added stuff

app.py

@@ -3,9 +3,162 @@ 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 
-from qa import 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"
+# }
+#     )
 
 
 #####
@@ -21,24 +174,29 @@ 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['answer'] + "\n\n\n Sources: \n\n\n" + src_list
-
-
-        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['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 infer(question, history):
         query =  question
         result = qa({"query": query, "history": history, "question": question})
         return result
 
-    css="""
-    #col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-    """
+css="""
+#col-container {min-width: 800px; max-width: 1920px; margin-left: auto; margin-right: auto;}
+"""
 
     title = """
     <div style="text-align: center;max-width: 1920px;">