COG-3050 - remove insights search (#1506)

<!-- .github/pull_request_template.md -->

## Description
<!--
Please provide a clear, human-generated description of the changes in
this PR.
DO NOT use AI-generated descriptions. We want to understand your thought
process and reasoning.
-->

As per COG-3050:
1. Remove insights search type and clean up any orphaned code
2. Replace callsites with default search type - `GRAPH_COMPLETION` -
where applicable

## Type of Change
<!-- Please check the relevant option -->
- [ ] Bug fix (non-breaking change that fixes an issue)
- [ ] New feature (non-breaking change that adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to change)
- [ ] Documentation update
- [x] Code refactoring
- [ ] Performance improvement
- [ ] Other (please specify):

## Screenshots/Videos (if applicable)
<!-- Add screenshots or videos to help explain your changes -->

## Pre-submission Checklist
<!-- Please check all boxes that apply before submitting your PR -->
- [ ] **I have tested my changes thoroughly before submitting this PR**
- [ ] **This PR contains minimal changes necessary to address the
issue/feature**
- [ ] My code follows the project's coding standards and style
guidelines
- [ ] I have added tests that prove my fix is effective or that my
feature works
- [ ] I have added necessary documentation (if applicable)
- [ ] All new and existing tests pass
- [ ] I have searched existing PRs to ensure this change hasn't been
submitted already
- [ ] I have linked any relevant issues in the description
- [ ] My commits have clear and descriptive messages

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin.

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

@@ -255,7 +255,7 @@ async def cognify(
         # 2. Get entity relationships and connections
         relationships = await cognee.search(
             "connections between concepts",
-            query_type=SearchType.INSIGHTS
+            query_type=SearchType.GRAPH_COMPLETION
         )
 
         # 3. Find relevant document chunks
@@ -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/cognify/cognify.py

@@ -148,7 +148,7 @@ async def cognify(
         # 2. Get entity relationships and connections
         relationships = await cognee.search(
             "connections between concepts",
-            query_type=SearchType.INSIGHTS
+            query_type=SearchType.GRAPH_COMPLETION
         )
 
