> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pinecone.io/llms.txt
> Use this file to discover all available pages before exploring further.
# Search with text
> Search a namespace with a query text, query vector, or record ID and return the most similar records, along with their similarity scores. Optionally, rerank the initial results based on their relevance to the query.
Searching with text is supported only for indexes with [integrated embedding](https://docs.pinecone.io/guides/index-data/indexing-overview#vector-embedding). Searching with a query vector or record ID is supported for all indexes.
For guidance and examples, see [Search](https://docs.pinecone.io/guides/search/search-overview).
```shell curl theme={null}
INDEX_HOST="INDEX_HOST"
NAMESPACE="YOUR_NAMESPACE"
PINECONE_API_KEY="YOUR_API_KEY"
# Search with a query text and rerank the results
# Supported only for indexes with integrated embedding
curl "https://$INDEX_HOST/records/namespaces/$NAMESPACE/search" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "X-Pinecone-Api-Version: 2026-04" \
-d '{
"query": {
"inputs": {"text": "Disease prevention"},
"top_k": 4,
},
"fields": ["category", "chunk_text"]
"rerank": {
"model": "bge-reranker-v2-m3",
"top_n": 2,
"rank_fields": ["chunk_text"] # Specified field must also be included in 'fields'
}
}'
# Search with a query vector and rerank the results
curl "https://$INDEX_HOST/records/namespaces/$NAMESPACE/search" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "X-Pinecone-Api-Version: 2026-04" \
-d '{
"query": {
"vector": {
"values": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3]
},
"top_k": 4,
},
"fields": ["category", "chunk_text"]
"rerank": {
"query": "Disease prevention",
"model": "bge-reranker-v2-m3",
"top_n": 2,
"rank_fields": ["chunk_text"] # Specified field must also be included in 'fields'
}
}'
# Search with a record ID and rerank the results
# Supported only for indexes with integrated embedding
curl "https://$INDEX_HOST/records/namespaces/$NAMESPACE/search" \
-H "Accept: application/json" \
-H "Content-Type: application/json" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "X-Pinecone-Api-Version: 2026-04" \
-d '{
"query": {
"id": "rec1",
"top_k": 4,
},
"fields": ["category", "chunk_text"]
"rerank": {
"query": "Disease prevention",
"model": "bge-reranker-v2-m3",
"top_n": 2,
"rank_fields": ["chunk_text"]
}
}'
```
```json curl theme={null}
{
"result": {
"hits": [
{
"_id": "rec3",
"_score": 0.004433765076100826,
"fields": {
"category": "immune system",
"chunk_text": "Rich in vitamin C and other antioxidants, apples contribute to immune health and may reduce the risk of chronic diseases."
}
},
{
"_id": "rec4",
"_score": 0.0029121784027665854,
"fields": {
"category": "endocrine system",
"chunk_text": "The high fiber content in apples can also help regulate blood sugar levels, making them a favorable snack for people with diabetes."
}
}
]
},
"usage": {
"embed_total_tokens": 8,
"read_units": 6,
"rerank_units": 1
}
}
```
## OpenAPI
````yaml https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-04/db_data_2026-04.oas.yaml post /records/namespaces/{namespace}/search
openapi: 3.0.3
info:
title: Pinecone Data Plane API
description: >-
Pinecone is a vector database that makes it easy to search and retrieve
billions of high-dimensional vectors.
contact:
name: Pinecone Support
url: https://support.pinecone.io
email: support@pinecone.io
license:
name: Apache 2.0
url: https://www.apache.org/licenses/LICENSE-2.0
version: 2026-04
servers:
- url: https://{index_host}
variables:
index_host:
default: unknown
description: host of the index
security:
- ApiKeyAuth: []
tags:
- name: Vector Operations
- name: Bulk Operations
- name: Namespace Operations
externalDocs:
description: More Pinecone.io API docs
url: https://docs.pinecone.io/introduction
paths:
/records/namespaces/{namespace}/search:
post:
tags:
- Vector Operations
summary: Search with text
description: >-
Search a namespace with a query text, query vector, or record ID and
return the most similar records, along with their similarity scores.
Optionally, rerank the initial results based on their relevance to the
query.
Searching with text is supported only for indexes with [integrated
embedding](https://docs.pinecone.io/guides/index-data/indexing-overview#vector-embedding).
Searching with a query vector or record ID is supported for all
indexes.
For guidance and examples, see
[Search](https://docs.pinecone.io/guides/search/search-overview).
operationId: searchRecordsNamespace
parameters:
- in: header
name: X-Pinecone-Api-Version
description: Required date-based version header
required: true
schema:
default: 2026-04
type: string
style: simple
- in: path
name: namespace
description: The namespace to search.
required: true
schema:
type: string
style: simple
requestBody:
content:
application/json:
schema:
$ref: '#/components/schemas/SearchRecordsRequest'
required: true
responses:
'200':
description: A successful search namespace response.
content:
application/json:
schema:
$ref: '#/components/schemas/SearchRecordsResponse'
'400':
description: Bad request. The request body included invalid request parameters.
content:
application/json:
schema:
$ref: '#/components/schemas/rpcStatus'
4XX:
description: An unexpected error response.
content:
application/json:
schema:
$ref: '#/components/schemas/rpcStatus'
5XX:
description: An unexpected error response.
content:
application/json:
schema:
$ref: '#/components/schemas/rpcStatus'
components:
schemas:
SearchRecordsRequest:
example:
fields:
- chunk_text
query:
inputs:
text: your query text
top_k: 10
description: A search request for records in a specific namespace.
