Building Advanced AI Data Analyst Systems: Beyond Text-to-SQL with Semantic Layers and Multi-Agent Planning
By
pedromnasc
Toasted golden, schmeared with insight. Top of the rack.
Summary
This article discusses building advanced AI data analyst systems that go beyond simple text-to-SQL capabilities. It emphasizes the importance of multi-step planning, external tools like coding, and semantic layers (specifically Malloy) to encode business meaning and reduce SQL complexity. The approach involves multi-agent, research-oriented systems that break down problems using domain knowledge, precise retrieval, code writing, and environmental interaction. The article also covers retrieval optimization using keyword search, embeddings, and fine-tuned rerankers for precision, recall, and latency.
Key quotes
· 4 pulledText-to-SQL is not enough. Answering real user questions requires going the extra mile like multi-step plans, external tools (coding) and external context.
Context is the product. A semantic layer (we use Malloy) encodes business meaning and sharply reduces SQL complexity.
Use a multi-agent, research-oriented system. Break problems down using context / domain knowledge, retrieve precisely, write code, interact with the environment and learn from it.
Retrieval is a recommendation problem. Mix keyword, embeddings, and a fine-tuned reranker; optimise for precision, recall, and latency.
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