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# https://towardsai.net/p/machine-learning/deploying-a-langchain-large-language-model-llm-with-streamlit-pinecone?amp=1
"""Python file to serve as the frontend"""
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.query_constructor.base import AttributeInfo
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",
),
]
document_content_description = "Excerpt's from Reid Hoffman's book Impromptu"
embeddings = OpenAIEmbeddings()
pinecone.init(
api_key=str(os.environ['PINECONE_API_KEY']),
environment=str(os.environ['PINECONE_ENV']))
index_name = str(os.environ['PINECONE_INDEX_NAME'])
def load_chain():
vectorstore = Pinecone.from_existing_index(index_name, embeddings)
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
document_content_description,
metadata_field_info,
verbose=True)
return retriever
# docsearch = Pinecone.from_existing_index(index_name, embeddings)
# return docsearch
# def load_chain():
# """Logic for loading the chain you want to use should go here."""
# llm = OpenAI(temperature=0)
# chain = ConversationChain(llm=llm)
# return chain
chain = load_chain()
# From here down is all the StreamLit UI.
st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
st.header("LangChain Demo")
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 the four types of hallucinations?", key="input")
return input_text
user_input = get_text()
if user_input:
docs = chain.get_relevant_documents(user_input)
print('docs')
print(docs)
print('docs[0]')
print(docs[0])
print('docs[0].page_content')
print(docs[0].page_content)
output = docs[0].page_content
# output = 'nothing right now'
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))
message(st.session_state["past"][i], is_user=True, key=str(i) + "_user") |