         # 3. Find relevant document chunks

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/insights_retriever.py

@@ -1,133 +0,0 @@
-import asyncio
-from typing import Any, Optional
-
-from cognee.modules.graph.cognee_graph.CogneeGraphElements import Edge, Node
-from cognee.modules.retrieval.base_graph_retriever import BaseGraphRetriever
-from cognee.shared.logging_utils import get_logger
-from cognee.infrastructure.databases.graph import get_graph_engine
-from cognee.infrastructure.databases.vector import get_vector_engine
-from cognee.modules.retrieval.exceptions.exceptions import NoDataError
-from cognee.infrastructure.databases.vector.exceptions.exceptions import CollectionNotFoundError
-
-logger = get_logger("InsightsRetriever")
-
-
-class InsightsRetriever(BaseGraphRetriever):
-    """
-    Retriever for handling graph connection-based insights.
-
-    Public methods include:
-    - get_context
-    - get_completion
-
-    Instance variables include:
-    - exploration_levels
-    - top_k
-    """
-
-    def __init__(self, exploration_levels: int = 1, top_k: Optional[int] = 5):
-        """Initialize retriever with exploration levels and search parameters."""
-        self.exploration_levels = exploration_levels
-        self.top_k = top_k
-
-    async def get_context(self, query: str) -> list:
-        """
-        Find neighbours of a given node in the graph.
-
-        If the provided query does not correspond to an existing node,
-        search for similar entities and retrieve their connections.
-        Reraises NoDataError if there is no data found in the system.
-
-        Parameters:
-        -----------
-
-            - query (str): A string identifier for the node whose neighbours are to be
-              retrieved.
-
-        Returns:
-        --------
-
-            - list: A list of unique connections found for the queried node.
-        """
-        if query is None:
-            return []
-
-        node_id = query
-        graph_engine = await get_graph_engine()
-        exact_node = await graph_engine.extract_node(node_id)
-
-        if exact_node is not None and "id" in exact_node:
-            node_connections = await graph_engine.get_connections(str(exact_node["id"]))
-        else:
-            vector_engine = get_vector_engine()
-
-            try:
-                results = await asyncio.gather(
-                    vector_engine.search("Entity_name", query_text=query, limit=self.top_k),
-                    vector_engine.search("EntityType_name", query_text=query, limit=self.top_k),
-                )
-            except CollectionNotFoundError as error:
-                logger.error("Entity collections not found")
-                raise NoDataError("No data found in the system, please add data first.") from error
-
-            results = [*results[0], *results[1]]
-            relevant_results = [result for result in results if result.score < 0.5][: self.top_k]
-
-            if len(relevant_results) == 0:
-                return []
-
-            node_connections_results = await asyncio.gather(
-                *[graph_engine.get_connections(result.id) for result in relevant_results]
-            )
-
-            node_connections = []
-            for neighbours in node_connections_results:
-                node_connections.extend(neighbours)
-
-        unique_node_connections_map = {}
-        unique_node_connections = []
-
-        for node_connection in node_connections:
-            if "id" not in node_connection[0] or "id" not in node_connection[2]:
-                continue
-
-            unique_id = f"{node_connection[0]['id']} {node_connection[1]['relationship_name']} {node_connection[2]['id']}"
-            if unique_id not in unique_node_connections_map:
-                unique_node_connections_map[unique_id] = True
-                unique_node_connections.append(node_connection)
-
-        return unique_node_connections
-        # return [
-        #     Edge(
-        #         node1=Node(node_id=connection[0]["id"], attributes=connection[0]),
-        #         node2=Node(node_id=connection[2]["id"], attributes=connection[2]),
-        #         attributes={
-        #             **connection[1],
-        #             "relationship_type": connection[1]["relationship_name"],
-        #         },
-        #     )
-        #     for connection in unique_node_connections
-        # ]
-
-    async def get_completion(self, query: str, context: Optional[Any] = None) -> Any:
-        """
-        Returns the graph connections context.
-
-        If a context is not provided, it fetches the context using the query provided.
-
-        Parameters:
-        -----------
-
-            - query (str): A string identifier used to fetch the context.
-            - context (Optional[Any]): An optional context to use for the completion; if None,
-              it fetches the context based on the query. (default None)
-
-        Returns:
-        --------
-
-            - Any: The context used for the completion, which is either provided or fetched
-              based on the query.
-        """
-        if context is None:
-            context = await self.get_context(query)
-        return context

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/modules/search/methods/get_search_type_tools.py

@@ -9,7 +9,6 @@ from cognee.modules.search.exceptions import UnsupportedSearchTypeError
 # Retrievers
 from cognee.modules.retrieval.user_qa_feedback import UserQAFeedback
 from cognee.modules.retrieval.chunks_retriever import ChunksRetriever
-from cognee.modules.retrieval.insights_retriever import InsightsRetriever
 from cognee.modules.retrieval.summaries_retriever import SummariesRetriever
 from cognee.modules.retrieval.completion_retriever import CompletionRetriever
 from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever
@@ -44,10 +43,6 @@ async def get_search_type_tools(
             SummariesRetriever(top_k=top_k).get_completion,
             SummariesRetriever(top_k=top_k).get_context,
         ],
-        SearchType.INSIGHTS: [
-            InsightsRetriever(top_k=top_k).get_completion,
-            InsightsRetriever(top_k=top_k).get_context,
-        ],
         SearchType.CHUNKS: [
             ChunksRetriever(top_k=top_k).get_completion,
             ChunksRetriever(top_k=top_k).get_context,

cognee/modules/search/types/SearchType.py

@@ -3,7 +3,6 @@ from enum import Enum
 
 class SearchType(Enum):
     SUMMARIES = "SUMMARIES"
-    INSIGHTS = "INSIGHTS"
     CHUNKS = "CHUNKS"
     RAG_COMPLETION = "RAG_COMPLETION"
     GRAPH_COMPLETION = "GRAPH_COMPLETION"

