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JamAIBase

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Basic Information

JamAI Base is an open-source backend platform for Retrieval-Augmented Generation (RAG) that combines an embedded relational database (SQLite) and an embedded vector database (LanceDB) with managed memory, embedding storage, and reranker orchestration. It exposes a simple REST API and a spreadsheet-like UI that lets users declaratively define AI-enhanced tables and pipelines. The project provides specialized table types—Generative Tables, Action Tables, Knowledge Tables, and Chat Tables—to convert static data into dynamic, context-aware content, enable real-time LLM interactions, store structured documents for retrieval, and build chat applications. JamAI Base is designed to be used via a hosted cloud service or self-hosted deployment and includes documentation, examples for chatbot frontends, and SDKs for integrating its RAG and orchestration capabilities into applications.

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Stars
1040
App URL
https://github.com/EmbeddedLLM/JamAIBase
Github Repository
https://github.com/EmbeddedLLM/JamAIBase/blob/main/README.md

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App Details

Features
Embedded SQLite and LanceDB integration for combined relational and vector storage. Managed memory and RAG capabilities for retrieval-augmented generation workflows. Built-in LLM support, vector embeddings, and reranker orchestration to streamline ranking and generation. Spreadsheet-like UI that provides a declarative interface to define Generative, Action, Knowledge, and Chat tables. Simple REST API endpoints for programmatic access and integration. LanceDB support for efficient multi-modal embedding storage and scalable querying. Declarative paradigm to define outcomes rather than procedural code. RAG optimizations including query rewriting, hybrid keyword/structured/vector search, reranking, adaptive chunking, and use of BGE M3-style multilingual embeddings.
Use Cases
JamAI Base lowers the barrier to build RAG-enabled applications by removing the need to assemble storage, vector database, embedding, and reranking components individually. The spreadsheet-like UI and declarative approach let non-expert users define data relationships and prompt-driven behaviors while developers use the REST API or SDKs to integrate features. Built-in table types accelerate chatbot creation, context-aware generation, and document-backed retrieval without hand-rolling pipelines. Scalability is supported via LanceDB and a serverless-friendly design, and flexibility is maintained by supporting any LLMs including commercial and open models. The project supplies examples, documentation, and both cloud and self-hosted options to fit experimentation and production needs.

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