models import KnowledgeGraph, MonitoringTool from cognee.shared.data_models import KnowledgeGraph, MonitoringTool
from cognee.tasks.documents import classify_documents, extract_chunks_from_documents from cognee.tasks.documents import classify_documents, extract_chunks_from_documents
from cognee.tasks.graph import extract_graph_from_data from cognee.tasks.graph import extract_graph_from_data
from cognee.tasks.ingestion import ingest_data_with_metadata from cognee.tasks.ingestion import ingest_data
from cognee.tasks.repo_processor import ( from cognee.tasks.repo_processor import (
enrich_dependency_graph, enrich_dependency_graph,
expand_dependency_graph, expand_dependency_graph,
@ -68,7 +68,7 @@ async def run_code_graph_pipeline(repo_path, include_docs=True):
if include_docs: if include_docs:
non_code_tasks = [ non_code_tasks = [
Task(get_non_py_files, task_config={"batch_size": 50}), Task(get_non_py_files, task_config={"batch_size": 50}),
Task(ingest_data_with_metadata, dataset_name="repo_docs", user=user), Task(ingest_data, dataset_name="repo_docs", user=user),
Task(get_data_list_for_user, dataset_name="repo_docs", user=user), Task(get_data_list_for_user, dataset_name="repo_docs", user=user),
Task(classify_documents), Task(classify_documents),
Task(extract_chunks_from_documents, max_tokens=cognee_config.max_tokens), Task(extract_chunks_from_documents, max_tokens=cognee_config.max_tokens),

4
cognee/infrastructure/databases/graph/networkx/adapter.py
View file

@ -247,7 +247,7 @@ class NetworkXAdapter(GraphDBInterface):
if not file_path: if not file_path:
file_path = self.filename 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: async with aiofiles.open(file_path, "w") as file:
json_data = json.dumps(graph_data, cls=JSONEncoder) json_data = json.dumps(graph_data, cls=JSONEncoder)
@ -310,7 +310,7 @@ class NetworkXAdapter(GraphDBInterface):
edge["updated_at"], "%Y-%m-%dT%H:%M:%S.%f%z" 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): for node_id, node_data in self.graph.nodes(data=True):
node_data["id"] = node_id node_data["id"] = node_id

2
cognee/infrastructure/databases/vector/embeddings/LiteLLMEmbeddingEngine.py
View file

@ -95,7 +95,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
return await self.embed_text(text) return await self.embed_text(text)
except (litellm.exceptions.BadRequestError, litellm.llms.OpenAI.openai.OpenAIError): except litellm.exceptions.BadRequestError:
raise EmbeddingException("Failed to index data points.") raise EmbeddingException("Failed to index data points.")
except Exception as error: except Exception as error:

4
cognee/modules/chunking/TextChunker.py
View file

@ -54,7 +54,6 @@ class TextChunker:
contains=[], contains=[],
_metadata={ _metadata={
"index_fields": ["text"], "index_fields": ["text"],
"metadata_id": self.document.metadata_id,
}, },
) )
paragraph_chunks = [] paragraph_chunks = []
@ -74,7 +73,6 @@ class TextChunker:
contains=[], contains=[],
_metadata={ _metadata={
"index_fields": ["text"], "index_fields": ["text"],
"metadata_id": self.document.metadata_id,
}, },
) )
except Exception as e: except Exception as e:
@ -95,7 +93,7 @@ class TextChunker:
chunk_index=self.chunk_index, chunk_index=self.chunk_index,
cut_type=paragraph_chunks[len(paragraph_chunks) - 1]["cut_type"], cut_type=paragraph_chunks[len(paragraph_chunks) - 1]["cut_type"],
contains=[], contains=[],
_metadata={"index_fields": ["text"], "metadata_id": self.document.metadata_id}, _metadata={"index_fields": ["text"]},
) )
except Exception as e: except Exception as e:
print(e) print(e)

14
cognee/modules/data/models/Data.py
View file

@ -1,13 +1,11 @@
from datetime import datetime, timezone from datetime import datetime, timezone
from typing import List
from uuid import uuid4 from uuid import uuid4
from sqlalchemy import UUID, Column, DateTime, String from sqlalchemy import UUID, Column, DateTime, String, JSON
from sqlalchemy.orm import Mapped, relationship from sqlalchemy.orm import relationship
from cognee.infrastructure.databases.relational import Base from cognee.infrastructure.databases.relational import Base
from .DatasetData import DatasetData from .DatasetData import DatasetData
from .Metadata import Metadata
class Data(Base): class Data(Base):
@ -21,6 +19,7 @@ class Data(Base):
raw_data_location = Column(String) raw_data_location = Column(String)
owner_id = Column(UUID, index=True) owner_id = Column(UUID, index=True)
content_hash = Column(String) content_hash = Column(String)
external_metadata = Column(JSON)
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc)) created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
updated_at = Column(DateTime(timezone=True), onupdate=lambda: datetime.now(timezone.utc)) updated_at = Column(DateTime(timezone=True), onupdate=lambda: datetime.now(timezone.utc))
@ -32,13 +31,6 @@ class Data(Base):
cascade="all, delete", cascade="all, delete",
) )
metadata_relationship = relationship(
"Metadata",
back_populates="data",
lazy="noload",
cascade="all, delete",
)
def to_json(self) -> dict: def to_json(self) -> dict:
return { return {
"id": str(self.id), "id": str(self.id),

21
cognee/modules/data/models/Metadata.py
View file

@ -1,21 +0,0 @@
from datetime import datetime, timezone
from uuid import uuid4
from sqlalchemy import UUID, Column, DateTime, String, ForeignKey
from sqlalchemy.orm import relationship
from cognee.infrastructure.databases.relational import Base
class Metadata(Base):
__tablename__ = "metadata_table"
id = Column(UUID, primary_key=True, default=uuid4)
metadata_repr = Column(String)
metadata_source = Column(String)
created_at = Column(DateTime(timezone=True), default=lambda: datetime.now(timezone.utc))
updated_at = Column(DateTime(timezone=True), onupdate=lambda: datetime.now(timezone.utc))
data_id = Column(UUID, ForeignKey("data.id", ondelete="CASCADE"), primary_key=False)
data = relationship("Data", back_populates="metadata_relationship")

19
cognee/modules/data/operations/delete_metadata.py
View file

@ -1,19 +0,0 @@
import warnings
from uuid import UUID
from sqlalchemy import select
from cognee.infrastructure.databases.relational import get_relational_engine
from ..models.Metadata import Metadata
async def delete_metadata(metadata_id: UUID):
db_engine = get_relational_engine()
async with db_engine.get_async_session() as session:
metadata = await session.get(Metadata, metadata_id)
if metadata is None:
warnings.warn(f"metadata for metadata_id: {metadata_id} not found")
session.delete(metadata)
session.commit()