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",

cognee/tests/test_chromadb.py

@@ -155,7 +155,7 @@ async def main():
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(
-        query_type=SearchType.INSIGHTS, query_text=random_node_name
+        query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
     )
     assert len(search_results) != 0, "The search results list is empty."
     print("\n\nExtracted sentences are:\n")

cognee/tests/test_kuzu.py

@@ -58,7 +58,7 @@ async def main():
         random_node_name = random_node.payload["text"]
 
         search_results = await cognee.search(
-            query_type=SearchType.INSIGHTS, query_text=random_node_name
+            query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
         )
         assert len(search_results) != 0, "The search results list is empty."
         print("\n\nExtracted sentences are:\n")

cognee/tests/test_lancedb.py

@@ -153,7 +153,7 @@ async def main():
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(
-        query_type=SearchType.INSIGHTS, query_text=random_node_name
+        query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
     )
     assert len(search_results) != 0, "The search results list is empty."
     print("\n\nExtracted sentences are:\n")

cognee/tests/test_library.py

@@ -51,7 +51,7 @@ async def main():
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(
-        query_type=SearchType.INSIGHTS, query_text=random_node_name
+        query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
     )
     assert len(search_results) != 0, "The search results list is empty."
     print("\n\nExtracted sentences are:\n")

cognee/tests/test_memgraph.py

@@ -52,7 +52,7 @@ async def main():
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(
-        query_type=SearchType.INSIGHTS, query_text=random_node_name
+        query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
     )
     assert len(search_results) != 0, "The search results list is empty."
     print("\n\nExtracted sentences are:\n")

cognee/tests/test_neo4j.py

@@ -52,7 +52,7 @@ async def main():
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(
-        query_type=SearchType.INSIGHTS, query_text=random_node_name
+        query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
     )
     assert len(search_results) != 0, "The search results list is empty."
     print("\n\nExtracted sentences are:\n")

cognee/tests/test_neptune_analytics_vector.py

@@ -56,7 +56,7 @@ async def main():
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(
-        query_type=SearchType.INSIGHTS, query_text=random_node_name
+        query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
     )
     assert len(search_results) != 0, "The search results list is empty."
     print("\n\nExtracted sentences are:\n")

cognee/tests/test_pgvector.py

@@ -167,7 +167,7 @@ async def main():
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(
-        query_type=SearchType.INSIGHTS, query_text=random_node_name
+        query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
     )
     assert len(search_results) != 0, "The search results list is empty."
     print("\n\nExtracted sentences are:\n")

cognee/tests/test_remote_kuzu.py

@@ -62,7 +62,7 @@ async def main():
         random_node_name = random_node.payload["text"]
 
         search_results = await cognee.search(
-            query_type=SearchType.INSIGHTS, query_text=random_node_name
+            query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
         )
         assert len(search_results) != 0, "The search results list is empty."
         print("\n\nExtracted sentences are:\n")

cognee/tests/test_s3_file_storage.py

@@ -47,7 +47,7 @@ async def main():
     random_node_name = random_node.payload["text"]
 
     search_results = await cognee.search(
-        query_type=SearchType.INSIGHTS, query_text=random_node_name
+        query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
     )
     assert len(search_results) != 0, "The search results list is empty."
     print("\n\nExtracted sentences are:\n")

