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| # logging | |
| import logging | |
| # access .env file | |
| import os | |
| from dotenv import load_dotenv | |
| import time | |
| #boto3 for S3 access | |
| import boto3 | |
| from botocore import UNSIGNED | |
| from botocore.client import Config | |
| # HF libraries | |
| from langchain_community.llms import HuggingFaceHub | |
| from langchain_community.embeddings import HuggingFaceHubEmbeddings | |
| # vectorestore | |
| from langchain_community.vectorstores import Chroma | |
| from langchain_community.vectorstores import FAISS | |
| import zipfile | |
| # retrieval chain | |
| from langchain.chains import RetrievalQAWithSourcesChain | |
| # prompt template | |
| from langchain.prompts import PromptTemplate | |
| from langchain.memory import ConversationBufferMemory | |
| # github issues | |
| #from langchain.document_loaders import GitHubIssuesLoader | |
| # debugging | |
| from langchain.globals import set_verbose | |
| # caching | |
| from langchain.globals import set_llm_cache | |
| # We can do the same thing with a SQLite cache | |
| from langchain.cache import SQLiteCache | |
| # template for prompt | |
| from prompt import template | |
| set_verbose(True) | |
| # set up logging for the chain | |
| logging.basicConfig() | |
| logging.getLogger("langchain.retrievers").setLevel(logging.INFO) | |
| logging.getLogger("langchain.chains.qa_with_sources").setLevel(logging.INFO) | |
| # load .env variables | |
| config = load_dotenv(".env") | |
| HUGGINGFACEHUB_API_TOKEN=os.getenv('HUGGINGFACEHUB_API_TOKEN') | |
| AWS_S3_LOCATION=os.getenv('AWS_S3_LOCATION') | |
| AWS_S3_FILE=os.getenv('AWS_S3_FILE') | |
| VS_DESTINATION=os.getenv('VS_DESTINATION') | |
| # remove old vectorstore | |
| if os.path.exists(VS_DESTINATION): | |
| os.remove(VS_DESTINATION) | |
| # remove old sqlite cache | |
| if os.path.exists('.langchain.sqlite'): | |
| os.remove('.langchain.sqlite') | |
| # initialize Model config | |
| llm_model_name = "mistralai/Mistral-7B-Instruct-v0.1" | |
| # changed named to model_id to llm as is common | |
| llm = HuggingFaceHub(repo_id=llm_model_name, model_kwargs={ | |
| # "temperature":0.1, | |
| "max_new_tokens":1024, | |
| "repetition_penalty":1.2, | |
| # "streaming": True, | |
| "return_full_text":False | |
| }) | |
| # initialize Embedding config | |
| embedding_model_name = "sentence-transformers/multi-qa-mpnet-base-dot-v1" | |
| embeddings = HuggingFaceHubEmbeddings(repo_id=embedding_model_name) | |
| set_llm_cache(SQLiteCache(database_path=".langchain.sqlite")) | |
| # retrieve vectorsrore | |
| s3 = boto3.client('s3', config=Config(signature_version=UNSIGNED)) | |
| ## download vectorstore from S3 | |
| s3.download_file(AWS_S3_LOCATION, AWS_S3_FILE, VS_DESTINATION) | |
| with zipfile.ZipFile(VS_DESTINATION, 'r') as zip_ref: | |
| zip_ref.extractall('./vectorstore/') | |
| FAISS_INDEX_PATH='./vectorstore/lc-faiss-multi-qa-mpnet' | |
| db = FAISS.load_local(FAISS_INDEX_PATH, embeddings) | |
| # use the cached embeddings instead of embeddings to speed up re-retrival | |
| # db = Chroma(persist_directory="./vectorstore", embedding_function=embeddings) | |
| # db.get() | |
| retriever = db.as_retriever(search_type="mmr", search_kwargs={'k': 3, 'lambda_mult': 0.25}) | |
| prompt = PromptTemplate( | |
| input_variables=["history", "context", "question"], | |
| template=template, | |
| ) | |
| memory = ConversationBufferMemory(memory_key="history", input_key="question") | |
| qa = RetrievalQAWithSourcesChain.from_chain_type(llm=llm, retriever=retriever, return_source_documents=True, verbose=True, chain_type_kwargs={ | |
| "verbose": True, | |
| "memory": memory, | |
| "prompt": prompt, | |
| "document_variable_name": "context" | |
| } | |
| ) | |