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from langchain_core.prompts import PromptTemplate
import os
from langchain_community.embeddings import HuggingFaceBgeEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.llms.ctransformers import CTransformers
#from langchain.chains import RetrievalQA
from langchain.chains.retrieval_qa.base import RetrievalQA
import streamlit as st
DB_FAISS_PATH = 'vectorstores/'
custom_prompt_template = '''use the following pieces of information to answer the user's questions.
If you don't know the answer, please just say that don't know the answer, don't try to make uo an answer.
Context : {context}
Question : {question}
only return the helpful answer below and nothing else.
'''
def set_custom_prompt():
"""
Prompt template for QA retrieval for vector stores
"""
prompt = PromptTemplate(template = custom_prompt_template,
input_variables = ['context','question'])
return prompt
def load_llm():
llm = CTransformers(
#model = 'TheBloke/Llama-2-7B-Chat-GGML',
#model = AutoModel.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML"),
model = 'MaziyarPanahi/BioMistral-7B-GGUF'
model_type = 'llama',
max_new_token = 512,
temperature = 0.5
)
return llm
def retrieval_qa_chain(llm,prompt,db):
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():
embeddings = HuggingFaceBgeEmbeddings(model_name = 'NeuML/pubmedbert-base-embeddings',
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
import streamlit as st
# Initialize the bot
bot = qa_bot()
# def process_query(query):
# # Here you would include the logic to process the query and return a response
# response, sources = bot.answer_query(query) # Modify this according to your bot implementation
# if sources:
# response += f"\nSources: {', '.join(sources)}"
# else:
# response += "\nNo Sources Found"
# return response
# Streamlit webpage title
st.title('Medical Chatbot')
# User input
user_query = st.text_input("Please enter your question:")
# Button to get answer
if st.button('Get Answer'):
if user_query:
# Call the function from your chatbot script
response = final_result(user_query)
if response:
# Displaying the response
st.write("### Answer")
st.write(response['result'])
#Displaying source document details if available
if 'source_documents' in response:
st.write("### Source Document Information")
for doc in response['source_documents']:
# Retrieve and format page content by replacing '\n' with new line
formatted_content = doc.page_content.replace("\\n", "\n")
st.write("#### Document Content")
st.text_area(label="Page Content", value=formatted_content, height=300)
# Retrieve source and page from metadata
source = doc.metadata['source']
page = doc.metadata['page']
st.write(f"Source: {source}")
st.write(f"Page Number: {page}")
else:
st.write("Sorry, I couldn't find an answer to your question.")
else:
st.write("Please enter a question to get an answer.") |