Builds retrieval-augmented generation pipelines: embedding chunking strategies, vector store selection, hybrid search blending, and re-ranking so agents answer from your documents rather than hallucinating generic responses.
Use cases
- Knowledge base Q&A
- Document-grounded agents
- Citation-heavy answers
- Domain-specific retrieval
Key features
- Select chunking strategy
- Configure embedding model
- Set up vector store
- Add hybrid search and re-ranking
Related
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Brainstorming before build
Surfaces goals, constraints, and design options before implementation so you do not paint yourself into a corner on product or UX decisions.
Fine-tuning preparation
Curates,清洗, and formats training datasets for fine-tuning—deduplication, quality filtering, and output formatting—so the resulting model actually improves on your target behavior.
Library docs in the loop
Pins assistant answers to the README, changelog, and typed exports you actually ship—using MCP doc retrieval or pasted snippets—so refactors start from real signatures instead of confident guesses.