Monitor usage and costs ↗

from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("example-index")

response = index.query(
    vector=[0.22,0.43,0.16,1,...], 
    namespace='example-namespace', 
    top_k=3,
    include_values=False,
    include_metadata=False
)

print(response)

import { Pinecone } from '@pinecone-database/pinecone'

const pc = new Pinecone({ apiKey: "YOUR_API_KEY" })
const index = pc.index("example-index")

const queryResponse = await index.namespace('example-namespace').query({
    vector: [0.22,0.43,0.16,1,...],
    topK: 3,
    includeValues: false,
    includeMetadata: false,
});

console.log(queryResponse);

import io.pinecone.clients.Index;
import io.pinecone.configs.PineconeConfig;
import io.pinecone.configs.PineconeConnection;
import io.pinecone.unsigned_indices_model.QueryResponseWithUnsignedIndices;

import java.util.Arrays;
import java.util.List;

public class QueryByVector {
    public static void main(String[] args) {
        PineconeConfig config = new PineconeConfig("YOUR_API_KEY");
        // To get the unique host for an index,
        // see https://docs.pinecone.io/guides/manage-data/target-an-index
        config.setHost("INDEX_HOST");
        PineconeConnection connection = new PineconeConnection(config);
        Index index = new Index(config, connection, "example-index");

        List<Float> query = Arrays.asList(0.22f,0.43f,0.16f,1f,...);
        QueryResponseWithUnsignedIndices queryResponse = index.query(3, query, null, null, null, "example-namespace", null, false, false);
        System.out.println(queryResponse);
    }
}

package main

import (
	"context"
	"encoding/json"
	"fmt"
	"log"

	"github.com/pinecone-io/go-pinecone/v4/pinecone"
)

func prettifyStruct(obj interface{}) string {
	bytes, _ := json.MarshalIndent(obj, "", "  ")
	return string(bytes)
}

func main() {
    ctx := context.Background()

    pc, err := pinecone.NewClient(pinecone.NewClientParams{
        ApiKey: "YOUR_API_KEY",
    })
    if err != nil {
        log.Fatalf("Failed to create Client: %v", err)
    }

	// To get the unique host for an index,
	// see https://docs.pinecone.io/guides/manage-data/target-an-index
	idxConnection, err := pc.Index(pinecone.NewIndexConnParams{Host: "INDEX_HOST", Namespace: "example-namespace"})
	if err != nil {
		log.Fatalf("Failed to create IndexConnection for Host: %v", err)
	}

	queryVector := []float32{0.22, 0.43, 0.16, 1, ...}

	res, err := idxConnection.QueryByVectorValues(ctx, &pinecone.QueryByVectorValuesRequest{
		Vector:        queryVector,
		TopK:          3,
		IncludeValues: false,
	})
	if err != nil {
		log.Fatalf("Error encountered when querying by vector: %v", err)
	} else {
		fmt.Printf(prettifyStruct(res))
	}

using Pinecone;

var pinecone = new PineconeClient("YOUR_API_KEY");

// To get the unique host for an index, 
// see https://docs.pinecone.io/guides/manage-data/target-an-index
var index = pinecone.Index(host: "INDEX_HOST");

var queryResponse = await index.QueryAsync(new QueryRequest {
    Vector = new[] { 0.22f,0.43f,0.16f,1f,... },
    Namespace = "example-namespace",
    TopK = 3,
    IncludeMetadata = false,
});

{'matches': [{'id': 'record_193027', 'score': 0.00405937387, 'values': []},
             {'id': 'record_137452', 'score': 0.00405937387, 'values': []},
             {'id': 'record_132264', 'score': 0.00405937387, 'values': []}],
 'namespace': 'example-namespace',
 'usage': {'read_units': 1}}