cognee/tests/unit/modules/retrieval/insights_retriever_test.py

@@ -1,251 +0,0 @@
-import os
-import pytest
-import pathlib
-
-import cognee
-from cognee.low_level import setup
-from cognee.tasks.storage import add_data_points
-from cognee.modules.engine.models import Entity, EntityType
-from cognee.infrastructure.databases.graph import get_graph_engine
-from cognee.infrastructure.databases.vector import get_vector_engine
-from cognee.modules.retrieval.exceptions.exceptions import NoDataError
-from cognee.modules.retrieval.insights_retriever import InsightsRetriever
-
-
-class TestInsightsRetriever:
-    @pytest.mark.asyncio
-    async def test_insights_context_simple(self):
-        system_directory_path = os.path.join(
-            pathlib.Path(__file__).parent, ".cognee_system/test_insights_context_simple"
-        )
-        cognee.config.system_root_directory(system_directory_path)
-        data_directory_path = os.path.join(
-            pathlib.Path(__file__).parent, ".data_storage/test_insights_context_simple"
-        )
-        cognee.config.data_root_directory(data_directory_path)
-
-        await cognee.prune.prune_data()
-        await cognee.prune.prune_system(metadata=True)
-        await setup()
-
-        entityTypePerson = EntityType(
-            name="Person",
-            description="An individual",
-        )
-
-        person1 = Entity(
-            name="Steve Rodger",
-            is_a=entityTypePerson,
-            description="An American actor, comedian, and filmmaker",
-        )
-
-        person2 = Entity(
-            name="Mike Broski",
-            is_a=entityTypePerson,
-            description="Financial advisor and philanthropist",
-        )
-
-        person3 = Entity(
-            name="Christina Mayer",
-            is_a=entityTypePerson,
-            description="Maker of next generation of iconic American music videos",
-        )
-
-        entityTypeCompany = EntityType(
-            name="Company",
-            description="An organization that operates on an annual basis",
-        )
-
-        company1 = Entity(
-            name="Apple",
-            is_a=entityTypeCompany,
-            description="An American multinational technology company headquartered in Cupertino, California",
-        )
-
-        company2 = Entity(
-            name="Google",
-            is_a=entityTypeCompany,
-            description="An American multinational technology company that specializes in Internet-related services and products",
-        )
-
-        company3 = Entity(
-            name="Facebook",
-            is_a=entityTypeCompany,
-            description="An American social media, messaging, and online platform",
-        )
-
-        entities = [person1, person2, person3, company1, company2, company3]
-
-        await add_data_points(entities)
-
-        retriever = InsightsRetriever()
-
-        context = await retriever.get_context("Mike")
-
-        assert context[0][0]["name"] == "Mike Broski", "Failed to get Mike Broski"
-
-    @pytest.mark.asyncio
-    async def test_insights_context_complex(self):
-        system_directory_path = os.path.join(
-            pathlib.Path(__file__).parent, ".cognee_system/test_insights_context_complex"
-        )
-        cognee.config.system_root_directory(system_directory_path)
-        data_directory_path = os.path.join(
-            pathlib.Path(__file__).parent, ".data_storage/test_insights_context_complex"
-        )
-        cognee.config.data_root_directory(data_directory_path)
-
-        await cognee.prune.prune_data()
-        await cognee.prune.prune_system(metadata=True)
-        await setup()
-
-        entityTypePerson = EntityType(
-            name="Person",
-            description="An individual",
-        )
-
-        person1 = Entity(
-            name="Steve Rodger",
-            is_a=entityTypePerson,
-            description="An American actor, comedian, and filmmaker",
-        )
-
-        person2 = Entity(
-            name="Mike Broski",
-            is_a=entityTypePerson,
-            description="Financial advisor and philanthropist",
-        )
-
-        person3 = Entity(
-            name="Christina Mayer",
-            is_a=entityTypePerson,
-            description="Maker of next generation of iconic American music videos",
-        )
-
-        person4 = Entity(
-            name="Jason Statham",
-            is_a=entityTypePerson,
-            description="An American actor",
