name
for the index.cloud
and region
to the cloud and region where the index should be deployed.embed.model
to one of Pinecone’s hosted embedding models.embed.field_map
to the name of the field in your source document that contains the data for embedding.name
for the index.vector_type
to dense
.dimension
and similarity metric
of the vectors you’ll store in the index. This should match the dimension and metric supported by your embedding model.spec.cloud
and spec.region
to the cloud and region where the index should be deployed. For Python, you also need to import the ServerlessSpec
class.name
for the index.cloud
and region
to the cloud and region where the index should be deployed.embed.model
to one of Pinecone’s hosted sparse embedding models.embed.field_map
to the name of the field in your source document that contains the text for embedding.name
for the index.vector_type
to sparse
.metric
to dotproduct
. Sparse indexes do not support other distance metrics.spec.cloud
and spec.region
to the cloud and region where the index should be deployed.2025-10
version of the API.schema
object with the names of the metadata fields explicitly indexed during index or namespace creation.
Cloud | Region | Supported plans | Availability phase |
---|---|---|---|
aws | us-east-1 (Virginia) | Starter, Standard, Enterprise | General availability |
aws | us-west-2 (Oregon) | Standard, Enterprise | General availability |
aws | eu-west-1 (Ireland) | Standard, Enterprise | General availability |
gcp | us-central1 (Iowa) | Standard, Enterprise | General availability |
gcp | europe-west4 (Netherlands) | Standard, Enterprise | General availability |
azure | eastus2 (Virginia) | Standard, Enterprise | General availability |
us-east-1
region of AWS only. To create indexes in other regions, upgrade your plan.dotproduct
metric.Euclidean
metric='euclidean'
, the most similar results are those with the lowest similarity score.Cosine
Dotproduct
multilingual-e5-large
multilingual-e5-large
is an efficient dense embedding model trained on a mixture of multilingual datasets. It works well on messy data and short queries expected to return medium-length passages of text (1-2 paragraphs).Detailsmultilingual-e5-large
model supports the following parameters:Parameter | Type | Required/Optional | Description | Default |
---|---|---|---|---|
input_type | string | Required | The type of input data. Accepted values: query or passage . | |
truncate | string | Optional | How to handle inputs longer than those supported by the model. Accepted values: END or NONE .END truncates the input sequence at the input token limit. NONE returns an error when the input exceeds the input token limit. | END |
llama-text-embed-v2
llama-text-embed-v2
is a high-performance dense embedding model optimized for text retrieval and ranking tasks. It is trained on a diverse range of text corpora and provides strong performance on longer passages and structured documents.Detailsllama-text-embed-v2
model supports the following parameters:Parameter | Type | Required/Optional | Description | Default |
---|---|---|---|---|
input_type | string | Required | The type of input data. Accepted values: query or passage . | |
truncate | string | Optional | How to handle inputs longer than those supported by the model. Accepted values: END or NONE .END truncates the input sequence at the input token limit. NONE returns an error when the input exceeds the input token limit. | END |
dimension | integer | Optional | Dimension of the vector to return. | 1024 |
pinecone-sparse-english-v0
pinecone-sparse-english-v0
is a sparse embedding model for converting text to sparse vectors for keyword or hybrid semantic/keyword search. Built on the innovations of the DeepImpact architecture, the model directly estimates the lexical importance of tokens by leveraging their context, unlike traditional retrieval models like BM25, which rely solely on term frequency.Detailspinecone-sparse-english-v0
model supports the following parameters:Parameter | Type | Required/Optional | Description | Default |
---|---|---|---|---|
input_type | string | Required | The type of input data. Accepted values: query or passage . | |
truncate | string | Optional | How to handle inputs longer than those supported by the model. Accepted values: END or NONE .END truncates the input sequence at the input token limit. NONE returns an error when the input exceeds the input token limit. | END |
return_tokens | boolean | Optional | Whether to return the string tokens. | False |