Merge branch 'dev' into fix-insert-data
This commit is contained in:
Igor Ilic
GitHub
commit
ffbb387580
18 changed files with 1265 additions and 3083 deletions
40
.github/workflows/profiling.yaml
vendored
40
.github/workflows/profiling.yaml
vendored
|
|
@ -68,32 +68,32 @@ jobs:
|
|||
echo "HEAD_SHA=${{ github.event.pull_request.head.sha }}" >> $GITHUB_ENV
|
||||
|
||||
# Run profiler on the base branch
|
||||
- name: Run profiler on base branch
|
||||
env:
|
||||
BASE_SHA: ${{ env.BASE_SHA }}
|
||||
run: |
|
||||
echo "Profiling the base branch for code_graph_pipeline.py"
|
||||
echo "Checking out base SHA: $BASE_SHA"
|
||||
git checkout $BASE_SHA
|
||||
echo "This is the working directory: $PWD"
|
||||
# Ensure the script is executable
|
||||
chmod +x cognee/api/v1/cognify/code_graph_pipeline.py
|
||||
# Run Scalene
|
||||
poetry run pyinstrument --renderer json -o base_results.json cognee/api/v1/cognify/code_graph_pipeline.py
|
||||
|
||||
# Run profiler on head branch
|
||||
# - name: Run profiler on head branch
|
||||
# - name: Run profiler on base branch
|
||||
# env:
|
||||
# HEAD_SHA: ${{ env.HEAD_SHA }}
|
||||
# BASE_SHA: ${{ env.BASE_SHA }}
|
||||
# run: |
|
||||
# echo "Profiling the head branch for code_graph_pipeline.py"
|
||||
# echo "Checking out head SHA: $HEAD_SHA"
|
||||
# git checkout $HEAD_SHA
|
||||
# echo "Profiling the base branch for code_graph_pipeline.py"
|
||||
# echo "Checking out base SHA: $BASE_SHA"
|
||||
# git checkout $BASE_SHA
|
||||
# echo "This is the working directory: $PWD"
|
||||
# # Ensure the script is executable
|
||||
# chmod +x cognee/api/v1/cognify/code_graph_pipeline.py
|
||||
# # Run Scalene
|
||||
# poetry run pyinstrument --renderer json -o head_results.json cognee/api/v1/cognify/code_graph_pipeline.py
|
||||
# poetry run pyinstrument --renderer json -o base_results.json cognee/api/v1/cognify/code_graph_pipeline.py
|
||||
|
||||
# Run profiler on head branch
|
||||
- name: Run profiler on head branch
|
||||
env:
|
||||
HEAD_SHA: ${{ env.HEAD_SHA }}
|
||||
run: |
|
||||
echo "Profiling the head branch for code_graph_pipeline.py"
|
||||
echo "Checking out head SHA: $HEAD_SHA"
|
||||
git checkout $HEAD_SHA
|
||||
echo "This is the working directory: $PWD"
|
||||
# Ensure the script is executable
|
||||
chmod +x cognee/api/v1/cognify/code_graph_pipeline.py
|
||||
# Run Scalene
|
||||
poetry run pyinstrument --renderer json -o head_results.json cognee/api/v1/cognify/code_graph_pipeline.py
|
||||
|
||||
# # Compare profiling results
|
||||
# - name: Compare profiling results
|
||||
|
|
|
|||
|
|
@ -7,6 +7,10 @@ on:
|
|||
description: "Location of example script to run"
|
||||
required: true
|
||||
type: string
|
||||
arguments:
|
||||
description: "Arguments for example script"
|
||||
required: false
|
||||
type: string
|
||||
secrets:
|
||||
GRAPHISTRY_USERNAME:
|
||||
required: true
|
||||
|
|
@ -53,4 +57,4 @@ jobs:
|
|||
LLM_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
|
||||
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
|
||||
run: poetry run python ${{ inputs.example-location }}
|
||||
run: poetry run python ${{ inputs.example-location }} ${{ inputs.arguments }}
|
||||
|
|
|
|||
22
.github/workflows/test_code_graph_example.yml
vendored
Normal file
22
.github/workflows/test_code_graph_example.yml
vendored
Normal file
|
|
@ -0,0 +1,22 @@
|
|||
name: test | code graph example
|
||||
|
||||
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_simple_example_test:
|
||||
uses: ./.github/workflows/reusable_python_example.yml
|
||||
with:
|
||||
example-location: ./examples/python/code_graph_example.py
|
||||
arguments: "--repo_path ./evals"
|
||||
secrets:
|
||||
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
GRAPHISTRY_USERNAME: ${{ secrets.GRAPHISTRY_USERNAME }}
|
||||
GRAPHISTRY_PASSWORD: ${{ secrets.GRAPHISTRY_PASSWORD }}
|
||||
32
Dockerfile_modal
Normal file
32
Dockerfile_modal
Normal file
|
|
@ -0,0 +1,32 @@
|
|||
FROM python:3.11-slim
|
||||
|
||||
# Set environment variables
|
||||
ENV PIP_NO_CACHE_DIR=true
|
||||
ENV PATH="${PATH}:/root/.poetry/bin"
|
||||
ENV PYTHONPATH=/app
|
||||
ENV RUN_MODE=modal
|
||||
ENV SKIP_MIGRATIONS=true
|
||||
|
||||
# System dependencies
|
||||
RUN apt-get update && apt-get install -y \
|
||||
gcc \
|
||||
libpq-dev \
|
||||
git \
|
||||
curl \
|
||||
build-essential \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
|
||||
