80 lines
2.8 KiB
Python
80 lines
2.8 KiB
Python
from models import ChatBody
|
|
from bs4 import BeautifulSoup
|
|
|
|
from langchain.docstore.document import Document as LDocument
|
|
from langchain.vectorstores.faiss import FAISS
|
|
from langchain.embeddings.openai import OpenAIEmbeddings
|
|
from langchain.llms import OpenAI
|
|
from langchain.text_splitter import CharacterTextSplitter
|
|
from langchain.chains import ConversationalRetrievalChain
|
|
from langchain.prompts.chat import (
|
|
ChatPromptTemplate,
|
|
SystemMessagePromptTemplate,
|
|
HumanMessagePromptTemplate
|
|
)
|
|
|
|
async def chat_extension_handler(body: ChatBody):
|
|
try:
|
|
soup = BeautifulSoup(body.html, 'lxml')
|
|
|
|
iframe = soup.find('iframe', id='pageassist-iframe')
|
|
if iframe:
|
|
iframe.decompose()
|
|
div = soup.find('div', id='pageassist-icon')
|
|
if div:
|
|
div.decompose()
|
|
div = soup.find('div', id='__plasmo-loading__')
|
|
if div:
|
|
div.decompose()
|
|
text = soup.get_text()
|
|
|
|
result = [LDocument(page_content=text, metadata={"source": "test"})]
|
|
token_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
|
|
doc = token_splitter.split_documents(result)
|
|
|
|
print(f'Number of documents: {len(doc)}')
|
|
|
|
|
|
vectorstore = FAISS.from_documents(doc, OpenAIEmbeddings())
|
|
|
|
|
|
messages = [
|
|
SystemMessagePromptTemplate.from_template("""I want you to act as a webpage that I am having a conversation witu. Your name is "OpenChatX". You will provide me with answers from the given text from webpage. Your answer should be original, concise, accurate, and helpful. You can recommend, translate and can do anything based on the context given. If the answer is not included in the text and you know the answer you can resonpond the answer othwerwise say exactly "I don't know the answer " and stop after that. Never break character. Answer must be in markdown format.
|
|
-----------------
|
|
{context}
|
|
"""),
|
|
HumanMessagePromptTemplate.from_template("{question}")
|
|
]
|
|
|
|
prompt = ChatPromptTemplate.from_messages(messages)
|
|
|
|
|
|
chat = ConversationalRetrievalChain.from_llm(OpenAI(temperature=0, model_name="gpt-3.5-turbo"), vectorstore.as_retriever(), return_source_documents=True, qa_prompt=prompt,)
|
|
|
|
history = [(d["human_message"], d["bot_response"]) for d in body.history]
|
|
|
|
print(history)
|
|
|
|
response = chat({
|
|
"question": body.user_message,
|
|
"chat_history": history
|
|
})
|
|
|
|
|
|
answer = response["answer"]
|
|
answer = answer[answer.find(":")+1:].strip()
|
|
|
|
|
|
return {
|
|
"bot_response": answer,
|
|
"human_message": body.user_message,
|
|
}
|
|
|
|
except Exception as e:
|
|
print(e)
|
|
return {
|
|
"bot_response": "Something went wrong please try again later",
|
|
"human_message": body.user_message,
|
|
}
|
|
|