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import os
os.environ['OPENAI_API_KEY'] = "<>"
###Chat model
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage
)
from langchain.chat_models import ChatOpenAI
chat = ChatOpenAI()
###Memory
from langchain.llms import OpenAI
from langchain.memory import ConversationSummaryMemory
llm = OpenAI(temperature=0)
memory = ConversationSummaryMemory(llm=llm,memory_key="chat_history",return_messages=True)
####Retrieval
from langchain.document_loaders import DirectoryLoader
# from langchain.document_loaders import WebBaseLoader
# loader = WebBaseLoader("https://www.hdfclife.com/insurance-knowledge-centre/about-life-insurance/health-insurance-meaning-and-types")
loader = DirectoryLoader('beshak/', glob="**/*.md")
data = loader.load()
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
vectorstore = Chroma.from_documents(documents=all_splits, embedding=OpenAIEmbeddings())
from langchain.chains import ConversationalRetrievalChain
llm = ChatOpenAI()
retriever = vectorstore.as_retriever()
qa = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, memory=memory)
print(qa("You're a helpful AI assistant that answers questions about health insurance.")["answer"])
print(qa("What is health insurance?")["answer"])
import gradio as gr
def chatbot_response(message, history):
return qa(message)["answer"]
gr.ChatInterface(
chatbot_response,
chatbot=gr.Chatbot(height=400),
textbox=gr.Textbox(placeholder="Ask me question about health insurance", container=False, scale=7),
title="Get Simple Health",
description="Ask any health insurance related question",
theme="soft",
examples=["Hello", "What is health insurance?", "What is critical ilness?"],
cache_examples=True,
retry_btn=None,
undo_btn="Delete Previous",
clear_btn="Clear",
).launch(share=True)
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