ENV PYTHONPATH=/app
|
||||
WORKDIR /app
|
||||
COPY pyproject.toml poetry.lock /app/
|
||||
|
||||
|
||||
RUN pip install poetry
|
||||
|
||||
RUN poetry install --all-extras --no-root --without dev
|
||||
|
||||
COPY cognee/ /app/cognee
|
||||
COPY README.md /app/README.md
|
||||
22
README.md
22
README.md
|
|
@ -241,6 +241,28 @@ Please see the cognee [Development Guide](https://docs.cognee.ai/quickstart/) fo
|
|||
```bash
|
||||
pip install cognee
|
||||
```
|
||||
### Deployment at Scale (Modal)
|
||||
|
||||
Scale cognee in 4(+1) simple steps to handle enterprise workloads using [Modal](https://modal.com)'s GPU-powered infrastructure
|
||||
|
||||
**1. Install the modal python client**
|
||||
```bash
|
||||
pip install modal
|
||||
```
|
||||
**2. Create a free account on [Modal](https://modal.com)**
|
||||
|
||||
**3. Set Up Modal API Key**
|
||||
```bash
|
||||
modal token set --token-id TOKEN_ID --token-secret TOKEN_SECRET --profile=PROFILE
|
||||
modal profile activate PROFILE
|
||||
```
|
||||
**4. Run cognee example**
|
||||
|
||||
This simple example will deploy separate cognee instances building their own memory stores and answering a list of questions at scale.
|
||||
```bash
|
||||
modal run -d modal_deployment.py
|
||||
```
|
||||
**5. Change the modal_deploy script and develop your own AI memory at scale 🚀**
|
||||
|
||||
## 💫 Contributors
|
||||
|
||||
|
|
|
|||
|
|
@ -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`
|
||||
|
|
|
|||
|
|
@ -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()
|
||||
|
|
@ -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())
|
||||
|
|
@ -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"
|
||||
|
|
|
|||
6
cognee-mcp/src/__init__.py
Normal file
6
cognee-mcp/src/__init__.py
Normal file
|
|
@ -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
45
cognee-mcp/src/client.py
Normal file
|
|
@ -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
119
cognee-mcp/src/server.py
Normal file
|
|
@ -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
3057
cognee-mcp/uv.lock
generated
File diff suppressed because it is too large
Load diff
|
|
@ -247,7 +247,7 @@ 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:
|
||||
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"
|
||||
)
|
||||
|
||||
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
|
||||
|
|
|
|||
|
|
@ -8,7 +8,6 @@ from cognee.modules.data.methods import get_datasets
|
|||
from cognee.modules.data.methods.get_dataset_data import get_dataset_data
|
||||
from cognee.modules.data.methods.get_datasets_by_name import get_datasets_by_name
|
||||
from cognee.modules.data.models import Data
|
||||
from cognee.modules.data.operations.write_metadata import write_metadata
|
||||
from cognee.modules.ingestion.data_types import BinaryData
|
||||
from cognee.modules.users.methods import get_default_user
|
||||
from cognee.shared.CodeGraphEntities import Repository
|
||||
|
|
|
|||
94
modal_deployment.py
Normal file
94
modal_deployment.py
Normal file
|
|
@ -0,0 +1,94 @@
|
|||
import modal
|
||||
import os
|
||||
import logging
|
||||
import asyncio
|
||||
import cognee
|
||||
import signal
|
||||
|
||||
from cognee.api.v1.search import SearchType
|
||||
from cognee.shared.utils import setup_logging
|
||||
|
||||
app = modal.App("cognee-runner")
|
||||
|
||||
image = (
|
||||
modal.Image.from_dockerfile(path="Dockerfile_modal", force_build=False)
|
||||
.copy_local_file("pyproject.toml", "pyproject.toml")
|
||||
.copy_local_file("poetry.lock", "poetry.lock")
|
||||
.env({"ENV": os.getenv("ENV"), "LLM_API_KEY": os.getenv("LLM_API_KEY")})
|
||||
.poetry_install_from_file(poetry_pyproject_toml="pyproject.toml")
|
||||
.pip_install("protobuf", "h2")
|
||||
)
|
||||
|
||||
|
||||
@app.function(image=image, concurrency_limit=10)
|
||||
async def entry(text: str, query: str):
|
||||
setup_logging(logging.ERROR)
|
||||
await cognee.prune.prune_data()
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
await cognee.add(text)
|
||||
await cognee.cognify()
|
||||
search_results = await cognee.search(SearchType.GRAPH_COMPLETION, query_text=query)
|
||||
|
||||
return {
|
||||
"text": text,
|
||||
"query": query,
|
||||
"answer": search_results[0] if search_results else None,
|
||||
}
|
||||
|
||||
|
||||
@app.local_entrypoint()
|
||||
async def main():
|
||||
text_queries = [
|
||||
{
|
||||
