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indexes
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create-for-model
# pip install --upgrade pinecone
from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

index_name = "integrated-dense-py"

index_model = pc.create_index_for_model(
    name=index_name,
    cloud="aws",
    region="us-east-1",
    embed={
        "model":"llama-text-embed-v2",
        "field_map":{"text": "chunk_text"}
    }
)

# Import specific classes to get type hints and autocompletions
from pinecone import CloudProvider, AwsRegion, IndexEmbed, EmbedModel

index_model = pc.create_index_for_model(
    name=index_name,
    cloud=CloudProvider.AWS,
    region=AwsRegion.US_EAST_1,
    embed=IndexEmbed(
        model=EmbedModel.Multilingual_E5_Large,
        field_map={"text": "chunk_text"},
        metric='cosine'
    )
)

{'deletion_protection': 'disabled',
 'dimension': 1024,
 'embed': {'dimension': 1024,
           'field_map': {'text': 'chunk_text'},
           'metric': 'cosine',
           'model': 'llama-text-embed-v2',
           'read_parameters': {'input_type': 'query', 'truncate': 'END'},
           'write_parameters': {'input_type': 'passage', 'truncate': 'END'}},
 'host': 'integrated-dense-py-govk0nt.svc.aped-4627-b74a.pinecone.io',
 'id': '9dabb7cb-ec0a-4e2e-b79e-c7c997e592ce',
 'metric': 'cosine',
 'name': 'integrated-dense-py',
 'spec': {'serverless': {'cloud': 'aws', 'region': 'us-east-1'}},
 'status': {'ready': True, 'state': 'Ready'},
 'tags': None}

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.

# pip install --upgrade pinecone
from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

index_name = "integrated-dense-py"

index_model = pc.create_index_for_model(
    name=index_name,
    cloud="aws",
    region="us-east-1",
    embed={
        "model":"llama-text-embed-v2",
        "field_map":{"text": "chunk_text"}
    }
)

# Import specific classes to get type hints and autocompletions
from pinecone import CloudProvider, AwsRegion, IndexEmbed, EmbedModel

index_model = pc.create_index_for_model(
    name=index_name,
    cloud=CloudProvider.AWS,
    region=AwsRegion.US_EAST_1,
    embed=IndexEmbed(
        model=EmbedModel.Multilingual_E5_Large,
        field_map={"text": "chunk_text"},
        metric='cosine'
    )
)

{'deletion_protection': 'disabled',
 'dimension': 1024,
 'embed': {'dimension': 1024,
           'field_map': {'text': 'chunk_text'},
           'metric': 'cosine',
           'model': 'llama-text-embed-v2',
           'read_parameters': {'input_type': 'query', 'truncate': 'END'},
           'write_parameters': {'input_type': 'passage', 'truncate': 'END'}},
 'host': 'integrated-dense-py-govk0nt.svc.aped-4627-b74a.pinecone.io',
 'id': '9dabb7cb-ec0a-4e2e-b79e-c7c997e592ce',
 'metric': 'cosine',
 'name': 'integrated-dense-py',
 'spec': {'serverless': {'cloud': 'aws', 'region': 'us-east-1'}},
 'status': {'ready': True, 'state': 'Ready'},
 'tags': None}

Authorizations

​
Api-Key
string
header
required

An API Key is required to call Pinecone APIs. Get yours from the console.

Body

application/json

The desired configuration for the index and associated embedding model.

The desired configuration for the index and associated embedding model.

​
name
string
required

The name of the index. Resource name must be 1-45 characters long, start and end with an alphanumeric character, and consist only of lower case alphanumeric characters or '-'.

Required string length: 1 - 45
Example:

"example-index"

​
cloud
enum<string>
required

The public cloud where you would like your index hosted.

Available options:
gcp,
aws,
azure
Example:

"aws"

​
region
string
required

The region where you would like your index to be created.

Example:

"us-east-1"

​
embed
object
required

Specify the integrated inference embedding configuration for the index.

Once set the model cannot be changed, but you can later update the embedding configuration for an integrated inference index including field map, read parameters, or write parameters.

Refer to the model guide for available models and model details.

Example:
{
  "field_map": { "text": "your-text-field" },
  "metric": "cosine",
  "model": "multilingual-e5-large",
  "read_parameters": { "input_type": "query", "truncate": "NONE" },
  "write_parameters": { "input_type": "passage" }
}
​
deletion_protection
enum<string>
default:disabled

Whether deletion protection is enabled/disabled for the index.

Available options:
disabled,
enabled
​
tags
object

Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.

Example:
{ "tag0": "val0", "tag1": "val1" }

Response

The index has successfully been created for the embedding model.

The IndexModel describes the configuration and status of a Pinecone index.

​
name
string
required

The name of the index. Resource name must be 1-45 characters long, start and end with an alphanumeric character, and consist only of lower case alphanumeric characters or '-'.

Required string length: 1 - 45
Example:

"example-index"

​
metric
enum<string>
required

The distance metric to be used for similarity search. You can use 'euclidean', 'cosine', or 'dotproduct'. If the 'vector_type' is 'sparse', the metric must be 'dotproduct'. If the vector_type is dense, the metric defaults to 'cosine'.

Available options:
cosine,
euclidean,
dotproduct
​
host
string
required

The URL address where the index is hosted.

Example:

"semantic-search-c01b5b5.svc.us-west1-gcp.pinecone.io"

​
spec
object
required
Example:
{
  "pod": {
    "environment": "us-east-1-aws",
    "metadata_config": {
      "indexed": ["genre", "title", "imdb_rating"]
    },
    "pod_type": "p1.x1",
    "pods": 1,
    "replicas": 1,
    "shards": 1
  }
}
​
status
object
required
Example:
{
  "ready": true,
  "state": "ScalingUpPodSize"
}
​
vector_type
string
default:dense
required

The index vector type. You can use 'dense' or 'sparse'. If 'dense', the vector dimension must be specified. If 'sparse', the vector dimension should not be specified.

​
dimension
integer<int32>

The dimensions of the vectors to be inserted in the index.

Required range: 1 <= x <= 20000
Example:

1536

​
private_host
string

The private endpoint URL of an index.

Example:

"semantic-search-c01b5b5.svc.private.us-west1-gcp.pinecone.io"

​
deletion_protection
enum<string>
default:disabled

Whether deletion protection is enabled/disabled for the index.

Available options:
disabled,
enabled
​
tags
object

Custom user tags added to an index. Keys must be 80 characters or less. Values must be 120 characters or less. Keys must be alphanumeric, '', or '-'. Values must be alphanumeric, ';', '@', '', '-', '.', '+', or ' '. To unset a key, set the value to be an empty string.

Example:
{ "tag0": "val0", "tag1": "val1" }
​
embed
object

The embedding model and document fields mapped to embedding inputs.

Example:
{
  "field_map": { "text": "your-text-field" },
  "metric": "cosine",
  "model": "multilingual-e5-large",
  "read_parameters": { "input_type": "query", "truncate": "NONE" },
  "write_parameters": { "input_type": "passage" }
}