Update app.py

app.py

@@ -26,7 +26,7 @@ embeddings = VoyageAIEmbeddings(voyage_api_key=voyage_api_key, model="voyage-law
 # 🔹 Query Expansion using GPT-4
 def expand_query(query):
     llm = ChatOpenAI(model="gpt-4", openai_api_key=openai.api_key, temperature=0.3)
-    prompt = f"Rewrite this vague query into a more specific one:\nQuery: {query}\nSpecific Query:"
+    prompt = f"Rewrite this vague query for searching a document into a more specific one:\nQuery: {query}\nSpecific Query:"
     refined_query = llm([HumanMessage(content=prompt)]).content.strip()
     return refined_query if refined_query else query
 
@@ -46,7 +46,7 @@ def hybrid_search(query, user_groups, index_name="briefmeta", min_score=0.01, fe
         doc_id, doc_groups = doc.metadata.get("id"), doc.metadata.get("groups", [])
         semantic_score = float(doc.metadata.get("score", 0))
         keyword_score = float(keyword_scores[i])
-        final_score = 0.7 * semantic_score + 0.3 * keyword_score  # Hybrid score
+        final_score = 0.65 * semantic_score + 0.35 * keyword_score  # Hybrid score
 
         if doc_id not in seen_ids and any(group in user_groups for group in doc_groups) and final_score > min_score:
             seen_ids.add(doc_id)
@@ -85,17 +85,17 @@ def rerank(query, context):
     return final_reranked
 
 # 🔹 Intelligent Search Summary Generator
-def generate_search_summary(search_results, query):
+def generate_search_summary(search_results, document_titles, query):
     if not search_results:
         return "No relevant documents found. Try refining your query."
 
-    num_results = len(search_results)
+    num_results = len(document_titles)
     doc_titles = [doc.get("title", "Unknown Document") for doc in search_results]
     doc_pages = [doc.get("page_number", "N/A") for doc in search_results]
     relevance_scores = [float(doc.get("score", 0)) for doc in search_results]
 
     summary_prompt = f"""
-    Generate a concise 1-3 sentence summary:
+    Generate a concise 1-3 sentence summary for the search results found:
     - User Query: "{query}"
     - Matching Documents: {num_results} found
     - Titles: {", ".join(set(doc_titles))}
@@ -143,7 +143,7 @@ def complete_workflow(query, user_groups, index_name="briefmeta"):
 
         document_titles = list({os.path.basename(doc["title"]) for doc in context_data})
         formatted_titles = " " + "\n".join(document_titles)
-        intelligent_search_summary = generate_search_summary(context_data, refined_query)
+        intelligent_search_summary = generate_search_summary(context_data, document_titles, refined_query)
 
         results = {
             "results": [