Samarth991/RAG-PDF_With_LLAMA-3B

1from langchain.chains.combine_documents import create_stuff_documents_chain2from langchain_core.prompts import ChatPromptTemplate3from langchain.chains import create_retrieval_chain4from langchain.chains.summarize.chain import load_summarize_chain5from langchain_community.llms.huggingface_hub import HuggingFaceHub6from langchain.retrievers.document_compressors import LLMChainExtractor7from langchain.retrievers import ContextualCompressionRetriever8from langchain.chains.question_answering import load_qa_chain9 10#from Api_Key import google_plam11from langchain_groq import ChatGroq12import os13from dotenv import load_dotenv14load_dotenv()15 16 17def prompt_template_to_analyze_resume():18    template = """19    You are provided with the Resume of the Candidate in the context below . 20    As an Talent Aquistion bot , your task is to provide insights about the candidate in precise manner.21    22    \n\n:{context}23    """24    prompt = ChatPromptTemplate.from_messages(25        [26            ('system',template),27            ('human','input'),28        ]29        )30    return prompt31    32def prompt_template_for_relaibility():33    template ="""34    You are provided with the Resume of the Candidate in the context below35    If asked about reliability , check How frequently the candidate has switched from one company to another. 36    Grade him on the given basis: 37        If less than 2 Year - very less Reliable  38        if more than 2 years but less than 5 years - Reliable 39        if more than 5 Years - Highly Reliable40    and generate verdict . 41    42    \n\n:{context}43    44    """45    prompt = ChatPromptTemplate.from_messages(46        [47            ('system',template),48            ('human','input'),49        ]50        )51    return prompt52    53 54def summarize(documents,llm):55    summarize_chain = load_summarize_chain(llm=llm, chain_type='refine', verbose = True)56    results = summarize_chain.invoke({'input_documents':documents})57    return results['output_text']58 59 60def get_hugging_face_model(model_id='mistralai/Mistral-7B-Instruct-v0.2',temperature=0.01,max_tokens=4096,api_key=None):61    llm = HuggingFaceHub(62        huggingfacehub_api_token =api_key,63        repo_id=model_id, 64        model_kwargs={"temperature":temperature, "max_new_tokens":max_tokens}65        )66    return llm67 68def get_groq_model(api_key):69    os.environ["GROQ_API_KEY"] = api_key70    llm = ChatGroq(model="llama3-8b-8192") # (model="gemma2-9b-it")71    return llm72    73 74def Q_A(vectorstore,question,API_KEY,compressor=False):75    76    if API_KEY.startswith('gsk'):77       chat_llm = get_groq_model(api_key=API_KEY)78    elif API_KEY.startswith('hf'):79        chat_llm = get_hugging_face_model(api_key=API_KEY)80    81    # Create a retriever82    retriever = vectorstore.as_retriever(search_type = 'similarity',search_kwargs = {'k':2},)83    84    if compressor:85        #Create a contextual compressor86        compressor = LLMChainExtractor.from_llm(chat_llm)87        compression_retriever = ContextualCompressionRetriever(base_compressor=compressor,base_retriever=retriever)88        retriever = compression_retriever89        90    if  'reliable' in question.lower() or 'relaibility' in question.lower():91        prompt = prompt_template_for_relaibility()92        93    else:94        prompt = prompt_template_to_analyze_resume()95    # question_answer_chain = load_qa_chain(chat_llm, chain_type="stuff", prompt=prompt)96    question_answer_chain = create_stuff_documents_chain(chat_llm, prompt)97    98    chain = create_retrieval_chain(retriever, question_answer_chain)99    result = chain.invoke({'input':question})100    return result['answer']101