feat: use fastmcp for mcp server
This commit is contained in:
Boris Arzentar
parent
bbb8e8951c
commit
00f302c37a
11 changed files with 1065 additions and 3065 deletions
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@ -1,31 +1,19 @@
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# cognee MCP server
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### Installing Manually
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A MCP server project
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=======
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1. Clone the [cognee](https://github.com/topoteretes/cognee) repo
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2. Install dependencies
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```
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pip install uv
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```
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```
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brew install postgresql
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```
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```
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brew install rust
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brew install uv
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```
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```jsx
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cd cognee-mcp
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uv sync --dev --all-extras
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uv sync --dev --all-extras --reinstall
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```
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3. Activate the venv with
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@ -48,8 +36,6 @@ nano claude_desktop_config.json
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```
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```
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{
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"mcpServers": {
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"cognee": {
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@ -65,16 +51,7 @@ nano claude_desktop_config.json
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"TOKENIZERS_PARALLELISM": "false",
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"LLM_API_KEY": "sk-"
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}
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},
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"filesystem": {
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"command": "npx",
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"args": [
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"-y",
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"@modelcontextprotocol/server-filesystem",
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"/Users/{user}/Desktop",
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"/Users/{user}/Projects"
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]
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}
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}
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}
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}
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```
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@ -89,19 +66,21 @@ To install Cognee for Claude Desktop automatically via [Smithery](https://smithe
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npx -y @smithery/cli install cognee --client claude
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```
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Define cognify tool in server.py
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Define cognify tool in server.py.
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Restart your Claude desktop.
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To use debugger, run:
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```bash
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npx @modelcontextprotocol/inspector uv --directory /Users/name/folder run cognee
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mcp dev src/server.py
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```
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Open inspector with timeout passed:
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```
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http://localhost:5173?timeout=120000
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```
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To apply new changes while development you do:
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1. Poetry lock in cognee folder
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2. uv sync --dev --all-extras --reinstall
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3. npx @modelcontextprotocol/inspector uv --directory /Users/vasilije/cognee/cognee-mcp run cognee
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To apply new changes while developing cognee you need to do:
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1. `poetry lock` in cognee folder
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2. `uv sync --dev --all-extras --reinstall `
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3. `mcp dev src/server.py`
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@ -1,15 +0,0 @@
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import asyncio
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from . import server
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def main():
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"""Main entry point for the package."""
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asyncio.run(server.main())
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# Optionally expose other important items at package level
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__all__ = ["main", "server"]
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if __name__ == "__main__":
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main()
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@ -1,235 +0,0 @@
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import importlib.util
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import os
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import asyncio
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from contextlib import redirect_stderr, redirect_stdout
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from sqlalchemy.testing.plugin.plugin_base import logging
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import cognee
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import mcp.server.stdio
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import mcp.types as types
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from cognee.api.v1.search import SearchType
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from cognee.shared.data_models import KnowledgeGraph
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from mcp.server import NotificationOptions, Server
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from mcp.server.models import InitializationOptions
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from PIL import Image
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server = Server("cognee-mcp")
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def node_to_string(node):
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# keys_to_keep = ["chunk_index", "topological_rank", "cut_type", "id", "text"]
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# keyset = set(keys_to_keep) & node.keys()
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# return "Node(" + " ".join([key + ": " + str(node[key]) + "," for key in keyset]) + ")"
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node_data = ", ".join(
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[f'{key}: "{value}"' for key, value in node.items() if key in ["id", "name"]]
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)
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return f"Node({node_data})"
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def retrieved_edges_to_string(search_results):
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edge_strings = []
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for triplet in search_results:
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node1, edge, node2 = triplet
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relationship_type = edge["relationship_name"]
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edge_str = f"{node_to_string(node1)} {relationship_type} {node_to_string(node2)}"
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edge_strings.append(edge_str)
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return "\n".join(edge_strings)
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def load_class(model_file, model_name):
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model_file = os.path.abspath(model_file)
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spec = importlib.util.spec_from_file_location("graph_model", model_file)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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model_class = getattr(module, model_name)
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return model_class
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@server.list_tools()
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async def handle_list_tools() -> list[types.Tool]:
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"""
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List available tools.
