merge done
This commit is contained in:
vasilije
commit
a5b28983bd
9 changed files with 18 additions and 225 deletions
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@ -97,7 +97,7 @@ git push origin feature/your-feature-name
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2. Create a Pull Request:
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- Go to the [**cognee** repository](https://github.com/topoteretes/cognee)
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- Click "Compare & Pull Request" and open a PR against dev branch
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- Click "Compare & Pull Request"
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- Fill in the PR template with details about your changes
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## 5. 📜 Developer Certificate of Origin (DCO)
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@ -8,7 +8,7 @@ requires-python = ">=3.10"
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dependencies = [
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# For local cognee repo usage remove comment bellow and add absolute path to cognee
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#"cognee[postgres,codegraph,gemini,huggingface] @ file:/Users/<username>/Desktop/cognee",
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"cognee[postgres,codegraph,gemini,huggingface,docs]==0.1.40",
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"cognee[postgres,codegraph,gemini,huggingface]==0.1.40",
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"fastmcp>=1.0",
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"mcp==1.5.0",
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"uv>=0.6.3",
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@ -24,46 +24,9 @@ log_file = get_log_file_location()
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@mcp.tool()
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async def cognify(data: str, graph_model_file: str = None, graph_model_name: str = None) -> list:
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"""
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Transform data into a structured knowledge graph in Cognee's memory layer.
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This function launches a background task that processes the provided text/file location and
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generates a knowledge graph representation. The function returns immediately while
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the processing continues in the background due to MCP timeout constraints.
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Parameters
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----------
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data : str
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The data to be processed and transformed into structured knowledge.
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This can include natural language, file location, or any text-based information
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that should become part of the agent's memory.
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graph_model_file : str, optional
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Path to a custom schema file that defines the structure of the generated knowledge graph.
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If provided, this file will be loaded using importlib to create a custom graph model.
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Default is None, which uses Cognee's built-in KnowledgeGraph model.
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graph_model_name : str, optional
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Name of the class within the graph_model_file to instantiate as the graph model.
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Required if graph_model_file is specified.
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Default is None, which uses the default KnowledgeGraph class.
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Returns
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-------
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list
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A list containing a single TextContent object with information about the
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background task launch and how to check its status.
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Notes
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-----
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- The function launches a background task and returns immediately
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- The actual cognify process may take significant time depending on text length
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- Use the cognify_status tool to check the progress of the operation
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"""
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async def cognify(text: str, graph_model_file: str = None, graph_model_name: str = None) -> list:
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async def cognify_task(
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data: str, graph_model_file: str = None, graph_model_name: str = None
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text: str, graph_model_file: str = None, graph_model_name: str = None
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) -> str:
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"""Build knowledge graph from the input text"""
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# NOTE: MCP uses stdout to communicate, we must redirect all output
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@ -75,7 +38,7 @@ async def cognify(data: str, graph_model_file: str = None, graph_model_name: str
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else:
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graph_model = KnowledgeGraph
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await cognee.add(data)
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await cognee.add(text)
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try:
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await cognee.cognify(graph_model=graph_model)
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@ -86,7 +49,7 @@ async def cognify(data: str, graph_model_file: str = None, graph_model_name: str
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asyncio.create_task(
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cognify_task(
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data=data,
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text=text,
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graph_model_file=graph_model_file,
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graph_model_name=graph_model_name,
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)
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@ -108,35 +71,6 @@ async def cognify(data: str, graph_model_file: str = None, graph_model_name: str
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@mcp.tool()
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async def codify(repo_path: str) -> list:
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"""
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Analyze and generate a code-specific knowledge graph from a software repository.
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This function launches a background task that processes the provided repository
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and builds a code knowledge graph. The function returns immediately while
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the processing continues in the background due to MCP timeout constraints.
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Parameters
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----------
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repo_path : str
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Path to the code repository to analyze. This can be a local file path or a
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relative path to a repository. The path should point to the root of the
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repository or a specific directory within it.
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Returns
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-------
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list
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A list containing a single TextContent object with information about the
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background task launch and how to check its status.
