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.

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:

• SDK and Platform Documentation
• API Documentation
• Changelog
• Versioning

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:

• Discord: Join our Discord
• GitHub: Star our GitHub repository

Community Videos

• Build Mixture of Agents (MoA) & RAG with Open Source Models in Minutes with JamAI Base
• Llama 3.1 405B & 70B vs MacBook Pro. Apple Silicon is overpowered! Bonus: Apple's OpenELM

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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