17
cognee/modules/data/operations/get_metadata.py
View file

@ -1,17 +0,0 @@
import json
from uuid import UUID
from sqlalchemy import select
from cognee.infrastructure.databases.relational import get_relational_engine
from ..models.Metadata import Metadata
async def get_metadata(metadata_id: UUID) -> Metadata:
db_engine = get_relational_engine()
async with db_engine.get_async_session() as session:
metadata = await session.get(Metadata, metadata_id)
return metadata

65
cognee/modules/data/operations/write_metadata.py
View file

@ -1,65 +0,0 @@
import inspect
import json
import re
import warnings
from uuid import UUID
from sqlalchemy import select
from typing import Any, BinaryIO, Union
from cognee.infrastructure.databases.relational import get_relational_engine
from cognee.infrastructure.files.utils.get_file_metadata import FileMetadata
from ..models.Metadata import Metadata
async def write_metadata(
data_item: Union[BinaryIO, str, Any], data_id: UUID, file_metadata: FileMetadata
) -> UUID:
metadata_dict = get_metadata_dict(data_item, file_metadata)
db_engine = get_relational_engine()
async with db_engine.get_async_session() as session:
metadata = (
await session.execute(select(Metadata).filter(Metadata.data_id == data_id))
).scalar_one_or_none()
if metadata is not None:
metadata.metadata_repr = json.dumps(metadata_dict)
metadata.metadata_source = parse_type(type(data_item))
await session.merge(metadata)
else:
metadata = Metadata(
id=data_id,
metadata_repr=json.dumps(metadata_dict),
metadata_source=parse_type(type(data_item)),
data_id=data_id,
)
session.add(metadata)
await session.commit()
def parse_type(type_: Any) -> str:
pattern = r".+'([\w_\.]+)'"
match = re.search(pattern, str(type_))
if match:
return match.group(1)
else:
raise Exception(f"type: {type_} could not be parsed")
def get_metadata_dict(
data_item: Union[BinaryIO, str, Any], file_metadata: FileMetadata
) -> dict[str, Any]:
if isinstance(data_item, str):
return file_metadata
elif isinstance(data_item, BinaryIO):
return file_metadata
elif hasattr(data_item, "dict") and inspect.ismethod(getattr(data_item, "dict")):
return {**file_metadata, **data_item.dict()}
else:
warnings.warn(
f"metadata of type {type(data_item)}: {str(data_item)[:20]}... does not have dict method. Defaulting to string method"
)
try:
return {**dict(file_metadata), "content": str(data_item)}
except Exception as e:
raise Exception(f"Could not cast metadata to string: {e}")

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

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

5
cognee/tasks/documents/classify_documents.py
View file

@ -1,4 +1,5 @@
from cognee.modules.data.models import Data from cognee.modules.data.models import Data
import json
from cognee.modules.data.processing.document_types import ( from cognee.modules.data.processing.document_types import (
Document, Document,
PdfDocument, PdfDocument,
@ -7,7 +8,6 @@ from cognee.modules.data.processing.document_types import (
TextDocument, TextDocument,
UnstructuredDocument, UnstructuredDocument,
) )
from cognee.modules.data.operations.get_metadata import get_metadata
EXTENSION_TO_DOCUMENT_CLASS = { EXTENSION_TO_DOCUMENT_CLASS = {
"pdf": PdfDocument, # Text documents "pdf": PdfDocument, # Text documents
@ -59,14 +59,13 @@ async def classify_documents(data_documents: list[Data]) -> list[Document]:
""" """
documents = [] documents = []
for data_item in data_documents: for data_item in data_documents:
metadata = await get_metadata(data_item.id)
document = EXTENSION_TO_DOCUMENT_CLASS[data_item.extension]( document = EXTENSION_TO_DOCUMENT_CLASS[data_item.extension](
id=data_item.id, id=data_item.id,
title=f"{data_item.name}.{data_item.extension}", title=f"{data_item.name}.{data_item.extension}",
raw_data_location=data_item.raw_data_location, raw_data_location=data_item.raw_data_location,
name=data_item.name, name=data_item.name,
mime_type=data_item.mime_type, mime_type=data_item.mime_type,
metadata_id=metadata.id, external_metadata=json.dumps(data_item.external_metadata, indent=4),
) )
documents.append(document) documents.append(document)

5
cognee/tasks/ingestion/__init__.py
View file

@ -1,6 +1,3 @@
from .ingest_data import ingest_data
from .save_data_to_storage import save_data_to_storage
from .save_data_item_to_storage import save_data_item_to_storage from .save_data_item_to_storage import save_data_item_to_storage
from .save_data_item_with_metadata_to_storage import save_data_item_with_metadata_to_storage from .ingest_data import ingest_data
from .ingest_data_with_metadata import ingest_data_with_metadata
from .resolve_data_directories import resolve_data_directories from .resolve_data_directories import resolve_data_directories