-        )
-
-        person5 = Entity(
-            name="Mike Tyson",
-            is_a=entityTypePerson,
-            description="A former professional boxer from the United States",
-        )
-
-        entityTypeCompany = EntityType(
-            name="Company",
-            description="An organization that operates on an annual basis",
-        )
-
-        company1 = Entity(
-            name="Apple",
-            is_a=entityTypeCompany,
-            description="An American multinational technology company headquartered in Cupertino, California",
-        )
-
-        company2 = Entity(
-            name="Google",
-            is_a=entityTypeCompany,
-            description="An American multinational technology company that specializes in Internet-related services and products",
-        )
-
-        company3 = Entity(
-            name="Facebook",
-            is_a=entityTypeCompany,
-            description="An American social media, messaging, and online platform",
-        )
-
-        entities = [person1, person2, person3, company1, company2, company3]
-
-        await add_data_points(entities)
-
-        graph_engine = await get_graph_engine()
-
-        await graph_engine.add_edges(
-            [
-                (
-                    (str)(person1.id),
-                    (str)(company1.id),
-                    "works_for",
-                    dict(
-                        relationship_name="works_for",
-                        source_node_id=person1.id,
-                        target_node_id=company1.id,
-                    ),
-                ),
-                (
-                    (str)(person2.id),
-                    (str)(company2.id),
-                    "works_for",
-                    dict(
-                        relationship_name="works_for",
-                        source_node_id=person2.id,
-                        target_node_id=company2.id,
-                    ),
-                ),
-                (
-                    (str)(person3.id),
-                    (str)(company3.id),
-                    "works_for",
-                    dict(
-                        relationship_name="works_for",
-                        source_node_id=person3.id,
-                        target_node_id=company3.id,
-                    ),
-                ),
-                (
-                    (str)(person4.id),
-                    (str)(company1.id),
-                    "works_for",
-                    dict(
-                        relationship_name="works_for",
-                        source_node_id=person4.id,
-                        target_node_id=company1.id,
-                    ),
-                ),
-                (
-                    (str)(person5.id),
-                    (str)(company1.id),
-                    "works_for",
-                    dict(
-                        relationship_name="works_for",
-                        source_node_id=person5.id,
-                        target_node_id=company1.id,
-                    ),
-                ),
-            ]
-        )
-
-        retriever = InsightsRetriever(top_k=20)
-
-        context = await retriever.get_context("Christina")
-
-        assert context[0][0]["name"] == "Christina Mayer", "Failed to get Christina Mayer"
-
-    @pytest.mark.asyncio
-    async def test_insights_context_on_empty_graph(self):
-        system_directory_path = os.path.join(
-            pathlib.Path(__file__).parent, ".cognee_system/test_insights_context_on_empty_graph"
-        )
-        cognee.config.system_root_directory(system_directory_path)
-        data_directory_path = os.path.join(
-            pathlib.Path(__file__).parent, ".data_storage/test_insights_context_on_empty_graph"
-        )
-        cognee.config.data_root_directory(data_directory_path)
-
-        await cognee.prune.prune_data()
-        await cognee.prune.prune_system(metadata=True)
-
-        retriever = InsightsRetriever()
-
-        with pytest.raises(NoDataError):
-            await retriever.get_context("Christina Mayer")
-
-        vector_engine = get_vector_engine()
-        await vector_engine.create_collection("Entity_name", payload_schema=Entity)
-        await vector_engine.create_collection("EntityType_name", payload_schema=EntityType)
-
-        context = await retriever.get_context("Christina Mayer")
-        assert context == [], "Returned context should be empty on an empty graph"

