rag-fix

app.py

@@ -17,7 +17,7 @@ GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
 llm_rag = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0.5,google_api_key=GOOGLE_API_KEY)
 
 prompt = PromptTemplate.from_template(
-    "you are rag bot designed to answer questions on aakash jammula "
+    "you are rag bot tasked to answer questions on aakash jammula "
     "(document contains my information/resume) and be engaging and only respond in text "
     "like a human. Use context only when they ask about me (aakash jammula).\n\n"
     "Context:\n{context}\n\nQuestion:\n{question}\nAnswer:"
@@ -73,6 +73,7 @@ async def ask(req: Query):
         raise HTTPException(status_code=400, detail="Missing question text")
     context = retrieve_context(req.q, k=req.k)
     user_prompt = prompt.invoke({"context": context, "question": req.q})
+    print ("User prompt:", user_prompt)
     resp = llm_rag.invoke(user_prompt)
     return {"context": context, "answer": resp.content}
 

rag.py

@@ -23,7 +23,7 @@ embeddings = GoogleGenerativeAIEmbeddings(
 )
 
 
-vectorstore = PineconeVectorStore(index=pinecone_index, embedding=embeddings)
+vectorstore = PineconeVectorStore(index=pinecone_index, embedding=embeddings,namespace="pdf-docs")
 
 def retrieve_context(question: str, k: int = 5) -> str:
     docs = vectorstore.similarity_search(question, k=k)