137
cognee/tasks/ingestion/ingest_data.py
View file

@ -1,16 +1,24 @@
from typing import Any, List
import dlt import dlt
import cognee.modules.ingestion as ingestion import cognee.modules.ingestion as ingestion
from uuid import UUID
from cognee.shared.utils import send_telemetry
from cognee.modules.users.models import User
from cognee.infrastructure.databases.relational import get_relational_engine from cognee.infrastructure.databases.relational import get_relational_engine
from cognee.modules.data.methods import create_dataset from cognee.modules.data.methods import create_dataset
from cognee.modules.data.models.DatasetData import DatasetData
from cognee.modules.users.models import User
from cognee.modules.users.permissions.methods import give_permission_on_document from cognee.modules.users.permissions.methods import give_permission_on_document
from cognee.shared.utils import send_telemetry
from .get_dlt_destination import get_dlt_destination from .get_dlt_destination import get_dlt_destination
from .save_data_item_to_storage import (
save_data_item_to_storage,
)
from typing import Union, BinaryIO
import inspect
import warnings
async def ingest_data(file_paths: list[str], dataset_name: str, user: User): async def ingest_data(data: Any, dataset_name: str, user: User):
destination = get_dlt_destination() destination = get_dlt_destination()
pipeline = dlt.pipeline( pipeline = dlt.pipeline(
@ -18,12 +26,21 @@ async def ingest_data(file_paths: list[str], dataset_name: str, user: User):
destination=destination, destination=destination,
) )
@dlt.resource(standalone=True, merge_key="id") def get_external_metadata_dict(data_item: Union[BinaryIO, str, Any]) -> dict[str, Any]:
async def data_resources(file_paths: str): if hasattr(data_item, "dict") and inspect.ismethod(getattr(data_item, "dict")):
return {"metadata": data_item.dict(), "origin": str(type(data_item))}
else:
warnings.warn(
f"Data of type {type(data_item)}... does not have dict method. Returning empty metadata."
)
return {}
@dlt.resource(standalone=True, primary_key="id", merge_key="id")
async def data_resources(file_paths: List[str], user: User):
for file_path in file_paths: for file_path in file_paths:
with open(file_path.replace("file://", ""), mode="rb") as file: with open(file_path.replace("file://", ""), mode="rb") as file:
classified_data = ingestion.classify(file) classified_data = ingestion.classify(file)
data_id = ingestion.identify(classified_data) data_id = ingestion.identify(classified_data, user)
file_metadata = classified_data.get_metadata() file_metadata = classified_data.get_metadata()
yield { yield {
"id": data_id, "id": data_id,
@ -31,71 +48,111 @@ async def ingest_data(file_paths: list[str], dataset_name: str, user: User):
"file_path": file_metadata["file_path"], "file_path": file_metadata["file_path"],
"extension": file_metadata["extension"], "extension": file_metadata["extension"],
"mime_type": file_metadata["mime_type"], "mime_type": file_metadata["mime_type"],
"content_hash": file_metadata["content_hash"],
"owner_id": str(user.id),
} }
async def data_storing(table_name, dataset_name, user: User): async def data_storing(data: Any, dataset_name: str, user: User):
db_engine = get_relational_engine() if not isinstance(data, list):
# Convert data to a list as we work with lists further down.
data = [data]
file_paths = []
# Process data
for data_item in data:
file_path = await save_data_item_to_storage(data_item, dataset_name)
file_paths.append(file_path)
# Ingest data and add metadata
with open(file_path.replace("file://", ""), mode="rb") as file:
classified_data = ingestion.classify(file)
# data_id is the hash of file contents + owner id to avoid duplicate data
data_id = ingestion.identify(classified_data, user)
file_metadata = classified_data.get_metadata()
async with db_engine.get_async_session() as session:
# Read metadata stored with dlt
files_metadata = await db_engine.get_all_data_from_table(table_name, dataset_name)
for file_metadata in files_metadata:
from sqlalchemy import select from sqlalchemy import select
from cognee.modules.data.models import Data from cognee.modules.data.models import Data
dataset = await create_dataset(dataset_name, user.id, session) db_engine = get_relational_engine()
data = ( async with db_engine.get_async_session() as session:
await session.execute(select(Data).filter(Data.id == UUID(file_metadata["id"]))) dataset = await create_dataset(dataset_name, user.id, session)
).scalar_one_or_none()
if data is not None: # Check to see if data should be updated
data.name = file_metadata["name"] data_point = (
data.raw_data_location = file_metadata["file_path"] await session.execute(select(Data).filter(Data.id == data_id))
data.extension = file_metadata["extension"] ).scalar_one_or_none()
data.mime_type = file_metadata["mime_type"]
await session.merge(data) if data_point is not None:
await session.commit() data_point.name = file_metadata["name"]
else: data_point.raw_data_location = file_metadata["file_path"]
data = Data( data_point.extension = file_metadata["extension"]
id=UUID(file_metadata["id"]), data_point.mime_type = file_metadata["mime_type"]
name=file_metadata["name"], data_point.owner_id = user.id
raw_data_location=file_metadata["file_path"], data_point.content_hash = file_metadata["content_hash"]
extension=file_metadata["extension"], data_point.external_metadata = (get_external_metadata_dict(data_item),)
mime_type=file_metadata["mime_type"], await session.merge(data_point)
) else:
data_point = Data(
id=data_id,
name=file_metadata["name"],
raw_data_location=file_metadata["file_path"],
extension=file_metadata["extension"],
mime_type=file_metadata["mime_type"],
owner_id=user.id,
content_hash=file_metadata["content_hash"],
external_metadata=get_external_metadata_dict(data_item),
)
# Check if data is already in dataset
dataset_data = (
await session.execute(
select(DatasetData).filter(
DatasetData.data_id == data_id, DatasetData.dataset_id == dataset.id
)
)
).scalar_one_or_none()
# If data is not present in dataset add it
if dataset_data is None:
dataset.data.append(data_point)
dataset.data.append(data)
await session.commit() await session.commit()
await give_permission_on_document(user, UUID(file_metadata["id"]), "read") await give_permission_on_document(user, data_id, "read")
await give_permission_on_document(user, UUID(file_metadata["id"]), "write") await give_permission_on_document(user, data_id, "write")
return file_paths
send_telemetry("cognee.add EXECUTION STARTED", user_id=user.id) send_telemetry("cognee.add EXECUTION STARTED", user_id=user.id)
db_engine = get_relational_engine() db_engine = get_relational_engine()
file_paths = await data_storing(data, dataset_name, user)
# Note: DLT pipeline has its own event loop, therefore objects created in another event loop # Note: DLT pipeline has its own event loop, therefore objects created in another event loop
# can't be used inside the pipeline # can't be used inside the pipeline
if db_engine.engine.dialect.name == "sqlite": if db_engine.engine.dialect.name == "sqlite":
# To use sqlite with dlt dataset_name must be set to "main". # To use sqlite with dlt dataset_name must be set to "main".
# Sqlite doesn't support schemas # Sqlite doesn't support schemas
run_info = pipeline.run( run_info = pipeline.run(
data_resources(file_paths), data_resources(file_paths, user),
table_name="file_metadata", table_name="file_metadata",
dataset_name="main", dataset_name="main",
write_disposition="merge", write_disposition="merge",
) )
else: else:
# Data should be stored in the same schema to allow deduplication
run_info = pipeline.run( run_info = pipeline.run(
data_resources(file_paths), data_resources(file_paths, user),
table_name="file_metadata", table_name="file_metadata",
dataset_name=dataset_name, dataset_name="public",
write_disposition="merge", write_disposition="merge",
) )
await data_storing("file_metadata", dataset_name, user)
send_telemetry("cognee.add EXECUTION COMPLETED", user_id=user.id) send_telemetry("cognee.add EXECUTION COMPLETED", user_id=user.id)
return run_info return run_info