"text": "NASA's Artemis program aims to return humans to the Moon by 2026, focusing on sustainable exploration and preparing for future Mars missions.",
|
||||
"query": "When does NASA plan to return humans to the Moon under the Artemis program?",
|
||||
},
|
||||
{
|
||||
"text": "According to a 2022 UN report, global food waste amounts to approximately 931 million tons annually, with households contributing 61% of the total.",
|
||||
"query": "How much food waste do households contribute annually according to the 2022 UN report?",
|
||||
},
|
||||
{
|
||||
"text": "The 2021 census data revealed that Tokyo's population reached 14 million, reflecting a 2.1% increase compared to the previous census conducted in 2015.",
|
||||
"query": "What was Tokyo's population according to the 2021 census data?",
|
||||
},
|
||||
{
|
||||
"text": "A recent study published in the Journal of Nutrition found that consuming 30 grams of almonds daily can lower LDL cholesterol levels by 7% over a 12-week period.",
|
||||
"query": "How much can daily almond consumption lower LDL cholesterol according to the study?",
|
||||
},
|
||||
{
|
||||
"text": "Amazon's Prime membership grew to 200 million subscribers in 2023, marking a 10% increase from the previous year, driven by exclusive content and faster delivery options.",
|
||||
"query": "How many Prime members did Amazon have in 2023?",
|
||||
},
|
||||
{
|
||||
"text": "A new report by the International Energy Agency states that global renewable energy capacity increased by 295 gigawatts in 2022, primarily driven by solar and wind power expansion.",
|
||||
"query": "By how much did global renewable energy capacity increase in 2022 according to the report?",
|
||||
},
|
||||
{
|
||||
"text": "The World Health Organization reported in 2023 that the global life expectancy has risen to 73.4 years, an increase of 5.5 years since the year 2000.",
|
||||
"query": "What is the current global life expectancy according to the WHO's 2023 report?",
|
||||
},
|
||||
{
|
||||
"text": "The FIFA World Cup 2022 held in Qatar attracted a record-breaking audience of 5 billion people across various digital and traditional broadcasting platforms.",
|
||||
"query": "How many people watched the FIFA World Cup 2022?",
|
||||
},
|
||||
{
|
||||
"text": "The European Space Agency's JUICE mission, launched in 2023, aims to explore Jupiter's icy moons, including Ganymede, Europa, and Callisto, over the next decade.",
|
||||
"query": "Which moons is the JUICE mission set to explore?",
|
||||
},
|
||||
{
|
||||
"text": "According to a report by the International Labour Organization, the global unemployment rate in 2023 was estimated at 5.4%, reflecting a slight decrease compared to the previous year.",
|
||||
"query": "What was the global unemployment rate in 2023 according to the ILO?",
|
||||
},
|
||||
]
|
||||
|
||||
tasks = [entry.remote.aio(item["text"], item["query"]) for item in text_queries]
|
||||
|
||||
results = await asyncio.gather(*tasks)
|
||||
|
||||
print("\nFinal Results:")
|
||||
|
||||
for result in results:
|
||||
print(result)
|
||||
print("----")
|
||||
|
||||
os.kill(os.getpid(), signal.SIGTERM)
|
||||
516
poetry.lock
generated
516
poetry.lock
generated
|
|
@ -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"},
|
||||
{file = "botocore-1.36.2.tar.gz", hash = "sha256:a1fe6610983f0214b0c7655fe6990b6a731746baf305b182976fc7b568fc3cb0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1246,13 +1246,13 @@ optimize = ["orjson"]
|
|||
|
||||
[[package]]
|
||||
name = "deepeval"
|
||||
version = "2.1.7"
|
||||
version = "2.1.9"
|
||||
description = "The Open-Source LLM Evaluation Framework."
|
||||
optional = true
|
||||
python-versions = "<3.13,>=3.9"
|
||||
files = [
|
||||
{file = "deepeval-2.1.7-py3-none-any.whl", hash = "sha256:ca0ce48067e4fc9e405c13abcf4187b8a1ff94d61a0b22daf8011e72f8ba1b65"},
|
||||
{file = "deepeval-2.1.7.tar.gz", hash = "sha256:ba71e568339a274246cb00327d25704e75438a76c5f22540af7bd843b2a0762a"},
|
||||
{file = "deepeval-2.1.9-py3-none-any.whl", hash = "sha256:c225f8ab6ab910de50026dfd46e2ea38541b3697b189831482a6f02162ead536"},
|
||||
{file = "deepeval-2.1.9.tar.gz", hash = "sha256:b6c9e90fd0ab639c5b0af5023f2e3fd20ce1906b05d7dc9bfc0bd2f46d0545e0"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -2138,13 +2138,13 @@ test = ["objgraph", "psutil"]
|
|||
|
||||
[[package]]
|
||||
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."