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Each tool specifies its arguments using JSON Schema validation.
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"""
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return [
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types.Tool(
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name="cognify",
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description="Build knowledge graph from the input text.",
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inputSchema={
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"type": "object",
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"properties": {
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"text": {"type": "string"},
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"graph_model_file": {"type": "string"},
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"graph_model_name": {"type": "string"},
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},
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"required": ["text"],
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},
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),
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types.Tool(
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name="search",
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description="Search the knowledge graph.",
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inputSchema={
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"type": "object",
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"properties": {
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"query": {"type": "string"},
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},
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"required": ["query"],
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},
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),
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types.Tool(
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name="prune",
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description="Reset the knowledge graph.",
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inputSchema={
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"type": "object",
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"properties": {
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"query": {"type": "string"},
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},
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},
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),
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types.Tool(
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name="visualize",
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description="Visualize the knowledge graph.",
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inputSchema={
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"type": "object",
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"properties": {
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"query": {"type": "string"},
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},
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},
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),
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]
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def get_freshest_png(directory: str) -> Image.Image:
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if not os.path.exists(directory):
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raise FileNotFoundError(f"Directory {directory} does not exist")
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# List all files in 'directory' that end with .png
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files = [f for f in os.listdir(directory) if f.endswith(".png")]
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if not files:
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raise FileNotFoundError("No PNG files found in the given directory.")
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# Sort by integer value of the filename (minus the '.png')
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# Example filename: 1673185134.png -> integer 1673185134
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try:
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files_sorted = sorted(files, key=lambda x: int(x.replace(".png", "")))
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except ValueError as e:
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raise ValueError("Invalid PNG filename format. Expected timestamp format.") from e
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# The "freshest" file has the largest timestamp
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freshest_filename = files_sorted[-1]
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freshest_path = os.path.join(directory, freshest_filename)
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# Open the image with PIL and return the PIL Image object
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try:
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return Image.open(freshest_path)
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except (IOError, OSError) as e:
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raise IOError(f"Failed to open PNG file {freshest_path}") from e
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@server.call_tool()
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async def handle_call_tool(
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name: str, arguments: dict | None
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) -> list[types.TextContent | types.ImageContent | types.EmbeddedResource]:
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"""
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Handle tool execution requests.
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Tools can modify server state and notify clients of changes.
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"""
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if name == "cognify":
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with open(os.devnull, "w") as fnull:
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with redirect_stdout(fnull), redirect_stderr(fnull):
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if not arguments:
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raise ValueError("Missing arguments")
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text = arguments.get("text")
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if ("graph_model_file" in arguments) and ("graph_model_name" in arguments):
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model_file = arguments.get("graph_model_file")
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model_name = arguments.get("graph_model_name")
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graph_model = load_class(model_file, model_name)
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else:
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graph_model = KnowledgeGraph
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await cognee.add(text)
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await cognee.cognify(graph_model=graph_model)
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return [
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types.TextContent(
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type="text",
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text="Ingested",
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)
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]
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elif name == "search":
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with open(os.devnull, "w") as fnull:
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with redirect_stdout(fnull), redirect_stderr(fnull):
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if not arguments:
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raise ValueError("Missing arguments")
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search_query = arguments.get("query")
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search_results = await cognee.search(SearchType.INSIGHTS, query_text=search_query)
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results = retrieved_edges_to_string(search_results)
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return [
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types.TextContent(
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type="text",
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text=results,
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)
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]
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elif name == "prune":
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with open(os.devnull, "w") as fnull:
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with redirect_stdout(fnull), redirect_stderr(fnull):
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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return [
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types.TextContent(
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type="text",
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text="Pruned",
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)
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]
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elif name == "visualize":
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with open(os.devnull, "w") as fnull:
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with redirect_stdout(fnull), redirect_stderr(fnull):
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try:
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results = await cognee.visualize_graph()
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return [
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types.TextContent(
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type="text",
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text=results,
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)
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]
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except (FileNotFoundError, IOError, ValueError) as e:
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raise ValueError(f"Failed to create visualization: {str(e)}")
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else:
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raise ValueError(f"Unknown tool: {name}")
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async def main():
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# Run the server using stdin/stdout streams
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async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
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await server.run(
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read_stream,
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write_stream,
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InitializationOptions(
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server_name="cognee-mcp",
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server_version="0.1.0",
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capabilities=server.get_capabilities(
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notification_options=NotificationOptions(),
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experimental_capabilities={},
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),
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),
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)
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# This is needed if you'd like to connect to a custom client
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if __name__ == "__main__":
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asyncio.run(main())
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@ -6,73 +6,8 @@ readme = "README.md"
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requires-python = ">=3.10"
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dependencies = [
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"mcp>=1.1.1",
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"openai==1.59.4",
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"pydantic==2.8.2",
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"python-dotenv==1.0.1",
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"fastapi>=0.109.2,<0.110.0",
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"uvicorn==0.22.0",
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"requests==2.32.3",
