Remove all references to SearchType.INSIGHTS across the codebase, meaningfully replacing it with SearchType.GRAPH_COMPLETION where applicable.

cognee-frontend/src/modules/chat/hooks/useChat.ts

@@ -89,15 +89,6 @@ export default function useChat(dataset: Dataset) {
 }
 
 
-interface Node {
-  name: string;
-}
-
-interface Relationship {
-  relationship_name: string;
-}
-
-type InsightMessage = [Node, Relationship, Node];
 
 // eslint-disable-next-line @typescript-eslint/no-explicit-any
 function convertToSearchTypeOutput(systemMessage: any[] | any, searchType: string): string {
@@ -106,14 +97,6 @@ function convertToSearchTypeOutput(systemMessage: any[] | any, searchType: strin
   }
 
   switch (searchType) {
-    case "INSIGHTS":
-      return systemMessage.map((message: InsightMessage) => {
-        const [node1, relationship, node2] = message;
-        if (node1.name && node2.name) {
-          return `${node1.name} ${relationship.relationship_name} ${node2.name}.`;
-        }
-        return "";
-      }).join("\n");
     case "SUMMARIES":
       return systemMessage.map((message: { text: string }) => message.text).join("\n");
     case "CHUNKS":

cognee-mcp/README.md

@@ -266,7 +266,7 @@ The MCP server exposes its functionality through tools. Call them from any MCP c
 
 - **codify**: Analyse a code repository, build a code graph, stores it in memory
 
-- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS, INSIGHTS
+- **search**: Query memory – supports GRAPH_COMPLETION, RAG_COMPLETION, CODE, CHUNKS
 
 - **list_data**: List all datasets and their data items with IDs for deletion operations
 

cognee-mcp/src/server.py

@@ -478,11 +478,6 @@ async def search(search_query: str, search_type: str) -> list:
             Best for: Direct document retrieval, specific fact-finding.
             Returns: LLM responses based on relevant text chunks.
 
-        **INSIGHTS**:
-            Structured entity relationships and semantic connections.
-            Best for: Understanding concept relationships, knowledge mapping.
-            Returns: Formatted relationship data and entity connections.
-
         **CHUNKS**:
             Raw text segments that match the query semantically.
             Best for: Finding specific passages, citations, exact content.
@@ -524,7 +519,6 @@ async def search(search_query: str, search_type: str) -> list:
         - "RAG_COMPLETION": Returns an LLM response based on the search query and standard RAG data
         - "CODE": Returns code-related knowledge in JSON format
         - "CHUNKS": Returns raw text chunks from the knowledge graph
-        - "INSIGHTS": Returns relationships between nodes in readable format
         - "SUMMARIES": Returns pre-generated hierarchical summaries
         - "CYPHER": Direct graph database queries
         - "FEELING_LUCKY": Automatically selects best search type
@@ -537,7 +531,6 @@ async def search(search_query: str, search_type: str) -> list:
         A list containing a single TextContent object with the search results.
         The format of the result depends on the search_type:
         - **GRAPH_COMPLETION/RAG_COMPLETION**: Conversational AI response strings
-        - **INSIGHTS**: Formatted relationship descriptions and entity connections
         - **CHUNKS**: Relevant text passages with source metadata
         - **SUMMARIES**: Hierarchical summaries from general to specific
         - **CODE**: Structured code information with context
@@ -547,7 +540,6 @@ async def search(search_query: str, search_type: str) -> list:
     Performance & Optimization:
         - **GRAPH_COMPLETION**: Slower but most intelligent, uses LLM + graph context
         - **RAG_COMPLETION**: Medium speed, uses LLM + document chunks (no graph traversal)
-        - **INSIGHTS**: Fast, returns structured relationships without LLM processing
         - **CHUNKS**: Fastest, pure vector similarity search without LLM
         - **SUMMARIES**: Fast, returns pre-computed summaries
         - **CODE**: Medium speed, specialized for code understanding
@@ -586,9 +578,6 @@ async def search(search_query: str, search_type: str) -> list:
                 return str(search_results[0])
             elif search_type.upper() == "CHUNKS":
                 return str(search_results)
-            elif search_type.upper() == "INSIGHTS":
-                results = retrieved_edges_to_string(search_results)
-                return results
             else:
                 return str(search_results)
 

cognee/api/v1/responses/default_tools.py

@@ -14,7 +14,6 @@ DEFAULT_TOOLS = [
                     "type": "string",
                     "description": "Type of search to perform",
                     "enum": [
-                        "INSIGHTS",
                         "CODE",
                         "GRAPH_COMPLETION",
                         "NATURAL_LANGUAGE",

