80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
from models import ChatBody
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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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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. Follow the user's instructions carefully and generate answer from given context and You can recommend, translate and can do anything one the given context. If the answer is not included in the context say exactly "Sorry, I don't know" and if you know the answer you can resonpond it. Respond using 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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