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"aiohttp==3.10.10",
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"typing_extensions==4.12.2",
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"nest_asyncio==1.6.0",
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"numpy==1.26.4",
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"datasets==3.1.0",
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"falkordb==1.0.9", # Optional
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"boto3>=1.26.125,<2.0.0",
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"botocore>=1.35.54,<2.0.0",
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"gunicorn>=20.1.0,<21.0.0",
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"sqlalchemy==2.0.36",
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"instructor==1.7.2",
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"networkx>=3.2.1,<4.0.0",
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"aiosqlite>=0.20.0,<0.21.0",
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"pandas==2.2.3",
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"filetype>=1.2.0,<2.0.0",
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"nltk>=3.8.1,<4.0.0",
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"dlt[sqlalchemy]>=1.4.1,<2.0.0",
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"aiofiles>=23.2.1,<24.0.0",
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"qdrant-client>=1.9.0,<2.0.0", # Optional
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"graphistry>=0.33.5,<0.34.0",
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"tenacity>=9.0.0",
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"weaviate-client==4.6.7", # Optional
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"scikit-learn>=1.5.0,<2.0.0",
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"pypdf>=4.1.0,<5.0.0",
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"neo4j>=5.20.0,<6.0.0", # Optional
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"jinja2>=3.1.3,<4.0.0",
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"matplotlib>=3.8.3,<4.0.0",
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"tiktoken==0.7.0",
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"langchain_text_splitters==0.3.2", # Optional
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"langsmith==0.1.139", # Optional
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"langdetect==1.0.9",
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"posthog>=3.5.0,<4.0.0", # Optional
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"lancedb==0.16.0",
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"litellm==1.57.2",
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"groq==0.8.0", # Optional
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"langfuse>=2.32.0,<3.0.0", # Optional
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"pydantic-settings>=2.2.1,<3.0.0",
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"anthropic>=0.26.1,<1.0.0",
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"sentry-sdk[fastapi]>=2.9.0,<3.0.0",
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"fastapi-users[sqlalchemy]>=14.0.0", # Optional
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"alembic>=1.13.3,<2.0.0",
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"asyncpg==0.30.0", # Optional
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"pgvector>=0.3.5,<0.4.0", # Optional
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"psycopg2>=2.9.10,<3.0.0", # Optional
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"llama-index-core>=0.12.0", # Optional
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"deepeval>=2.0.1,<3.0.0", # Optional
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"transformers>=4.46.3,<5.0.0",
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"pymilvus>=2.5.0,<3.0.0", # Optional
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"unstructured[csv,doc,docx,epub,md,odt,org,ppt,pptx,rst,rtf,tsv,xlsx]>=0.16.10,<1.0.0", # Optional
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"pytest>=7.4.0,<8.0.0",
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"pytest-asyncio>=0.21.1,<0.22.0",
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"coverage>=7.3.2,<8.0.0",
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"mypy>=1.7.1,<2.0.0",
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"deptry>=0.20.0,<0.21.0",
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"debugpy==1.8.2",
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"pylint>=3.0.3,<4.0.0",
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"ruff>=0.2.2,<0.3.0",
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"tweepy==4.14.0",
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"gitpython>=3.1.43,<4.0.0",
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"cognee",
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"mcp==1.2.0",
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]
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[[project.authors]]
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@ -80,16 +15,14 @@ name = "Rita Aleksziev"
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email = "rita@topoteretes.com"
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[build-system]
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requires = [ "hatchling",]
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requires = [ "hatchling", ]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["src"]
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[tool.uv.sources]
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cognee = { path = "../../cognee" }
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[dependency-groups]
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dev = [
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"cognee",
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]
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[project.scripts]
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cognee = "cognee_mcp:main"
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cognee = "src:main"
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5
cognee-mcp/src/__init__.py
Normal file
5
cognee-mcp/src/__init__.py
Normal file
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@ -0,0 +1,5 @@
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from .server import mcp