145
cognee/tasks/ingestion/ingest_data_with_metadata.py
View file

@ -1,145 +0,0 @@
from typing import Any, List
import dlt
import cognee.modules.ingestion as ingestion
from cognee.infrastructure.databases.relational import get_relational_engine
from cognee.modules.data.methods import create_dataset
from cognee.modules.data.models.DatasetData import DatasetData
from cognee.modules.users.models import User
from cognee.modules.users.permissions.methods import give_permission_on_document
from cognee.shared.utils import send_telemetry
from cognee.modules.data.operations.write_metadata import write_metadata
from .get_dlt_destination import get_dlt_destination
from .save_data_item_with_metadata_to_storage import (
save_data_item_with_metadata_to_storage,
)
async def ingest_data_with_metadata(data: Any, dataset_name: str, user: User):
destination = get_dlt_destination()
pipeline = dlt.pipeline(
pipeline_name="file_load_from_filesystem",
destination=destination,
)
@dlt.resource(standalone=True, primary_key="id", merge_key="id")
async def data_resources(file_paths: List[str], user: User):
for file_path in file_paths:
with open(file_path.replace("file://", ""), mode="rb") as file:
classified_data = ingestion.classify(file)
data_id = ingestion.identify(classified_data, user)
file_metadata = classified_data.get_metadata()
yield {
"id": data_id,
"name": file_metadata["name"],
"file_path": file_metadata["file_path"],
"extension": file_metadata["extension"],
"mime_type": file_metadata["mime_type"],
"content_hash": file_metadata["content_hash"],
"owner_id": str(user.id),
}
async def data_storing(data: Any, dataset_name: str, user: User):
if not isinstance(data, list):
# Convert data to a list as we work with lists further down.
data = [data]
file_paths = []
# Process data
for data_item in data:
file_path = await save_data_item_with_metadata_to_storage(data_item, dataset_name)
file_paths.append(file_path)
# Ingest data and add metadata
with open(file_path.replace("file://", ""), mode="rb") as file:
classified_data = ingestion.classify(file)
# data_id is the hash of file contents + owner id to avoid duplicate data
data_id = ingestion.identify(classified_data, user)
file_metadata = classified_data.get_metadata()
from sqlalchemy import select
from cognee.modules.data.models import Data
db_engine = get_relational_engine()
async with db_engine.get_async_session() as session:
dataset = await create_dataset(dataset_name, user.id, session)
# Check to see if data should be updated
data_point = (
await session.execute(select(Data).filter(Data.id == data_id))
).scalar_one_or_none()
if data_point is not None:
data_point.name = file_metadata["name"]
data_point.raw_data_location = file_metadata["file_path"]
data_point.extension = file_metadata["extension"]
data_point.mime_type = file_metadata["mime_type"]
data_point.owner_id = user.id
data_point.content_hash = file_metadata["content_hash"]
await session.merge(data_point)
else:
data_point = Data(
id=data_id,
name=file_metadata["name"],
raw_data_location=file_metadata["file_path"],
extension=file_metadata["extension"],
mime_type=file_metadata["mime_type"],
owner_id=user.id,
content_hash=file_metadata["content_hash"],
)
# Check if data is already in dataset
dataset_data = (
await session.execute(
select(DatasetData).filter(
DatasetData.data_id == data_id, DatasetData.dataset_id == dataset.id
)
)
).scalar_one_or_none()
# If data is not present in dataset add it
if dataset_data is None:
dataset.data.append(data_point)
await session.commit()
await write_metadata(data_item, data_point.id, file_metadata)
await give_permission_on_document(user, data_id, "read")
await give_permission_on_document(user, data_id, "write")
return file_paths
send_telemetry("cognee.add EXECUTION STARTED", user_id=user.id)
db_engine = get_relational_engine()
file_paths = await data_storing(data, dataset_name, user)
# Note: DLT pipeline has its own event loop, therefore objects created in another event loop
# can't be used inside the pipeline
if db_engine.engine.dialect.name == "sqlite":
# To use sqlite with dlt dataset_name must be set to "main".
# Sqlite doesn't support schemas
run_info = pipeline.run(
data_resources(file_paths, user),
table_name="file_metadata",
dataset_name="main",
write_disposition="merge",
)
else:
# Data should be stored in the same schema to allow deduplication
run_info = pipeline.run(
data_resources(file_paths, user),
table_name="file_metadata",
dataset_name="public",
write_disposition="merge",
)
send_telemetry("cognee.add EXECUTION COMPLETED", user_id=user.id)
return run_info

12
cognee/tasks/ingestion/save_data_item_to_storage.py
View file

@ -1,12 +1,18 @@
from typing import Union, BinaryIO from typing import Union, BinaryIO, Any
from cognee.modules.ingestion.exceptions import IngestionError from cognee.modules.ingestion.exceptions import IngestionError
from cognee.modules.ingestion import save_data_to_file from cognee.modules.ingestion import save_data_to_file
def save_data_item_to_storage(data_item: Union[BinaryIO, str], dataset_name: str) -> str: async def save_data_item_to_storage(data_item: Union[BinaryIO, str, Any], dataset_name: str) -> str:
if "llama_index" in str(type(data_item)):
# Dynamic import is used because the llama_index module is optional.
from .transform_data import get_data_from_llama_index
file_path = get_data_from_llama_index(data_item, dataset_name)
# data is a file object coming from upload. # data is a file object coming from upload.
if hasattr(data_item, "file"): elif hasattr(data_item, "file"):
file_path = save_data_to_file(data_item.file, filename=data_item.filename) file_path = save_data_to_file(data_item.file, filename=data_item.filename)
elif isinstance(data_item, str): elif isinstance(data_item, str):

30
cognee/tasks/ingestion/save_data_item_with_metadata_to_storage.py
View file

@ -1,30 +0,0 @@
from typing import Union, BinaryIO, Any
from cognee.modules.ingestion.exceptions import IngestionError
from cognee.modules.ingestion import save_data_to_file
async def save_data_item_with_metadata_to_storage(
data_item: Union[BinaryIO, str, Any], dataset_name: str
) -> str:
if "llama_index" in str(type(data_item)):
# Dynamic import is used because the llama_index module is optional.
from .transform_data import get_data_from_llama_index
file_path = get_data_from_llama_index(data_item, dataset_name)
# data is a file object coming from upload.
elif hasattr(data_item, "file"):
file_path = save_data_to_file(data_item.file, filename=data_item.filename)
elif isinstance(data_item, str):
# data is a file path
if data_item.startswith("file://") or data_item.startswith("/"):
file_path = data_item.replace("file://", "")
# data is text
else:
file_path = save_data_to_file(data_item)
else:
raise IngestionError(message=f"Data type not supported: {type(data_item)}")
return file_path

16
cognee/tasks/ingestion/save_data_to_storage.py
View file

@ -1,16 +0,0 @@
from typing import Union, BinaryIO
from cognee.tasks.ingestion.save_data_item_to_storage import save_data_item_to_storage
def save_data_to_storage(data: Union[BinaryIO, str], dataset_name) -> list[str]:
if not isinstance(data, list):
# Convert data to a list as we work with lists further down.
data = [data]
file_paths = []
for data_item in data:
file_path = save_data_item_to_storage(data_item, dataset_name)
file_paths.append(file_path)
return file_paths

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

@ -29,7 +29,7 @@ def test_AudioDocument():
id=uuid.uuid4(), id=uuid.uuid4(),
name="audio-dummy-test", name="audio-dummy-test",
raw_data_location="", raw_data_location="",
metadata_id=uuid.uuid4(), external_metadata="",
mime_type="", mime_type="",
) )
with patch.object(AudioDocument, "create_transcript", return_value=TEST_TEXT): 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(), id=uuid.uuid4(),
name="image-dummy-test", name="image-dummy-test",
raw_data_location="", raw_data_location="",
metadata_id=uuid.uuid4(), external_metadata="",
mime_type="", mime_type="",
) )
with patch.object(ImageDocument, "transcribe_image", return_value=TEST_TEXT): 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(), id=uuid.uuid4(),
name="Test document.pdf", name="Test document.pdf",
raw_data_location=test_file_path, raw_data_location=test_file_path,
metadata_id=uuid.uuid4(), external_metadata="",
mime_type="", 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(), id=uuid.uuid4(),
name=input_file, name=input_file,
raw_data_location=test_file_path, raw_data_location=test_file_path,
metadata_id=uuid.uuid4(), external_metadata="",
mime_type="", mime_type="",
) )

