{"id":15076541,"url":"https://github.com/EmbeddedLLM/JamAIBase","last_synced_at":"2025-09-25T23:31:48.861Z","repository":{"id":242074315,"uuid":"808203681","full_name":"EmbeddedLLM/JamAIBase","owner":"EmbeddedLLM","description":"The collaborative spreadsheet for AI. Chain cells into powerful pipelines, experiment with prompts and models, and evaluate LLM responses in real-time. 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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.\n\n![JamAI Base Demo](jamaibase.webp)\n\n## Key Features\n\n- Embedded database (SQLite) and vector database (LanceDB)\n- Managed memory and RAG capabilities\n- Built-in LLM, vector embeddings, and reranker orchestration\n- Intuitive spreadsheet-like UI\n- Simple REST API\n\n### Generative Tables\n\nTransform static database tables into dynamic, AI-enhanced entities.\n\n- **Dynamic Data Generation**: Automatically populate columns with relevant data generated by LLMs.\n- **Built-in REST API Endpoint**: Streamline the process of integrating AI capabilities into applications.\n\n### Action Tables\n\nFacilitate real-time interactions between the application frontend and the LLM backend.\n\n- **Real-Time Responsiveness**: Provide a responsive AI interaction layer for applications.\n- **Automated Backend Management**: Eliminate the need for manual backend management of user inputs and outputs.\n- **Complex Workflow Orchestration**: Enable the creation of sophisticated LLM workflows.\n\n### Knowledge Tables\n\nAct as repositories for structured data and documents, enhancing the LLM’s contextual understanding.\n\n- **Rich Contextual Backdrop**: Provide a rich contextual backdrop for LLM operations.\n- **Enhanced Data Retrieval**: Support other generative tables by supplying detailed, structured contextual information.\n- **Efficient Document Management**: Enable uploading and synchronization of documents and data.\n\n### Chat Tables\n\nSimplify the creation and management of intelligent chatbot applications.\n\n- **Intelligent Chatbot Development**: Simplify the development and operational management of chatbots.\n- **Context-Aware Interactions**: Enhance user engagement through intelligent and context-aware interactions.\n- **Seamless Integration**: Integrate with Retrieval-Augmented Generation (RAG) to utilize content from any Knowledge Table.\n\n### LanceDB Integration\n\nEfficient management and querying of large-scale multi-modal data.\n\n- **Optimized Data Handling**: Store, manage, query, and retrieve embeddings on large-scale multi-modal data efficiently.\n- **Scalability**: Ensure optimal performance and seamless scalability.\n\n### Declarative Paradigm\n\nFocus on defining \"what\" you want to achieve rather than \"how\" to achieve it.\n\n- **Simplified Development**: Allow users to define relationships and desired outcomes.\n- **Non-Procedural Approach**: Eliminate the need to write procedures.\n- **Functional Flexibility**: Support functional programming through LLMs.\n\n## Key Benefits\n\n### Ease of Use\n\n- **Interface**: Simple, intuitive spreadsheet-like interface.\n- **Focus**: Define data requirements through natural language prompts.\n\n### Scalability\n\n- **Foundation**: Built on LanceDB, an open-source vector database designed for AI workloads.\n- **Performance**: Serverless design ensures optimal performance and seamless scalability.\n\n### Flexibility\n\n- **LLM Support**: Supports any LLMs, including OpenAI GPT-4, Anthropic Claude 3, and Meta Llama3.\n- **Capabilities**: Leverage state-of-the-art AI capabilities effortlessly.\n\n### Declarative Paradigm\n\n- **Approach**: Define the \"what\" rather than the \"how.\"\n- **Simplification**: Simplifies complex data operations, making them accessible to users with varying levels of technical expertise.\n\n### Innovative RAG Techniques\n\n- **Effortless RAG**: Built-in RAG features, no need to build the RAG pipeline yourself.\n- **Query Rewriting**: Boosts the accuracy and relevance of your search queries.\n- **Hybrid Search \u0026 Reranking**: Combines keyword-based search, structured search, and vector search for the best results.\n- **Structured RAG Content Management**: Organizes and manages your structured content seamlessly.\n- **Adaptive Chunking**: Automatically determines the best way to chunk your data.\n- **BGE M3-Embedding**: Leverages multi-lingual, multi-functional, and multi-granular text embeddings for free.\n\n## Getting Started\n\n### Option 1: Use the JamAI Base Cloud\n\n[Sign up for a free account!](https://cloud.jamaibase.com/) Did we mention that you can get free LLM tokens?\n\n### Option 2: Launch self-hosted services\n\n[Follow our step-by-step guide.](https://docs.jamaibase.com/sdk/python-sdk-documentation#oss)\n\n### Explore the Documentation:\n\n- [SDK and Platform Documentation](https://docs.jamaibase.com)\n- [API Documentation](https://jamaibase.readme.io)\n- [Changelog](CHANGELOG.md)\n- [Versioning](VERSIONING.md)\n\n## Examples\n\nWant 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.\n\nHere are a couple of cool frontend examples:\n\n1. [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!\n2. [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.\n3. [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!\n\nLet us know if you have any questions – we're here to help! Happy coding! 😊\n\n## Community and Support\n\nJoin our vibrant developer community for comprehensive documentation, tutorials, and resources:\n\n- **Discord**: [Join our Discord](https://discord.gg/rV6DECA8Dw)\n- **GitHub**: [Star our GitHub repository](https://github.com/EmbeddedLLM/JamAIBase)\n\n## Contributing\n\nWe welcome contributions! Please read our [Contributing Guide](Contributing_Guide_Link) to get started.\n\n## License\n\nThis project is released under the Apache 2.0 License. - see the [LICENSE](https://github.com/EmbeddedLLM/JamAIBase/blob/main/LICENSE) file for details.\n\n## Contact\n\nFollow us on [X](https://x.com/EmbeddedLLM) and [LinkedIn](https://www.linkedin.com/company/embedded-llm/) for updates and news.\n","funding_links":[],"categories":["NLP","Python","Industry Strength Information Retrieval","UIs"],"sub_categories":["3. 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