{
  matches: [
    {
      id: 'record_186225',
      score: 0.00405937387,
      values: [],
      sparseValues: undefined,
      metadata: undefined
    },
    {
      id: 'record_164994',
      score: 0.00405937387,
      values: [],
      sparseValues: undefined,
      metadata: undefined
    },
    {
      id: 'record_186333',
      score: 0.00405937387,
      values: [],
      sparseValues: undefined,
      metadata: undefined
    }
  ],
  namespace: 'example-namespace',
  usage: { readUnits: 1 }
}

class QueryResponseWithUnsignedIndices {
    matches: [ScoredVectorWithUnsignedIndices {
        score: 0.004059374
        id: record_170370
        values: []
        metadata: 
        sparseValuesWithUnsignedIndices: SparseValuesWithUnsignedIndices {
            indicesWithUnsigned32Int: []
            values: []
        }
    }, ScoredVectorWithUnsignedIndices {
        score: 0.004059374
        id: record_107423
        values: []
        metadata: 
        sparseValuesWithUnsignedIndices: SparseValuesWithUnsignedIndices {
            indicesWithUnsigned32Int: []
            values: []
        }
    }, ScoredVectorWithUnsignedIndices {
        score: 0.004059374
        id: record_171426
        values: []
        metadata: 
        sparseValuesWithUnsignedIndices: SparseValuesWithUnsignedIndices {
            indicesWithUnsigned32Int: []
            values: []
        }
    }]
    namespace: example-index
    usage: read_units: 1
}

{
  "matches": [
    {
      "vector": {
        "id": "record_193027"
      },
      "score": 0.004059374
    },
    {
      "vector": {
        "id": "record_137452"
      },
      "score": 0.004059374
    },
    {
      "vector": {
        "id": "record_132264"
      },
      "score": 0.004059374
    }
  ],
  "usage": {
    "read_units": 1
  },
  "namespace": "example-index"
}

{
  "results": [],
  "matches": [
    {
      "id": "record_193027",
      "score": 0.004059374,
      "values": []
    },
    {
      "id": "record_137452",
      "score": 0.004059374,
      "values": []
    },
    {
      "id": "record_132264",
      "score": 0.004059374,
      "values": []
    }
  ],
  "namespace": "example-namespace",
  "usage": {
    "readUnits": 1
  }
}

# Import the Pinecone library
from pinecone.grpc import PineconeGRPC as Pinecone
from pinecone import ServerlessSpec
import time

# Initialize a Pinecone client with your API key
pc = Pinecone(api_key="YOUR_API_KEY")

# Define a sample dataset where each item has a unique ID and piece of text
data = [
    {"id": "vec1", "text": "Apple is a popular fruit known for its sweetness and crisp texture."},
    {"id": "vec2", "text": "The tech company Apple is known for its innovative products like the iPhone."},
    {"id": "vec3", "text": "Many people enjoy eating apples as a healthy snack."},
    {"id": "vec4", "text": "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
    {"id": "vec5", "text": "An apple a day keeps the doctor away, as the saying goes."},
    {"id": "vec6", "text": "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."}
]

# Convert the text into numerical vectors that Pinecone can index
embeddings = pc.inference.embed(
    model="llama-text-embed-v2",
    inputs=[d['text'] for d in data],
    parameters={"input_type": "passage", "truncate": "END"}
)

print(embeddings)

// Import the Pinecone library
import { Pinecone } from '@pinecone-database/pinecone';

// Initialize a Pinecone client with your API key
const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });

// Define a sample dataset where each item has a unique ID and piece of text
const data = [
  { id: 'vec1', text: 'Apple is a popular fruit known for its sweetness and crisp texture.' },
  { id: 'vec2', text: 'The tech company Apple is known for its innovative products like the iPhone.' },
  { id: 'vec3', text: 'Many people enjoy eating apples as a healthy snack.' },
  { id: 'vec4', text: 'Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces.' },
  { id: 'vec5', text: 'An apple a day keeps the doctor away, as the saying goes.' },
  { id: 'vec6', text: 'Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership.' }
];

