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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"
}
)
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