Upload indexer.py

indexer.py

@@ -21,34 +21,37 @@ def index_text():
     return vectorstore
 
 
-def answer_query(query, vectorstore):
+def answer_query(query, history,vectorstore):
+
+    os.environ["GEMINI_API_KEY"] = os.getenv("GEMINI_API_KEY")
+    client = genai.Client()
+
     RAG_TEMPLATE = """
 #CONTEXT:
 {context}
-
-QUERY:
-{query}
-
-Use the provided context to answer the user query. Only use the provided context to answer the query.
-If you do not know the answer, or it's not contained in the provided context, respond with "I don't know".
+Use the provided context to answer the user query.
 """
-    os.environ["GEMINI_API_KEY"] = os.getenv("GEMINI_API_KEY")
-    client = genai.Client()
-    # Get relevant documents
     retriever = vectorstore.as_retriever()
     search_results = retriever.invoke(query, k=2)
-    
-    # Combine context from retrieved documents
     context = " ".join([doc.page_content for doc in search_results])
-    
-    # Build prompt
     prompt = RAG_TEMPLATE.format(context=context, query=query)
 
-    # Generate response using Gemini
-    response = client.models.generate_content(
-        model="gemini-2.5-pro",
-        contents=prompt,
-        config=types.GenerateContentConfig(),
+
+    gemini_history = []
+    for msg in history:
+        # The Gemini API uses 'model' for the assistant's role
+        
+        role = 'model' if msg['role'] == 'assistant' else 'user'
+        gemini_history.append(
+            types.Content(role=role, parts=[types.Part(text=msg['content'])])
+        )
+
+    chat = client.chats.create(
+        model="gemini-2.0-flash",
+        history=gemini_history,
+        config=types.GenerateContentConfig(
+            system_instruction=prompt)
     )
 
-    return response.text
+    response=chat.send_message(message=query)
+    return response.text