cognee/api/v1/responses/dispatch_function.py

@@ -59,7 +59,7 @@ async def handle_search(arguments: Dict[str, Any], user) -> list:
     valid_search_types = (
         search_tool["parameters"]["properties"]["search_type"]["enum"]
         if search_tool
-        else ["INSIGHTS", "CODE", "GRAPH_COMPLETION", "NATURAL_LANGUAGE"]
+        else ["CODE", "GRAPH_COMPLETION", "NATURAL_LANGUAGE"]
     )
 
     if search_type_str not in valid_search_types:

cognee/api/v1/responses/routers/default_tools.py

@@ -14,7 +14,6 @@ DEFAULT_TOOLS = [
                     "type": "string",
                     "description": "Type of search to perform",
                     "enum": [
-                        "INSIGHTS",
                         "CODE",
                         "GRAPH_COMPLETION",
                         "NATURAL_LANGUAGE",

cognee/api/v1/search/search.py

@@ -52,11 +52,6 @@ async def search(
             Best for: Direct document retrieval, specific fact-finding.
             Returns: LLM responses based on relevant text chunks.
 
-        **INSIGHTS**:
-            Structured entity relationships and semantic connections.
-            Best for: Understanding concept relationships, knowledge mapping.
-            Returns: Formatted relationship data and entity connections.
-
         **CHUNKS**:
             Raw text segments that match the query semantically.
             Best for: Finding specific passages, citations, exact content.
@@ -124,9 +119,6 @@ async def search(
             **GRAPH_COMPLETION/RAG_COMPLETION**:
                 [List of conversational AI response strings]
 
-            **INSIGHTS**:
-                [List of formatted relationship descriptions and entity connections]
-
             **CHUNKS**:
                 [List of relevant text passages with source metadata]
 
@@ -146,7 +138,6 @@ async def search(
     Performance & Optimization:
         - **GRAPH_COMPLETION**: Slower but most intelligent, uses LLM + graph context
         - **RAG_COMPLETION**: Medium speed, uses LLM + document chunks (no graph traversal)
-        - **INSIGHTS**: Fast, returns structured relationships without LLM processing
         - **CHUNKS**: Fastest, pure vector similarity search without LLM
         - **SUMMARIES**: Fast, returns pre-computed summaries
         - **CODE**: Medium speed, specialized for code understanding

cognee/cli/commands/search_command.py

@@ -31,10 +31,6 @@ Search Types & Use Cases:
     Traditional RAG using document chunks without graph structure.
     Best for: Direct document retrieval, specific fact-finding.
 
-**INSIGHTS**:
-    Structured entity relationships and semantic connections.
-    Best for: Understanding concept relationships, knowledge mapping.
-
 **CHUNKS**:
     Raw text segments that match the query semantically.
     Best for: Finding specific passages, citations, exact content.

cognee/cli/config.py

@@ -19,7 +19,6 @@ COMMAND_DESCRIPTIONS = {
 SEARCH_TYPE_CHOICES = [
     "GRAPH_COMPLETION",
     "RAG_COMPLETION",
-    "INSIGHTS",
     "CHUNKS",
     "SUMMARIES",
     "CODE",

cognee/infrastructure/llm/prompts/search_type_selector_prompt.txt

@@ -10,8 +10,6 @@ Here are the available `SearchType` tools and their specific functions:
   - Summarizing large amounts of information 
   - Quick understanding of complex subjects
 
-* **`INSIGHTS`**: The `INSIGHTS` search type discovers connections and relationships between entities in the knowledge graph.
-
   **Best for:**
 
   - Discovering how entities are connected
@@ -95,9 +93,6 @@ Here are the available `SearchType` tools and their specific functions:
 Query: "Summarize the key findings from these research papers"
 Response: `SUMMARIES`
 
-Query: "What is the relationship between the methodologies used in these papers?"
-Response: `INSIGHTS`
-
 Query: "When was Einstein born?"
 Response: `CHUNKS`
 

cognee/modules/retrieval/utils/description_to_codepart_search.py

@@ -62,7 +62,7 @@ async def code_description_to_code_part(
 
     try:
         if include_docs:
-            search_results = await search(query_text=query, query_type="INSIGHTS")
+            search_results = await search(query_text=query, query_type="GRAPH_COMPLETION")
 
             concatenated_descriptions = " ".join(
                 obj["description"]

cognee/tests/cli_tests/cli_unit_tests/test_cli_utils.py

@@ -53,7 +53,6 @@ class TestCliConfig:
         expected_types = [
             "GRAPH_COMPLETION",
             "RAG_COMPLETION",
-            "INSIGHTS",
             "CHUNKS",
             "SUMMARIES",
             "CODE",