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utils.py
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# Importing Dependencies
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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from langchain import PromptTemplate, HuggingFacePipeline
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from langchain.embeddings import HuggingFaceEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.chains import RetrievalQA
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# Faiss Index Path
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FAISS_INDEX = "vectorstore/"
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# Custom prompt template
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custom_prompt_template = """[INST] <<SYS>>
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You are a trained bot to guide people about Indian Law. You will answer user's query with your knowledge and the context provided.
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If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.
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Do not say thank you and tell you are an AI Assistant and be open about everything.
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<</SYS>>
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Use the following pieces of context to answer the users question.
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Context : {context}
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Question : {question}
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Answer : [/INST]
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"""
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# Return the custom prompt template
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def set_custom_prompt_template():
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"""
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Set the custom prompt template for the LLMChain
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"""
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prompt = PromptTemplate(template=custom_prompt_template, input_variables=["context", "question"])
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return prompt
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# Return the LLM
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def load_llm():
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"""
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Load the LLM
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"""
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# Model ID
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repo_id = 'meta-llama/Llama-2-7b-chat-hf'
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# Load the model
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model = AutoModelForCausalLM.from_pretrained(
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repo_id,
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device_map='auto',
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load_in_4bit=True
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)
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# Load the tokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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repo_id,
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use_fast=True
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)
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# Create pipeline
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pipe = pipeline(
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'text-generation',
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model=model,
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tokenizer=tokenizer,
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max_length=512
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)
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# Load the LLM
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llm = HuggingFacePipeline(pipeline=pipe)
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return llm
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# Return the chain
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def retrieval_qa_chain(llm, prompt, db):
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"""
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Create the Retrieval QA chain
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"""
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# Create the chain
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qa_chain = RetrievalQA.from_chain_type(
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llm=llm,
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chain_type='stuff',
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retriever=db.as_retriever(search_kwargs={'k': 2}),
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return_source_documents=True,
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chain_type_kwargs={'prompt': prompt}
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)
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return qa_chain
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# Return the chain
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def qa_pipeline():
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"""
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Create the QA pipeline
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"""
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# Load the HuggingFace embeddings
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embeddings = HuggingFaceEmbeddings()
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# Load the index
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db = FAISS.load_local("vectorstore/", embeddings)
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# Load the LLM
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llm = load_llm()
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# Set the custom prompt template
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qa_prompt = set_custom_prompt_template()
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# Create the retrieval QA chain
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chain = retrieval_qa_chain(llm, qa_prompt, db)
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return chain
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