seperated QA from app.py

app.py

@@ -1,122 +1,11 @@
-
-# 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
+# import for typing
 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
 
 # gradio
 import gradio as gr
 
-# 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
-
-
 global qa 
-
-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"
-}
-    )
+from qa import qa
 
 
 #####
@@ -124,57 +13,57 @@ qa = RetrievalQAWithSourcesChain.from_chain_type(llm=llm, retriever=retriever, r
 # Gradio fns
 ####
 
-def add_text(history, text):
-    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
+def create_gradio_interface(qa:RetrievalQAWithSourcesChain):
+    def add_text(history, text):
+        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 infer(question, history):
-    query =  question
-    result = qa({"query": query, "history": history, "question": question})
-    return result
+        history[-1][1] = print_this #response['answer']
+        return history
 
-css="""
-#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
-"""
+    def infer(question, history):
+        query =  question
+        result = qa({"query": query, "history": history, "question": question})
+        return result
 
-title = """
-<div style="text-align: center;max-width: 1920px;">
-    <h1>Chat with your Documentation</h1>
-    <p style="text-align: center;">This is a privately hosten Docs AI Buddy, <br />
-    It will help you with any question regarding the documentation of Ray ;)</p>
-</div>
-"""
+    css="""
+    #col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
+    """
 
+    title = """
+    <div style="text-align: center;max-width: 1920px;">
+        <h1>Chat with your Documentation</h1>
+        <p style="text-align: center;">This is a privately hosten Docs AI Buddy, <br />
+        It will help you with any question regarding the documentation of Ray ;)</p>
+    </div>
+    """
 
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(min_width=900, elem_id="col-container"):
-        gr.HTML(title)      
-        chatbot = gr.Chatbot([], elem_id="chatbot")
-        #with gr.Row():
-        #    clear = gr.Button("Clear")
 
-        with gr.Row():
-            question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
-        with gr.Row():
-            clear = gr.ClearButton([chatbot, question])
+    with gr.Blocks(css=css) as demo:
+        with gr.Column(min_width=900, elem_id="col-container"):
+            gr.HTML(title)      
+            chatbot = gr.Chatbot([], elem_id="chatbot")
+            #with gr.Row():
+            #    clear = gr.Button("Clear")
 
-    question.submit(add_text, [chatbot, question], [chatbot, question], queue=False).then(
-        bot, chatbot, chatbot
-    )
-    #clear.click(lambda: None, None, chatbot, queue=False)
+            with gr.Row():
+                question = gr.Textbox(label="Question", placeholder="Type your question and hit Enter ")
+            with gr.Row():
+                clear = gr.ClearButton([chatbot, question])
 
-demo.queue().launch()
+        question.submit(add_text, [chatbot, question], [chatbot, question], queue=False).then(
+            bot, chatbot, chatbot
+        )
+        #clear.click(lambda: None, None, chatbot, queue=False)
 
-def create_gradio_interface(qa:RetrievalQAWithSourcesChain, ):
-    pass
+if __name__ == "__main__":
+    demo = create_gradio_interface(qa)
+    demo.queue().launch()