Returns the top-k most similar documents along with their scores and requested fields.

A request includes a `score_by` array selecting one of the following scoring types:

- **`type: "text"`**, BM25 token matching over one or more text fields named in `fields`; naming several scores the query against all of them. Multi-word queries use OR-style matching (case-insensitive). For exact-phrase ranking, use `query_string` with quoted terms.
- **`type: "query_string"`**, Lucene query syntax. Supports boolean operators, phrase prefix matching, boosting, fuzzy matching (`term~`, `term~N`), and cross-field queries. See the [query syntax reference](/guides/index-data-search-full-text-search-query-syntax). **Does not accept a `field` or `fields` parameter.** Target specific fields using Lucene field qualifiers in the query string itself: `fieldname:value` or `title:(alpha) OR body:(beta)`.
- **`type: "dense_vector"`**, dense vector similarity ranking against a `dense_vector` field.
- **`type: "sparse_vector"`**, sparse vector similarity ranking against a `sparse_vector` field.

Any scoring method can be combined with metadata filters (including text match operators `$match_phrase` / `$match_all` / `$match_any` and logical operators `$and` / `$or` / `$not`). Filters are applied **before** scoring: the search only considers documents that match the filter. Scoring-only operators are available in `query_string` scoring but cannot be used inside `filter`: phrase slop (`"phrase"~N`), term boosting (`^N`), and phrase prefix (`"phrase pre"*`).

`include_fields` defaults to `[]` (returns only `_id` and `_score`); use `["*"]` to return all stored fields.

:::callout{intent="note"}
A single search request ranks by one scoring type. Multi-field BM25 is supported: name several fields in one `text` clause's `fields` array, or pass multiple `text` clauses, which the server combines into one ranking; a `query_string` clause can also target several fields. Every contributing field weighs equally in `2026-07`; there is no per-field weight parameter. To combine BM25 ranking with `dense_vector` or `sparse_vector` ranking, restrict the dense (or sparse) search with a text-match filter (`$match_phrase`, `$match_all`, `$match_any`) on the full-text field, or run separate searches and merge the results client-side.
:::

:::callout{intent="warning"}
Text-match operators (`$match_phrase`, `$match_all`, `$match_any`) are supported on this endpoint and on [fetch](/guides/database-2026-07-data-plane-fetch-documents). A filtered [update](/guides/database-2026-07-data-plane-update-documents) or [delete](/guides/database-2026-07-data-plane-delete-documents) rejects them with a `400` and accepts plain metadata filters only. To update or delete by text match, search or fetch for the matching IDs first, then act on those IDs.
:::

```python Python theme={null}
# pip install --upgrade pinecone
import os
from pinecone import Pinecone

pc = Pinecone(api_key=os.environ["PINECONE_API_KEY"])
index = pc.Index(name="articles")

NAMESPACE = "example-namespace"

# BM25 token matching
response = index.documents.search(
    namespace=NAMESPACE,
    top_k=10,
    score_by=[{"type": "text", "fields": ["body"], "query": "machine learning"}],
    include_fields=["title", "body", "category", "year"],
)
for match in response.matches:
    print(match._id, match._score, getattr(match, "title", ""))

# Lucene query string
response = index.documents.search(
    namespace=NAMESPACE,
    top_k=10,
    score_by=[{"type": "query_string", "query": "title:(quantum) OR body:(machine learning)"}],
    include_fields=["title", "body"],
)

# Dense vector ranking with phrase-match filter
query_vector = [0.12, 0.34, 0.56]  # replace with your actual query vector
response = index.documents.search(
    namespace=NAMESPACE,
    top_k=10,
    score_by=[{
        "type": "dense_vector",
        "fields": ["embedding"],
        "values": query_vector,
    }],
    filter={"body": {"$match_phrase": "machine learning"}},
    include_fields=["title", "body"],
)
```

```shell curl theme={null}
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="articles-abc123.svc.us-east-1.pinecone.io"

# EXAMPLE REQUEST 1: BM25 token matching (type: "text")
curl "https://$INDEX_HOST/namespaces/__default__/documents/search" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "include_fields": ["title", "body", "category", "year"],
    "score_by": [{
      "type": "text",
      "fields": ["body"],
      "query": "machine learning"
    }],
    "top_k": 10
  }'

# EXAMPLE REQUEST 2: Cross-field boolean query (type: "query_string")
curl "https://$INDEX_HOST/namespaces/__default__/documents/search" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "include_fields": ["title", "body"],
    "score_by": [{
      "type": "query_string",
      "query": "title:(quantum) OR body:(machine learning)"
    }],
    "top_k": 10
  }'

# EXAMPLE REQUEST 3: Dense vector ranking with phrase-match filter
curl "https://$INDEX_HOST/namespaces/__default__/documents/search" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "include_fields": ["title", "body"],
    "filter": { "body": { "$match_phrase": "machine learning" } },
    "score_by": [{
      "type": "dense_vector",
      "fields": ["embedding"],
      "values": [0.12, 0.34, 0.56]
    }],
    "top_k": 10
  }'

# EXAMPLE REQUEST 4: Sparse vector ranking
curl "https://$INDEX_HOST/namespaces/__default__/documents/search" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "include_fields": ["title", "body"],
    "score_by": [{
      "type": "sparse_vector",
      "fields": ["sparse_embedding"],
      "sparse_values": {
        "indices": [12, 287, 4096],
        "values": [0.41, 0.33, 0.18]
      }
    }],
    "top_k": 10
  }'

# EXAMPLE REQUEST 5: Compound filter ($and + $match_all + metadata)
curl "https://$INDEX_HOST/namespaces/__default__/documents/search" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
    "include_fields": ["body", "category", "year"],
    "filter": {
      "$and": [
        { "body": { "$match_all": "federal reserve" } },
        { "category": { "$eq": "finance" } },
        { "year": { "$gte": 2024 } }
      ]
    },
    "score_by": [{
      "type": "text",
      "fields": ["body"],
      "query": "monetary policy impact"
    }],
    "top_k": 10
  }'
```

