Spaces:
Runtime error
Runtime error
File size: 3,793 Bytes
c1869f7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 |
# pip install streamlit langchain lanchain-openai beautifulsoup4 python-dotenv chromadb
import streamlit as st
from langchain_core.messages import AIMessage, HumanMessage
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 dotenv import load_dotenv
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
load_dotenv()
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()
document_chunks = text_splitter.split_documents(document)
# create a vectorstore from the chunks
vector_store = Chroma.from_documents(document_chunks, OpenAIEmbeddings())
return vector_store
def get_context_retriever_chain(vector_store):
llm = ChatOpenAI()
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()
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)
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_query
})
return response['answer']
# app config
st.set_page_config(page_title="Chat with websites", page_icon="🤖")
st.title("Chat with websites")
# sidebar
with st.sidebar:
st.header("Settings")
website_url = st.text_input("Website URL")
if website_url is None or website_url == "":
st.info("Please enter a website URL")
else:
# session state
if "chat_history" not in st.session_state:
st.session_state.chat_history = [
AIMessage(content="Hello, I am a bot. How can I help you?"),
]
if "vector_store" not in st.session_state:
st.session_state.vector_store = get_vectorstore_from_url(website_url)
# user input
user_query = st.chat_input("Type your message here...")
if user_query is not None and user_query != "":
response = get_response(user_query)
st.session_state.chat_history.append(HumanMessage(content=user_query))
st.session_state.chat_history.append(AIMessage(content=response))
# conversation
for message in st.session_state.chat_history:
if isinstance(message, AIMessage):
with st.chat_message("AI"):
st.write(message.content)
elif isinstance(message, HumanMessage):
with st.chat_message("Human"):
st.write(message.content)
|