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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Bring LLMs to your data
LangDB allows you to leverage the power of large language models (LLMs) directly on top of your data warehouse, whether it's Snowflake, Databricks, or ClickHouse. Unlock the full potential of your data with just a few lines of SQL or Python.
Route your data through any LLM
LangDB integrates with all major LLM platforms, including OpenAI, Anthropic, Gemini, AWS Bedrock, Mistral, and more. Effortlessly route your data through various models based on performance, accuracy, or cost-efficiency. Test with the latest LLMs to ensure you're using the right model for your specific purpose.
RAG in minutes
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.
Experiment & Evaluate
LangDB automatically tracks all interactions, enabling users to experiment with multiple Large Language Models (LLMs) at once. It provides tracing data in table format, allowing you to run experiments, evaluate results, and seamlessly integrate them into your Retrieval-Augmented Generation (RAG) applications.
For Agile Data Teams
Work with SQL, Python and Notebooks to create your applications, and effortlessly share your work for seamless team collaboration. With LangDB, your team can build on each other's work, fostering continuous improvement.
A Fully Integrated Workflow for RAG Application Development
From Prototype to Production in a Snap
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End-to-End Interaction ObservabilityGet full visibility into your LLM application workflow. Track every SQL query, API call, or model interaction in real-time with detailed interaction trees for transparency and quick troubleshooting. | Semantic Caching and RoutingLangDB boosts performance with semantic caching, storing query results by meaning for faster retrieval. Semantic routing directs requests to the most suitable LLM based on context, ensuring optimal model selection, lower latency, and more relevant responses. |
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Seamless Integration with Data CatalogsLangDB integrates with major data catalog and governance platforms, generating precise SQL queries based on your data's structure for accurate results and improved LLM workflow efficiency. | Declarative Agent WorkflowsLangDB agents, defined in SQL or Python, run entirely within your data warehouse, enabling rapid experimentation and seamless deployment without complex microservices, accelerating time-to-value and speeding up iteration for LLM-driven applications. |
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 LangDB
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 ➔