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "griffe-1.5.4-py3-none-any.whl", hash = "sha256:ed33af890586a5bebc842fcb919fc694b3dc1bc55b7d9e0228de41ce566b4a1d"},
|
||||
{file = "griffe-1.5.4.tar.gz", hash = "sha256:073e78ad3e10c8378c2f798bd4ef87b92d8411e9916e157fd366a17cc4fd4e52"},
|
||||
{file = "griffe-1.5.5-py3-none-any.whl", hash = "sha256:2761b1e8876c6f1f9ab1af274df93ea6bbadd65090de5f38f4cb5cc84897c7dd"},
|
||||
{file = "griffe-1.5.5.tar.gz", hash = "sha256:35ee5b38b93d6a839098aad0f92207e6ad6b70c3e8866c08ca669275b8cba585"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3379,13 +3379,13 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
|
|||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.3.29"
|
||||
version = "0.3.30"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = true
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "langchain_core-0.3.29-py3-none-any.whl", hash = "sha256:817db1474871611a81105594a3e4d11704949661008e455a10e38ca9ff601a1a"},
|
||||
{file = "langchain_core-0.3.29.tar.gz", hash = "sha256:773d6aeeb612e7ce3d996c0be403433d8c6a91e77bbb7a7461c13e15cfbe5b06"},
|
||||
{file = "langchain_core-0.3.30-py3-none-any.whl", hash = "sha256:0a4c4e02fac5968b67fbb0142c00c2b976c97e45fce62c7ac9eb1636a6926493"},
|
||||
{file = "langchain_core-0.3.30.tar.gz", hash = "sha256:0f1281b4416977df43baf366633ad18e96c5dcaaeae6fcb8a799f9889c853243"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3402,17 +3402,17 @@ typing-extensions = ">=4.7"
|
|||
|
||||
[[package]]
|
||||
name = "langchain-openai"
|
||||
version = "0.3.0"
|
||||
version = "0.3.1"
|
||||
description = "An integration package connecting OpenAI and LangChain"
|
||||
optional = true
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "langchain_openai-0.3.0-py3-none-any.whl", hash = "sha256:49c921a22d272b04749a61e78bffa83aecdb8840b24b69f2909e115a357a9a5b"},
|
||||
{file = "langchain_openai-0.3.0.tar.gz", hash = "sha256:88d623eeb2aaa1fff65c2b419a4a1cfd37d3a1d504e598b87cf0bc822a3b70d0"},
|
||||
{file = "langchain_openai-0.3.1-py3-none-any.whl", hash = "sha256:5cf2a1e115b12570158d89c22832fa381803c3e1e11d1eb781195c8d9e454bd5"},
|
||||
{file = "langchain_openai-0.3.1.tar.gz", hash = "sha256:cce314f1437b2cad73e0ed2b55e74dc399bc1bbc43594c4448912fb51c5e4447"},
|
||||
]
|
||||
|
||||
[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"
|
||||
tiktoken = ">=0.7,<1"
|
||||
|
||||
|
|
@ -3446,13 +3446,13 @@ six = "*"
|
|||
|
||||
[[package]]
|
||||
name = "langfuse"
|
||||
version = "2.57.10"
|
||||
version = "2.57.11"
|
||||
description = "A client library for accessing langfuse"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "langfuse-2.57.10-py3-none-any.whl", hash = "sha256:db7e8f7cf8d0204e17074e6729b144e7f9c7198499cd84a824bbc81fb5e37e4a"},
|
||||
{file = "langfuse-2.57.10.tar.gz", hash = "sha256:751dd03271809f4bf50f6e4e0d1138b0e0eb028efefc984fdc6948d2bfddd95d"},
|
||||
{file = "langfuse-2.57.11-py3-none-any.whl", hash = "sha256:c9a074c68de62b7a7b144c02577a1a124df84274f13c80488f077147e93d6e78"},
|
||||
{file = "langfuse-2.57.11.tar.gz", hash = "sha256:f1c220decdd9c858fb58916af1775ac999836859553c6ffef33ebf2197030697"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3524,13 +3524,13 @@ proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "
|
|||
|
||||
[[package]]
|
||||
name = "llama-index-core"
|
||||
version = "0.12.11"
|
||||
version = "0.12.12"
|
||||
description = "Interface between LLMs and your data"
|
||||
optional = true
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "llama_index_core-0.12.11-py3-none-any.whl", hash = "sha256:3b1e019c899e9e011dfa01c96b7e3f666e0c161035fbca6cb787b4c61e0c94db"},
|
||||
{file = "llama_index_core-0.12.11.tar.gz", hash = "sha256:9a41ca91167ea5eec9ebaac7f5e958b7feddbd8af3bfbf7c393a5edfb994d566"},
|
||||
{file = "llama_index_core-0.12.12-py3-none-any.whl", hash = "sha256:cea491e87f65e6b775b5aef95720de302b85af1bdc67d779c4b09170a30e5b98"},
|
||||
{file = "llama_index_core-0.12.12.tar.gz", hash = "sha256:068b755bbc681731336e822f5977d7608585e8f759c6293ebd812e2659316a37"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4067,13 +4067,13 @@ pyyaml = ">=5.1"
|
|||
|
||||
[[package]]
|
||||
name = "mkdocs-material"
|
||||
version = "9.5.49"
|
||||
version = "9.5.50"
|
||||
description = "Documentation that simply works"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "mkdocs_material-9.5.49-py3-none-any.whl", hash = "sha256:c3c2d8176b18198435d3a3e119011922f3e11424074645c24019c2dcf08a360e"},
|
||||
