The outcome

Produce a searchable index that can be evaluated, refreshed, migrated, corrected, and deleted without mixing incompatible vectors or access scopes.

Voyage AI is a embedding and reranking platform for semantic search, retrieval-augmented generation, code and domain retrieval, and multimodal indexing. Creating high-quality text or multimodal embeddings and reranking candidate results for semantic search, RAG, code search, and domain-specific retrieval. This guide narrows that broad capability into one repeatable outcome, with checkpoints that keep the source material and your judgment in the loop.

Before you begin

Set the boundary before the tool starts.

Choose one real task, identify who will use the result, and decide what evidence or test will make the result acceptable. Gather only the source material needed for that task. If the work contains confidential, personal, regulated, or client-owned information, confirm that the platform and account are approved before sharing it.

Troiana principle

AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.

Step by step

A workflow you can repeat.

  1. 01

    Define corpus owners, users, source permissions, languages and modalities, freshness, chunking, metadata, access filters, relevance targets, latency, retention, and deletion requirements.

  2. 02

    Create a dedicated server-side Voyage key, confirm the organization's data opt-out setting, select an exact current model, and record tokenizer, input_type, dimension, datatype, normalization and truncation behavior.

  3. 03

    Parse approved copies, remove secrets and irrelevant metadata, assign stable source and chunk IDs, preserve version and access labels, and embed corpus chunks with input_type=document in bounded batches.

  4. 04

    Store model and preprocessing metadata beside every vector, enforce source permissions before ranking, and test duplicate, empty, oversized, malformed, multilingual, image, deletion, retry and partial-batch cases.

  5. 05

    Publish a versioned index, monitor ingestion failures and drift, re-embed changed sources deliberately, verify removal from index and backups, and keep the previous complete index for rollback.

Working standard

What good use looks like.

  • Version every vector with its model.
  • Filter permissions before returning results.
  • Make deletion traceable to the source.

Embedding compatibility, dimensions, tokenizers, limits, and recommended models change; automatic truncation can silently remove decisive content, and relevance scores are not truth or permission checks. Voyage says hosted-API customers can opt out of storage and training use for zero-day retention through an organization-admin control. Keep keys server-side, confirm current data settings, preserve source permissions and deletion, separate query and document inputs, version every vector with its model configuration, evaluate reranking on labeled data, and reindex deliberately when compatibility changes.

Official references

Check the current product documentation.

Features, plan limits, availability, and data controls change. These official pages are the starting points used for this collection.