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https://github.com/EmbeddedLLM/JamAIBase

The collaborative spreadsheet for AI. Chain cells into powerful pipelines, experiment with prompts and models, and evaluate LLM responses in real-time. Work together seamlessly to build and iterate on AI applications.
https://github.com/EmbeddedLLM/JamAIBase

agents ai ai-agents-framework baas backend-as-a-service chatbot chatgpt intelligent-spreadsheet lancedb llama3-1 llm llm-ops orchestration python rag retrieval-augmented-generation serverless spreadsheet svelte workflow

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The collaborative spreadsheet for AI. Chain cells into powerful pipelines, experiment with prompts and models, and evaluate LLM responses in real-time. Work together seamlessly to build and iterate on AI applications.

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README 

        
# JamAI Base



![JamAI Base Cover](JamAI_Base_Cover.png)



![Linting](https://github.com/EmbeddedLLM/JamAIBase/actions/workflows/lint.yml/badge.svg)

![CI](https://github.com/EmbeddedLLM/JamAIBase/actions/workflows/ci.yml/badge.svg)



> [!TIP]

> [Explore our docs](#explore-the-documentation)



## 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](jamaibase.webp)



## 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, 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!](https://cloud.jamaibase.com/) Did we mention that you can get free LLM tokens?



### Option 2: Launch self-hosted services



[Follow our step-by-step guide.](https://docs.jamaibase.com/sdk/python-sdk-documentation#oss)



### Explore the Documentation:



- [SDK and Platform Documentation](https://docs.jamaibase.com)

- [API Documentation](https://jamaibase.readme.io)

- [Changelog](CHANGELOG.md)

- [Versioning](VERSIONING.md)



## Examples



Want to try building apps with JamAI Base? We've got some awesome examples to get you started! Check out our [example docs](https://docs.jamaibase.com/getting-started/use-case) for inspiration.



Here are a couple of cool frontend examples:



1. [Simple Chatbot Bot using NLUX](https://docs.jamaibase.com/getting-started/quick-start/nlux-frontend-only): 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](https://docs.jamaibase.com/getting-started/quick-start/nlux-+-express.js): Take it a step further and add some backend power with Express.js.

3. [Simple Chatbot Bot using Streamlit](https://docs.jamaibase.com/sdk/python-sdk-documentation#streamlit-chat-app): 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](https://discord.gg/rV6DECA8Dw)

- **GitHub**: [Star our GitHub repository](https://github.com/EmbeddedLLM/JamAIBase)



## Contributing



We welcome contributions! Please read our [Contributing Guide](Contributing_Guide_Link) to get started.



## License



This project is released under the Apache 2.0 License. - see the [LICENSE](https://github.com/EmbeddedLLM/JamAIBase/blob/main/LICENSE) file for details.



## Contact



Follow us on [X](https://x.com/EmbeddedLLM) and [LinkedIn](https://www.linkedin.com/company/embedded-llm/) for updates and news.