{file = "mkdocs_material-9.5.49.tar.gz", hash = "sha256:3671bb282b4f53a1c72e08adbe04d2481a98f85fed392530051f80ff94a9621d"},
|
||||
{file = "mkdocs_material-9.5.50-py3-none-any.whl", hash = "sha256:f24100f234741f4d423a9d672a909d859668a4f404796be3cf035f10d6050385"},
|
||||
{file = "mkdocs_material-9.5.50.tar.gz", hash = "sha256:ae5fe16f3d7c9ccd05bb6916a7da7420cf99a9ce5e33debd9d40403a090d5825"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4090,7 +4090,7 @@ regex = ">=2022.4"
|
|||
requests = ">=2.26,<3.0"
|
||||
|
||||
[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)"]
|
||||
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]]
|
||||
name = "orjson"
|
||||
version = "3.10.14"
|
||||
version = "3.10.15"
|
||||
description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "orjson-3.10.14-cp310-cp310-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:849ea7845a55f09965826e816cdc7689d6cf74fe9223d79d758c714af955bcb6"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b5947b139dfa33f72eecc63f17e45230a97e741942955a6c9e650069305eb73d"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:cde6d76910d3179dae70f164466692f4ea36da124d6fb1a61399ca589e81d69a"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c6dfbaeb7afa77ca608a50e2770a0461177b63a99520d4928e27591b142c74b1"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:fa45e489ef80f28ff0e5ba0a72812b8cfc7c1ef8b46a694723807d1b07c89ebb"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4f5007abfdbb1d866e2aa8990bd1c465f0f6da71d19e695fc278282be12cffa5"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1b49e2af011c84c3f2d541bb5cd1e3c7c2df672223e7e3ea608f09cf295e5f8a"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:164ac155109226b3a2606ee6dda899ccfbe6e7e18b5bdc3fbc00f79cc074157d"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-musllinux_1_2_armv7l.whl", hash = "sha256:6b1225024cf0ef5d15934b5ffe9baf860fe8bc68a796513f5ea4f5056de30bca"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:d6546e8073dc382e60fcae4a001a5a1bc46da5eab4a4878acc2d12072d6166d5"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:9f1d2942605c894162252d6259b0121bf1cb493071a1ea8cb35d79cb3e6ac5bc"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-win32.whl", hash = "sha256:397083806abd51cf2b3bbbf6c347575374d160331a2d33c5823e22249ad3118b"},
|
||||
{file = "orjson-3.10.14-cp310-cp310-win_amd64.whl", hash = "sha256:fa18f949d3183a8d468367056be989666ac2bef3a72eece0bade9cdb733b3c28"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-macosx_10_15_x86_64.macosx_11_0_arm64.macosx_10_15_universal2.whl", hash = "sha256:f506fd666dd1ecd15a832bebc66c4df45c1902fd47526292836c339f7ba665a9"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:efe5fd254cfb0eeee13b8ef7ecb20f5d5a56ddda8a587f3852ab2cedfefdb5f6"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:4ddc8c866d7467f5ee2991397d2ea94bcf60d0048bdd8ca555740b56f9042725"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3af8e42ae4363773658b8d578d56dedffb4f05ceeb4d1d4dd3fb504950b45526"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:84dd83110503bc10e94322bf3ffab8bc49150176b49b4984dc1cce4c0a993bf9"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:36f5bfc0399cd4811bf10ec7a759c7ab0cd18080956af8ee138097d5b5296a95"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:868943660fb2a1e6b6b965b74430c16a79320b665b28dd4511d15ad5038d37d5"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:33449c67195969b1a677533dee9d76e006001213a24501333624623e13c7cc8e"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-musllinux_1_2_armv7l.whl", hash = "sha256:e4c9f60f9fb0b5be66e416dcd8c9d94c3eabff3801d875bdb1f8ffc12cf86905"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:0de4d6315cfdbd9ec803b945c23b3a68207fd47cbe43626036d97e8e9561a436"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:83adda3db595cb1a7e2237029b3249c85afbe5c747d26b41b802e7482cb3933e"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-win32.whl", hash = "sha256:998019ef74a4997a9d741b1473533cdb8faa31373afc9849b35129b4b8ec048d"},
|
||||
{file = "orjson-3.10.14-cp311-cp311-win_amd64.whl", hash = "sha256:9d034abdd36f0f0f2240f91492684e5043d46f290525d1117712d5b8137784eb"},
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||||
[[package]]
|
||||
|
|
@ -5357,13 +5361,13 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
|
|||
|
||||
[[package]]
|
||||
name = "posthog"
|
||||
version = "3.8.3"
|
||||
version = "3.8.4"
|
||||
description = "Integrate PostHog into any python application."
