Update model.py

model.py

@@ -1,110 +1,111 @@
-"""
-The code in this script subjects to a licence of 96harsh52/LLaMa_2_chatbot (https://github.com/96harsh52/LLaMa_2_chatbot)
-Youtube instruction (https://www.youtube.com/watch?v=kXuHxI5ZcG0&list=PLrLEqwuz-mRIdQrfeCjeCyFZ-Pl6ffPIN&index=18)
-Llama 2 Model (Quantized one by the Bloke): https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q8_0.bin
-Llama 2 HF Model (Original One): https://huggingface.co/meta-llama
-Chainlit docs: https://github.com/Chainlit/chainlit
-"""
-
-from langchain import PromptTemplate
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
-from langchain.chains import RetrievalQA
-from langchain_community.llms import CTransformers
-import chainlit as cl
-
-DB_FAISS_PATH = 'vectorstore/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='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):
-    """
-    Create a retrieval QA chain
-    """
-    qa_chain = 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 qa_chain
-
-
-def qa_bot():
-    """
-    Create a QA bot
-    """
-    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
-                                       model_kwargs={'device': 'cpu'})
-    db = FAISS.load_local(DB_FAISS_PATH, embeddings, allow_dangerous_deserialization=True)
-    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 Medical Chatbot. What is your query?"
-    await msg.update()
-    cl.user_session.set("chain", chain)
-
-
-@cl.on_message
-async def main(message: cl.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.content, 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()
-
-
+"""
+The code in this script subjects to a licence of 96harsh52/LLaMa_2_chatbot (https://github.com/96harsh52/LLaMa_2_chatbot)
+Youtube instruction (https://www.youtube.com/watch?v=kXuHxI5ZcG0&list=PLrLEqwuz-mRIdQrfeCjeCyFZ-Pl6ffPIN&index=18)
+Llama 2 Model (Quantized one by the Bloke): https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/blob/main/llama-2-7b-chat.ggmlv3.q8_0.bin
+Llama 2 HF Model (Original One): https://huggingface.co/meta-llama
+Chainlit docs: https://github.com/Chainlit/chainlit
+"""
+
+from langchain import PromptTemplate
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import FAISS
+from langchain.chains import RetrievalQA
+from langchain_community.llms import CTransformers
+import chainlit as cl
+
+DB_FAISS_PATH = 'vectorstore/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='TheBloke/Llama-2-7b-Chat-GGUF',
+                        model_file='llama-2-7b-chat.Q8_0.gguf',
+                        model_type='llama',
+                        max_new_tokens=512,
+                        temperature=0.5)
+    return llm
+
+
+def retrieval_qa_chain(llm, prompt, db):
+    """
+    Create a retrieval QA chain
+    """
+    qa_chain = 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 qa_chain
+
+
+def qa_bot():
+    """
+    Create a QA bot
+    """
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
+                                       model_kwargs={'device': 'cpu'})
+    db = FAISS.load_local(DB_FAISS_PATH, embeddings, allow_dangerous_deserialization=True)
+    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 Medical Chatbot. What is your query?"
+    await msg.update()
+    cl.user_session.set("chain", chain)
+
+
+@cl.on_message
+async def main(message: cl.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.content, 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()
+
+