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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.create_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)