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import streamlit as st
from langchain_community.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain.chains import create_history_aware_retriever,create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from dotenv import load_dotenv
import os
load_dotenv()
KEY=os.getenv("MY_KEY")
def get_vectorstore_from_url(url):
# get the text in document form
loader = WebBaseLoader(url)
document = loader.load()
# split the document into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=200,
chunk_overlap=10,
length_function=len)
document_chunks = text_splitter.split_documents(document)
# create a vectorstore from the chunks
vector_store = Chroma.from_documents(document_chunks,
OpenAIEmbeddings(openai_api_key=KEY))
return vector_store
def get_response(user_input):
retriever_chain = get_context_retriever_chain(st.session_state.vector_store)
conversation_rag_chain = get_conversational_rag_chain(retriever_chain)
response = conversation_rag_chain.invoke({
"chat_history": st.session_state.chat_history,
"input": user_input
})
return response['answer']
def get_context_retriever_chain(vector_store):
llm = ChatOpenAI(openai_api_key=KEY)
retriever = vector_store.as_retriever()
prompt = ChatPromptTemplate.from_messages([
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
("user", "Given the above conversation, generate a search query to look up in order to get information relevant to the conversation")
])
retriever_chain = create_history_aware_retriever(llm, retriever, prompt)
return retriever_chain
def get_conversational_rag_chain(retriever_chain):
llm = ChatOpenAI(openai_api_key=KEY)
prompt = ChatPromptTemplate.from_messages([
("system", "Answer the user's questions based on the below context:\n\n{context}"),
MessagesPlaceholder(variable_name="chat_history"),
("user", "{input}"),
])
stuff_documents_chain = create_stuff_documents_chain(llm,prompt)
return create_retrieval_chain(retriever_chain, stuff_documents_chain)
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