// Convert the text into numerical vectors that Pinecone can index
const model = 'llama-text-embed-v2';

const embeddings = await pc.inference.embed(
  model,
  data.map(d => d.text),
  { inputType: 'passage', truncate: 'END' }
);

console.log(embeddings);

// Import the required classes
import io.pinecone.clients.Index;
import io.pinecone.clients.Inference;
import io.pinecone.clients.Pinecone;
import org.openapitools.inference.client.ApiException;
import org.openapitools.inference.client.model.Embedding;
import org.openapitools.inference.client.model.EmbeddingsList;

import java.math.BigDecimal;
import java.util.*;
import java.util.stream.Collectors;

public class GenerateEmbeddings {
    public static void main(String[] args) throws ApiException {
        // Initialize a Pinecone client with your API key
        Pinecone pc = new Pinecone.Builder("YOUR_API_KEY").build();
        Inference inference = pc.getInferenceClient();

        // Prepare input sentences to be embedded
        List<DataObject> data = Arrays.asList(
            new DataObject("vec1", "Apple is a popular fruit known for its sweetness and crisp texture."),
            new DataObject("vec2", "The tech company Apple is known for its innovative products like the iPhone."),
            new DataObject("vec3", "Many people enjoy eating apples as a healthy snack."),
            new DataObject("vec4", "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."),
            new DataObject("vec5", "An apple a day keeps the doctor away, as the saying goes."),
            new DataObject("vec6", "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership.")
        );

        List<String> inputs = data.stream()
            .map(DataObject::getText)
            .collect(Collectors.toList());

        // Specify the embedding model and parameters
        String embeddingModel = "llama-text-embed-v2";

        Map<String, Object> parameters = new HashMap<>();
        parameters.put("input_type", "passage");
        parameters.put("truncate", "END");

        // Generate embeddings for the input data
        EmbeddingsList embeddings = inference.embed(embeddingModel, parameters, inputs);

        // Get embedded data
        List<Embedding> embeddedData = embeddings.getData();
    }

    private static List<Float> convertBigDecimalToFloat(List<BigDecimal> bigDecimalValues) {
        return bigDecimalValues.stream()
            .map(BigDecimal::floatValue)
            .collect(Collectors.toList());
    }
}

class DataObject {
    private String id;
    private String text;

    public DataObject(String id, String text) {
        this.id = id;
        this.text = text;
    }

    public String getId() {
        return id;
    }
    public String getText() {
        return text;
    }
}

package main

// Import the required packages
import (
    "context"
   	"encoding/json"
    "fmt"
    "log"

    "github.com/pinecone-io/go-pinecone/v4/pinecone"
)

type Data struct {
    ID   string
    Text string
}

type Query struct {
	Text string
}

func prettifyStruct(obj interface{}) string {
    bytes, _ := json.MarshalIndent(obj, "", "  ")
    return string(bytes)
}

func main() {
    ctx := context.Background()

    // Initialize a Pinecone client with your API key
    pc, err := pinecone.NewClient(pinecone.NewClientParams{
        ApiKey: "YOUR_API_KEY",
    })
    if err != nil {
        log.Fatalf("Failed to create Client: %v", err)
    }

    // Define a sample dataset where each item has a unique ID and piece of text
    data := []Data{
        {ID: "vec1", Text: "Apple is a popular fruit known for its sweetness and crisp texture."},
        {ID: "vec2", Text: "The tech company Apple is known for its innovative products like the iPhone."},
        {ID: "vec3", Text: "Many people enjoy eating apples as a healthy snack."},
        {ID: "vec4", Text: "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."},
        {ID: "vec5", Text: "An apple a day keeps the doctor away, as the saying goes."},
        {ID: "vec6", Text: "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."},
    }

    // Specify the embedding model and parameters
    embeddingModel := "llama-text-embed-v2"

    docParameters := pinecone.EmbedParameters{
        InputType: "passage",
        Truncate:  "END",
    }

