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| from langchain.vectorstores import FAISS | |
| from langchain.llms import GooglePalm | |
| from langchain.document_loaders import PyPDFLoader | |
| from langchain.document_loaders import TextLoader | |
| from langchain.document_loaders import Docx2txtLoader | |
| from langchain.embeddings import HuggingFaceInstructEmbeddings | |
| from langchain.prompts import PromptTemplate | |
| from langchain.chains import RetrievalQA | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| import os | |
| from dotenv import load_dotenv | |
| vector_index_path = "assets/vectordb/faiss_index" | |
| def load_env_variables(): | |
| load_dotenv() # take environment variables from .env | |
| def create_vector_db(filename, instructor_embeddings): | |
| if filename.endswith(".pdf"): | |
| loader = PyPDFLoader(file_path=filename) | |
| elif filename.endswith(".doc") or filename.endswith(".docx"): | |
| loader = Docx2txtLoader(filename) | |
| elif filename.endswith("txt") or filename.endswith("TXT"): | |
| loader = TextLoader(filename) | |
| # Split documents | |
| text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=10) | |
| splits = text_splitter.split_documents(loader.load()) | |
| # data = loader.load() | |
| # Create a FAISS instance for vector database from 'data' | |
| vectordb = FAISS.from_documents(documents=splits, | |
| embedding=instructor_embeddings) | |
| # Save vector database locally | |
| vectordb.save_local(vector_index_path) | |
| def get_qa_chain(instructor_embeddings, llm): | |
| # Load the vector database from the local folder | |
| vectordb = FAISS.load_local(vector_index_path, instructor_embeddings) | |
| # Create a retriever for querying the vector database | |
| retriever = vectordb.as_retriever(search_type="similarity") | |
| prompt_template = """ | |
| You are a question answer agent and you must strictly follow below prompt template. | |
| Given the following context and a question, generate an answer based on this context only. | |
| In the answer try to provide as much text as possible from "response" section in the source document context without making much changes. | |
| Keep answers brief and well-structured. Do not give one word answers. | |
| If the answer is not found in the context, kindly state "I don't know." Don't try to make up an answer. | |
| CONTEXT: {context} | |
| QUESTION: {question}""" | |
| PROMPT = PromptTemplate( | |
| template=prompt_template, input_variables=["context", "question"] | |
| ) | |
| chain = RetrievalQA.from_chain_type(llm=llm, | |
| chain_type="stuff", # or map-reduce | |
| retriever=retriever, | |
| input_key="query", | |
| return_source_documents=True, # return source document from the vector db | |
| chain_type_kwargs={"prompt": PROMPT}, | |
| verbose=True) | |
| return chain | |
| def load_model_params(): | |
| load_env_variables() | |
| # Create Google Palm LLM model | |
| llm = GooglePalm(google_api_key=os.environ["GOOGLE_API_KEY"], temperature=0.1) | |
| # # Initialize instructor embeddings using the Hugging Face model | |
| instructor_embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-large") | |
| return llm, instructor_embeddings | |
| def document_parser(instructor_embeddings, llm): | |
| chain = get_qa_chain(instructor_embeddings=instructor_embeddings, llm=llm) | |
| return chain | |