added the code needed to qa

qa.py

@@ -0,0 +1,112 @@
+
+# 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.llms import HuggingFaceHub
+from langchain.embeddings import HuggingFaceHubEmbeddings
+# vectorestore
+from langchain.vectorstores import Chroma
+
+# retrieval chain
+from langchain.chains import RetrievalQAWithSourcesChain
+# prompt template
+from langchain.prompts import PromptTemplate
+from langchain.memory import ConversationBufferMemory
+from langchain.retrievers import BM25Retriever, EnsembleRetriever
+# reorder retrived documents
+# 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":True
+    })
+
+# initialize Embedding config
+embedding_model_name = "sentence-transformers/all-mpnet-base-v2"
+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))
+
+## Chroma DB
+s3.download_file(AWS_S3_LOCATION, AWS_S3_FILE, VS_DESTINATION)
+# 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})
+
+# asks LLM to create 3 alternatives baed on user query
+# asks LLM to extract relevant parts from retrieved documents
+
+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"
+}
+    )