Retrieval
Embeddings and Reranking
Embeddings and reranking improve search, RAG, classification, clustering, recommendation, and document retrieval quality.
Who this is for
Teams building knowledge-base or search systems.
Configuration reference
Values to confirm before setup
Embedding use
Convert text into vectors
Reranking use
Reorder candidate search results
Quote factor
Document volume, refresh frequency, query volume, vector dimensions
Setup flow
Practical steps
- 01Inventory document volume and update frequency.
- 02Choose embedding dimensions and chunking strategy.
- 03Choose vector database or search backend.
- 04Add reranking for high-value queries.
- 05Measure retrieval quality with customer examples.
RAG setup
A model key alone does not create good retrieval. Chunking, metadata, filters, reranking, and evaluation samples matter as much as the embedding model.
Common mistakes
Check these before escalating
- Large backfills can create one-time token spikes.
- Bad chunking produces bad answers even with a strong model.
- Embedding model changes may require re-indexing.
Related guides
OpenAI-Compatible Chat API
Most OpenAI-compatible integrations need only three changes: API key, base URL, and model name. The hard part is choosing the correct plan and endpoint.
Billing and Pricing Structure
A trustworthy quote separates official model usage, Token Plan subscription, shared quota, payment costs, taxes, and ModelSmarter service fees.
Usage Monitoring and Cost Control
Production buyers need visibility into call volume, token consumption, success rate, quota remaining, and monthly replenishment.