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
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]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -5377,17 +5381,17 @@ six = ">=1.5"
|
|||
dev = ["black", "flake8", "flake8-print", "isort", "pre-commit"]
|
||||
langchain = ["langchain (>=0.2.0)"]
|
||||
sentry = ["django", "sentry-sdk"]
|
||||
test = ["coverage", "django", "flake8", "freezegun (==0.3.15)", "langchain-community (>=0.2.0)", "langchain-openai (>=0.2.0)", "mock (>=2.0.0)", "pylint", "pytest", "pytest-asyncio", "pytest-timeout"]
|
||||
test = ["anthropic", "coverage", "django", "flake8", "freezegun (==0.3.15)", "langchain-anthropic (>=0.2.0)", "langchain-community (>=0.2.0)", "langchain-openai (>=0.2.0)", "mock (>=2.0.0)", "openai", "pylint", "pytest", "pytest-asyncio", "pytest-timeout"]
|
||||
|
||||
[[package]]
|
||||
name = "pre-commit"
|
||||
version = "4.0.1"
|
||||
version = "4.1.0"
|
||||
description = "A framework for managing and maintaining multi-language pre-commit hooks."
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
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[package.dependencies]
|
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|
|
@ -5413,13 +5417,13 @@ twisted = ["twisted"]
|
|||
|
||||
[[package]]
|
||||
name = "prompt-toolkit"
|
||||
version = "3.0.48"
|
||||
version = "3.0.50"
|
||||
description = "Library for building powerful interactive command lines in Python"
|
||||
optional = true
|
||||
python-versions = ">=3.7.0"
|
||||
python-versions = ">=3.8.0"
|
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[package.dependencies]
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|
|
@ -5695,119 +5699,131 @@ files = [
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|||
|
||||
[[package]]
|
||||
name = "pydantic"
|
||||
version = "2.8.2"
|
||||
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|
||||
description = "Data validation using Python type hints"
|
||||
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[package.dependencies]
|
||||
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|
||||
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|
||||
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|
||||
annotated-types = ">=0.6.0"
|
||||
pydantic-core = "2.27.2"
|
||||
typing-extensions = ">=4.12.2"
|
||||
|
||||
[package.extras]
|
||||
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|
||||
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|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
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|
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[package.dependencies]
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[[package]]
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name = "qdrant-client"
|
||||
version = "1.12.2"
|
||||
version = "1.13.0"
|
||||
description = "Client library for the Qdrant vector search engine"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "qdrant_client-1.12.2-py3-none-any.whl", hash = "sha256:a0ae500a46a679ff3521ba3f1f1cf3d72b57090a768cec65fc317066bcbac1e6"},
|
||||
{file = "qdrant_client-1.12.2.tar.gz", hash = "sha256:2777e09b3e89bb22bb490384d8b1fa8140f3915287884f18984f7031a346aba5"},
|
||||
{file = "qdrant_client-1.13.0-py3-none-any.whl", hash = "sha256:63a063d5232618b609f2c438caf6f3afd3bd110dd80d01be20c596e516efab6b"},
|
||||
{file = "qdrant_client-1.13.0.tar.gz", hash = "sha256:9708e3194081619b38194c99e7c369064e3f3f328d8a8ef1d71a87425a5ddf0c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -6487,8 +6503,8 @@ pydantic = ">=1.10.8"
|
|||
urllib3 = ">=1.26.14,<3"
|
||||
|
||||
[package.extras]
|
||||
fastembed = ["fastembed (==0.5.0)"]
|
||||
fastembed-gpu = ["fastembed-gpu (==0.5.0)"]
|
||||
fastembed = ["fastembed (==0.5.1)"]
|
||||
fastembed-gpu = ["fastembed-gpu (==0.5.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "rapidfuzz"
|
||||
|
|
@ -6610,19 +6626,19 @@ ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==23.2.1)", "requests (>=2.31.0)"