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

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

8
notebooks/cognee_llama_index.ipynb
View file

@ -118,10 +118,10 @@
] ]
}, },
{ {
"cell_type": "code",
"execution_count": null,
"metadata": {}, "metadata": {},
"cell_type": "code",
"outputs": [], "outputs": [],
"execution_count": null,
"source": [ "source": [
"from typing import Union, BinaryIO\n", "from typing import Union, BinaryIO\n",
"\n", "\n",
@ -133,7 +133,7 @@
")\n", ")\n",
"from cognee.modules.users.models import User\n", "from cognee.modules.users.models import User\n",
"from cognee.modules.users.methods import get_default_user\n", "from cognee.modules.users.methods import get_default_user\n",
"from cognee.tasks.ingestion.ingest_data_with_metadata import ingest_data_with_metadata\n", "from cognee.tasks.ingestion.ingest_data import ingest_data\n",
"import cognee\n", "import cognee\n",
"\n", "\n",
"# Create a clean slate for cognee -- reset data and system state\n", "# Create a clean slate for cognee -- reset data and system state\n",
@ -153,7 +153,7 @@
" if user is None:\n", " if user is None:\n",
" user = await get_default_user()\n", " user = await get_default_user()\n",
"\n", "\n",
" await ingest_data_with_metadata(data, dataset_name, user)\n", " await ingest_data(data, dataset_name, user)\n",
"\n", "\n",
"\n", "\n",
"await add(documents)\n", "await add(documents)\n",

