Merge branch 'topoteretes:main' into fix/token-expiration-time

CONTRIBUTING.md

@@ -97,6 +97,21 @@ git checkout -b feature/your-feature-name
 python cognee/cognee/tests/test_library.py
 ```
 
+### Running Simple Example
+
+Change .env.example into .env and provide your OPENAI_API_KEY as LLM_API_KEY
+
+Make sure to run ```shell uv sync ``` in the root cloned folder or set up a virtual environment to run cognee
+
+```shell
+python cognee/cognee/examples/python/simple_example.py
+```
+or 
+
+```shell
+uv run python cognee/cognee/examples/python/simple_example.py
+```
+
 ## 4. 📤 Submitting Changes
 
 1. Install ruff on your system

cognee-mcp/src/cognee_client.py

@@ -151,7 +151,7 @@ class CogneeClient:
         query_type: str,
         datasets: Optional[List[str]] = None,
         system_prompt: Optional[str] = None,
-        top_k: int = 10,
+        top_k: int = 5,
     ) -> Any:
         """
         Search the knowledge graph.
@@ -192,7 +192,9 @@ class CogneeClient:
 
             with redirect_stdout(sys.stderr):
                 results = await self.cognee.search(
-                    query_type=SearchType[query_type.upper()], query_text=query_text
+                    query_type=SearchType[query_type.upper()],
+                    query_text=query_text,
+                    top_k=top_k
                 )
                 return results
 

cognee-mcp/src/server.py

@@ -316,7 +316,7 @@ async def save_interaction(data: str) -> list:
 
 
 @mcp.tool()
-async def search(search_query: str, search_type: str) -> list:
+async def search(search_query: str, search_type: str, top_k: int = 5) -> list:
     """
     Search and query the knowledge graph for insights, information, and connections.
 
@@ -389,6 +389,13 @@ async def search(search_query: str, search_type: str) -> list:
 
         The search_type is case-insensitive and will be converted to uppercase.
 
+    top_k : int, optional
+        Maximum number of results to return (default: 5).
+        Controls the amount of context retrieved from the knowledge graph.
+        - Lower values (3-5): Faster, more focused results
+        - Higher values (10-20): More comprehensive, but slower and more context-heavy
+        Helps manage response size and context window usage in MCP clients.
+
     Returns
     -------
     list
@@ -425,13 +432,32 @@ async def search(search_query: str, search_type: str) -> list:
 
     """
 
-    async def search_task(search_query: str, search_type: str) -> str:
-        """Search the knowledge graph"""
+    async def search_task(search_query: str, search_type: str, top_k: int) -> str:
+        """
+        Internal task to execute knowledge graph search with result formatting.
+
+        Handles the actual search execution and formats results appropriately
+        for MCP clients based on the search type and execution mode (API vs direct).
+
+        Parameters
+        ----------
+        search_query : str
+            The search query in natural language
+        search_type : str
+            Type of search to perform (GRAPH_COMPLETION, CHUNKS, etc.)
+        top_k : int
+            Maximum number of results to return
+
+        Returns
+        -------
+        str
+            Formatted search results as a string, with format depending on search_type
+        """
         # NOTE: MCP uses stdout to communicate, we must redirect all output
         #       going to stdout ( like the print function ) to stderr.
         with redirect_stdout(sys.stderr):
             search_results = await cognee_client.search(
-                query_text=search_query, query_type=search_type
+                query_text=search_query, query_type=search_type, top_k=top_k
             )
 
             # Handle different result formats based on API vs direct mode
@@ -465,7 +491,7 @@ async def search(search_query: str, search_type: str) -> list:
                 else:
                     return str(search_results)
 
-    search_results = await search_task(search_query, search_type)
+    search_results = await search_task(search_query, search_type, top_k)
     return [types.TextContent(type="text", text=search_results)]