evals/src/qa/qa_benchmark_cognee.py

@@ -34,7 +34,7 @@ class CogneeConfig(QABenchmarkConfig):
     system_prompt_path: str = "answer_simple_question_benchmark2.txt"
 
     # Search parameters (fallback if not using eval framework)
-    search_type: SearchType = SearchType.INSIGHTS
+    search_type: SearchType = SearchType.GRAPH_COMPLETION
 
     # Clean slate on initialization
     clean_start: bool = True

examples/database_examples/chromadb_example.py

@@ -57,7 +57,9 @@ async def main():
 
     # Now let's perform some searches
     # 1. Search for insights related to "ChromaDB"
-    insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="ChromaDB")
+    insights_results = await cognee.search(
+        query_type=SearchType.GRAPH_COMPLETION, query_text="ChromaDB"
+    )
     print("\nInsights about ChromaDB:")
     for result in insights_results:
         print(f"- {result}")

examples/database_examples/kuzu_example.py

@@ -55,7 +55,9 @@ async def main():
 
     # Now let's perform some searches
     # 1. Search for insights related to "KuzuDB"
-    insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="KuzuDB")
+    insights_results = await cognee.search(
+        query_type=SearchType.GRAPH_COMPLETION, query_text="KuzuDB"
+    )
     print("\nInsights about KuzuDB:")
     for result in insights_results:
         print(f"- {result}")

examples/database_examples/neo4j_example.py

@@ -64,7 +64,9 @@ async def main():
 
     # Now let's perform some searches
     # 1. Search for insights related to "Neo4j"
-    insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="Neo4j")
+    insights_results = await cognee.search(
+        query_type=SearchType.GRAPH_COMPLETION, query_text="Neo4j"
+    )
     print("\nInsights about Neo4j:")
     for result in insights_results:
         print(f"- {result}")

examples/database_examples/neptune_analytics_example.py

@@ -79,7 +79,7 @@ async def main():
     # Now let's perform some searches
     # 1. Search for insights related to "Neptune Analytics"
     insights_results = await cognee.search(
-        query_type=SearchType.INSIGHTS, query_text="Neptune Analytics"
+        query_type=SearchType.GRAPH_COMPLETION, query_text="Neptune Analytics"
     )
     print("\n========Insights about Neptune Analytics========:")
     for result in insights_results:

examples/database_examples/pgvector_example.py

@@ -69,7 +69,9 @@ async def main():
 
     # Now let's perform some searches
     # 1. Search for insights related to "PGVector"
-    insights_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text="PGVector")
+    insights_results = await cognee.search(
+        query_type=SearchType.GRAPH_COMPLETION, query_text="PGVector"
+    )
     print("\nInsights about PGVector:")
     for result in insights_results:
         print(f"- {result}")

examples/python/simple_example.py

@@ -50,7 +50,9 @@ async def main():
     query_text = "Tell me about NLP"
     print(f"Searching cognee for insights with query: '{query_text}'")
     # Query cognee for insights on the added text
-    search_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text=query_text)
+    search_results = await cognee.search(
+        query_type=SearchType.GRAPH_COMPLETION, query_text=query_text
+    )
 
     print("Search results:")
     # Display results

notebooks/cognee_demo.ipynb

@@ -1795,7 +1795,7 @@
     }
    ],
    "source": [
-    "search_results = await cognee.search(query_type=SearchType.INSIGHTS, query_text=node_name)\n",
+    "search_results = await cognee.search(query_type=SearchType.GRAPH_COMPLETION, query_text=node_name)\n",
     "print(\"\\n\\nExtracted sentences are:\\n\")\n",
     "for result in search_results:\n",
     "    print(f\"{result}\\n\")"

notebooks/neptune-analytics-example.ipynb

@@ -295,7 +295,7 @@
    "cell_type": "code",
    "source": [
     "# Search graph insights\n",
-    "insights_results = await search(query_text=\"Neptune Analytics\", query_type=SearchType.INSIGHTS)\n",
+    "insights_results = await search(query_text=\"Neptune Analytics\", query_type=SearchType.GRAPH_COMPLETION)\n",
     "print(\"\\nInsights about Neptune Analytics:\")\n",
     "for result in insights_results:\n",
     "    src_node = result[0].get(\"name\", result[0][\"type\"])\n",