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AI Book Chat (Springboot, LangChain4j, AstraDB)

Overview

Upload a book in PDF format and ask questions through REST API.

Terminology

  • Embedding (a.k.a. vector): numeric representation of real-world objects (like text, images, or videos). Simply a format similar to a list of cordinates that machine learning models can easily process.
  • Embedding model: AI model that converts real-world objects into numeric representation. e.g. For text: LLMs like Open AI GPT-3, GPT-4
  • Embedding store (a.k.a. vector database): used for efficient storage and retrieval of embeddings. e.g. AstraDB (based on Apache Cassandra)

Architecture

Transform book text into embeddings and store on vector database.

Generate Embeddings

Retrieve relevant text embeddings for queries from vector database, feed them to chat language model, and receive responses.

Book Chat

Tech Stack

  • Java 21
  • Springboot 3.2.3: Java web application framework.
  • Maven 3.9.6: Java dependency manager and build tool.
  • Lombok: Java annotation library to reduce boilerplate code.
  • LangChain4j 0.25: Java implementation of LangChain framework. Used to build LLM-based application functionalities.
  • AstraDB: Serverless DBaaS built on Apache Cassandra. Used as the embedding store (a.k.a vector database).
  • Open AI GPT LLM: Chat language model. Used to feed embeddings from vector DB to GPT LLM and get responses for queries.

Initial Setup

Generate a fresh project with Springboot Web dependency (https://start.spring.io/).

cd chat-api
mvn -N wrapper:wrapper -Dmaven=3.9.6

Create Serverless Vector DB instance on AstraDB (https://astra.datastax.com/).

DB Name: vector-db-1
Provider: AWS
Region: us-east-1
CQL Table: vectordb
Namespace: ai_book_chat

Create API Key from OpenAI (https://platform.openai.com/api-keys).

Set required environment variables.

export ASTRA_DB_ID="XXXXX"
export ASTRA_DB_TOKEN="AstraCS:XXXXXXX"
export ASTRA_DB_REGION="us-east-1"
export ASTRA_DB_NAMESPACE="ai_book_chat"
export ASTRA_DB_TABLE="vectordb"
export OPENAI_API_KEY="XXXXX"

Build and run application.

./mvnw clean install
./mvnw spring-boot:run

Test REST endpoint via a tool like Postman or curl.

curl --location 'http://localhost:8080/api/chat' --header 'Content-Type: application/json' --data 'What is Cassandra?'