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import gradio as gr
from langchain.document_loaders import DirectoryLoader, PyPDFLoader
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.agents import Tool
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import HuggingFacePipeline
from transformers import LlamaTokenizer, LlamaForCausalLM, pipeline
# Load and process the text files
loader = DirectoryLoader('./new_papers/', glob="./*.pdf", loader_cls=PyPDFLoader)
documents = loader.load()
# Splitting the text into chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
texts = text_splitter.split_documents(documents)
# HF Instructor Embeddings
instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl", model_kwargs={"device": "cuda"})
# Embed and store the texts
persist_directory = 'db'
embedding = instructor_embeddings
vectordb = Chroma.from_documents(documents=texts, embedding=embedding, persist_directory=persist_directory)
# Make a retriever
retriever = vectordb.as_retriever(search_kwargs={"k": 3})
# Setup LLM for text generation
tokenizer = LlamaTokenizer.from_pretrained("TheBloke/wizardLM-7B-HF")
model = LlamaForCausalLM.from_pretrained("TheBloke/wizardLM-7B-HF", load_in_8bit=True, device_map='auto', torch_dtype=torch.float16, low_cpu_mem_usage=True)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=1024, temperature=0, top_p=0.95, repetition_penalty=1.15)
local_llm = HuggingFacePipeline(pipeline=pipe)
# Make a chain
qa_chain = RetrievalQA.from_chain_type(llm=local_llm, chain_type="stuff", retriever=retriever, return_source_documents=True)
class VectorStoreRetrieverTool(Tool):
name = "vectorstore_retriever"
description = "This tool uses LangChain's RetrievalQA to find relevant answers from a vector store based on a given query."
inputs = ["text"]
outputs = ["text"]
def __call__(self, query: str):
# Run the query through the RetrievalQA chain
llm_response = qa_chain(query)
return llm_response['result']
# Create the Gradio interface using the HuggingFaceTool
tool = gr.Interface(
VectorStoreRetrieverTool(),
live=True,
title="LangChain-Application: Vectorstore-Retriever",
description="This tool uses LangChain's RetrievalQA to find relevant answers from a vector store based on a given query.",
)
# Launch the Gradio interface
tool.launch()
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