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Notes
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-----
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- The function launches a background task and returns immediately
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- The code graph generation may take significant time for larger repositories
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- Use the codify_status tool to check the progress of the operation
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- Process results are logged to the standard Cognee log file
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- All stdout is redirected to stderr to maintain MCP communication integrity
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"""
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async def codify_task(repo_path: str):
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# NOTE: MCP uses stdout to communicate, we must redirect all output
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# going to stdout ( like the print function ) to stderr.
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@ -169,46 +103,6 @@ async def codify(repo_path: str) -> list:
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@mcp.tool()
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async def search(search_query: str, search_type: str) -> list:
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"""
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Search the Cognee knowledge graph for information relevant to the query.
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This function executes a search against the Cognee knowledge graph using the
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specified query and search type. It returns formatted results based on the
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search type selected.
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Parameters
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----------
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search_query : str
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The search query in natural language. This can be a question, instruction, or
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any text that expresses what information is needed from the knowledge graph.
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search_type : str
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The type of search to perform. Valid options include:
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- "GRAPH_COMPLETION": Returns an LLM response based on the search query and Cognee's memory
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- "RAG_COMPLETION": Returns an LLM response based on the search query and standard RAG data
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- "CODE": Returns code-related knowledge in JSON format
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- "CHUNKS": Returns raw text chunks from the knowledge graph
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- "INSIGHTS": Returns relationships between nodes in readable format
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The search_type is case-insensitive and will be converted to uppercase.
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Returns
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-------
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list
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A list containing a single TextContent object with the search results.
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The format of the result depends on the search_type:
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- For CODE: JSON-formatted search results
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- For GRAPH_COMPLETION/RAG_COMPLETION: A single text completion
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- For CHUNKS: String representation of the raw chunks
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- For INSIGHTS: Formatted string showing node relationships
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- For other types: String representation of the search results
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Notes
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-----
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- Different search types produce different output formats
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- The function handles the conversion between Cognee's internal result format and MCP's output format
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"""
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async def search_task(search_query: str, search_type: str) -> str:
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"""Search the knowledge graph"""
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# NOTE: MCP uses stdout to communicate, we must redirect all output
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@ -238,24 +132,7 @@ async def search(search_query: str, search_type: str) -> list:
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@mcp.tool()
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async def prune():
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"""
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Reset the Cognee knowledge graph by removing all stored information.
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This function performs a complete reset of both the data layer and system layer
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of the Cognee knowledge graph, removing all nodes, edges, and associated metadata.
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It is typically used during development or when needing to start fresh with a new
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knowledge base.
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Returns
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-------
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list
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A list containing a single TextContent object with confirmation of the prune operation.
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Notes
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-----
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- This operation cannot be undone. All memory data will be permanently deleted.
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- The function prunes both data content (using prune_data) and system metadata (using prune_system)
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"""
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"""Reset the knowledge graph"""
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with redirect_stdout(sys.stderr):
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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@ -264,25 +141,7 @@ async def prune():
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@mcp.tool()
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async def cognify_status():
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"""
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Get the current status of the cognify pipeline.
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This function retrieves information about current and recently completed cognify operations
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in the main_dataset. It provides details on progress, success/failure status, and statistics
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about the processed data.
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Returns
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-------
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list
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A list containing a single TextContent object with the status information as a string.
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The status includes information about active and completed jobs for the cognify_pipeline.
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Notes
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-----
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- The function retrieves pipeline status specifically for the "cognify_pipeline" on the "main_dataset"
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- Status information includes job progress, execution time, and completion status
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- The status is returned in string format for easy reading
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"""
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"""Get status of cognify pipeline"""
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with redirect_stdout(sys.stderr):
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user = await get_default_user()
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status = await get_pipeline_status(
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@ -293,25 +152,7 @@ async def cognify_status():
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@mcp.tool()
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async def codify_status():
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"""
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Get the current status of the codify pipeline.
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This function retrieves information about current and recently completed codify operations
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in the codebase dataset. It provides details on progress, success/failure status, and statistics
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about the processed code repositories.
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Returns
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-------
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list
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A list containing a single TextContent object with the status information as a string.
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The status includes information about active and completed jobs for the cognify_code_pipeline.
