JamAI Base provides an open-source RAG backend platform designed for building AI applications, targeting developers and researchers who need to create complex LLM workflows. It simplifies AI development by offering a spreadsheet-like UI and a REST API for chaining AI models, managing prompts, and evaluating LLM responses, with embedded SQLite and LanceDB for data and vector storage.

How It Works

JamAI Base utilizes a declarative paradigm, allowing users to define "what" they want rather than "how," simplifying complex data operations. It integrates embedded SQLite for structured data and LanceDB for efficient, scalable vector storage and querying of multi-modal data. The platform orchestrates LLM, vector embeddings, and reranker components, offering features like query rewriting, hybrid search, and adaptive chunking for enhanced RAG capabilities.

Quick Start & Requirements

• Cloud: Sign up for a free account at https://jammaibase.com/.
• Self-hosted: Follow the step-by-step guide in the documentation.
• Dependencies: No specific Python version or hardware requirements are listed for basic usage. LanceDB integration implies potential for large datasets.
• Documentation: https://docs.jammaibase.com/
• Examples: https://docs.jammaibase.com/examples

Highlighted Details

• Supports various LLMs including OpenAI GPT-4, Anthropic Claude 3, and Meta Llama3.
• Features built-in RAG with query rewriting, hybrid search, and reranking.
• Offers "Generative Tables" to transform static data into AI-enhanced entities and "Action Tables" for real-time LLM backend interactions.
• Utilizes BGE M3-Embedding for multi-lingual, multi-functional text embeddings.

Maintenance & Community

• Active community support via Discord.
• GitHub repository available for contributions and stargazing.
• Links to X and LinkedIn for updates.

Licensing & Compatibility

• Licensed under Apache 2.0.
• Permissive license suitable for commercial use and integration into closed-source applications.

Limitations & Caveats

The project appears to be in active development, with a focus on core RAG and data management features. Advanced customization or specific model integrations beyond the listed ones might require custom development.