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import streamlit as st
import os
import textwrap
import torch
import chromadb
import langchain
import openai
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import TextLoader, UnstructuredPDFLoader, YoutubeLoader
from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.indexes import VectorstoreIndexCreator
from langchain.llms import OpenAI, HuggingFacePipeline
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain.prompts import PromptTemplate
#from auto_gptq import AutoGPTQForCausalLM
from transformers import AutoTokenizer, pipeline, logging, TextStreamer
from langchain.document_loaders.image import UnstructuredImageLoader
x = st.slider('Select a value')
st.write(x, 'squared is', x * x)
current_working_directory = os.getcwd()
print(current_working_directory)
st.write('current dir:', current_working_directory)
arr = os.listdir('.')
st.write('dir contents:',arr)
def print_response(response: str):
print("\n".join(textwrap.wrap(response, width=100)))
pdf_loader = UnstructuredPDFLoader("./pdfs/Predicting issue types on GitHub.pdf")
pdf_pages = pdf_loader.load_and_split()
st.write('total pages from PDFs:', len(pdf_pages))
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=512)
texts = text_splitter.split_documents(pdf_pages)
st.write('total chunks from pages:', len(texts))
st.write('loading chunks into vector db')
model_name = "hkunlp/instructor-large"
hf_embeddings = HuggingFaceInstructEmbeddings(
model_name = model_name)
# db = Chroma.from_documents(texts, hf_embeddings)
st.write('loading tokenizer')
#model_name_or_path = "TheBloke/Llama-2-13B-chat-GPTQ"
model_name_or_path = "TheBloke/Llama-2-13B-chat-GGUF"
#tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model_basename = "model"
use_triton = False
DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
st.write('loading LLM')
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
model_basename=model_basename,
use_safetensors=True,
trust_remote_code=True,
device=DEVICE,
use_triton=use_triton,
quantize_config=None)
st.write('setting up the chain')
streamer = TextStreamer(tokenizer, skip_prompt = True, skip_special_tokens = True)
text_pipeline = pipeline(task = 'text-generation', model = model, tokenizer = tokenizer, streamer = streamer)
llm = HuggingFacePipeline(pipeline = text_pipeline)
def generate_prompt(prompt, sys_prompt):
return f"[INST] <<SYS>> {sys_prompt} <</SYS>> {prompt} [/INST]"
sys_prompt = "Use following piece of context to answer the question in less than 20 words"
template = generate_prompt(
"""
{context}
Question : {question}
"""
, sys_prompt)
prompt = PromptTemplate(template=template, input_variables=["context", "question"])
chain_type_kwargs = {"prompt": prompt}
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=db.as_retriever(search_kwargs={"k": 2}),
return_source_documents = True,
chain_type_kwargs=chain_type_kwargs,
)
st.write('READY!!!')
q1="what the author worked on ?"
q2="where did author study?"
q3="what author did ?"
result = qa_chain(q1)
st.write('question:', q1, 'result:', result)
result = qa_chain(q2)
st.write('question:', q2, 'result:', result)
result = qa_chain(q3)
st.write('question:', q3, 'result:', result)
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