type: object
properties:
query:
description: .
type: object
properties:
top_k:
example: 10
description: The number of similar records to return.
type: integer
format: int32
filter:
description: >-
The filter to apply. You can use vector metadata to limit your
search. See [Understanding
metadata](https://docs.pinecone.io/guides/index-data/indexing-overview#metadata).
type: object
inputs:
$ref: '#/components/schemas/EmbedInputs'
vector:
$ref: '#/components/schemas/SearchRecordsVector'
id:
example: example-vector-1
description: The unique ID of the vector to be used as a query vector.
type: string
maxLength: 512
match_terms:
$ref: '#/components/schemas/SearchMatchTerms'
required:
- top_k
fields:
example:
- chunk_text
description: >-
The fields to return in the search results. If not specified, the
response will include all fields.
type: array
items:
type: string
maxLength: 100
rerank:
description: Parameters for reranking the initial search results.
type: object
properties:
model:
example: bge-reranker-v2-m3
description: >-
The name of the [reranking
model](https://docs.pinecone.io/guides/search/rerank-results#reranking-models)
to use.
type: string
rank_fields:
example:
- chunk_text
- title
description: >
The field(s) to consider for reranking. If not provided, the
default is `["text"]`.
The number of fields supported is
[model-specific](https://docs.pinecone.io/guides/search/rerank-results#reranking-models).
type: array
items:
type: string
top_n:
example: 5
description: >-
The number of top results to return after reranking. Defaults to
top_k.
type: integer
format: int32
parameters:
example:
truncate: END
description: >-
Additional model-specific parameters. Refer to the [model
guide](https://docs.pinecone.io/guides/search/rerank-results#reranking-models)
for available model parameters.
type: object
additionalProperties: true
query:
example: What is the capital of France?
description: >-
The query to rerank documents against. If a specific rerank
query is specified, it overwrites the query input that was
provided at the top level.
type: string
required:
- model
- rank_fields
required:
- query
SearchRecordsResponse:
example:
result:
hits:
- _id: example-record-1
_score: 0.9281134605407715
fields:
data: your example text
usage:
embed_total_tokens: 10
read_units: 5
description: The records search response.
type: object
properties:
result:
type: object
properties:
hits:
description: The hits for the search document request.
type: array
items:
$ref: '#/components/schemas/Hit'
required:
- hits
usage:
$ref: '#/components/schemas/SearchUsage'
required:
- usage
- result
rpcStatus:
type: object
properties:
code:
type: integer
format: int32
message:
type: string
details:
type: array
items:
$ref: '#/components/schemas/protobufAny'
EmbedInputs:
example:
text: chunk_text
type: object
SearchRecordsVector:
type: object
properties:
values:
$ref: '#/components/schemas/VectorValues'
sparse_values:
example:
- 0.1
- 0.2
- 0.3
description: The sparse embedding values.
type: array
items:
type: number
format: float
sparse_indices:
example:
- 10
- 3
- 156
description: The sparse embedding indices.
type: array
items:
type: integer
format: int32
minimum: 0
SearchMatchTerms:
example:
strategy: all
terms:
- animal
- CHARACTER
- donald Duck
description: >-
Specifies which terms must be present in the text of each search hit
based on the specified strategy. The match is performed
against the text field specified in the integrated index `field_map`
configuration.
Terms are normalized and tokenized into single tokens before matching,
and order does not matter.
Example:
`"match_terms": {"terms": ["animal", "CHARACTER", "donald Duck"], "strategy": "all"}` will tokenize
to `["animal", "character", "donald", "duck"]`, and would match
`"Donald F. Duck is a funny animal character"` but would not match `"A duck is a funny animal"`.
Match terms filtering is supported only for sparse indexes with
[integrated
embedding](https://docs.pinecone.io/guides/index-data/indexing-overview#vector-embedding)
configured to use the
[pinecone-sparse-english-v0](https://docs.pinecone.io/models/pinecone-sparse-english-v0)
model.
type: object
properties:
strategy:
description: >-
The strategy for matching terms in the text. Currently, only `all`
is supported, which means all specified terms must be present.
x-enum:
- all
type: string
terms:
description: >-
A list of terms that must be present in the text of each search hit
based on the specified strategy.
type: array
items:
type: string
Hit:
example:
_id: example-record-1
_score: 0.9281134605407715
fields:
data: your example text
more_data:
text: your example text
description: A record whose vector values are similar to the provided search query.
type: object
properties:
_id:
description: The record id of the search hit.
type: string
_score:
description: The similarity score of the returned record.
type: number
format: float
fields:
description: The selected record fields associated with the search hit.
type: object
required:
- _id
- _score
- fields
SearchUsage:
type: object
properties:
read_units:
example: 5
description: The number of read units consumed by this operation.
type: integer
format: int32
minimum: 0
embed_total_tokens:
example: 2
description: The number of embedding tokens consumed by this operation.
type: integer
format: int32
minimum: 0
rerank_units:
example: 1
description: The number of rerank units consumed by this operation.
type: integer
format: int32
minimum: 0
required:
- read_units
protobufAny:
type: object
properties:
typeUrl:
type: string
value:
type: string
format: byte
VectorValues:
description: This is the vector data included in the request.
type: array
items:
type: number
format: float
minLength: 1
maxLength: 20000
securitySchemes:
ApiKeyAuth:
type: apiKey
in: header
name: Api-Key
description: >-
An API Key is required to call Pinecone APIs. Get yours from the
[console](https://app.pinecone.io/).
````