fix the retreveing of the tools

prepare_env.py

@@ -128,14 +128,15 @@ def create_vectorstore_from_json(json_path: str, collection_name: str, embedding
     print(f"✅ Vector store created with collection: {collection_name}")
     return vectorstore, documents
 
-def create_retriever(vectorstore, docs, llm, get_all: bool = False):
+def create_retriever(vectorstore, docs, llm, bm25_k=3,vector_k=6):
     """Create ensemble retriever with vector and BM25 search
     
     Args:
         vectorstore: The vector store for similarity search
         docs: Documents for BM25 retriever
         llm: Language model for multi-query generation
-        get_all: If True, configure retriever to return all documents
+        bm25_k: Number of documents to retrieve with BM25
+        vector_k: Number of documents to retrieve with vector search
     
     Returns:
         Configured retriever (MultiQueryRetriever or EnsembleRetriever)
@@ -162,15 +163,7 @@ def create_retriever(vectorstore, docs, llm, get_all: bool = False):
     Provide only the alternative questions, one per line."""
     )
     
-    # Determine k values based on get_all parameter
-    if get_all:
-        vector_k = len(docs)  # Get all documents
-        bm25_k = len(docs)    # Get all documents
-        print(f"📄 GET_ALL mode: Setting k={len(docs)} (total documents)")
-    else:
-        vector_k = 6
-        bm25_k = 3
-        print(f"🎯 FILTERED mode: Vector k={vector_k}, BM25 k={bm25_k}")
+
     
     # Vector retriever
     vector_retriever = vectorstore.as_retriever(
@@ -191,10 +184,6 @@ def create_retriever(vectorstore, docs, llm, get_all: bool = False):
     )
     print("✅ Ensemble retriever created (weights: 0.5, 0.5)")
     
-    # If get_all is True, return ensemble retriever directly to avoid query processing overhead
-    if get_all:
-        print("📋 Returning ensemble retriever (bypassing MultiQuery for get_all mode)")
-        return ensemble_retriever
     
     # Multi-query expanding retriever (only for filtered mode)
     expanding_retriever = MultiQueryRetriever.from_llm(
@@ -295,7 +284,7 @@ def create_section_tools(embedding_function, llm):
             if os.path.exists(path):
                 print(f"📁 Creating retriever for section {section} from {path}")
                 vstore, docs = create_vectorstore_from_json(path, f"Guide_2023_{section}", embedding_function)
-                section_retrievers[section] = create_retriever(vstore, docs, llm, get_all=True)
+                section_retrievers[section] = create_retriever(vstore, docs, llm, bm25_k=5, vector_k=7)
                 print(f"✅ Successfully created retriever for section {section}")
             else:
                 print(f"⚠️ Warning: File not found for section {section}: {path}")