/JamAIBase

Firebase for AI Agents: Open-source backend platform that puts powerful generative models at the core of your database. With managed memory and RAG capabilities, developers can easily build AI agents, enhance their apps with generative tables, and create magical UI experiences.

Primary LanguagePythonApache License 2.0Apache-2.0

JamAI Base

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Overview

JamAI Base is an open-source RAG (Retrieval-Augmented Generation) backend platform that integrates an embedded database (SQLite) and an embedded vector database (LanceDB) with managed memory and RAG capabilities. It features built-in LLM, vector embeddings, and reranker orchestration and management, all accessible through a convenient, intuitive, spreadsheet-like UI and a simple REST API.

JamAI Base Demo

Key Features

  • Embedded database (SQLite) and vector database (LanceDB)
  • Managed memory and RAG capabilities
  • Built-in LLM, vector embeddings, and reranker orchestration
  • Intuitive spreadsheet-like UI
  • Simple REST API

Generative Tables

Transform static database tables into dynamic, AI-enhanced entities.

  • Dynamic Data Generation: Automatically populate columns with relevant data generated by LLMs.
  • Built-in REST API Endpoint: Streamline the process of integrating AI capabilities into applications.

Action Tables

Facilitate real-time interactions between the application frontend and the LLM backend.

  • Real-Time Responsiveness: Provide a responsive AI interaction layer for applications.
  • Automated Backend Management: Eliminate the need for manual backend management of user inputs and outputs.
  • Complex Workflow Orchestration: Enable the creation of sophisticated LLM workflows.

Knowledge Tables

Act as repositories for structured data and documents, enhancing the LLM’s contextual understanding.

  • Rich Contextual Backdrop: Provide a rich contextual backdrop for LLM operations.
  • Enhanced Data Retrieval: Support other generative tables by supplying detailed, structured contextual information.
  • Efficient Document Management: Enable uploading and synchronization of documents and data.

Chat Tables

Simplify the creation and management of intelligent chatbot applications.

  • Intelligent Chatbot Development: Simplify the development and operational management of chatbots.
  • Context-Aware Interactions: Enhance user engagement through intelligent and context-aware interactions.
  • Seamless Integration: Integrate with Retrieval-Augmented Generation (RAG) to utilize content from any Knowledge Table.

LanceDB Integration

Efficient management and querying of large-scale multi-modal data.

  • Optimized Data Handling: Store, manage, query, and retrieve embeddings on large-scale multi-modal data efficiently.
  • Scalability: Ensure optimal performance and seamless scalability.

Declarative Paradigm

Focus on defining "what" you want to achieve rather than "how" to achieve it.

  • Simplified Development: Allow users to define relationships and desired outcomes.
  • Non-Procedural Approach: Eliminate the need to write procedures.
  • Functional Flexibility: Support functional programming through LLMs.

Key Benefits

Ease of Use

  • Interface: Simple, intuitive spreadsheet-like interface.
  • Focus: Define data requirements through natural language prompts.

Scalability

  • Foundation: Built on LanceDB, an open-source vector database designed for AI workloads.
  • Performance: Serverless design ensures optimal performance and seamless scalability.

Flexibility

  • LLM Support: Supports any LLMs, including OpenAI GPT-4, Anthropic Claude 3, Mistral AI Mixtral, and Meta Llama3.
  • Capabilities: Leverage state-of-the-art AI capabilities effortlessly.

Declarative Paradigm

  • Approach: Define the "what" rather than the "how."
  • Simplification: Simplifies complex data operations, making them accessible to users with varying levels of technical expertise.

Innovative RAG Techniques

  • Effortless RAG: Built-in RAG features, no need to build the RAG pipeline yourself.
  • Query Rewriting: Boosts the accuracy and relevance of your search queries.
  • Hybrid Search & Reranking: Combines keyword-based search, structured search, and vector search for the best results.
  • Structured RAG Content Management: Organizes and manages your structured content seamlessly.
  • Adaptive Chunking: Automatically determines the best way to chunk your data.
  • BGE M3-Embedding: Leverages multi-lingual, multi-functional, and multi-granular text embeddings for free.

Getting Started

Option 1: Use the JamAI Base Cloud

Sign up for a free account! Did we mention that you can get free LLM tokens?

Option 2: Launch self-hosted services

Follow our step-by-step guide.

Explore the Documentation:

Examples

Want to try building apps with JamAI Base? We've got some awesome examples to get you started! Check out our example docs for inspiration.

Here are a couple of cool frontend examples:

  1. Simple Chatbot Bot using NLUX: Build a basic chatbot without any backend setup. It's a great way to dip your toes in!
  2. Simple Chatbot Bot using NLUX + Express.js: Take it a step further and add some backend power with Express.js.
  3. Simple Chatbot Bot using Streamlit: Are you a Python dev? Checkout this Streamlit demo!

Let us know if you have any questions – we're here to help! Happy coding! 😊

Community and Support

Join our vibrant developer community for comprehensive documentation, tutorials, and resources:

Contributing

We welcome contributions! Please read our Contributing Guide to get started.

License

This project is released under the Apache 2.0 License. - see the LICENSE file for details.

Contact

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