Update app.py

app.py

@@ -69,21 +69,41 @@ async def lifespan(app: FastAPI):
                                max_output_tokens=300
                            )
         ml_models["prompt_template"] = PromptTemplate.from_template("""
-**Role**: You are an expert decision maker Assistant in the domain such as insurance, legal compliance, human resources, and contract management.
+You are an expert decision maker Assistant in the domain such as insurance, legal compliance, human resources, and contract management.
+Do not add any irrelevent information, also do not use bullet points, make it in a perfect sentence answer.
+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}
 
-**Context**: 
+We retrieved the following policy clauses and rules relevant to this case:
 {context}
 
-**Query**: {full_query}
+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.
+### 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 .
+}}
 
-**Instructions**:
-1. If query contains age ,gender,procedure,duration , location ,and query like can i get a knee surgery ,if i am male and duration is 3months or any query is similar to the previus example :
-   - Output ONLY JSON: {{"decision":"approved/rejected","amount":"₹X","justification":"Clause reference"}}
-2. Else: Provide concise answer
-3. NEVER mention document sources
-4. If unsure, respond: "Insufficient information"
-
-**Response**:
 """
     )
         print("✅ Models and prompt loaded successfully!")
@@ -153,7 +173,7 @@ async def run_hackrx(req: RunRequest):
     )
 
     # dense_retriever = vectorstore.as_retriever(search_type="mmr",search_kwargs={"k": 8})
-    dense_retriever = vectorstore.as_retriever(search_type="mmr",search_kwargs={"k": 8 ,"lambda_mult": 0.6})
+    dense_retriever = vectorstore.as_retriever(search_type="mmr",search_kwargs={"k": 8 ,"lambda_mult": 0.5})
     
 
     # Create retrievers using the pre-loaded models from our ml_models dictionary
@@ -263,13 +283,13 @@ async def run_hackrx(req: RunRequest):
     #     # Extract the content from each result and parse it
     tasks = [hybrid_rag_chain.ainvoke({"full_query": q}) for q in req.questions]
     results = await asyncio.gather(*tasks)
-    # answers = [parse_llm_response(result.content) for result in results]
-    answers = []
+    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())    
+    # for msg in results:
+    #     # Safely access the content field
+    #     if hasattr(msg, "content"):
+    #         answers.append(msg.content.strip())    
 
     return JSONResponse({"answers": answers}, status_code=200)