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Real-time document Q&A using Pulsar, Cassandra, LangChain, and open-source language models.

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wikipedia_demo

Real-time document Q&A using Pulsar, Cassandra, LangChain, and open-source language models.

Don't want to complete the exercises? The complete working code is available on the complete branch.

Project overview

This workshop code runs a Retrieval Augmented Generation (RAG) application stack that takes data from Wikipedia, stores it in a vector database (Astra DB), and provides a chat interface for asking questions about the Wikipedia documents.

The project uses Astra Streaming (serverless Apache Pulsar) and Astra DB (serverless Apache Cassandra) and 4 microservices built using:

  • Python
  • LangChain for the LLM framework
  • Open source Instructor Embedding model
  • Open source Mistral 7B LLM
  • Gradio for a simple chat web UI
  • Fast API to provide the document embedding service

Running the project

The project consists of 4 microservices

  • docstream Gets random Wikipedia articles in English and adds them to a Pulsar topic for processing
  • embeddings A RESTful API service that turns text into embeddings.
  • procstream Consumes articles from the Pulsar topic, scrapes the webpage to get the full text, generates embeddings, and stores in Astra DB
  • chatbot Provides both the UI for the chatbot and the agent code for running the chatbot

With docker

docker compose up --build

Individual services can also be started directly. Note that procstream and chatbot require that the embeddings microservice is running.

  • docker compose up --build docstream
  • docker compose up --build embeddings
  • docker compose up --build procstream
  • docker compose up --build chatbot

Without docker

If you do not wish to run with docker, you can run each of the 4 microservices separately. Use pip to install the requirements for each microservice and then run it directly with python.

cd docstream
pip install -r requirements.txt
python app.py
cd embeddings
pip install -r requirements.txt
gunicorn --workers 1 -k uvicorn.workers.UvicornWorker app:app --bind 0.0.0.0:8000
cd procstream
pip install -r requirements.txt
python app.py
cd chatbot
pip install -r requirements.txt
python app.py

Using the services

You can access the embeddings API in your Chrome browser at http://127.0.0.1:8000/docs.

The chatbot can be opened in your Chrome browser at http://127.0.0.1:7860.

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Real-time document Q&A using Pulsar, Cassandra, LangChain, and open-source language models.

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