|
|||
|
||||
[[package]]
|
||||
name = "referencing"
|
||||
version = "0.36.0"
|
||||
version = "0.36.1"
|
||||
description = "JSON Referencing + Python"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "referencing-0.36.0-py3-none-any.whl", hash = "sha256:01fc2916bab821aa3284d645bbbb41ba39609e7ff47072416a39ec2fb04d10d9"},
|
||||
{file = "referencing-0.36.0.tar.gz", hash = "sha256:246db964bb6101905167895cd66499cfb2aabc5f83277d008c52afe918ef29ba"},
|
||||
{file = "referencing-0.36.1-py3-none-any.whl", hash = "sha256:363d9c65f080d0d70bc41c721dce3c7f3e77fc09f269cd5c8813da18069a6794"},
|
||||
{file = "referencing-0.36.1.tar.gz", hash = "sha256:ca2e6492769e3602957e9b831b94211599d2aade9477f5d44110d2530cf9aade"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
attrs = ">=22.2.0"
|
||||
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]]
|
||||
name = "regex"
|
||||
|
|
@ -6980,20 +6996,20 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "s3transfer"
|
||||
version = "0.11.0"
|
||||
version = "0.11.1"
|
||||
description = "An Amazon S3 Transfer Manager"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "s3transfer-0.11.0-py3-none-any.whl", hash = "sha256:f43b03931c198743569bbfb6a328a53f4b2b4ec723cd7c01fab68e3119db3f8b"},
|
||||
{file = "s3transfer-0.11.0.tar.gz", hash = "sha256:6563eda054c33bdebef7cbf309488634651c47270d828e594d151cd289fb7cf7"},
|
||||
{file = "s3transfer-0.11.1-py3-none-any.whl", hash = "sha256:8fa0aa48177be1f3425176dfe1ab85dcd3d962df603c3dbfc585e6bf857ef0ff"},
|
||||
{file = "s3transfer-0.11.1.tar.gz", hash = "sha256:3f25c900a367c8b7f7d8f9c34edc87e300bde424f779dc9f0a8ae4f9df9264f6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
botocore = ">=1.33.2,<2.0a.0"
|
||||
botocore = ">=1.36.0,<2.0a.0"
|
||||
|
||||
[package.extras]
|
||||
crt = ["botocore[crt] (>=1.33.2,<2.0a.0)"]
|
||||
crt = ["botocore[crt] (>=1.36.0,<2.0a.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "safetensors"
|
||||
|
|
@ -7835,13 +7851,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
|
|||
|
||||
[[package]]
|
||||
name = "transformers"
|
||||
version = "4.48.0"
|
||||
version = "4.48.1"
|
||||
description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
|
||||
optional = false
|
||||
python-versions = ">=3.9.0"
|
||||
files = [
|
||||
{file = "transformers-4.48.0-py3-none-any.whl", hash = "sha256:6d3de6d71cb5f2a10f9775ccc17abce9620195caaf32ec96542bd2a6937f25b0"},
|
||||
{file = "transformers-4.48.0.tar.gz", hash = "sha256:03fdfcbfb8b0367fb6c9fbe9d1c9aa54dfd847618be9b52400b2811d22799cb1"},
|
||||
{file = "transformers-4.48.1-py3-none-any.whl", hash = "sha256:24be0564b0a36d9e433d9a65de248f1545b6f6edce1737669605eb6a8141bbbb"},
|
||||
{file = "transformers-4.48.1.tar.gz", hash = "sha256:7c1931facc3ee8adcbf86fc7a87461d54c1e40eca3bb57fef1ee9f3ecd32187e"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8090,13 +8106,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "unstructured"
|
||||
version = "0.16.13"
|
||||
version = "0.16.14"
|
||||
description = "A library that prepares raw documents for downstream ML tasks."
|
||||
optional = true
|
||||
python-versions = "<3.13,>=3.9.0"
|
||||
files = [
|
||||
{file = "unstructured-0.16.13-py3-none-any.whl", hash = "sha256:d578d3ebd78c6bf3ea837a13b7e2942671920f9e7361e8532c5eb00f9cf359e6"},
|
||||
{file = "unstructured-0.16.13.tar.gz", hash = "sha256:6195744a203e65bf6b8460cbfccd9bef67a1f5d44e79229a13e7e37f528abbcd"},
|
||||
{file = "unstructured-0.16.14-py3-none-any.whl", hash = "sha256:7b3c2eb21e65d2f61240de7a5241fd7734d97be2c9cfa5f70934e10470318131"},
|
||||
{file = "unstructured-0.16.14.tar.gz", hash = "sha256:cec819461090226cd478429c1e0fda19a66ba49ab9ade1ea1fd9ec79c279d7ac"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8221,21 +8237,22 @@ zstd = ["zstandard (>=0.18.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "uvicorn"
|
||||
version = "0.22.0"
|
||||
version = "0.34.0"
|
||||
