My first article for the LangChain-MySQL project showed how a multi-stage LangChain agent lets you ask natural-language questions over MySQL. Heavy prompts and repeated LLM calls, however, made the system sluggish and prone to OpenAI 429 Too Many Requests errors.
What’s new in Part 2:
Persistent FAISS vector store loads only the schema chunks you need — columns, keys, indexes — so prompts shrink and latency falls.
Comprehensive test suite — unit, integration, and mock-DB tests wired into CI — catches schema-drift and retrieval edge cases before they hit production.
LLM rate-limit resolution — Added foreign key relationships to the vector DB, which improves schema representation and reduces token usage.
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