Java version of LangChain

A project to show howto use SpringAI with OpenAI to chat with the documents in a library. Documents are stored in a normal/vector database. The AI is used to create embeddings from documents that are stored in the vector database. The vector database is used to query for the nearest document. That document is used by the AI to generate the answer.

a rag implementation example for document-based QA, using spring-ai, ollama, and postgres-pgvector vetor db.

AI implementation using langchain4j and springAI frameworks with Java

A dynamic learning assistant designed to simplify the onboarding and training process for new hires. Users can upload documents or enter URLs for training materials. Built with Spring Boot, @langchain4j and spring-ai

Sanford utilizes LLMs, a storage bucket, and a Vector store to search for and/or summarize documents that you upload.

A utility service backed by Spring AI that will help you refactor source in a Git repository. Contains a naive implementation to support refactoring Java application source.

A Spring AI Exploring Project[Caicai].

Practicing AI with Spring

Simple spring-boot application which ingest a document to apply RAG pattern in order to enrich model knowledge about doc content.

Spring AI, Ollama, llama3.1, nomic-embed-text, PGVector

a rag implementation example for document-based QA, using spring-ai, ollama, and postgres-pgvector vetor db.

SpringAI Study