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Wren AI

The open source SQL AI agent makes text-to-SQL conversion simple.

#Open source
#data analysis
#AI agent
#data access
#SQL generation
Wren AI

Product Details

Wren AI is an open source SQL AI agent designed to help data and product teams interact with data through natural language to generate SQL queries, charts, spreadsheets, reports and BI. It adopts a semantic engine architecture to provide business context for LLM, processing metadata, schema, terminology, data relationships, and the logic behind calculation and aggregation through "Modeling Definition Language" to generate accurate SQL queries with semantic context. The main advantages of Wren AI include being easy to use, safe and reliable, open source and free, supporting a variety of data sources and analysis tools, such as BigQuery, DuckDB, PostgreSQL, etc., and can be integrated with popular tools such as Excel and Google Sheets. It also supports multiple LLM models, whether hosted in the cloud or on-premises. Wren AI is positioned to provide data teams with a powerful tool to improve the efficiency of data access and analysis.

Main Features

1
Semantic indexing: Through the semantic engine architecture, it provides business context for LLM and helps it better understand the data architecture.
2
Generate accurate SQL: Use the "Modeling Definition Language" to process data-related logic and generate accurate SQL queries with semantic context.
3
GenBI Features: Provides AI-generated summaries and key insights, instantly converting query results into reports and charts.
4
End-to-end workflow: Seamlessly connect data with popular analysis tools such as Excel and Google Sheets for further analysis.
5
Safe and reliable: Using RAG architecture, there is no need to expose or upload data to the LLM model.
6
Open source and free: deploy anywhere you like, no fees required.
7
Extensive integration: Supports multiple databases, data warehouses and analysis tools, as well as popular LLM models.

How to Use

1
1. Visit the Wren AI official website, click the install or deploy button, and follow the instructions to install Wren AI in a local or cloud environment.
2
2. Connect to the data source: In the Wren AI interface, select the database or data warehouse to be connected, such as BigQuery, PostgreSQL, etc., and configure it.
3
3. Define the data architecture: Use the ‘Modeling Definition Language’ to add a semantic layer to the data architecture to help Wren AI better understand the business context.
4
4. Questions and queries: Ask business questions in Wren AI’s dialogue interface through natural language, and Wren AI will automatically generate corresponding SQL queries.
5
5. View the results: The query results will be displayed in table form, and the GenBI function will provide AI-generated summaries and charts.
6
6. Export and analysis: Export query results to tools such as Excel or Google Sheets for further data analysis and visualization.
7
7. Feedback and optimization: Provide feedback to Wren AI based on the accuracy and practicality of the query results to help it continuously optimize and improve.

Target Users

The target audience is data teams and product teams, especially non-technical teams who want to improve the efficiency of data access and analysis, but do not want to delve into writing SQL code. By providing natural language interaction, Wren AI allows them to obtain data insights more quickly without relying on professional data analysts or developers. In addition, Wren AI is also an ideal choice for enterprises that want to quickly build analysis and reporting functions on existing data architecture.

Examples

An e-commerce company uses Wren AI to quickly generate SQL queries, analyze user purchasing behavior data, and optimize marketing strategies.

Data analysts use Wren AI's GenBI function to transform complex query results into intuitive charts and reports, which are presented to management.

The product team takes advantage of Wren AI's integration with Excel to obtain data in real time and conduct further analysis and visualization.

Quick Access

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Categories

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› data analysis
› AI search

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