Supercharge your |
with LLMs
Build Agentic workflows on your data using SQL and Python.
Deploy in minutes. Integrate with any LLM.
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Integration of Structured and Unstructured Data
LangDB seamlessly works with unstructured data such as PDFs, text files, or JSON documents, while also connecting to relational databases like PostgreSQL and MySQL, simplifying the development and deployment of complex RAG applications.
From data to interactive production-grade chat applications with SQL
LangDB enables users to create interactive RAG applications using just a few SQL statements. Everything just runs on the database, making production deployments a snap.
Easy experimentation and tracking
LangDB allows users to experiment with multiple Large Language Models (LLMs) simultaneously. Each interaction is captured for easy tracing and analysis, facilitating complex logic such as Self-Retrieval-Augmented Generation (Sel-RAG) or Chain of Thought (CoT) processes.
Notebook as a Primary Interface
LangDB uses notebooks as the primary interface, allowing for interactive development. A notebook can be instantly deployed as a chat application and directly integrated with the customer's website using LangDB's JavaScript SDK.
Extend your data warehouse with LLM capabilities
Connect your data warehouse to LangDB to extend its capabilities with LLM capabilities. Work with multiple LLM providers and experiment with different models.
Samples Gallery
QA on PDF Documents & RAG using LangDB
Extract data from PDFs and create a RAG function that leverages vector search using LangDB agent APIs
Open Notebook ➔
Combining Insights from Structured and Unstructured Data Using PostgreSQL
Combine insights from structured data (SQL tables) and unstructured data (Wikipedia articles) to answer user queries.
Open Notebook ➔
Structured Layout Extraction from PDFs and Images
Lodas and extract complex tables from PDF files, store it in a structured format, and use it for further analysis.
Open Notebook ➔