521
poetry.lock generated
View file

@ -1,4 +1,4 @@
# This file is automatically @generated by Poetry 1.8.5 and should not be changed by hand. # This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand.
[[package]] [[package]]
name = "aiofiles" name = "aiofiles"
@ -168,13 +168,13 @@ docs = ["sphinx (==7.2.6)", "sphinx-mdinclude (==0.5.3)"]
[[package]] [[package]]
name = "alembic" name = "alembic"
version = "1.14.0" version = "1.14.1"
description = "A database migration tool for SQLAlchemy." description = "A database migration tool for SQLAlchemy."
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "alembic-1.14.0-py3-none-any.whl", hash = "sha256:99bd884ca390466db5e27ffccff1d179ec5c05c965cfefc0607e69f9e411cb25"}, {file = "alembic-1.14.1-py3-none-any.whl", hash = "sha256:1acdd7a3a478e208b0503cd73614d5e4c6efafa4e73518bb60e4f2846a37b1c5"},
{file = "alembic-1.14.0.tar.gz", hash = "sha256:b00892b53b3642d0b8dbedba234dbf1924b69be83a9a769d5a624b01094e304b"}, {file = "alembic-1.14.1.tar.gz", hash = "sha256:496e888245a53adf1498fcab31713a469c65836f8de76e01399aa1c3e90dd213"},
] ]
[package.dependencies] [package.dependencies]
@ -183,7 +183,7 @@ SQLAlchemy = ">=1.3.0"
typing-extensions = ">=4" typing-extensions = ">=4"
[package.extras] [package.extras]
tz = ["backports.zoneinfo"] tz = ["backports.zoneinfo", "tzdata"]
[[package]] [[package]]
name = "annotated-types" name = "annotated-types"
@ -609,17 +609,17 @@ xyzservices = ">=2021.09.1"
[[package]] [[package]]
name = "boto3" name = "boto3"
version = "1.36.0" version = "1.36.2"
description = "The AWS SDK for Python" description = "The AWS SDK for Python"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "boto3-1.36.0-py3-none-any.whl", hash = "sha256:d0ca7a58ce25701a52232cc8df9d87854824f1f2964b929305722ebc7959d5a9"}, {file = "boto3-1.36.2-py3-none-any.whl", hash = "sha256:76cfc9a705be46e8d22607efacc8d688c064f923d785a01c00b28e9a96425d1a"},
{file = "boto3-1.36.0.tar.gz", hash = "sha256:159898f51c2997a12541c0e02d6e5a8fe2993ddb307b9478fd9a339f98b57e00"}, {file = "boto3-1.36.2.tar.gz", hash = "sha256:fde1c29996b77274a60b7bc9f741525afa6267bb1716eb644a764fb7c124a0d2"},
] ]
[package.dependencies] [package.dependencies]
botocore = ">=1.36.0,<1.37.0" botocore = ">=1.36.2,<1.37.0"
jmespath = ">=0.7.1,<2.0.0" jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.11.0,<0.12.0" s3transfer = ">=0.11.0,<0.12.0"
@ -628,13 +628,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]] [[package]]
name = "botocore" name = "botocore"
version = "1.36.0" version = "1.36.2"
description = "Low-level, data-driven core of boto 3." description = "Low-level, data-driven core of boto 3."
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "botocore-1.36.0-py3-none-any.whl", hash = "sha256:b54b11f0cfc47fc1243ada0f7f461266c279968487616720fa8ebb02183917d7"}, {file = "botocore-1.36.2-py3-none-any.whl", hash = "sha256:bc3b7e3b573a48af2bd7116b80fe24f9a335b0b67314dcb2697a327d009abf29"},
{file = "botocore-1.36.0.tar.gz", hash = "sha256:0232029ff9ae3f5b50cdb25cbd257c16f87402b6d31a05bd6483638ee6434c4b"}, {file = "botocore-1.36.2.tar.gz", hash = "sha256:a1fe6610983f0214b0c7655fe6990b6a731746baf305b182976fc7b568fc3cb0"},
] ]
[package.dependencies] [package.dependencies]
@ -1246,13 +1246,13 @@ optimize = ["orjson"]
[[package]] [[package]]
name = "deepeval" name = "deepeval"
version = "2.1.7" version = "2.1.9"
description = "The Open-Source LLM Evaluation Framework." description = "The Open-Source LLM Evaluation Framework."
optional = true optional = true
python-versions = "<3.13,>=3.9" python-versions = "<3.13,>=3.9"
files = [ files = [
{file = "deepeval-2.1.7-py3-none-any.whl", hash = "sha256:ca0ce48067e4fc9e405c13abcf4187b8a1ff94d61a0b22daf8011e72f8ba1b65"}, {file = "deepeval-2.1.9-py3-none-any.whl", hash = "sha256:c225f8ab6ab910de50026dfd46e2ea38541b3697b189831482a6f02162ead536"},
{file = "deepeval-2.1.7.tar.gz", hash = "sha256:ba71e568339a274246cb00327d25704e75438a76c5f22540af7bd843b2a0762a"}, {file = "deepeval-2.1.9.tar.gz", hash = "sha256:b6c9e90fd0ab639c5b0af5023f2e3fd20ce1906b05d7dc9bfc0bd2f46d0545e0"},
] ]
[package.dependencies] [package.dependencies]
@ -2138,13 +2138,13 @@ test = ["objgraph", "psutil"]
[[package]] [[package]]
name = "griffe" name = "griffe"
version = "1.5.4" version = "1.5.5"
description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API." description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
optional = false optional = false
python-versions = ">=3.9" python-versions = ">=3.9"
files = [ files = [
{file = "griffe-1.5.4-py3-none-any.whl", hash = "sha256:ed33af890586a5bebc842fcb919fc694b3dc1bc55b7d9e0228de41ce566b4a1d"}, {file = "griffe-1.5.5-py3-none-any.whl", hash = "sha256:2761b1e8876c6f1f9ab1af274df93ea6bbadd65090de5f38f4cb5cc84897c7dd"},
{file = "griffe-1.5.4.tar.gz", hash = "sha256:073e78ad3e10c8378c2f798bd4ef87b92d8411e9916e157fd366a17cc4fd4e52"}, {file = "griffe-1.5.5.tar.gz", hash = "sha256:35ee5b38b93d6a839098aad0f92207e6ad6b70c3e8866c08ca669275b8cba585"},
] ]
[package.dependencies] [package.dependencies]
@ -3379,13 +3379,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
[[package]] [[package]]
name = "langchain-core" name = "langchain-core"
version = "0.3.29" version = "0.3.30"
description = "Building applications with LLMs through composability" description = "Building applications with LLMs through composability"
optional = true optional = true
python-versions = "<4.0,>=3.9" python-versions = "<4.0,>=3.9"
files = [ files = [
{file = "langchain_core-0.3.29-py3-none-any.whl", hash = "sha256:817db1474871611a81105594a3e4d11704949661008e455a10e38ca9ff601a1a"}, {file = "langchain_core-0.3.30-py3-none-any.whl", hash = "sha256:0a4c4e02fac5968b67fbb0142c00c2b976c97e45fce62c7ac9eb1636a6926493"},
{file = "langchain_core-0.3.29.tar.gz", hash = "sha256:773d6aeeb612e7ce3d996c0be403433d8c6a91e77bbb7a7461c13e15cfbe5b06"}, {file = "langchain_core-0.3.30.tar.gz", hash = "sha256:0f1281b4416977df43baf366633ad18e96c5dcaaeae6fcb8a799f9889c853243"},
] ]
[package.dependencies] [package.dependencies]
@ -3402,17 +3402,17 @@ typing-extensions = ">=4.7"
[[package]] [[package]]
name = "langchain-openai" name = "langchain-openai"
version = "0.3.0" version = "0.3.1"
description = "An integration package connecting OpenAI and LangChain" description = "An integration package connecting OpenAI and LangChain"
optional = true optional = true
python-versions = "<4.0,>=3.9" python-versions = "<4.0,>=3.9"
files = [ files = [
{file = "langchain_openai-0.3.0-py3-none-any.whl", hash = "sha256:49c921a22d272b04749a61e78bffa83aecdb8840b24b69f2909e115a357a9a5b"}, {file = "langchain_openai-0.3.1-py3-none-any.whl", hash = "sha256:5cf2a1e115b12570158d89c22832fa381803c3e1e11d1eb781195c8d9e454bd5"},
{file = "langchain_openai-0.3.0.tar.gz", hash = "sha256:88d623eeb2aaa1fff65c2b419a4a1cfd37d3a1d504e598b87cf0bc822a3b70d0"}, {file = "langchain_openai-0.3.1.tar.gz", hash = "sha256:cce314f1437b2cad73e0ed2b55e74dc399bc1bbc43594c4448912fb51c5e4447"},