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Notes
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-----
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- The function retrieves pipeline status specifically for the "cognify_code_pipeline" on the "codebase" dataset
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- Status information includes job progress, execution time, and completion status
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- The status is returned in string format for easy reading
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"""
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"""Get status of codify pipeline"""
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with redirect_stdout(sys.stderr):
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user = await get_default_user()
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status = await get_pipeline_status(
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@ -178,18 +178,10 @@ class MilvusAdapter(VectorDBInterface):
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):
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from pymilvus import MilvusException, exceptions
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if limit <= 0:
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return []
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client = self.get_milvus_client()
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if query_text is None and query_vector is None:
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raise ValueError("One of query_text or query_vector must be provided!")
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if not client.has_collection(collection_name=collection_name):
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logger.warning(
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f"Collection '{collection_name}' not found in MilvusAdapter.search; returning []."
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)
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return []
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try:
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query_vector = query_vector or (await self.embed_data([query_text]))[0]
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@ -216,19 +208,12 @@ class MilvusAdapter(VectorDBInterface):
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)
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for result in results[0]
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]
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except exceptions.CollectionNotExistException:
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logger.warning(
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f"Collection '{collection_name}' not found (exception) in MilvusAdapter.search; returning []."
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)
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return []
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except exceptions.CollectionNotExistException as error:
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raise CollectionNotFoundError(
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f"Collection '{collection_name}' does not exist!"
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) from error
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except MilvusException as e:
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# Catch other Milvus errors that are "collection not found" (paranoid safety)
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if "collection not found" in str(e).lower() or "schema" in str(e).lower():
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logger.warning(
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f"Collection '{collection_name}' not found (MilvusException) in MilvusAdapter.search; returning []."
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)
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return []
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logger.error(f"Error searching Milvus collection '{collection_name}': {e}")
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logger.error(f"Error during search in collection '{collection_name}': {str(e)}")
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raise e
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async def batch_search(
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@ -159,24 +159,12 @@ class QDrantAdapter(VectorDBInterface):
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query_vector: Optional[List[float]] = None,
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limit: int = 15,
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with_vector: bool = False,
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) -> List[ScoredResult]:
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):
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from qdrant_client.http.exceptions import UnexpectedResponse
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if query_text is None and query_vector is None:
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raise InvalidValueError(message="One of query_text or query_vector must be provided!")
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if limit <= 0:
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return []
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if not await self.has_collection(collection_name):
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logger.warning(
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f"Collection '{collection_name}' not found in QdrantAdapter.search; returning []."
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)
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return []
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if query_vector is None:
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query_vector = (await self.embed_data([query_text]))[0]
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try:
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client = self.get_qdrant_client()
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@ -113,7 +113,7 @@ class WeaviateAdapter(VectorDBInterface):
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# )
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else:
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data_point: DataObject = data_points[0]
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if await collection.data.exists(data_point.uuid):
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if collection.data.exists(data_point.uuid):
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return await collection.data.update(
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uuid=data_point.uuid,
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vector=data_point.vector,
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@ -146,7 +146,7 @@ async def brute_force_search(
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async def search_in_collection(collection_name: str):
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try:
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return await vector_engine.search(
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collection_name=collection_name, query_text=query, limit=50
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collection_name=collection_name, query_text=query, limit=0
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)
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except CollectionNotFoundError:
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return []
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@ -136,27 +136,6 @@ Repository = "https://github.com/topoteretes/cognee"
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build]
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exclude = [
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"/bin",
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"/dist",
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"/.data",
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"/.github",
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"/alembic",
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"/distributed",
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"/deployment",
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"/cognee-mcp",
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"/cognee-frontend",
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"/examples",
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"/helm",
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"/licenses",
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"/logs",
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"/notebooks",
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"/profiling",
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"/tests",
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"/tools",
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]
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[tool.ruff]
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line-length = 100
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exclude = [
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|
|
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2
uv.lock
generated
2
uv.lock
generated
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@ -860,7 +860,7 @@ wheels = [
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[[package]]
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name = "cognee"
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version = "0.1.40"
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version = "0.1.39"
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source = { editable = "." }
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dependencies = [
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||||
{ name = "aiofiles" },
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|
|
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|||
Loading…
Add table
Reference in a new issue