`POST /namespaces/{namespace}/documents/search`

:::code-group
```bash title="cURL"
curl --request POST \
  --url https://{index_host}/namespaces/{namespace}/documents/search \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "include_fields": [
    "title",
    "content"
  ],
  "score_by": [
    {
      "fields": [
        "content"
      ],
      "query": "What is machine learning?",
      "type": "text"
    }
  ],
  "top_k": 10
}'
```

```json title="200"
{
  "matches": [
    {
      "_id": "doc-1",
      "_score": 0.9281134605407715,
      "title": "Introduction to Machine Learning"
    }
  ],
  "namespace": "my-namespace",
  "usage": {
    "read_units": 5
  }
}
```
:::

## Authorizations

- `Authorization` (header, string, required) — Bearer authentication header of the form `Bearer <token>`.

## Path Parameters

- `namespace` (path, string, required) — The namespace to search.

## Headers

- `X-Pinecone-Api-Version` (header, string, required) — Required date-based version header

## Body

- `score_by` (body, object\[], required) — The list of scoring methods to use for ranking documents. A single clause of any type is always valid. Several clauses may be combined only when every one of them is `text` or `query_string`; a `dense_vector` or `sparse_vector` clause must appear on its own.
- `top_k` (body, integer, required) — The number of top-ranked documents to return.
- `include_fields` (body, string\[]) — The document fields to return on each match alongside `_id` and `_score`. When omitted or empty, no fields are returned. Pass `["*"]` to return every field.
- `filter` (body, object) — A metadata filter expression to restrict the documents searched.

## Response

- `200` — A successful search response.
- `400` — Bad request. The request body included invalid request parameters.
- `401` — Unauthorized. Possible causes: missing or invalid API key.
- `4XX` — An unexpected error response.
- `5XX` — An unexpected error response.

## Related pages

- [Upsert documents](./database-2026-07-data-plane-upsert-documents.md)
- [Fetch documents](./database-2026-07-data-plane-fetch-documents.md)
- [List documents](./database-2026-07-data-plane-list-documents.md)
- [Update documents](./database-2026-07-data-plane-update-documents.md)
- [Delete documents](./database-2026-07-data-plane-delete-documents.md)

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