Introduction to LangDB

LangDB enables you to build, deploy, and evaluate Large Language Model (LLM) applications directly on your data. By leveraging SQL and Python, you can construct Retrieval-Augmented Generation (RAG) pipelines and multi-agent workflows without the need for additional infrastructure.

Benefits

Work Closer to Your Data

LangDB integrates seamlessly with your existing data warehouse, eliminating the need for extra infrastructure. This simplifies setup and management, making it easier to get started quickly and efficiently.

Simplicity

Use SQL or Python to analyze and construct RAG pipelines, just as you would query data. LangDB allows you to work with familiar tools, reducing the learning curve and letting you focus on building your solutions.

Notebooks

LangDB offers a Jupyter-like notebook environment where you can build iteratively while interacting with your data. You can share notebooks and models with your team effortlessly. All models are automatically saved in the database, enabling the reuse of models you've already built.

One Syntax, All Models

With LangDB, the same SQL or Python-based model can be tested and compared across multiple model providers. As new models become available, you can easily test your code against them and compare results using SQL queries.

Easy Deployment

Deploying solutions with LangDB is as simple as running a few SQL statements. Everything you build becomes immediately accessible as a chat widget or API, ready for production use without complex deployment steps.

Robust Security

Leverage familiar row-level permissions to create advanced access control patterns. This allows for precise management of data access in LLM applications without requiring developers to learn new security mechanisms.

Design Goals

Why this approach?

While building a basic RAG example is straightforward, scaling it to a fully functional solution requires a complex architecture capable of handling multiple queries and constructing the right context for LLMs to generate accurate results. LangDB addresses these challenges with a data-first approach, integrating directly with your enterprise data to ensure better context and data quality, resulting in more precise and reliable outcomes.

All queries are automatically traced, allowing for further evaluation, optimization, and auditability. This is essential for ensuring compliance and governance in AI applications.

By treating data as a first-class citizen, LangDB allows businesses to fine-tune models directly on their own data, improving performance without relying on external systems or complex integrations.

LangDB builds on top of powerful data warehouse functionalities, offering a SQL-native experience for various tasks, including generating embeddings, chunking data, retrieval, and creating custom models and agents.

Deployment

Everything you run on LangDB is instantly available as an API or through a client library, eliminating the need to maintain multiple servers and languages. This unlocks rapid integration, immediate impact on your data operations, and reduces overhead, allowing developers to focus on building robust, AI-driven product experiences.

Security is Key for RAG

Interacting with LLMs in retrieval-augmented generation (RAG) workflows presents unique security challenges, particularly when it comes to maintaining data privacy and integrity. LangDB runs on top of your data warehouse, ensuring predictable security using familiar tools for developers. With row-level access controls, LangDB offers robust, fine-grained security measures to protect sensitive data effectively.