changed prompt

app.py

@@ -58,43 +58,18 @@ async def lifespan(app: FastAPI):
         # ml_models["reranker_compressor"] = CrossEncoderReranker(model=cross_encoder_model, top_n=5)
         ml_models["llm"] = ChatGoogleGenerativeAI(model="gemini-2.0-flash", api_key=GOOGLE_API_KEY)
         ml_models["prompt_template"] = PromptTemplate.from_template(
-         """
-        You are an expert decision maker Assistant in the domain such as insurance, legal compliance, human resources, and contract management.
-
-The customer has submitted a query. If the query has details about age , gender ,procedure ,location , policy duration, then parse it and understand the query properly
-If not, the raw question is provided instead:
-- Query: {full_query}
-
-We retrieved the following policy clauses and rules relevant to this case:
-{context}
-
-Remember the below three points while answering or responsing the query :
-1:Do  not mention document id or its page number just ,mention clauses if applicable .
-2:Do not make such statement in the response that you need more document or information to answer the query, just answer from retrieved data.
-3:UNDERSTAND THE QUERY ASKED SMARTLY AND ANSWER IT.
-### Task:
-If the details about age , gender , procedure , location , policy duration are available, do all of the following:
-1. Decide whether the procedure is covered.
-2. Estimate the claimable amount.
-3. Justify with the relevant clause.
-and answer the query  precisely as insurance agent.
-
-Otherwise, answer the question concisely , relevently and clearly using the retrieved context.
-
-### Output format:
-If query involes age , gender , procedure , location , policy duration answer like below:
-{{
-  "decision": "approved / rejected",
-  "amount": "INR amount or null",
-  "justification": "Refer to specific clause"
-  Make it  concise ,perfect, relevent and clear .
-}}
-Else:
-{{
-  "response": "Concise natural language answer"
-   Make it  concise ,perfect, relevent and clear .
-}}
-"""
+"""You are an expert insurance assistant. Your task is to answer the user's question as concisely as possible using ONLY the provided context.
+
+    Do not add any irrelevent information, explanations, or clauses that are not directly required to answer the question , also do not use bullet points make it in a sentence and perfect.
+
+    Context:
+    {context}
+
+    Question:
+    {full_query}
+
+    Concise Answer:
+    """
     )
         print("✅ Models and prompt loaded successfully!")
     except Exception as e:
@@ -263,9 +238,14 @@ async def run_hackrx(req: RunRequest):
         tasks.append(ml_models["llm"].ainvoke(ml_models["prompt_template"].format_prompt(**prompt_input)))
     # tasks = [hybrid_rag_chain.ainvoke({"full_query": q}) for q in req.questions]
     results = await asyncio.gather(*tasks)
-    
-    # Extract the content from each result and parse it
-    answers = [parse_llm_response(result.content) for result in results]
+    answers = []
+
+    for msg in results:
+        # Safely access the content field
+        if hasattr(msg, "content"):
+            answers.append(msg.content.strip())
+        # Extract the content from each result and parse it
+    # answers = [parse_llm_response(result.content) for result in results]
 
     return JSONResponse({"answers": answers}, status_code=200)