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Delete lpphelper.py
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lpphelper.py
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import transformers
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import torch
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import os
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
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from transformers import pipeline
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from langchain.llms import HuggingFacePipeline
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from langchain.vectorstores import Chroma
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.chains import RetrievalQA
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from langchain.document_loaders import TextLoader
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from langchain.document_loaders import PyPDFLoader
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from langchain.document_loaders import DirectoryLoader
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from InstructorEmbedding import INSTRUCTOR
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from langchain.embeddings import HuggingFaceInstructEmbeddings
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from langchain_community.vectorstores import Chroma
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import textwrap
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def gen_vectordb():
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tokenizer = AutoTokenizer.from_pretrained("lmsys/fastchat-t5-3b-v1.0")
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model = AutoModelForSeq2SeqLM.from_pretrained("lmsys/fastchat-t5-3b-v1.0")
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pipe = pipeline(
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"text2text-generation",
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model=model,
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tokenizer=tokenizer,
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max_length=256
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)
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local_llm = HuggingFacePipeline(pipeline=pipe)
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loader = DirectoryLoader('C:/Users/SudheerRChinthala/sivallm/new_papers', glob="./*.pdf", loader_cls=PyPDFLoader)
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documents = loader.load()
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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texts = text_splitter.split_documents(documents)
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instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base")
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persist_directory = 'db'
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embedding = instructor_embeddings
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vectordb = Chroma.from_documents(documents=texts,
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embedding=embedding,
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persist_directory=persist_directory)
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retriever = vectordb.as_retriever(search_kwargs={"k": 3})
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qa_chain = RetrievalQA.from_chain_type(llm=local_llm,
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chain_type="stuff",
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retriever=retriever,
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return_source_documents=True)
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vectordb.persist()
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vectordb = None
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if __name__=="__main__":
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gen_vectordb()
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