The outcome

Learn the interaction loop using a small task with a clear outcome.

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 the search users, corpus owners, permissions, languages and modalities, relevance labels, freshness, latency, throughput, storage, data handling, cost, and deletion requirements.

  2. 02

    Create a server-side API key, choose a current embedding model by measured task fit, set input_type to document for the corpus and query for searches, and record model, dimension, datatype, tokenizer, and truncation choices.

  3. 03

    Index a small versioned corpus with stable IDs and metadata, retrieve candidates on labeled queries, then test whether a current reranker improves ranking enough to justify its latency and cost.

  4. 04

    Measure recall, ranking quality, no-answer behavior, permissions, multilingual and adversarial cases, latency, errors, and spend before canarying, monitoring drift, and preserving the prior index for rollback.

Working standard

What good use looks like.

  • State the outcome before the background.
  • Provide the real source material.
  • Review the result before expanding the task.

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.