    // Convert the text into numerical vectors that Pinecone can index
    var documents []string
    for _, d := range data {
        documents = append(documents, d.Text)
    }

    docEmbeddingsResponse, err := pc.Inference.Embed(ctx, &pinecone.EmbedRequest{
        Model:      embeddingModel,
        TextInputs: documents,
        Parameters: docParameters,
    }) 
    if err != nil {
        log.Fatalf("Failed to embed documents: %v", err)
    } else {
        fmt.Printf(prettifyStruct(docEmbeddingsResponse))
    }
}

using Pinecone;
using System;
using System.Collections.Generic;

// Initialize a Pinecone client with your API key
var pinecone = new PineconeClient("YOUR_API_KEY");

// Prepare input sentences to be embedded
var data = new[]
{
    new
    {
        Id = "vec1",
        Text = "Apple is a popular fruit known for its sweetness and crisp texture."
    },
    new
    {
        Id = "vec2",
        Text = "The tech company Apple is known for its innovative products like the iPhone."
    },
    new
    {
        Id = "vec3",
        Text = "Many people enjoy eating apples as a healthy snack."
    },
    new
    {
        Id = "vec4",
        Text = "Apple Inc. has revolutionized the tech industry with its sleek designs and user-friendly interfaces."
    },
    new
    {
        Id = "vec5",
        Text = "An apple a day keeps the doctor away, as the saying goes."
    },
    new
    {
        Id = "vec6",
        Text = "Apple Computer Company was founded on April 1, 1976, by Steve Jobs, Steve Wozniak, and Ronald Wayne as a partnership."
    }
};

// Specify the embedding model and parameters
var embeddingModel = "llama-text-embed-v2";

// Generate embeddings for the input data
var embeddings = await pinecone.Inference.EmbedAsync(new EmbedRequest
{
    Model = embeddingModel,
    Inputs = data.Select(item => new EmbedRequestInputsItem { Text = item.Text }),
    Parameters = new Dictionary<string, object?>
    {
        ["input_type"] = "passage",
        ["truncate"] = "END"
    }
});

Console.WriteLine(embeddings);

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: 2025-10" \
  -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."}
      ]
  }'

EmbeddingsList(
    model='llama-text-embed-v2',
    data=[
        {'values': [0.04925537109375, -0.01313018798828125, -0.0112762451171875, ...]},
        ...
    ],
    usage={'total_tokens': 130}
)

EmbeddingsList(1) [
  {
    values: [
      0.04925537109375, 
      -0.01313018798828125, 
      -0.0112762451171875,
      ...
    ]
  },
  ...
  model: 'llama-text-embed-v2',
  data: [ { values: [Array] } ],
  usage: { totalTokens: 130 }
]

class EmbeddingsList {
    model: llama-text-embed-v2
    data: [class Embedding {
        values: [0.04925537109375, -0.01313018798828125, -0.0112762451171875, ...]
        additionalProperties: null
    }, ...]
    usage: class EmbeddingsListUsage {
        totalTokens: 130
        additionalProperties: null
    }
    additionalProperties: null
}

{
  "data": [
    {
      "values": [
        0.03942871,
        -0.010177612,
        -0.046051025,
        ...
      ]
    },
    ...
  ], 
  "model": "llama-text-embed-v2",
  "usage": {
    "total_tokens": 130
  }
}

{
  "model": "llama-text-embed-v2",
  "data": [
    {
      "values": [
        0.04913330078125,
        -0.01306915283203125,
        -0.01116180419921875,
        ...
      ]
    },
    ...
  ],
  "usage": {
    "total_tokens": 130
  }
}

{
  "data": [
    {
      "values": [
        0.04925537109375,
        -0.01313018798828125,
        -0.0112762451171875,
        ...
      ]
    }, 
    ...
  ],
  "model": "llama-text-embed-v2",
  "usage": {
    "total_tokens": 130
  }
}