Create app.py

app.py

@@ -0,0 +1,91 @@
+from langchain import PromptTemplate
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import CTransformers
+from langchain.chains import RetrievalQA
+import chainlit as cl
+
+DB_FAISS_PATH = "vectorstores/db_faiss"
+
+custom_prompt_template = """Use the following pieces of information to answer the user's question.
+If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+Context: {context}
+Question: {question}
+
+only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+
+def set_custom_prompt():
+    """
+    Prompt template for QA retrieval for each vectorstore
+    """
+    prompt = PromptTemplate(template=custom_prompt_template,
+                            input_variables=['context', 'question'])
+    return prompt
+
+
+def load_llm():
+    """
+    Load the language model
+    """
+    llm = CTransformers(
+        model="C:/Users/sanath/Downloads/llama-2-7b-chat.ggmlv3.q8_0.bin",
+        model_type = "llama",
+        max_new_tokens = 512,
+        temperature = 0.5
+    )
+    return llm
+
+def retrieval_QA_chain(llm,prompt,db):
+  qachain = RetrievalQA.from_chain_type(
+      llm=llm,
+      chain_type="stuff",
+      retriever=db.as_retriever(search_kwargs={'k':2}),
+      return_source_documents=True,
+      chain_type_kwargs={'prompt':prompt}
+
+  )
+  return qachain
+
+
+def qa_bot():
+  embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-miniLM-L6-V2', model_kwargs={'device': 'cpu'})
+  db = FAISS.load_local(DB_FAISS_PATH,embeddings)
+  llm = load_llm()
+  qa_prompt = set_custom_prompt()
+  qa = retrieval_QA_chain(llm,qa_prompt,db)
+  return qa
+
+def final_result(query):
+  qa_result = qa_bot()
+  response = qa_result({'query':query})
+  return response
+
+@cl.on_chat_start
+async def start():
+  chain = qa_bot()
+  msg = cl.Message(content = "Starting the bot...")
+  await msg.send()
+  msg.content = "Hi, Welcome to the Medical bot. What is your query?"
+  await msg.update()
+
+  cl.user_session.set("chain",chain)
+
+@cl.on_message
+async def main(message):
+  chain = cl.user_session.get("chain")
+  cb = cl.AsyncLangchainCallbackHandler(
+      stream_final_answer = True, answer_prefix_tokens = ["FINAL","ANSWER"]
+  )
+  cb.answer_reached = True
+  res = await chain.acall(message, callbacks=[cb])
+  answer = res["result"]
+  sources = res["source_documents"]
+  if sources:
+    answer += f"\nSources:" + str(sources)
+  else:
+    answer += "\nNo sources found"
+  
+  await cl.Message(content=answer).send()