WrenAI
🚀 Open-source SQL AI Agent for Text-to-SQL. Supporting PostgreSQL, DuckDB, MySQL, MS SQL, ClickHouse, Trino, JSON, CSV, Parquet data sources, and more! 🚀
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WrenAI is a data assistant tool that helps users get results and insights faster by asking questions in natural language, without writing SQL. It leverages Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) technology to enhance comprehension of internal data. Key benefits include fast onboarding, secure design, and open-source availability. WrenAI consists of three core services: Wren UI (intuitive user interface), Wren AI Service (processes queries using a vector database), and Wren Engine (platform backbone). It is currently in alpha version, with new releases planned biweekly.
README:
Hacktoberfest 2024 is here, and we're inviting developers of all levels to join our open-source community. Together, we'll build Wren AI as a friendly community for all.
👉 Learn how to win a Wren AI Exclusive Swag Pack & Holopin from DigitalOcean Rewards!
Wren AI is a SQL AI Agent for data teams to get results and insights faster by asking business questions without writing SQL.
https://github.com/user-attachments/assets/737bbf1f-f9f0-483b-afb3-2c622c9b91ba
Wren AI’s mission is to democratize data by bringing AI agents with SQL ability to any data source.
Wren AI has implemented a semantic engine architecture to provide the LLM context of your business; you can easily establish a logical presentation layer on your data schema that helps LLM learn more about your business context.
With Wren AI, you can process metadata, schema, terminology, data relationships, and the logic behind calculations and aggregations with “Modeling Definition Language”, reducing duplicate coding and simplifying data joins.
When starting a new conversation in Wren AI, your question is used to find the most relevant tables. From these, LLM generates three relevant questions for the user to choose from. You can also ask follow-up questions to get deeper insights.
The AI self-learning feedback loop refines SQL augmentation and generation by collecting data from various sources. These include user query history, revision intentions, feedback, schema patterns, semantic enhancements, and query frequency.
We focus on providing an open, secure, and reliable SQL AI Agent for everyone.
Wren AI makes it easy to onboard your data. Discover and analyze your data with our user interface. Effortlessly generate results without needing to code.
Your database content will never be transmitted to the LLM. Only metadata, like schemas, documentation, and queries, will be used in semantic search.
Deploy Wren AI anywhere you like on your own data, LLM APIs, and environment, it's free.
Wren AI consists of three core services:
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Wren UI: An intuitive user interface for asking questions, defining data relationships, and integrating data sources.
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Wren AI Service: Processes queries using a vector database for context retrieval, guiding LLMs to produce precise SQL outputs.
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Wren Engine: Serves as the semantic engine, mapping business terms to data sources, defining relationships, and incorporating predefined calculations and aggregations.
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Using Wren AI is super simple, you can set it up within 3 minutes, and start to interact with your data!
- Visit our Installation Guide of Wren AI.
- Visit the Usage Guides to learn more about how to use Wren AI.
Visit Wren AI documentation to view the full documentation.
Want to contribute to Wren AI? Check out our Contribution Guidelines.
- Welcome to our Discord server to give us feedback!
- If there are any issues, please visit GitHub Issues.
Please note that our Code of Conduct applies to all Wren AI community channels. Users are highly encouraged to read and adhere to them to avoid repercussions.
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