Fixes to search and evals

cognee/api/v1/cognify/cognify.py

@@ -258,9 +258,9 @@ if __name__ == "__main__":
         from cognee.shared.SourceCodeGraph import SourceCodeGraph
         from cognee.api.v1.config import config
 
-        config.set_graph_model(SourceCodeGraph)
-        config.set_classification_model(CodeContentPrediction)
-        graph = await cognify()
+        # config.set_graph_model(SourceCodeGraph)
+        # config.set_classification_model(CodeContentPrediction)
+        # graph = await cognify()
         vector_client = infrastructure_config.get_config("vector_engine")
 
         out = await vector_client.search(collection_name ="basic_rag", query_text="show_all_processes", limit=10)

cognee/infrastructure/InfrastructureConfig.py

@@ -84,10 +84,12 @@ class InfrastructureConfig():
 
         if (config_entity is None or config_entity == "llm_engine") and self.llm_engine is None:
             self.llm_engine = OpenAIAdapter(config.openai_key, config.openai_model)
-
         if (config_entity is None or config_entity == "database_directory_path") and self.database_directory_path is None:
             self.database_directory_path = self.system_root_directory + "/" + config.db_path
 
+        if self.database_directory_path is None:
+            self.database_directory_path = self.system_root_directory + "/" + config.db_path
+
         if (config_entity is None or config_entity == "database_file_path") and self.database_file_path is None:
             self.database_file_path = self.system_root_directory + "/" + config.db_path + "/" + config.db_name
 
@@ -114,6 +116,9 @@ class InfrastructureConfig():
                     )
                 else:
                     from .databases.vector.lancedb.LanceDBAdapter import LanceDBAdapter
+                    print("Using LanceDB as vector engine", self.database_directory_path)
+                    print("Setting system root directory to", self.system_root_directory)
+
                     lance_db_path = self.database_directory_path + "/cognee.lancedb"
                     LocalStorage.ensure_directory_exists(lance_db_path)
 

cognee/modules/search/graph/search_categories.py

@@ -1,4 +1,6 @@
-from typing import Union, Dict, re
+from typing import Union, Dict
+import re
+
 
 from cognee.modules.search.llm.extraction.categorize_relevant_category import categorize_relevant_category
 
@@ -13,7 +15,7 @@ def strip_exact_regex(s, substring):
     # Regex to match the exact substring at the start and end
     return re.sub(f"^{pattern}|{pattern}$", "", s)
 
-async def search_categories(query:str, graph: Union[nx.Graph, any], query_label: str, infrastructure_config: Dict):
+async def search_categories(query:str, graph: Union[nx.Graph, any], query_label: str=None, infrastructure_config: Dict=None):
     """
     Filter nodes in the graph that contain the specified label and return their summary attributes.
     This function supports both NetworkX graphs and Neo4j graph databases.
@@ -29,6 +31,7 @@ async def search_categories(query:str, graph: Union[nx.Graph, any], query_label:
       each representing a node with 'nodeId' and 'summary'.
     """
     # Determine which client is in use based on the configuration
+    from cognee.infrastructure import infrastructure_config
     if infrastructure_config.get_config()["graph_engine"] == GraphDBType.NETWORKX:
 
         categories_and_ids = [

cognee/modules/search/llm/extraction/categorize_relevant_category.py

@@ -6,7 +6,7 @@ from cognee.infrastructure.llm.get_llm_client import get_llm_client
 async def categorize_relevant_category(query: str, summary, response_model: Type[BaseModel]):
     llm_client = get_llm_client()
 
-    enriched_query= render_prompt("categorize_category.txt", {"query": query, "categories": summary})
+    enriched_query= render_prompt("categorize_categories.txt", {"query": query, "categories": summary})
 
     print("enriched_query", enriched_query)
 

evals/simple_rag_vs_cognee_eval.py

@@ -84,17 +84,22 @@ async def run_cognify_base_rag():
 
 
 async def cognify_search_base_rag(content:str, context:str):
+    infrastructure_config.set_config({"database_directory_path": "/Users/vasa/Projects/cognee/cognee/.cognee_system/databases/cognee.lancedb"})
+
     vector_client = infrastructure_config.get_config("vector_engine")
 
-    return_ = await vector_client.search(collection_name="basic_rag", query_text="show_all_processes", limit=10)
+    return_ = await vector_client.search(collection_name="basic_rag", query_text=content, limit=10)
 
     print("results", return_)
     return return_
 
 async def cognify_search_graph(content:str, context:str):
     from cognee.api.v1.search.search import search
-    return_ = await search(content)
-    return return_
+    search_type = 'CATEGORIES'
+    params = {'query': 'Ministarstvo'}
+
+    results = await search(search_type, params)
+    return results
 
 
 
@@ -128,8 +133,9 @@ if __name__ == "__main__":
     import asyncio
 
     async def main():
-        await run_cognify_base_rag_and_search()
-
+        # await run_cognify_base_rag()
+        # await cognify_search_base_rag("show_all_processes", "context")
+        await cognify_search_graph("show_all_processes", "context")
     asyncio.run(main())
     # run_cognify_base_rag_and_search()
     # # Data preprocessing before setting the dataset test cases