# Search overview

:::callout{intent="tip"}
Searches consume [read units (RUs)](/guides/manage-cost-understanding-cost#read-units). See [Understanding cost](/guides/manage-cost-understanding-cost#query) for how query cost is calculated, [Pricing](https://www.pinecone.io/pricing/) for rates, and [Query limits](/guides/apis-database-limits-operation-limits) for `top_k` and result-size limits.
:::

## Choosing a search approach

Pinecone supports four retrieval approaches. They differ in the signal they rank on and the index shape they require.

### Quick decision tree

Walk through these questions in order. Pick the first match.

1. **Do your queries share specific tokens with the data?** (Product names, error messages, source code, named entities, technical jargon, identifiers.) → **[Full-text search](/guides/index-data-search-full-text-search)**. BM25 ranks results that share tokens with the query; Lucene syntax adds boolean and phrase operators.

2. **Are your queries natural language where meaning matters more than exact wording?** (Synonyms, paraphrases, conceptual similarity.) → **[Semantic search](/guides/index-data-search-semantic-search)** with a dense vector field.

3. **Do you need both keyword and semantic signals on the same data?** → **[Hybrid search](/guides/index-data-search-hybrid-search)**.
   - **On a JSON-document workload**, declare a `dense_vector` field alongside one or more FTS-enabled `string` fields, then add a text-match filter to a `dense_vector` query or run two searches and merge the results client-side (Documents API).
   - **On a vector-only records workload**, store a dense vector and a sparse vector on each record in a single index (Vectors API).

4. **Do you produce a learned sparse-vector representation upstream of Pinecone?** (For example, using [`pinecone-sparse-english-v0`](/guides/more-models-pinecone-sparse-english-v0) or your own sparse encoder.) → **[Sparse-vector search](/guides/index-data-search-lexical-search)**.

### Approach details

A useful gradient: **dense** ranks on concept (semantic similarity), **full-text search** ranks on strict character-level token matching (BM25), and **sparse-vector search** sits between them — token-aware, but with learned per-token weights and term expansion.

- **[Full-text search](/guides/index-data-search-full-text-search)** — **recommended** for keyword and phrase search over text content. You upsert typed JSON documents and rank with `score_by`: BM25 token matching on an FTS-enabled `string` field, Lucene query syntax (`query_string`), `dense_vector` similarity, or `sparse_vector` similarity. A single index with a document schema can mix all four field types, so it's also the recommended single-index path when a workload needs more than one signal (BM25 + dense, BM25 + sparse, etc.).

- **[Semantic search](/guides/index-data-search-semantic-search) (dense-vector)** — for queries where intent and meaning matter more than exact keyword matches (synonyms, paraphrases, conceptual similarity). Uses dense embeddings.

- **[Hybrid search](/guides/index-data-search-hybrid-search)** — combines a keyword signal with a semantic signal so one query benefits from both. There are three patterns: on the Documents API, a text-match filter on a `dense_vector` query, or client-side RRF fusion of two searches; on the Vectors API, a single index storing a dense and a sparse vector combined server-side. See [Hybrid search](/guides/index-data-search-hybrid-search) to choose.

- **[Sparse-vector search](/guides/index-data-search-lexical-search)** — recommended for workflows that use a learned sparse-vector model (for example, [`pinecone-sparse-english-v0`](/guides/more-models-pinecone-sparse-english-v0)) or where the application owns the sparse-vector representation directly. For general-purpose keyword and phrase retrieval over text, start with full-text search.

Once you've chosen an approach, see [Data modeling](/guides/index-data-data-modeling) to structure your documents or records, then [Create an index](/guides/index-data-create-an-index) to set it up.

## Optimization

- [Filter by metadata](/guides/index-data-search-filter-by-metadata)
- [Rerank results](/guides/index-data-search-rerank-results)
- [Parallel queries](/guides/index-data-search-semantic-search#parallel-queries)

## Related pages

- [Account management](./account-management-index.md)
- [Admin](./admin-2-index.md)
- [Admin](./admin-index.md)
- [APIs](./apis-index.md)
- [Architecture](./architecture-index.md)
- [Assistants](./assistants-index.md)
- [Bring Your Own Cloud](./bring-your-own-cloud-index.md)
- [Build an assistant](./build-an-assistant-index.md)
- [Build an integration](./build-an-integration-index.md)
- [Changelog](./changelog-index.md)
- [Full-text search overview](./index-data-search-full-text-search.md)
- [Full-text search query syntax](./index-data-search-full-text-search-query-syntax.md)
- [Full-text search text processing](./index-data-search-full-text-search-text-processing.md)

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