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import transformers
import torch
import os
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from transformers import pipeline
from langchain.llms import HuggingFacePipeline
from langchain.vectorstores import Chroma
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains import RetrievalQA
from langchain.document_loaders import TextLoader
from langchain.document_loaders import PyPDFLoader
from langchain.document_loaders import DirectoryLoader
from InstructorEmbedding import INSTRUCTOR
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain_community.vectorstores import Chroma
import textwrap
import streamlit as st
persist_directory = 'db'
instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base")
embedding = instructor_embeddings
#tokenizer = AutoTokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0")
tokenizer = AutoTokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0",use_fast=False, legacy=False)
model = AutoModelForSeq2SeqLM.from_pretrained("lmsys/fastchat-t5-3b-v1.0")
pipe = pipeline("text2text-generation",model=model, tokenizer=tokenizer,max_length=256)
local_llm = HuggingFacePipeline(pipeline=pipe)
vectordb = Chroma(persist_directory=persist_directory,embedding_function=embedding)
retriever = vectordb.as_retriever(search_kwargs={"k": 3})
def get_lpphelper_chain():
qa_chain = RetrievalQA.from_chain_type(llm=local_llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True)
return qa_chain
def wrap_text_preserve_newlines(text, width=110):
# Split the input text into lines based on newline characters
lines = text.split('\n')
# Wrap each line individually
wrapped_lines = [textwrap.fill(line, width=width) for line in lines]
# Join the wrapped lines back together using newline characters
wrapped_text = '\n'.join(wrapped_lines)
return wrapped_text
def process_llm_response(llm_response):
wrap_text = wrap_text_preserve_newlines(llm_response['result'])
sources = '\n\nSources:'
print('\n\nSources:')
for source in llm_response["source_documents"]:
sources.join(source.metadata['source'])
print(wrap_text.join(sources))
return wrap_text.replace("<pad>","")
if __name__=="__main__":
get_lpphelper_chain() |