Delete lppchain.py

lppchain.py

@@ -1,58 +0,0 @@
-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")
-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()