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import os
import streamlit as st
from apify_client import ApifyClient
from dotenv import load_dotenv
from langchain.callbacks.base import BaseCallbackHandler
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from langchain.memory import ConversationBufferMemory
from langchain.memory.chat_message_histories import StreamlitChatMessageHistory
from langchain.vectorstores import Chroma
from langchain.document_loaders import ApifyDatasetLoader
from langchain.document_loaders.base import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
load_dotenv()
st.set_page_config(page_title='Chat with a website')
website_url = st.text_input("Please enter the website URL to scrape:", value="https://www.example.com/")
st.title(f'Chat with {website_url}')
APIFY_API_TOKEN = os.environ.get('APIFY_API_TOKEN')
@st.cache_resource(ttl='1h')
def get_retriever():
embeddings = OpenAIEmbeddings()
vectordb = Chroma(persist_directory='db', embedding_function=embeddings)
retriever = vectordb.as_retriever(search_type='mmr')
return retriever
def scrape_website(website_url: str):
apify_client = ApifyClient(APIFY_API_TOKEN)
actor_run_info = apify_client.actor('apify/website-content-crawler').call(
run_input={'startUrls': [{'url': website_url}]}
)
loader = ApifyDatasetLoader(
dataset_id=actor_run_info['defaultDatasetId'],
dataset_mapping_function=lambda item: Document(
page_content=item['text'] or '', metadata={'source': item['url']}
),
)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=100)
docs = text_splitter.split_documents(documents)
embedding = OpenAIEmbeddings()
vectordb = Chroma.from_documents(
documents=docs,
embedding=embedding,
persist_directory='db2',
)
vectordb.persist()
if st.button("Start Scraping"):
scrape_website(website_url)
class StreamHandler(BaseCallbackHandler):
def __init__(self, container: st.delta_generator.DeltaGenerator, initial_text: str = ''):
self.container = container
self.text = initial_text
def on_llm_new_token(self, token: str, **kwargs) -> None:
self.text += token
self.container.markdown(self.text)
retriever = get_retriever()
msgs = StreamlitChatMessageHistory()
memory = ConversationBufferMemory(memory_key='chat_history', chat_memory=msgs, return_messages=True)
llm = ChatOpenAI(model_name='gpt-3.5-turbo', temperature=0, streaming=True)
qa_chain = ConversationalRetrievalChain.from_llm(
llm, retriever=retriever, memory=memory, verbose=False
)
if st.sidebar.button('Clear message history') or len(msgs.messages) == 0:
msgs.clear()
msgs.add_ai_message(f'Ask me anything about {website_url}!')
avatars = {'human': 'user', 'ai': 'assistant'}
for msg in msgs.messages:
st.chat_message(avatars[msg.type]).write(msg.content)
if user_query := st.chat_input(placeholder='Ask me anything!'):
st.chat_message('user').write(user_query)
with st.chat_message('assistant'):
stream_handler = StreamHandler(st.empty())
response = qa_chain.run(user_query, callbacks=[stream_handler]) |