Apache SkyWalking Natural Language to BydbQL

This solution addresses the problem that BanyanDB / BydbQL has a high usability barrier, making it difficult for users to accurately translate natural language queries into executable query statements. In real-world observability and operations scenarios, users often lack deep understanding of query syntax, schema structures, and query patterns, resulting in low efficiency and high error rates. To solve this, the proposal designs and implements an NL2BydbQL intelligent query agent. Leveraging large language models and the LangGraph reasoning framework, the agent converts natural language into executable BydbQL. The system combines RAG (grammar rules, few-shot examples, schema semantics), MCP-based real-time schema awareness, and a multi-stage reasoning pipeline to form a complete loop from intent understanding to query generation and validation. The core approach includes: Building a structured knowledge system (Grammar Rules, Few-shot Examples, Schema Semantic Docs) Designing a dual-path reasoning mechanism (QA vs. Workflow) with dynamic routing based on query complexity Introducing an intermediate representation (IR) to unify generation and repair Ensuring query correctness via parser validation and automatic repair Incorporating runtime feedback and session memory for continuous improvement The deliverables include: A production-ready NL2BydbQL agent (based on LangGraph) A set of structured RAG datasets (grammar, examples, schema descriptions) A complete and extensible reasoning workflow design End-to-end capabilities including query generation, auto-correction, clarification interaction, and execution feedback loop This solution significantly lowers the query barrier and improves the usability and efficiency of SkyWalking and BanyanDB in intelligent operations scenarios.