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Pinecone Inference API

Model inference

Pinecone Inference API is one of 9 APIs that Pinecone publishes on the APIs.io network, described by a machine-readable OpenAPI specification.

Tagged areas include Inference. The published artifact set on APIs.io includes an OpenAPI specification and API documentation.

This API exposes 4 operations across 4 paths, and defines 16 schemas. It is described by OpenAPI 3.2.0, at version 2025-10.

Requests are made against a single base URL, https://api.pinecone.io.

4 operations 4 paths 16 schemas 2 GET2 POST

Metadata

The identity and technical contract details declared by the specification.

Specification
OpenAPI 3.2.0
API Version
2025-10
Base URL
https://api.pinecone.io
Authentication
API Key
License
Resource Areas
1

Authentication & Security 1

Pinecone Inference API declares 1 security scheme for authenticating requests. An API key is passed in the header as Api-Key (ApiKeyAuth). By default, every request must be authenticated.

  • ApiKeyAuth — An API Key is required to call Pinecone APIs. Get yours from the [console](https://app.pinecone.io/).

Paths & Operations 4

Across 4 paths, the API surfaces 4 operations — 2 GET, 2 POST. Each is listed below with its method, path, parameters, and response codes.

Inference 4

Model inference

POST
/embed
Generate vectors
embed 1 param body → 200400401500
POST
/rerank
Rerank results
rerank 1 param body → 200400401500
GET
/models
List available models
list_models 3 params → 200401404500
GET
/models/{model_name}
Describe a model
get_model 2 params → 200401404500

Schemas 16

The contract defines 16 schemas that model the data the API accepts and returns. The most detailed are ModelInfo (12 properties), ModelInfoSupportedParameter (8 properties), RerankRequest (7 properties), SparseEmbedding (4 properties). Each schema is shown below with its type and property counts.

ModelInfoMetric
string
A distance metric that the embedding model supports for similarity searches. Possible values: cosine, euclidean, or dotproduct.
DenseEmbedding
object
A dense embedding of a single input
2 properties 2 required
Embedding
object
Embedding of a single input
ModelInfoSupportedParameter
object
Describes a parameter supported by the model, including parameter value constraints.
8 properties 4 required
VectorType
string
Indicates whether this is a 'dense' or 'sparse' embedding.
RerankResult
object
The result of a reranking request.
3 properties 3 required
RerankRequest
object
7 properties 3 required
ModelInfo
object
Represents the model configuration including model type, supported parameters, and other model details.
12 properties 4 required
EmbedRequest
object
3 properties 2 required
ModelInfoList
object
The list of available models.
1 property
ErrorResponse
object
The response shape used for all error responses.
2 properties 2 required
ModelInfoSupportedMetrics
array
The distance metrics supported by the model for similarity search.
SparseEmbedding
object
A sparse embedding of a single input
4 properties 3 required
RankedDocument
object
A ranked document with a relevance score and an index position.
3 properties 2 required
EmbeddingsList
object
Embeddings generated for the input.
4 properties 4 required
Document
object
Document for reranking

Specification

The full machine-readable OpenAPI contract behind this narrative.

Source

pinecone-inference-api-openapi.yml Raw ↑

Other APIs Pinecone publishes across the network.

Pinecone API Keys API
Pinecone Bulk Operations API
Pinecone Manage Assistants API
Pinecone Manage Indexes API
Pinecone Namespace Operations API
Pinecone Organizations API
Pinecone Projects API
Pinecone Vector Operations API
Where this information came from

This is an independent, third-party profile of Pinecone Inference API, published by API Evangelist. We do not operate, host, resell, or support these APIs, and we are not affiliated with or endorsed by the company unless stated above. Everything here is built from publicly available information — the company's own site, developer portal, documentation, public repositories, and the specifications it publishes for public use. Nothing is obtained by breaching a system, defeating an access control, or using credentials.

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