description = "The lightning-fast ASGI server."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "uvicorn-0.22.0-py3-none-any.whl", hash = "sha256:e9434d3bbf05f310e762147f769c9f21235ee118ba2d2bf1155a7196448bd996"},
|
||||
{file = "uvicorn-0.22.0.tar.gz", hash = "sha256:79277ae03db57ce7d9aa0567830bbb51d7a612f54d6e1e3e92da3ef24c2c8ed8"},
|
||||
{file = "uvicorn-0.34.0-py3-none-any.whl", hash = "sha256:023dc038422502fa28a09c7a30bf2b6991512da7dcdb8fd35fe57cfc154126f4"},
|
||||
{file = "uvicorn-0.34.0.tar.gz", hash = "sha256:404051050cd7e905de2c9a7e61790943440b3416f49cb409f965d9dcd0fa73e9"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
click = ">=7.0"
|
||||
h11 = ">=0.8"
|
||||
typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
|
||||
|
||||
[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]]
|
||||
name = "validators"
|
||||
|
|
@ -8253,13 +8270,13 @@ crypto-eth-addresses = ["eth-hash[pycryptodome] (>=0.7.0)"]
|
|||
|
||||
[[package]]
|
||||
name = "virtualenv"
|
||||
version = "20.29.0"
|
||||
version = "20.29.1"
|
||||
description = "Virtual Python Environment builder"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "virtualenv-20.29.0-py3-none-any.whl", hash = "sha256:c12311863497992dc4b8644f8ea82d3b35bb7ef8ee82e6630d76d0197c39baf9"},
|
||||
{file = "virtualenv-20.29.0.tar.gz", hash = "sha256:6345e1ff19d4b1296954cee076baaf58ff2a12a84a338c62b02eda39f20aa982"},
|
||||
{file = "virtualenv-20.29.1-py3-none-any.whl", hash = "sha256:4e4cb403c0b0da39e13b46b1b2476e505cb0046b25f242bee80f62bf990b2779"},
|
||||
{file = "virtualenv-20.29.1.tar.gz", hash = "sha256:b8b8970138d32fb606192cb97f6cd4bb644fa486be9308fb9b63f81091b5dc35"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8654,13 +8671,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "xyzservices"
|
||||
version = "2024.9.0"
|
||||
version = "2025.1.0"
|
||||
description = "Source of XYZ tiles providers"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "xyzservices-2024.9.0-py3-none-any.whl", hash = "sha256:776ae82b78d6e5ca63dd6a94abb054df8130887a4a308473b54a6bd364de8644"},
|
||||
{file = "xyzservices-2024.9.0.tar.gz", hash = "sha256:68fb8353c9dbba4f1ff6c0f2e5e4e596bb9e1db7f94f4f7dfbcb26e25aa66fde"},
|
||||
{file = "xyzservices-2025.1.0-py3-none-any.whl", hash = "sha256:fa599956c5ab32dad1689960b3bb08fdcdbe0252cc82d84fc60ae415dc648907"},
|
||||
{file = "xyzservices-2025.1.0.tar.gz", hash = "sha256:5cdbb0907c20be1be066c6e2dc69c645842d1113a4e83e642065604a21f254ba"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -8779,6 +8796,7 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
|
|||
type = ["pytest-mypy"]
|
||||
|
||||
[extras]
|
||||
codegraph = ["jedi", "parso"]
|
||||
deepeval = ["deepeval"]
|
||||
docs = ["unstructured"]
|
||||
falkordb = ["falkordb"]
|
||||
|
|
@ -8797,4 +8815,4 @@ weaviate = ["weaviate-client"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10.0,<3.13"
|
||||
content-hash = "097955773827cdf96b42e54328f66b79e2b92e5a7f221a06afe1a71fea2c33bc"
|
||||
content-hash = "9b5d0162e4fdaaded920a2c8b448e07ec794c55914c1d6e18c6ab9b48c42df2d"
|
||||
|
|
|
|||
|
|
@ -20,10 +20,10 @@ classifiers = [
|
|||
[tool.poetry.dependencies]
|
||||
python = ">=3.10.0,<3.13"
|
||||
openai = "1.59.4"
|
||||
pydantic = "2.8.2"
|
||||
pydantic = "2.10.5"
|
||||
python-dotenv = "1.0.1"
|
||||
fastapi = ">=0.109.2,<0.116.0"
|
||||
uvicorn = "0.22.0"
|
||||
uvicorn = "0.34.0"
|
||||
requests = "2.32.3"
|
||||
aiohttp = "3.10.10"
|
||||
typing_extensions = "4.12.2"
|
||||
|
|
@ -77,9 +77,8 @@ pre-commit = "^4.0.1"
|
|||
httpx = "0.27.0"
|
||||
bokeh="^3.6.2"
|
||||
nltk = "3.9.1"
|
||||
|
||||
|
||||
|
||||
parso = {version = "^0.8.4", optional = true}
|
||||
jedi = {version = "^0.19.2", optional = true}
|
||||
|
||||
|
||||
[tool.poetry.extras]
|
||||
|
|
@ -97,6 +96,7 @@ falkordb = ["falkordb"]
|
|||
groq = ["groq"]
|
||||
milvus = ["pymilvus"]
|
||||
docs = ["unstructured"]
|
||||
codegraph = ["parso", "jedi"]
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest = "^7.4.0"
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue