app.py

# https://towardsai.net/p/machine-learning/deploying-a-langchain-large-language-model-llm-with-streamlit-pinecone?amp=1

import os
import pinecone
import streamlit as st
from streamlit_chat import message
from langchain.llms import OpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Pinecone
from langchain.chains import ConversationChain
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains import RetrievalQA
from langchain.chains.query_constructor.base import AttributeInfo

pinecone.init(
     api_key=str(os.environ['PINECONE_API_KEY']), 
     environment=str(os.environ['PINECONE_ENV']))

llm = OpenAI(temperature=0)
embeddings = OpenAIEmbeddings()
vectorstore = Pinecone.from_existing_index("impromptu", embeddings)
document_content_description = ("Excerpts from the book Impromptu, "
                                "jointly authored by Reid Hoffman, " 
                                "GPT-3, and GPT 4.")
metadata_field_info=[
    AttributeInfo(
        name="author",
        description="The author of the excerpt", 
        type="string or list[string]", 
    ),
    AttributeInfo(
        name="chapter_number",
        description="The chapter number of excerpt", 
        type="integer", 
    ),
    AttributeInfo(
        name="chapter_name",
        description="The chapter name of the excerpt", 
        type="string", 
    ),
]

def load_chain():
    retriever = SelfQueryRetriever.from_llm(
        llm, 
        vectorstore, 
        document_content_description, 
        metadata_field_info, 
        verbose=True)
    qa = RetrievalQA.from_chain_type(
        llm=OpenAI(), 
        chain_type="stuff",
        retriever=retriever, 
        return_source_documents=False)
    return qa
    
chain = load_chain()

# From here down is all the StreamLit UI.
st.set_page_config(page_title="Impromptu GPT", page_icon=":robot:")
st.header("Impromptu GPT")
'''
> A secondary UI testing LangChain's Python-only [```SelfQueryRetriever```](https://python.langchain.com/en/latest/modules/indexes/retrievers/examples/self_query_retriever.html)
interface, which uses an LLM to structure vectorstore queries. 
Below, conversational requests for specific chapters and 
speakers are extracted into relevant metadata filters.
'''

if "generated" not in st.session_state:
    st.session_state["generated"] = []

if "past" not in st.session_state:
    st.session_state["past"] = []

def get_text():
    input_text = st.text_input("You: ", "What are some of the risks mentioned in chapter one?", key="input")
    return input_text

user_input = get_text()

if user_input:
    output = chain.run(user_input)   
    st.session_state.past.append(user_input)
    st.session_state.generated.append(output)

if st.session_state["generated"]:
    for i in range(len(st.session_state["generated"]) - 1, -1, -1):
        message(st.session_state["generated"][i], key=str(i), avatar_style="bottts")
        message(st.session_state["past"][i], is_user=True, key=str(i) + "_user", avatar_style="shapes")