From 00f302c37abc279f49bde7e3c0bb5ee8d2d5ea0c Mon Sep 17 00:00:00 2001 From: Boris Arzentar Date: Thu, 23 Jan 2025 11:45:40 +0100 Subject: [PATCH 1/2] feat: use fastmcp for mcp server --- cognee-mcp/README.md | 47 +- cognee-mcp/cognee_mcp/__init__.py | 15 - cognee-mcp/cognee_mcp/server.py | 235 -- cognee-mcp/pyproject.toml | 79 +- cognee-mcp/src/__init__.py | 5 + cognee-mcp/src/client.py | 42 + cognee-mcp/src/server.py | 118 + cognee-mcp/uv.lock | 3117 ++++------------- .../databases/graph/networkx/adapter.py | 4 +- poetry.lock | 521 +-- pyproject.toml | 7 +- 11 files changed, 1095 insertions(+), 3095 deletions(-) delete mode 100644 cognee-mcp/cognee_mcp/__init__.py delete mode 100644 cognee-mcp/cognee_mcp/server.py create mode 100644 cognee-mcp/src/__init__.py create mode 100644 cognee-mcp/src/client.py create mode 100644 cognee-mcp/src/server.py diff --git a/cognee-mcp/README.md b/cognee-mcp/README.md index dd3ea0b8d..4f3f726fe 100644 --- a/cognee-mcp/README.md +++ b/cognee-mcp/README.md @@ -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" - ] - } + } } } ``` @@ -89,19 +66,21 @@ To install Cognee for Claude Desktop automatically via [Smithery](https://smithe npx -y @smithery/cli install cognee --client claude ``` -Define cognify tool in server.py +Define cognify tool in server.py. 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` diff --git a/cognee-mcp/cognee_mcp/__init__.py b/cognee-mcp/cognee_mcp/__init__.py deleted file mode 100644 index 8c1ca4328..000000000 --- a/cognee-mcp/cognee_mcp/__init__.py +++ /dev/null @@ -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() diff --git a/cognee-mcp/cognee_mcp/server.py b/cognee-mcp/cognee_mcp/server.py deleted file mode 100644 index 6ae93934e..000000000 --- a/cognee-mcp/cognee_mcp/server.py +++ /dev/null @@ -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()) diff --git a/cognee-mcp/pyproject.toml b/cognee-mcp/pyproject.toml index 5da82f935..5b7ea57e8 100644 --- a/cognee-mcp/pyproject.toml +++ b/cognee-mcp/pyproject.toml @@ -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" \ No newline at end of file +cognee = "src:main" diff --git a/cognee-mcp/src/__init__.py b/cognee-mcp/src/__init__.py new file mode 100644 index 000000000..a9f366bde --- /dev/null +++ b/cognee-mcp/src/__init__.py @@ -0,0 +1,5 @@ +from .server import mcp + +def main(): + """Main entry point for the package.""" + mcp.run(transport="stdio") diff --git a/cognee-mcp/src/client.py b/cognee-mcp/src/client.py new file mode 100644 index 000000000..d972fb84c --- /dev/null +++ b/cognee-mcp/src/client.py @@ -0,0 +1,42 @@ +from datetime import timedelta +from mcp import ClientSession, StdioServerParameters +from mcp.client.stdio import stdio_client + +# Create server parameters for stdio connection +server_params = StdioServerParameters( + command="mcp", # Executable + args=["run", "src/server.py"], # Optional command line arguments + env=None # Optional environment variables +) + +text = """ +Artificial intelligence, or AI, is technology that enables computers +and machines to simulate human intelligence and problem-solving +capabilities. +On its own or combined with other technologies (e.g., sensors, +geolocation, robotics) AI can perform tasks that would otherwise +require human intelligence or intervention. Digital assistants, GPS +guidance, autonomous vehicles, and generative AI tools (like Open +AI's Chat GPT) are just a few examples of AI in the daily news and +our daily lives. +As a field of computer science, artificial intelligence encompasses +(and is often mentioned together with) machine learning and deep +learning. These disciplines involve the development of AI +algorithms, modeled after the decision-making processes of the human +brain, that can ‘learn’ from available data and make increasingly +more accurate classifications or predictions over time. +""" + +async def run(): + async with stdio_client(server_params) as (read, write): + async with ClientSession(read, write, timedelta(minutes=3)) as session: + await session.initialize() + + toolResult = await session.call_tool("cognify", arguments={"text": text}) + # toolResult = await session.call_tool("search", arguments={"search_query": "AI"}) + + print(f"Cognify result: {toolResult}") + +if __name__ == "__main__": + import asyncio + asyncio.run(run()) diff --git a/cognee-mcp/src/server.py b/cognee-mcp/src/server.py new file mode 100644 index 000000000..3584163f0 --- /dev/null +++ b/cognee-mcp/src/server.py @@ -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") diff --git a/cognee-mcp/uv.lock b/cognee-mcp/uv.lock index 503a1ba82..b79e6695b 100644 --- a/cognee-mcp/uv.lock +++ b/cognee-mcp/uv.lock @@ -1,11 +1,9 @@ version = 1 requires-python = ">=3.10" resolution-markers = [ - "python_full_version < '3.11'", + "python_full_version >= '3.12'", "python_full_version == '3.11.*'", - "python_full_version >= '3.12' and python_full_version < '3.12.4'", - "python_full_version >= '3.12.4' and python_full_version < '3.13'", - "python_full_version >= '3.13'", + 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= "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" diff --git a/pyproject.toml b/pyproject.toml index ea0b64404..14528777c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -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"] From eb22dc9889cc3600eb9c568d99c0b27a9b860349 Mon Sep 17 00:00:00 2001 From: Boris Arzentar Date: Thu, 23 Jan 2025 11:47:20 +0100 Subject: [PATCH 2/2] fix: remove unnecessary dot --- cognee-mcp/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cognee-mcp/README.md b/cognee-mcp/README.md index 4f3f726fe..c9926270c 100644 --- a/cognee-mcp/README.md +++ b/cognee-mcp/README.md @@ -66,7 +66,7 @@ To install Cognee for Claude Desktop automatically via [Smithery](https://smithe npx -y @smithery/cli install cognee --client claude ``` -Define cognify tool in server.py. +Define cognify tool in server.py Restart your Claude desktop.