> ## 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.
# Generate vectors
> Generate vector embeddings for input data. This endpoint uses Pinecone's [hosted embedding models](https://docs.pinecone.io/guides/index-data/create-an-index#embedding-models).
```shell curl theme={null}
PINECONE_API_KEY="YOUR_API_KEY"
curl https://api.pinecone.io/embed \
-H "Api-Key: $PINECONE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-Pinecone-Api-Version: 2026-04" \
-d '{
"model": "llama-text-embed-v2",
"parameters": {
"input_type": "passage",
"truncate": "END"
},
"inputs": [
{"text": "Apple is a popular fruit known for its sweetness and crisp texture."},
{"text": "The tech company Apple is known for its innovative products like the iPhone."},
{"text": "Many people enjoy eating apples as a healthy snack."},
{"text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
{"text": "An apple a day keeps the doctor away, as the saying goes."},
{"text": "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."}
]
}'
```
```json curl theme={null}
{
"data": [
{
"values": [
0.04925537109375,
-0.01313018798828125,
-0.0112762451171875,
...
]
},
...
],
"model": "llama-text-embed-v2",
"usage": {
"total_tokens": 130
}
}
```
## OpenAPI
````yaml https://raw.githubusercontent.com/pinecone-io/pinecone-api/refs/heads/main/2026-04/inference_2026-04.oas.yaml post /embed
openapi: 3.0.3
info:
title: Pinecone Inference 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://api.pinecone.io
description: Production API endpoints
security:
- ApiKeyAuth: []
tags:
- name: Inference
description: Model inference
externalDocs:
description: More Pinecone.io API docs
url: https://docs.pinecone.io/introduction
paths:
/embed:
post:
tags:
- Inference
summary: Generate vectors
description: >-
Generate vector embeddings for input data. This endpoint uses Pinecone's
[hosted embedding
models](https://docs.pinecone.io/guides/index-data/create-an-index#embedding-models).
operationId: embed
parameters:
- in: header
name: X-Pinecone-Api-Version
description: Required date-based version header
required: true
schema:
default: 2026-04
type: string
style: simple
requestBody:
description: Generate embeddings for inputs.
content:
application/json:
schema:
$ref: '#/components/schemas/EmbedRequest'
responses:
'200':
description: OK
content:
application/json:
schema:
$ref: '#/components/schemas/EmbeddingsList'
'400':
description: Bad request. The request body included invalid request parameters.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
examples:
index-metric-validation-error:
summary: Validation error
value:
error:
code: INVALID_ARGUMENT
message: >-
Bad request. The request body included invalid request
parameters.
status: 400
'401':
description: 'Unauthorized. Possible causes: Invalid API key.'
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
examples:
unauthorized:
summary: Unauthorized
value:
error:
code: UNAUTHENTICATED
message: Invalid API key.
status: 401
'500':
description: Internal server error.
content:
application/json:
schema:
$ref: '#/components/schemas/ErrorResponse'
examples:
internal-server-error:
summary: Internal server error
value:
error:
code: UNKNOWN
message: Internal server error
status: 500
components:
schemas:
EmbedRequest:
type: object
properties:
model:
example: multilingual-e5-large
description: >-
The
[model](https://docs.pinecone.io/guides/index-data/create-an-index#embedding-models)
to use for embedding generation.
type: string
parameters:
example:
input_type: passage
truncate: END
description: >-
Additional model-specific parameters. Refer to the [model
guide](https://docs.pinecone.io/guides/index-data/create-an-index#embedding-models)
for available model parameters.
type: object
additionalProperties: true
inputs:
description: List of inputs to generate embeddings for.
type: array
items:
type: object
properties:
text:
example: The quick brown fox jumps over the lazy dog.
description: The text input to generate embeddings for.
type: string
required:
- model
- inputs
EmbeddingsList:
description: Embeddings generated for the input.
type: object
properties:
model:
example: multilingual-e5-large
description: The model used to generate the embeddings
type: string
vector_type:
example: dense
description: >-
Indicates whether the response data contains 'dense' or 'sparse'
embeddings.
type: string
data:
description: The embeddings generated for the inputs.
type: array
items:
$ref: '#/components/schemas/Embedding'
usage:
description: Usage statistics for the model inference.
type: object
properties:
total_tokens:
example: 205
description: Total number of tokens consumed across all inputs.
type: integer
format: int32
minimum: 0
required:
- model
- vector_type
- data
- usage
ErrorResponse:
example:
error:
code: QUOTA_EXCEEDED
message: >-
The index exceeds the project quota of 5 pods by 2 pods. Upgrade
your account or change the project settings to increase the quota.
status: 429
description: The response shape used for all error responses.
type: object
properties:
status:
example: 500
description: The HTTP status code of the error.
type: integer
error:
example:
code: INVALID_ARGUMENT
message: >-
Index name must contain only lowercase alphanumeric characters or
hyphens, and must not begin or end with a hyphen.
description: Detailed information about the error that occurred.
type: object
properties:
code:
description: >-
The error code.
Possible values: `OK`, `UNKNOWN`, `INVALID_ARGUMENT`,
`DEADLINE_EXCEEDED`, `QUOTA_EXCEEDED`, `NOT_FOUND`,
`ALREADY_EXISTS`, `PERMISSION_DENIED`, `UNAUTHENTICATED`,
`RESOURCE_EXHAUSTED`, `FAILED_PRECONDITION`, `ABORTED`,
`OUT_OF_RANGE`, `UNIMPLEMENTED`, `INTERNAL`, `UNAVAILABLE`,
`DATA_LOSS`, or `FORBIDDEN`.
x-enum:
- OK
- UNKNOWN
- INVALID_ARGUMENT
- DEADLINE_EXCEEDED
- QUOTA_EXCEEDED
- NOT_FOUND
- ALREADY_EXISTS
- PERMISSION_DENIED
- UNAUTHENTICATED
- RESOURCE_EXHAUSTED
- FAILED_PRECONDITION
- ABORTED
- OUT_OF_RANGE
- UNIMPLEMENTED
- INTERNAL
- UNAVAILABLE
- DATA_LOSS
- FORBIDDEN
type: string
message:
example: >-
Index name must contain only lowercase alphanumeric characters
or hyphens, and must not begin or end with a hyphen.
description: A human-readable error message describing the error.
type: string
details:
description: >-
Additional information about the error. This field is not
guaranteed to be present.
type: object
required:
- code
- message
required:
- status
- error
Embedding:
description: Embedding of a single input
discriminator:
propertyName: vector_type
mapping:
dense:
$ref: '#/components/schemas/DenseEmbedding'
sparse:
$ref: '#/components/schemas/SparseEmbedding'
type: object
oneOf:
- $ref: '#/components/schemas/DenseEmbedding'
- $ref: '#/components/schemas/SparseEmbedding'
DenseEmbedding:
title: Dense embedding
description: A dense embedding of a single input
type: object
properties:
values:
example:
- 0.1
- 0.2
- 0.3
description: The dense embedding values.
type: array
items:
type: number
format: float
vector_type:
$ref: '#/components/schemas/VectorType'
required:
- values
- vector_type
SparseEmbedding:
title: Sparse embedding
description: A sparse embedding of a single input
type: object
properties:
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: int64
minimum: 0
maximum: 4294967295
sparse_tokens:
example:
- quick
- brown
- fox
description: The normalized tokens used to create the sparse embedding.
type: array
items:
type: string
vector_type:
$ref: '#/components/schemas/VectorType'
required:
- sparse_values
- sparse_indices
- vector_type
VectorType:
description: Indicates whether this is a 'dense' or 'sparse' embedding.
type: string
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/).
````