] ]
[package.dependencies] [package.dependencies]
langchain-core = ">=0.3.29,<0.4.0" langchain-core = ">=0.3.30,<0.4.0"
openai = ">=1.58.1,<2.0.0" openai = ">=1.58.1,<2.0.0"
tiktoken = ">=0.7,<1" tiktoken = ">=0.7,<1"
@ -3446,13 +3446,13 @@ six = "*"
[[package]] [[package]]
name = "langfuse" name = "langfuse"
version = "2.57.10" version = "2.57.11"
description = "A client library for accessing langfuse" description = "A client library for accessing langfuse"
optional = false optional = false
python-versions = "<4.0,>=3.9" python-versions = "<4.0,>=3.9"
files = [ files = [
{file = "langfuse-2.57.10-py3-none-any.whl", hash = "sha256:db7e8f7cf8d0204e17074e6729b144e7f9c7198499cd84a824bbc81fb5e37e4a"}, {file = "langfuse-2.57.11-py3-none-any.whl", hash = "sha256:c9a074c68de62b7a7b144c02577a1a124df84274f13c80488f077147e93d6e78"},
{file = "langfuse-2.57.10.tar.gz", hash = "sha256:751dd03271809f4bf50f6e4e0d1138b0e0eb028efefc984fdc6948d2bfddd95d"}, {file = "langfuse-2.57.11.tar.gz", hash = "sha256:f1c220decdd9c858fb58916af1775ac999836859553c6ffef33ebf2197030697"},
] ]
[package.dependencies] [package.dependencies]
@ -3524,13 +3524,13 @@ proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "
[[package]] [[package]]
name = "llama-index-core" name = "llama-index-core"
version = "0.12.11" version = "0.12.12"
description = "Interface between LLMs and your data" description = "Interface between LLMs and your data"
optional = true optional = true
python-versions = "<4.0,>=3.9" python-versions = "<4.0,>=3.9"
files = [ files = [
{file = "llama_index_core-0.12.11-py3-none-any.whl", hash = "sha256:3b1e019c899e9e011dfa01c96b7e3f666e0c161035fbca6cb787b4c61e0c94db"}, {file = "llama_index_core-0.12.12-py3-none-any.whl", hash = "sha256:cea491e87f65e6b775b5aef95720de302b85af1bdc67d779c4b09170a30e5b98"},
{file = "llama_index_core-0.12.11.tar.gz", hash = "sha256:9a41ca91167ea5eec9ebaac7f5e958b7feddbd8af3bfbf7c393a5edfb994d566"}, {file = "llama_index_core-0.12.12.tar.gz", hash = "sha256:068b755bbc681731336e822f5977d7608585e8f759c6293ebd812e2659316a37"},
] ]
[package.dependencies] [package.dependencies]
@ -4067,13 +4067,13 @@ pyyaml = ">=5.1"
[[package]] [[package]]
name = "mkdocs-material" name = "mkdocs-material"
version = "9.5.49" version = "9.5.50"
description = "Documentation that simply works" description = "Documentation that simply works"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "mkdocs_material-9.5.49-py3-none-any.whl", hash = "sha256:c3c2d8176b18198435d3a3e119011922f3e11424074645c24019c2dcf08a360e"}, {file = "mkdocs_material-9.5.50-py3-none-any.whl", hash = "sha256:f24100f234741f4d423a9d672a909d859668a4f404796be3cf035f10d6050385"},
{file = "mkdocs_material-9.5.49.tar.gz", hash = "sha256:3671bb282b4f53a1c72e08adbe04d2481a98f85fed392530051f80ff94a9621d"}, {file = "mkdocs_material-9.5.50.tar.gz", hash = "sha256:ae5fe16f3d7c9ccd05bb6916a7da7420cf99a9ce5e33debd9d40403a090d5825"},
] ]
[package.dependencies] [package.dependencies]
@ -4090,7 +4090,7 @@ regex = ">=2022.4"
requests = ">=2.26,<3.0" requests = ">=2.26,<3.0"
[package.extras] [package.extras]
git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2.4,<2.0)"] git = ["mkdocs-git-committers-plugin-2 (>=1.1,<3)", "mkdocs-git-revision-date-localized-plugin (>=1.2.4,<2.0)"]
imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=10.2,<11.0)"] imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=10.2,<11.0)"]
recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"] recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"]
@ -4810,86 +4810,90 @@ files = [
[[package]] [[package]]
name = "orjson" name = "orjson"
version = "3.10.14" version = "3.10.15"
description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy" description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
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{version = ">=0.3.7", markers = "python_version >= \"3.12\""},
{version = ">=0.3.6", markers = "python_version >= \"3.11\" and python_version < \"3.12\""}, {version = ">=0.3.6", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
{version = ">=0.3.7", markers = "python_version >= \"3.12\""},
] ]
isort = ">=4.2.5,<5.13.0 || >5.13.0,<6" isort = ">=4.2.5,<5.13.0 || >5.13.0,<6"
mccabe = ">=0.6,<0.8" mccabe = ">=0.6,<0.8"
@ -6465,13 +6481,13 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
[[package]] [[package]]
name = "qdrant-client" name = "qdrant-client"
version = "1.12.2" version = "1.13.0"
description = "Client library for the Qdrant vector search engine" description = "Client library for the Qdrant vector search engine"
optional = true optional = true
python-versions = ">=3.9" python-versions = ">=3.9"
files = [ files = [
{file = "qdrant_client-1.12.2-py3-none-any.whl", hash = "sha256:a0ae500a46a679ff3521ba3f1f1cf3d72b57090a768cec65fc317066bcbac1e6"}, {file = "qdrant_client-1.13.0-py3-none-any.whl", hash = "sha256:63a063d5232618b609f2c438caf6f3afd3bd110dd80d01be20c596e516efab6b"},
{file = "qdrant_client-1.12.2.tar.gz", hash = "sha256:2777e09b3e89bb22bb490384d8b1fa8140f3915287884f18984f7031a346aba5"}, {file = "qdrant_client-1.13.0.tar.gz", hash = "sha256:9708e3194081619b38194c99e7c369064e3f3f328d8a8ef1d71a87425a5ddf0c"},
] ]
[package.dependencies] [package.dependencies]
@ -6487,8 +6503,8 @@ pydantic = ">=1.10.8"
urllib3 = ">=1.26.14,<3" urllib3 = ">=1.26.14,<3"
[package.extras] [package.extras]
fastembed = ["fastembed (==0.5.0)"] fastembed = ["fastembed (==0.5.1)"]
fastembed-gpu = ["fastembed-gpu (==0.5.0)"] fastembed-gpu = ["fastembed-gpu (==0.5.1)"]
[[package]] [[package]]
name = "rapidfuzz" name = "rapidfuzz"
@ -6610,19 +6626,19 @@ ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==23.2.1)", "requests (>=2.31.0)"
[[package]] [[package]]
name = "referencing" name = "referencing"
version = "0.36.0" version = "0.36.1"
description = "JSON Referencing + Python" description = "JSON Referencing + Python"
optional = false optional = false
python-versions = ">=3.9" python-versions = ">=3.9"
files = [ files = [
{file = "referencing-0.36.0-py3-none-any.whl", hash = "sha256:01fc2916bab821aa3284d645bbbb41ba39609e7ff47072416a39ec2fb04d10d9"}, {file = "referencing-0.36.1-py3-none-any.whl", hash = "sha256:363d9c65f080d0d70bc41c721dce3c7f3e77fc09f269cd5c8813da18069a6794"},
{file = "referencing-0.36.0.tar.gz", hash = "sha256:246db964bb6101905167895cd66499cfb2aabc5f83277d008c52afe918ef29ba"}, {file = "referencing-0.36.1.tar.gz", hash = "sha256:ca2e6492769e3602957e9b831b94211599d2aade9477f5d44110d2530cf9aade"},
] ]
[package.dependencies] [package.dependencies]
attrs = ">=22.2.0" attrs = ">=22.2.0"
rpds-py = ">=0.7.0" rpds-py = ">=0.7.0"
typing-extensions = {version = "*", markers = "python_version < \"3.13\""} typing-extensions = {version = ">=4.4.0", markers = "python_version < \"3.13\""}
[[package]] [[package]]
name = "regex" name = "regex"
@ -6980,20 +6996,20 @@ files = [
[[package]] [[package]]
name = "s3transfer" name = "s3transfer"
version = "0.11.0" version = "0.11.1"
