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Artisan IMG > Pinecone (pinecone) (478b3ead-7cb5-455e-b264-41761fc8f45e)
Pinecone
OverviewOperations (sample payloads)Operations

Operations (sample payloads)

Main operations
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Create index
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This operation deploys a Pinecone index. This is where you specify the measure of similarity, the dimension of vectors to be stored in the index, which cloud provider you would like to deploy with, and more.

Sample Input

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{
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    "name": "product-embeddings",
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    "dimension": 768,
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    "metric": "cosine",
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    "spec": {
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        "pod": {
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            "environment": "gcp-starter",
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            "pod_type": "p1.x1",
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            "replicas": 1,
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            "shards": 1,
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            "pods": 1,
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            "metadata_config": {
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                "indexed": [
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                    "category",
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                    "brand",
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                    "price"
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                ]
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            }
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        }
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    }
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}

Sample Output

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{
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    "name": "product-embeddings",
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    "dimension": 768,
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    "metric": "cosine",
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    "host": "product-embeddings-12345.svc.gcp-starter.pinecone.io",
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    "spec": {
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        "pod": {
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            "environment": "gcp-starter",
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            "replicas": 1,
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            "shards": 1,
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            "pod_type": "p1.x1",
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            "pods": 1,
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            "metadata_config": {
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                "indexed": [
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                    "category",
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                    "brand",
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                    "price"
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                ]
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            },
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            "source_collection": ""
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        }
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    },
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    "status": {
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        "ready": true,
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        "state": "Ready"
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    }
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}

Delete vectors
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The delete operation deletes vectors, by id, from a single namespace.

Sample Input

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{
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    "index_host": "my-index-123456.svc.us-west1-gcp.pinecone.io",
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    "ids": [
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        "vec1",
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        "vec2",
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        "vec3"
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    ],
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    "namespace": "product_catalog",
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    "filter": {
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        "category": "electronics",
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        "price": {
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            "$gt": 500
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        }
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    }
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}

Sample Output

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{
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    "deleted": true
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}

Get index
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Get a description of an index.

Sample Input

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{
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    "index_name": "my-product-catalog"
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}

Sample Output

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{
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    "name": "my-product-catalog",
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    "dimension": 1536,
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    "metric": "cosine",
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    "host": "my-product-catalog-12345.svc.us-west1-gcp.pinecone.io",
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    "spec": {
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        "pod": {
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            "environment": "gcp-starter",
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            "replicas": 1,
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            "shard": 1,
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            "pod_type": "p1.x1",
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            "pods": 1,
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            "metadata_config": {
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                "indexed": [
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                    "category",
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                    "brand"
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                ],
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                "source_collection": "products"
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            }
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        }
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    },
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    "status": {
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        "ready": true,
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        "state": "Ready"
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    }
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}

Get index stats
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The describe_index_stats operation returns statistics about the contents of an index, including the vector count per namespace and the number of dimensions, and the index fullness.

Sample Input

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{
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    "index_host": "my-index-123456.svc.us-west1-gcp.pinecone.io",
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    "filter": {
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        "genre": "science fiction",
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        "year": {
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            "$gte": 2000
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        }
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    }
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}

Sample Output

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{
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    "namespaces": {
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        "default": {
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            "vector_count": 10000
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        },
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        "movies": {
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            "vector_count": 5000
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        }
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    },
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    "dimension": 1536,
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    "index_fullness": 0.75,
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    "total_vector_count": 15000
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}

List indexes
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This operation returns a list of all indexes in a project.

Sample Input

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{}

Sample Output

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{
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    "indexes": [
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        {
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            "name": "product-catalog",
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            "dimension": 512,
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            "metric": "cosine",
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            "host": "product-catalog-12345.svc.us-west1-gcp.pinecone.io",
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            "spec": {
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                "pod": {
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                    "environment": "gcp-starter",
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                    "replicas": 1,
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                    "shards": 1,
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                    "pod_type": "s1.x1",
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                    "pods": 1,
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                    "metadata_config": {
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                        "indexed": [
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                            "category",
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                            "brand",
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                            "price"
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                        ]
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                    },
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                    "source_collection": ""
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                }
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            },
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            "status": {
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                "ready": true,
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                "state": "Ready"
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            }
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        },
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        {
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            "name": "customer-embeddings",
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            "dimension": 768,
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            "metric": "euclidean",
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            "host": "customer-embeddings-67890.svc.us-east1-aws.pinecone.io",
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            "spec": {
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                "serverless": {
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                    "cloud": "aws",
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                    "region": "us-east-1"
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                }
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            },
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            "status": {
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                "ready": true,
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                "state": "Ready"
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            }
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        }
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    ]
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}

Query vectors
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The query operation searches a namespace, using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores.

Sample Input

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{
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    "index_host": "my-index-123456.svc.us-west1-gcp.pinecone.io",
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    "namespace": "products",
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    "topK": 3,
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    "filter": {
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        "category": "electronics",
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        "price": {
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            "$lte": 1000
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        }
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    },
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    "includeValues": true,
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    "includeMetadata": true,
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    "vector": [
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        0.1,
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        0.2,
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        0.3,
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        0.4,
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        0.5
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    ]
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}

Sample Output

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{
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    "matches": [
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        {
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            "id": "prod_001",
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            "score": 0.95,
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            "values": [
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                0.11,
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                0.21,
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                0.31,
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                0.41,
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                0.51
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            ],
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            "metadata": {
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                "name": "Smartphone X",
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                "category": "electronics",
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                "price": 799.99
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            }
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        },
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        {
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            "id": "prod_002",
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            "score": 0.87,
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            "values": [
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                0.12,
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                0.22,
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                0.32,
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                0.42,
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                0.52
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            ],
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            "metadata": {
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                "name": "Laptop Y",
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                "category": "electronics",
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                "price": 999.99
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            }
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        },
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        {
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            "id": "prod_003",
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            "score": 0.82,
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            "values": [
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                0.13,
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                0.23,
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                0.33,
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                0.43,
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                0.53
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            ],
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            "metadata": {
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                "name": "Tablet Z",
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                "category": "electronics",
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                "price": 599.99
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            }
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        }
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    ],
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    "namespace": "products",
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    "usage": {
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        "readUnits": 3
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    }
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}

Upsert vectors
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The upsert operation writes vectors into a namespace. If a new value is upserted for an existing vector id, it will overwrite the previous value.

Sample Input

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{
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    "index_host": "my-index-123abc.svc.us-east-1-aws.pinecone.io",
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    "vectors": [
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        {
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            "id": "vec1",
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            "values": [
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                0.1,
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                0.2,
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                0.3,
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                0.4,
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                0.5
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            ],
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            "sparseValues": {
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                "indices": [
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                    1,
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                    3,
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                    5
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                ],
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                "values": [
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                    0.5,
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                    0.8,
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                    0.2
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                ]
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            },
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            "metadata": {
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                "category": "electronics",
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                "price": 299.99
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            }
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        },
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        {
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            "id": "vec2",
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            "values": [
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                0.6,
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                0.7,
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                0.8,
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                0.9,
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                1
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            ],
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            "metadata": {
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                "category": "clothing",
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                "size": "medium"
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            }
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        }
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    ],
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    "namespace": "product_catalog"
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}

Sample Output

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{
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    "upsertedCount": 2
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}
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