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from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Chroma
from langchain import HuggingFacePipeline
from langchain.chains import RetrievalQA
from transformers import AutoTokenizer
import pickle
import os
with open('shakespeare.pkl', 'rb') as fp:
data = pickle.load(fp)
bloomz_tokenizer = AutoTokenizer.from_pretrained('bigscience/bloomz-1b7')
text_splitter = CharacterTextSplitter.from_huggingface_tokenizer(bloomz_tokenizer, chunk_size=100, chunk_overlap=0, separator='\n')
documents = text_splitter.split_documents(data)
embeddings = HuggingFaceEmbeddings()
persist_directory = "vector_db"
vectordb = Chroma.from_documents(documents=documents, embedding=embeddings, persist_directory=persist_directory)
vectordb.persist()
vectordb = None
vectordb_persist = Chroma(persist_directory=persist_directory, embedding_function=embeddings)
llm = HuggingFacePipeline.from_model_id(
model_id="bigscience/bloomz-1b7",
task="text-generation",
model_kwargs={"temperature" : 0, "max_length" : 500})
doc_retriever = vectordb_persist.as_retriever()
shakespeare_qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=doc_retriever)
def make_inference(query):
inference = shakespeare_qa.run(query)
return inference
if __name__ == "__main__":
# make a gradio interface
import gradio as gr
gr.Interface(
make_inference,
gr.inputs.Textbox(lines=2, label="Query"),
gr.outputs.Textbox(label="Response"),
title="Ask_Shakespeare",
description="️building_w_llms_qa_Shakespeare allows you to inquire about the Shakespeare's plays.",
).launch()