Revert "added stuff"

This reverts commit 8a93d3866170273593eb3e236a3a87d9dbbcc5a7.

revert changes from old containers

app.py

@@ -3,162 +3,9 @@ from langchain.chains import RetrievalQAWithSourcesChain
 
 # gradio
 import gradio as gr
-#import random
-import time
-#boto3 for S3 access
-import boto3
-from botocore import UNSIGNED
-from botocore.client import Config
-# access .env file
-import os
-from dotenv import load_dotenv
-#from bs4 import BeautifulSoup
-# HF libraries
-from langchain.llms import HuggingFaceHub
-from langchain.embeddings import HuggingFaceHubEmbeddings
-# vectorestore
-from langchain.vectorstores import Chroma
-#from langchain.vectorstores import FAISS
-# retrieval chain
-from langchain.chains import RetrievalQA
-# from langchain.chains import RetrievalQAWithSourcesChain
-# prompt template
-from langchain.prompts import PromptTemplate
-from langchain.memory import ConversationBufferMemory
-# logging
-import logging
-#import zipfile
-# improve results with retriever
-# from langchain.retrievers import ContextualCompressionRetriever
-# from langchain.retrievers.document_compressors import LLMChainExtractor
-# from langchain.retrievers.document_compressors import EmbeddingsFilter
-# from langchain.retrievers.multi_query import MultiQueryRetriever
-# from langchain.retrievers import BM25Retriever, EnsembleRetriever
-# reorder retrived documents
-# from langchain.document_transformers import LongContextReorder
-# github issues
-#from langchain.document_loaders import GitHubIssuesLoader
-# debugging
-from langchain.globals import set_verbose
-# caching
-from langchain.globals import set_llm_cache
-#from langchain.cache import InMemoryCache
-# We can do the same thing with a SQLite cache
-from langchain.cache import SQLiteCache
-#set_llm_cache(InMemoryCache())
-
-set_verbose(True)
-
-# 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')
-
-# initialize Model config
-# HuggingFaceH4/zephyr-7b-beta
-# mistralai/Mistral-7B-Instruct-v0.1
-model_id = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={
-    "temperature":0.1, 
-    "max_new_tokens":1024, 
-    "repetition_penalty":1.2, 
-#    "streaming": True, 
-#    "return_full_text":True
-    })
-
-#model_name = "sentence-transformers/multi-qa-mpnet-base-dot-v1"
-model_name = "sentence-transformers/all-mpnet-base-v2"
-embeddings = HuggingFaceHubEmbeddings(repo_id=model_name)
-
-# 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')
-
-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()
-
-## FAISS DB
-# s3.download_file('rad-rag-demos', 'vectorstores/faiss_db_ray.zip', './chroma_db/faiss_db_ray.zip')
-# with zipfile.ZipFile('./chroma_db/faiss_db_ray.zip', 'r') as zip_ref:
-#     zip_ref.extractall('./chroma_db/')
-
-# FAISS_INDEX_PATH='./chroma_db/faiss_db_ray'
-# db = FAISS.load_local(FAISS_INDEX_PATH, embeddings)
-
-# initialize the bm25 retriever and chroma/faiss retriever
-# bm25_retriever = BM25Retriever.
-# bm25_retriever.k = 2
-
-# Retrieve more documents with higher diversity useful if your dataset has many similar documents
-retriever = db.as_retriever(search_type="mmr")#, search_kwargs={'k': 3, 'lambda_mult': 0.25})
-# Above a certain threshold
-# retriever = db.as_retriever(
-#     search_type="similarity_score_threshold",
-#     search_kwargs={'score_threshold': 0.6}
-# )
-
-# # asks LLM to create 3 alternatives baed on user query
-# multi_retriever = MultiQueryRetriever.from_llm(retriever=retriever, llm=model_id)
-
-# # asks LLM to extract relevant parts from retrieved documents
-# compressor = LLMChainExtractor.from_llm(model_id)
-# compression_retriever = ContextualCompressionRetriever(base_compressor=compressor, base_retriever=multi_retriever)
 
 global qa 
-template = """
-You are the friendly documentation AI buddy Arti, who helps the Human in using RAY, the open-source unified framework for scaling AI and Python applications.
-    Use the following context (delimited by <ctx></ctx>) and the chat history (delimited by <hs></hs>) to answer the question :
-------
-<ctx>
-{context}
-</ctx>
-------
-<hs>
-{history}
-</hs>
-------
-{question}
-Answer:
-"""
-prompt = PromptTemplate(
-    input_variables=["history", "context", "question"],
-    template=template,
-)
-memory = ConversationBufferMemory(memory_key="history", input_key="question")
-
-# logging for the chain
-logging.basicConfig()
-logging.getLogger("langchain.retrievers").setLevel(logging.INFO)    
-logging.getLogger("langchain.chains").setLevel(logging.INFO)    
-
-
-
-qa = RetrievalQA.from_chain_type(llm=model_id, retriever=retriever, return_source_documents=True, verbose=True, chain_type_kwargs={
-    "verbose": True,
-    "memory": memory,
-    "prompt": prompt
-}
-    )
-# qa = RetrievalQAWithSourcesChain.from_chain_type(llm=model_id, retriever=retriever, return_source_documents=True, verbose=True, chain_type_kwargs={
-#     "verbose": True,
-#     "memory": memory,
-#     "prompt": prompt,
-#     "document_variable_name": "context"
-# }
-#     )
+from qa import qa
 
 
 #####
@@ -174,29 +21,24 @@ def create_gradio_interface(qa:RetrievalQAWithSourcesChain):
         history = history + [(text, None)]
         return history, ""
 
-def bot(history):
-    response = infer(history[-1][0], history)
-    sources = [doc.metadata.get("source") for doc in response['source_documents']]
-    src_list = '\n'.join(sources)
-    print_this = response['result'] + "\n\n\n Sources: \n\n\n" + src_list
-
-    history[-1][1] = ""
-    for character in response['result']: #print_this: # 
-        #print_this:
-        history[-1][1] += character
-        time.sleep(0.01)
-        yield history
-    # history[-1][1] = print_this #response['answer']
-    # return history
+    def bot(history):
+        response = infer(history[-1][0], history)
+        sources = [doc.metadata.get("source") for doc in response['source_documents']]
+        src_list = '\n'.join(sources)
+        print_this = response['answer'] + "\n\n\n Sources: \n\n\n" + src_list
+
+
+        history[-1][1] = print_this #response['answer']
+        return history
 
     def infer(question, history):
         query =  question
         result = qa({"query": query, "history": history, "question": question})
         return result
 
-css="""
-#col-container {min-width: 800px; max-width: 1920px; margin-left: auto; margin-right: auto;}
-"""
+    css="""
+    #col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
+    """
 
     title = """
     <div style="text-align: center;max-width: 1920px;">