Question answering over a group chat messages

In this tutorial, we are going to use Langchain + Deep Lake with GPT4 to semantically search and ask questions over a group chat.

View a working demo here

1. Install required packages

!python3 -m pip install --upgrade langchain deeplake openai tiktoken

2. Add API keys

import os
import getpass
from langchain.document_loaders import PyPDFLoader, TextLoader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
from langchain.vectorstores import DeepLake
from langchain.chains import ConversationalRetrievalChain, RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI

os.environ['OPENAI_API_KEY'] = getpass.getpass('OpenAI API Key:')
os.environ['ACTIVELOOP_TOKEN'] = getpass.getpass('Activeloop Token:')
os.environ['ACTIVELOOP_ORG'] = getpass.getpass('Activeloop Org:')

org = os.environ['ACTIVELOOP_ORG']
embeddings = OpenAIEmbeddings()

dataset_path = 'hub://' + org + '/data'

2. Create sample data

You can generate a sample group chat conversation using ChatGPT with this prompt:

Generate a group chat conversation with three friends talking about their day, referencing real places and fictional names. Make it funny and as detailed as possible.

I’ve already generated such a chat in messages.txt. We can keep it simple and use this for our example.

3. Ingest chat embeddings

We load the messages in the text file, chunk and upload to ActiveLoop Vector store.

with open("messages.txt") as f:
    state_of_the_union = f.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
pages = text_splitter.split_text(state_of_the_union)

text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
texts = text_splitter.split_documents(pages)

print (texts)

dataset_path = 'hub://'+org+'/data'
embeddings = OpenAIEmbeddings()
db = DeepLake.from_documents(texts, embeddings, dataset_path=dataset_path, overwrite=True)

4. Ask questions

Now we can ask a question and get an answer back with a semantic search:

db = DeepLake(dataset_path=dataset_path, read_only=True, embedding_function=embeddings)

retriever = db.as_retriever()
retriever.search_kwargs['distance_metric'] = 'cos'
retriever.search_kwargs['k'] = 4

qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=retriever, return_source_documents=False)

# What was the restaurant the group was talking about called?
query = input("Enter query:")

# The Hungry Lobster
ans = qa({"query": query})

print(ans)