description = "An Amazon S3 Transfer Manager" description = "An Amazon S3 Transfer Manager"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "s3transfer-0.11.0-py3-none-any.whl", hash = "sha256:f43b03931c198743569bbfb6a328a53f4b2b4ec723cd7c01fab68e3119db3f8b"}, {file = "s3transfer-0.11.1-py3-none-any.whl", hash = "sha256:8fa0aa48177be1f3425176dfe1ab85dcd3d962df603c3dbfc585e6bf857ef0ff"},
{file = "s3transfer-0.11.0.tar.gz", hash = "sha256:6563eda054c33bdebef7cbf309488634651c47270d828e594d151cd289fb7cf7"}, {file = "s3transfer-0.11.1.tar.gz", hash = "sha256:3f25c900a367c8b7f7d8f9c34edc87e300bde424f779dc9f0a8ae4f9df9264f6"},
] ]
[package.dependencies] [package.dependencies]
botocore = ">=1.33.2,<2.0a.0" botocore = ">=1.36.0,<2.0a.0"
[package.extras] [package.extras]
crt = ["botocore[crt] (>=1.33.2,<2.0a.0)"] crt = ["botocore[crt] (>=1.36.0,<2.0a.0)"]
[[package]] [[package]]
name = "safetensors" name = "safetensors"
@ -7835,13 +7851,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
[[package]] [[package]]
name = "transformers" name = "transformers"
version = "4.48.0" version = "4.48.1"
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow" description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
optional = false optional = false
python-versions = ">=3.9.0" python-versions = ">=3.9.0"
files = [ files = [
{file = "transformers-4.48.0-py3-none-any.whl", hash = "sha256:6d3de6d71cb5f2a10f9775ccc17abce9620195caaf32ec96542bd2a6937f25b0"}, {file = "transformers-4.48.1-py3-none-any.whl", hash = "sha256:24be0564b0a36d9e433d9a65de248f1545b6f6edce1737669605eb6a8141bbbb"},
{file = "transformers-4.48.0.tar.gz", hash = "sha256:03fdfcbfb8b0367fb6c9fbe9d1c9aa54dfd847618be9b52400b2811d22799cb1"}, {file = "transformers-4.48.1.tar.gz", hash = "sha256:7c1931facc3ee8adcbf86fc7a87461d54c1e40eca3bb57fef1ee9f3ecd32187e"},
] ]
[package.dependencies] [package.dependencies]
@ -8090,13 +8106,13 @@ files = [
[[package]] [[package]]
name = "unstructured" name = "unstructured"
version = "0.16.13" version = "0.16.14"
description = "A library that prepares raw documents for downstream ML tasks." description = "A library that prepares raw documents for downstream ML tasks."
optional = true optional = true
python-versions = "<3.13,>=3.9.0" python-versions = "<3.13,>=3.9.0"
files = [ files = [
{file = "unstructured-0.16.13-py3-none-any.whl", hash = "sha256:d578d3ebd78c6bf3ea837a13b7e2942671920f9e7361e8532c5eb00f9cf359e6"}, {file = "unstructured-0.16.14-py3-none-any.whl", hash = "sha256:7b3c2eb21e65d2f61240de7a5241fd7734d97be2c9cfa5f70934e10470318131"},
{file = "unstructured-0.16.13.tar.gz", hash = "sha256:6195744a203e65bf6b8460cbfccd9bef67a1f5d44e79229a13e7e37f528abbcd"}, {file = "unstructured-0.16.14.tar.gz", hash = "sha256:cec819461090226cd478429c1e0fda19a66ba49ab9ade1ea1fd9ec79c279d7ac"},
] ]
[package.dependencies] [package.dependencies]
@ -8221,21 +8237,22 @@ zstd = ["zstandard (>=0.18.0)"]
[[package]] [[package]]
name = "uvicorn" name = "uvicorn"
version = "0.22.0" version = "0.34.0"
description = "The lightning-fast ASGI server." description = "The lightning-fast ASGI server."
optional = false optional = false
python-versions = ">=3.7" python-versions = ">=3.9"
files = [ files = [
{file = "uvicorn-0.22.0-py3-none-any.whl", hash = "sha256:e9434d3bbf05f310e762147f769c9f21235ee118ba2d2bf1155a7196448bd996"}, {file = "uvicorn-0.34.0-py3-none-any.whl", hash = "sha256:023dc038422502fa28a09c7a30bf2b6991512da7dcdb8fd35fe57cfc154126f4"},
{file = "uvicorn-0.22.0.tar.gz", hash = "sha256:79277ae03db57ce7d9aa0567830bbb51d7a612f54d6e1e3e92da3ef24c2c8ed8"}, {file = "uvicorn-0.34.0.tar.gz", hash = "sha256:404051050cd7e905de2c9a7e61790943440b3416f49cb409f965d9dcd0fa73e9"},
] ]
[package.dependencies] [package.dependencies]
click = ">=7.0" click = ">=7.0"
h11 = ">=0.8" h11 = ">=0.8"
typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
[package.extras] [package.extras]
standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"] standard = ["colorama (>=0.4)", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.14.0,!=0.15.0,!=0.15.1)", "watchfiles (>=0.13)", "websockets (>=10.4)"]
[[package]] [[package]]
name = "validators" name = "validators"
@ -8253,13 +8270,13 @@ crypto-eth-addresses = ["eth-hash[pycryptodome] (>=0.7.0)"]
[[package]] [[package]]
name = "virtualenv" name = "virtualenv"
version = "20.29.0" version = "20.29.1"
description = "Virtual Python Environment builder" description = "Virtual Python Environment builder"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "virtualenv-20.29.0-py3-none-any.whl", hash = "sha256:c12311863497992dc4b8644f8ea82d3b35bb7ef8ee82e6630d76d0197c39baf9"}, {file = "virtualenv-20.29.1-py3-none-any.whl", hash = "sha256:4e4cb403c0b0da39e13b46b1b2476e505cb0046b25f242bee80f62bf990b2779"},
{file = "virtualenv-20.29.0.tar.gz", hash = "sha256:6345e1ff19d4b1296954cee076baaf58ff2a12a84a338c62b02eda39f20aa982"}, {file = "virtualenv-20.29.1.tar.gz", hash = "sha256:b8b8970138d32fb606192cb97f6cd4bb644fa486be9308fb9b63f81091b5dc35"},
] ]
[package.dependencies] [package.dependencies]
@ -8654,13 +8671,13 @@ files = [
[[package]] [[package]]
name = "xyzservices" name = "xyzservices"
version = "2024.9.0" version = "2025.1.0"
description = "Source of XYZ tiles providers" description = "Source of XYZ tiles providers"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
files = [ files = [
{file = "xyzservices-2024.9.0-py3-none-any.whl", hash = "sha256:776ae82b78d6e5ca63dd6a94abb054df8130887a4a308473b54a6bd364de8644"}, {file = "xyzservices-2025.1.0-py3-none-any.whl", hash = "sha256:fa599956c5ab32dad1689960b3bb08fdcdbe0252cc82d84fc60ae415dc648907"},
{file = "xyzservices-2024.9.0.tar.gz", hash = "sha256:68fb8353c9dbba4f1ff6c0f2e5e4e596bb9e1db7f94f4f7dfbcb26e25aa66fde"}, {file = "xyzservices-2025.1.0.tar.gz", hash = "sha256:5cdbb0907c20be1be066c6e2dc69c645842d1113a4e83e642065604a21f254ba"},
] ]
[[package]] [[package]]
@ -8797,4 +8814,4 @@ weaviate = ["weaviate-client"]
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = ">=3.10.0,<3.13" python-versions = ">=3.10.0,<3.13"
content-hash = "097955773827cdf96b42e54328f66b79e2b92e5a7f221a06afe1a71fea2c33bc" content-hash = "d40b127fc83e2f623276d7f001e726490a4ccad195350e8ff0b10c7e3b53775a"

7
pyproject.toml
View file

@ -20,10 +20,10 @@ classifiers = [
[tool.poetry.dependencies] [tool.poetry.dependencies]
python = ">=3.10.0,<3.13" python = ">=3.10.0,<3.13"
openai = "1.59.4" openai = "1.59.4"
pydantic = "2.8.2" pydantic = "2.10.5"
python-dotenv = "1.0.1" python-dotenv = "1.0.1"
fastapi = ">=0.109.2,<0.116.0" fastapi = ">=0.109.2,<0.116.0"
uvicorn = "0.22.0" uvicorn = "0.34.0"
requests = "2.32.3" requests = "2.32.3"
aiohttp = "3.10.10" aiohttp = "3.10.10"
typing_extensions = "4.12.2" typing_extensions = "4.12.2"
@ -79,9 +79,6 @@ bokeh="^3.6.2"
nltk = "3.9.1" nltk = "3.9.1"
[tool.poetry.extras] [tool.poetry.extras]
filesystem = ["s3fs", "botocore"] filesystem = ["s3fs", "botocore"]
weaviate = ["weaviate-client"] weaviate = ["weaviate-client"]