What's the most cost-effective way to do document retrieval using an embedding API?

The most cost-effective approach is usually:

1) Chunk documents once, embed offline, and store vectors

• Split documents into sensible chunks.
• Generate embeddings once for each chunk with a cheaper embedding model.
• Store the vectors in a vector database or even flat files + ANN index.

This avoids re-embedding on every query, which is where most cost can accumulate.

2) Use a small, cheap embedding model

For most retrieval use cases, a smaller embedding model is enough. Pick the lowest-cost model that still gives acceptable retrieval quality for your corpus.

Cost savings come from:

• lower per-token embedding price
• faster processing
• smaller vector storage footprint

3) Keep chunk sizes moderate

Very small chunks increase embedding count and storage. Very large chunks reduce retrieval quality.

A cost-effective balance is often:

• ~200–500 tokens per chunk
• some overlap if needed, but not too much

Overlap improves recall, but too much overlap multiplies cost.

4) Embed only what matters

Reduce cost before embedding:

• strip boilerplate
• remove duplicated sections
• exclude images/footers/nav text if irrelevant
• deduplicate near-identical documents

5) Use two-stage retrieval

A cheap and effective pattern:

1. Retrieve top 20–100 chunks with embeddings
2. Re-rank the short list with a more precise method or LLM only if needed

This keeps expensive downstream processing limited to a small candidate set.

6) Cache aggressively

If users ask repeated or similar questions:

• cache query embeddings
• cache retrieval results
• cache document chunk embeddings permanently unless content changes

7) Choose the right search architecture

If your corpus is small, you may not need a sophisticated vector database.

• Small corpus: in-memory vector search or SQLite + ANN can be enough
• Large corpus: use a vector DB or ANN index like FAISS, HNSW, etc.

The cheapest system is the simplest one that meets latency and scale needs.

8) Consider hybrid retrieval

Combine:

• lexical search (BM25)
• embedding search

This can improve recall, letting you use fewer embedding hits and less expensive reranking.

Practical recommendation

For most teams, the most cost-effective setup is:

• preprocess and deduplicate documents
• chunk into ~300 tokens
• embed once with a low-cost embedding model
• store in FAISS/HNSW/vector DB
• use top-k retrieval
• rerank only the top few results if necessary
• cache everything possible

If you want, I can also give you:

1. a reference architecture,
2. a cost breakdown formula, or
3. a concrete implementation example in Python.