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Question answering over a group chat messages using Activeloop's DeepLake

In this tutorial, we are going to use Langchain + Activeloop's 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[enterprise]' 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:")
activeloop_token = getpass.getpass("Activeloop Token:")
os.environ["ACTIVELOOP_TOKEN"] = activeloop_token
os.environ["ACTIVELOOP_ORG"] = getpass.getpass("Activeloop Org:")

org_id = os.environ["ACTIVELOOP_ORG"]
embeddings = OpenAIEmbeddings()

dataset_path = "hub://" + org_id + "/data"

API Reference:

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
)

Optional: You can also use Deep Lake's Managed Tensor Database as a hosting service and run queries there. In order to do so, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps.

# 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, runtime="tensor_db"
# )

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)