RAG Is More Than Just Vector Search
Blog post from Tiger Data
- Developed a question-answering system over GitHub issues using OpenAI's Parallel Tool Calling API. - Implemented text-to-SQL tools and semantic search tools to build the question-answering system. - Used Pydantic models for separation of concerns, making it easier to evaluate tool selection separately from implementation. - Utilized Instructor for testing function calling capabilities before moving to implementation. - Discussed best practices for text-to-SQL generation and provided a prompt example for TimescaleDB-specific query generation.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| Vector Search | 85 | 3,721 | 295 | 94 | +59% |
| Kubernetes | 32 | 1,448 | 201 | 90 | +5% |
| RAG | 19 | 1,977 | 263 | 84 | -21% |
| LLM | 9 | 4,126 | 494 | 151 | +3% |
| AI Agents | 1 | 776 | 115 | 53 | +109% |
| AI Coding Assistant | 1 | 706 | 110 | 47 | +46% |
| Data Pipeline | 1 | 1,437 | 344 | 74 | +108% |
| Real-time | 1 | 4,436 | 999 | 233 | +49% |
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