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def main():
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"""Main entry point for the package."""
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mcp.run(transport="stdio")
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42
cognee-mcp/src/client.py
Normal file
42
cognee-mcp/src/client.py
Normal file
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@ -0,0 +1,42 @@
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from datetime import timedelta
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from mcp import ClientSession, StdioServerParameters
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from mcp.client.stdio import stdio_client
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# Create server parameters for stdio connection
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server_params = StdioServerParameters(
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command="mcp", # Executable
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args=["run", "src/server.py"], # Optional command line arguments
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env=None # Optional environment variables
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)
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text = """
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Artificial intelligence, or AI, is technology that enables computers
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and machines to simulate human intelligence and problem-solving
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capabilities.
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On its own or combined with other technologies (e.g., sensors,
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geolocation, robotics) AI can perform tasks that would otherwise
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require human intelligence or intervention. Digital assistants, GPS
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guidance, autonomous vehicles, and generative AI tools (like Open
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AI's Chat GPT) are just a few examples of AI in the daily news and
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our daily lives.
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As a field of computer science, artificial intelligence encompasses
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(and is often mentioned together with) machine learning and deep
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learning. These disciplines involve the development of AI
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algorithms, modeled after the decision-making processes of the human
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brain, that can ‘learn’ from available data and make increasingly
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more accurate classifications or predictions over time.
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"""
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async def run():
|
||||
async with stdio_client(server_params) as (read, write):
|
||||
async with ClientSession(read, write, timedelta(minutes=3)) as session:
|
||||
await session.initialize()
|
||||
|
||||
toolResult = await session.call_tool("cognify", arguments={"text": text})
|
||||
# toolResult = await session.call_tool("search", arguments={"search_query": "AI"})
|
||||
|
||||
print(f"Cognify result: {toolResult}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
asyncio.run(run())
|
||||
118
cognee-mcp/src/server.py
Normal file
118
cognee-mcp/src/server.py
Normal file
|
|
@ -0,0 +1,118 @@
|
|||
import os
|
||||
import cognee
|
||||
import importlib.util
|
||||
# from PIL import Image as PILImage
|
||||
from mcp.server.fastmcp import FastMCP
|
||||
from cognee.api.v1.search import SearchType
|
||||
from cognee.shared.data_models import KnowledgeGraph
|
||||
|
||||
mcp = FastMCP("cognee", timeout=120000)
|
||||
|
||||
@mcp.tool()
|
||||
async def cognify(text: str, graph_model_file: str = None, graph_model_name: str = None) -> str:
|
||||
"""Build knowledge graph from the input text"""
|
||||
if graph_model_file and graph_model_name:
|
||||
graph_model = load_class(graph_model_file, graph_model_name)
|
||||
else:
|
||||
graph_model = KnowledgeGraph
|
||||
|
||||
await cognee.add(text)
|
||||
|
||||
try:
|
||||
await cognee.cognify(graph_model=graph_model)
|
||||
except Exception as e:
|
||||
raise ValueError(f"Failed to cognify: {str(e)}")
|
||||
|
||||
return "Ingested"
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def search(search_query: str) -> str:
|
||||
"""Search the knowledge graph"""
|
||||
search_results = await cognee.search(SearchType.INSIGHTS, query_text=search_query)
|
||||
|
||||
results = retrieved_edges_to_string(search_results)
|
||||
|
||||
return results
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def prune() -> str:
|
||||
"""Reset the knowledge graph"""
|
||||
await cognee.prune.prune_data()
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
|
||||
return "Pruned"
|
||||
|
||||
|
||||
# @mcp.tool()
|
||||
# async def visualize() -> Image:
|
||||
# """Visualize the knowledge graph"""
|
||||
# try:
|
||||
# image_path = await cognee.visualize_graph()
|
||||
|
||||
# img = PILImage.open(image_path)
|
||||
# return Image(data=img.tobytes(), format="png")
|
||||
# except (FileNotFoundError, IOError, ValueError) as e:
|
||||
# raise ValueError(f"Failed to create visualization: {str(e)}")
|
||||
|
||||
|
||||
|
||||
def node_to_string(node):
|
||||
node_data = ", ".join(
|
||||
[f'{key}: "{value}"' for key, value in node.items() if key in ["id", "name"]]
|
||||
)
|
||||
|
||||
return f"Node({node_data})"
|
||||
|
||||
|
||||
def retrieved_edges_to_string(search_results):
|
||||
edge_strings = []
|
||||
for triplet in search_results:
|
||||
node1, edge, node2 = triplet
|
||||
relationship_type = edge["relationship_name"]
|
||||
edge_str = f"{node_to_string(node1)} {relationship_type} {node_to_string(node2)}"
|
||||
edge_strings.append(edge_str)
|
||||
return "\n".join(edge_strings)
|
||||
|
||||
|
||||
def load_class(model_file, model_name):
|
||||
model_file = os.path.abspath(model_file)
|
||||
spec = importlib.util.spec_from_file_location("graph_model", model_file)
|
||||
module = importlib.util.module_from_spec(spec)
|
||||
spec.loader.exec_module(module)
|
||||
|
||||
model_class = getattr(module, model_name)
