ai-playground

AI implementations using java, stores and either of Langchain4j or springai framework

AI Verbiage

• LLM - Large Language Model
• Embedding Store - database equivalent to store vector embeddings
• Embeddings - Document converted to vector
• Document - information that is required for AI to process

Pre-requisite

• Java 17+
• Docker Engine

Implementations

Below is the summary of implementations in this repository

Category	Module	Description
playground	langchain4j playground	AI playground using Langchain4j
	spring ai playground	AI playground using SpringAI & SpringBoot
embeddingstores	neo4j store with springai	Embedding store implementation using springai, spring boot and neo4j as store
	opensearch store with langchain4j	Embedding store implementation using langchain4j and opensearch as store
	pgvector store with lanchain4j	Embedding store implementation using langchain4j and pgvector as store
	pgvector store with springai	Embedding store implementation using springai, spring boot and pgvector as store
RAG(Retrieval-Augmented-Generation)	rag implementation using langchain4j and AllMiniLmL6V2	RAG Implementation using Langchain4j, PGVector store and allMiniLmL6V2 LLM with spring boot
	rag implementation using springai with llama llm	RAG Implementation using springai, Redis store, PDF document reader and ollama LLM with llama2 model. Ollama LLM is offline version of LLM which will be downloaded once and can be used locally, as a result it will be very slow in responding based on your system configuration
	rag implementation using springai with openai llm	RAG Implementation using springai, PGVector store, Tika document reader and openai LLM with spring boot

Credits

Thanks to langchain4j for providing an openAI compatible API for learning and demo purposes.

How to run in local

Note All examples contains testcontainers, so can run directly in dev mode.