174 lines
5.4 KiB
Python
174 lines
5.4 KiB
Python
from models import ChatBody, ChatAppBody
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from bs4 import BeautifulSoup
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from langchain.docstore.document import Document as LDocument
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from langchain.vectorstores.faiss import FAISS
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.llms import OpenAI
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from langchain.text_splitter import CharacterTextSplitter
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from langchain.chains import ConversationalRetrievalChain
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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SystemMessagePromptTemplate,
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HumanMessagePromptTemplate
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)
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from langchain.vectorstores import Chroma
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from db.supa import SupaService
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supabase = SupaService()
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async def chat_app_handler(body: ChatAppBody, jwt: str):
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try:
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user = supabase.get_user(jwt)
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if not user:
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return {
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"bot_response": "You are not logged in",
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"human_message": body.user_message,
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}
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user_id = user.user.id
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website_response = supabase.find_website(body.id, user_id)
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website = website_response.data
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if len(website) == 0:
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return {
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"bot_response": "Website not found",
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"human_message": body.user_message,
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}
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website = website[0]
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text = website["html"]
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result = [LDocument(page_content=text, metadata={"source": "test"})]
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token_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
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doc = token_splitter.split_documents(result)
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print(f'Number of documents: {len(doc)}')
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vectorstore = Chroma.from_documents(doc, OpenAIEmbeddings())
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messages = [
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SystemMessagePromptTemplate.from_template("""You are PageAssist bot. Use the following pieces of context from this webpage to answer the question from the user.
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If you don't know the answer, just say you don't know. DO NOT try to make up an answer.
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If user want recommendation, help from the context, or any other information, please provide it.
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If the question is not related to the context, politely respond that you are tuned to only answer questions that are related to the context. Helpful answer in markdown:
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-----------------
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{context}
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"""),
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HumanMessagePromptTemplate.from_template("{question}")
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]
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prompt = ChatPromptTemplate.from_messages(messages)
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chat = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0, model_name="gpt-3.5-turbo"), vectorstore.as_retriever(search_kwargs={"k": 1}), return_source_documents=True, qa_prompt=prompt,)
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history = [(d["human_message"], d["bot_response"]) for d in body.history]
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response = chat({
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"question": body.user_message,
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"chat_history": history
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})
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answer = response["answer"]
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answer = answer[answer.find(":")+1:].strip()
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return {
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"bot_response": answer,
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"human_message": body.user_message,
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}
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except Exception as e:
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print(e)
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return {
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"bot_response": "Something went wrong please try again later",
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"human_message": body.user_message,
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}
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async def chat_extension_handler(body: ChatBody):
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try:
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soup = BeautifulSoup(body.html, 'lxml')
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iframe = soup.find('iframe', id='pageassist-iframe')
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if iframe:
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iframe.decompose()
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div = soup.find('div', id='pageassist-icon')
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if div:
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div.decompose()
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div = soup.find('div', id='__plasmo-loading__')
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if div:
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div.decompose()
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text = soup.get_text()
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result = [LDocument(page_content=text, metadata={"source": "test"})]
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token_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
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doc = token_splitter.split_documents(result)
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print(f'Number of documents: {len(doc)}')
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vectorstore = Chroma.from_documents(doc, OpenAIEmbeddings())
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messages = [
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SystemMessagePromptTemplate.from_template("""You are PageAssist bot. Use the following pieces of context from this webpage to answer the question from the user.
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If you don't know the answer, just say you don't know. DO NOT try to make up an answer.
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If user want recommendation, help from the context, or any other information, please provide it.
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If the question is not related to the context, politely respond that you are tuned to only answer questions that are related to the context. Helpful answer in markdown:
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-----------------
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{context}
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"""),
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HumanMessagePromptTemplate.from_template("{question}")
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]
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prompt = ChatPromptTemplate.from_messages(messages)
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chat = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0, model_name="gpt-3.5-turbo"), vectorstore.as_retriever(search_kwargs={"k": 1}), return_source_documents=True, qa_prompt=prompt,)
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history = [(d["human_message"], d["bot_response"]) for d in body.history]
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response = chat({
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"question": body.user_message,
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"chat_history": history
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})
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answer = response["answer"]
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answer = answer[answer.find(":")+1:].strip()
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return {
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"bot_response": answer,
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"human_message": body.user_message,
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}
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except Exception as e:
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print(e)
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return {
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"bot_response": "Something went wrong please try again later",
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"human_message": body.user_message,
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}
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