|
||||
|
||||
return model_class
|
||||
|
||||
|
||||
# def get_freshest_png(directory: str) -> Image:
|
||||
# if not os.path.exists(directory):
|
||||
# raise FileNotFoundError(f"Directory {directory} does not exist")
|
||||
|
||||
# # List all files in 'directory' that end with .png
|
||||
# files = [f for f in os.listdir(directory) if f.endswith(".png")]
|
||||
# if not files:
|
||||
# raise FileNotFoundError("No PNG files found in the given directory.")
|
||||
|
||||
# # Sort by integer value of the filename (minus the '.png')
|
||||
# # Example filename: 1673185134.png -> integer 1673185134
|
||||
# try:
|
||||
# files_sorted = sorted(files, key=lambda x: int(x.replace(".png", "")))
|
||||
# except ValueError as e:
|
||||
# raise ValueError("Invalid PNG filename format. Expected timestamp format.") from e
|
||||
|
||||
# # The "freshest" file has the largest timestamp
|
||||
# freshest_filename = files_sorted[-1]
|
||||
# freshest_path = os.path.join(directory, freshest_filename)
|
||||
|
||||
# # Open the image with PIL and return the PIL Image object
|
||||
# try:
|
||||
# return PILImage.open(freshest_path)
|
||||
# except (IOError, OSError) as e:
|
||||
# raise IOError(f"Failed to open PNG file {freshest_path}") from e
|
||||
|
||||
if __name__ == "__main__":
|
||||
# Initialize and run the server
|
||||
mcp.run(transport="stdio")
|
||||
3057
cognee-mcp/uv.lock
generated
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
|
||||
|
|
|
|||
521
poetry.lock
generated
521
poetry.lock
generated
|
|
@ -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]]
|
||||
name = "aiofiles"
|
||||
|
|
@ -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"
|
||||
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[package.dependencies]
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||||
|
|
@ -4090,7 +4090,7 @@ regex = ">=2022.4"
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requests = ">=2.26,<3.0"
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[package.extras]
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git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2.4,<2.0)"]
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git = ["mkdocs-git-committers-plugin-2 (>=1.1,<3)", "mkdocs-git-revision-date-localized-plugin (>=1.2.4,<2.0)"]
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@ -4810,86 +4810,90 @@ files = [
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[[package]]
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]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -4994,8 +4998,8 @@ files = [
|
|||
[package.dependencies]
|
||||
numpy = [
|
||||
{version = ">=1.22.4", markers = "python_version < \"3.11\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=1.23.2", markers = "python_version == \"3.11\""},
|
||||
{version = ">=1.26.0", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
python-dateutil = ">=2.8.2"
|
||||
pytz = ">=2020.1"
|
||||
|
|
@ -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 = "*"
|
||||
files = [
|
||||
{file = "posthog-3.8.3-py2.py3-none-any.whl", hash = "sha256:7215c4d7649b0c87905b42f460403311564996d776ab48d39852f46539a50f22"},
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|
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{file = "posthog-3.8.4.tar.gz", hash = "sha256:ba8cd14bca58686a199b1ba5655d3bad67c09a3a381062347eb30908282df1da"},
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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"
|
||||
files = [
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||||
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||||
]
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||||
|
||||
[package.dependencies]
|
||||
|
|
@ -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]]
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||||
name = "pydantic"
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||||
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[package.dependencies]
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||||
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||||
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||||
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[package.extras]
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||||
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[[package]]
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{file = "pydantic_core-2.27.2-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:f141ee28a0ad2123b6611b6ceff018039df17f32ada8b534e6aa039545a3efb2"},
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||||
{file = "pydantic_core-2.27.2-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:7d0c8399fcc1848491f00e0314bd59fb34a9c008761bcb422a057670c3f65e35"},
|
||||
{file = "pydantic_core-2.27.2.tar.gz", hash = "sha256:eb026e5a4c1fee05726072337ff51d1efb6f59090b7da90d30ea58625b1ffb39"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -5911,8 +5927,8 @@ astroid = ">=3.3.8,<=3.4.0-dev0"
|
|||
colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
|
||||
dill = [
|
||||
{version = ">=0.2", markers = "python_version < \"3.11\""},
|
||||
{version = ">=0.3.7", markers = "python_version >= \"3.12\""},
|
||||
{version = ">=0.3.6", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
|
||||
{version = ">=0.3.7", markers = "python_version >= \"3.12\""},
|
||||
]
|
||||
isort = ">=4.2.5,<5.13.0 || >5.13.0,<6"
|
||||
mccabe = ">=0.6,<0.8"
|
||||
|
|
@ -6465,13 +6481,13 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
|
|||
|
||||
[[package]]
|
||||
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"},
|
||||
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|
||||
{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"},
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||||
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|
||||
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|
||||
{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]]
|
||||
|
|
@ -8797,4 +8814,4 @@ weaviate = ["weaviate-client"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10.0,<3.13"
|
||||
content-hash = "097955773827cdf96b42e54328f66b79e2b92e5a7f221a06afe1a71fea2c33bc"
|
||||
content-hash = "d40b127fc83e2f623276d7f001e726490a4ccad195350e8ff0b10c7e3b53775a"
|
||||
|
|
|
|||
|
|
@ -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"
|
||||
|
|
@ -79,9 +79,6 @@ bokeh="^3.6.2"
|
|||
nltk = "3.9.1"
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
[tool.poetry.extras]
|
||||
filesystem = ["s3fs", "botocore"]
|
||||
weaviate = ["weaviate-client"]
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue