Update app.py

app.py

@@ -73,7 +73,7 @@ def create_retriever_from_chroma(vectorstore_path="./docs/chroma/", search_type=
         )
         st.write("VectorStore is created")
     
-    retriever=vectorstore.as_retriever(search_type = search_type, search_kwargs={"k": k})
+    retriever=vectorstore.as_retriever(search_type = search_type, search_kwargs={"k": k, 'lambda_mult': 0.7})
 
     
 
@@ -152,7 +152,7 @@ def create_conversational_rag_chain(retriever):
     llm = llamacpp.LlamaCpp(
         model_path = "qwen2-0_5b-instruct-q8_0.gguf",
         n_gpu_layers=0,
-        temperature=0.2,
+        temperature=0.0,
         top_p=0.9,
         n_ctx=22000,
         n_batch=2000,
@@ -163,9 +163,19 @@ def create_conversational_rag_chain(retriever):
         verbose=False,
     )
 
-    prompt = hub.pull("rlm/rag-prompt")
+    qa_system_prompt = """ Use the following pieces of retrieved context to answer the question{question} . \
+     Be  informative but dont make to long answers, be polite and formal.\
+    Make answer in Enlish Language.\
+     Make answer based on context .\
+     If you don't know the answer, say " Please provide more details about your question ." \
 
-    rag_chain = prompt | llm | StrOutputParser()
+
+     {context}"""
+    
+    
+    answer_prompt = ChatPromptTemplate.from_template(qa_system_prompt)
+
+    rag_chain = answer_prompt | llm | StrOutputParser()
 
 
     return rag_chain