import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const embeddings = await pc.inference.embed({
model: 'multilingual-e5-large',
inputs: ['Who created the first computer?'],
parameters: {
inputType: 'passage',
truncate: 'END',
}
});
console.log(embeddings);
// {
// model: 'multilingual-e5-large',
// vectorType: 'dense',
// data: [ { values: [Array], vectorType: 'dense' } ],
// usage: { totalTokens: 10 }
// }
```
@param options - The {@link EmbedOptions} for generating embeddings.
Get the information for a model hosted by Pinecone.
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const model = await pc.inference.getModel('pinecone-sparse-english-v0');
console.log(model);
// {
// model: 'pinecone-sparse-english-v0',
// shortDescription: '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.',
// type: 'embed',
// vectorType: 'sparse',
// defaultDimension: undefined,
// modality: 'text',
// maxSequenceLength: 512,
// maxBatchSize: 96,
// providerName: 'Pinecone',
// supportedDimensions: undefined,
// supportedMetrics: [ 'DotProduct' ],
// supportedParameters: [
// {
// parameter: 'input_type',
// type: 'one_of',
// valueType: 'string',
// required: true,
// allowedValues: [Array],
// min: undefined,
// max: undefined,
// _default: undefined
// },
// {
// parameter: 'truncate',
// type: 'one_of',
// valueType: 'string',
// required: false,
// allowedValues: [Array],
// min: undefined,
// max: undefined,
// _default: 'END'
// },
// {
// parameter: 'return_tokens',
// type: 'any',
// valueType: 'boolean',
// required: false,
// allowedValues: undefined,
// min: undefined,
// max: undefined,
// _default: false
// }
// ]
// }
```
@param modelName - The model name you would like to describe.
List available models hosted by Pinecone.
Optionaloptions: ListModelsOptionsimport { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const models = await pc.inference.listModels();
console.log(models);
// {
// models: [
// {
// model: 'llama-text-embed-v2',
// shortDescription: 'A high performance dense embedding model optimized for multilingual and cross-lingual text question-answering retrieval with support for long documents (up to 2048 tokens) and dynamic embedding size (Matryoshka Embeddings).',
// type: 'embed',
// vectorType: 'dense',
// defaultDimension: 1024,
// modality: 'text',
// maxSequenceLength: 2048,
// maxBatchSize: 96,
// providerName: 'NVIDIA',
// supportedDimensions: [Array],
// supportedMetrics: [Array],
// supportedParameters: [Array]
// },
// ...
// {
// model: 'pinecone-rerank-v0',
// shortDescription: 'A state of the art reranking model that out-performs competitors on widely accepted benchmarks. It can handle chunks up to 512 tokens (1-2 paragraphs)',
// type: 'rerank',
// vectorType: undefined,
// defaultDimension: undefined,
// modality: 'text',
// maxSequenceLength: 512,
// maxBatchSize: 100,
// providerName: 'Pinecone',
// supportedDimensions: undefined,
// supportedMetrics: undefined,
// supportedParameters: [Array]
// }
// ]
// }
```
@param options - (Optional) A {@link ListModelsOptions} object to filter the models returned.
Rerank documents against a query with a reranking model. Each document is ranked in descending relevance order against the query provided.
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const myQuery = 'What are some good Turkey dishes for Thanksgiving?';
// Option 1: Documents as an array of strings
const myDocsStrings = [
'I love turkey sandwiches with pastrami',
'A lemon brined Turkey with apple sausage stuffing is a classic Thanksgiving main',
'My favorite Thanksgiving dish is pumpkin pie',
'Turkey is a great source of protein',
];
// Option 1 response
const response = await pc.inference.rerank({
model: 'bge-reranker-v2-m3',
query: myQuery,
documents: myDocsStrings
});
console.log(response);
// {
// model: 'bge-reranker-v2-m3',
// data: [
// { index: 1, score: 0.5633179, document: [Object] },
// { index: 2, score: 0.02013874, document: [Object] },
// { index: 3, score: 0.00035419367, document: [Object] },
// { index: 0, score: 0.00021485926, document: [Object] }
// ],
// usage: { rerankUnits: 1 }
// }
// Option 2: Documents as an array of objects
const myDocsObjs = [
{
title: 'Turkey Sandwiches',
body: 'I love turkey sandwiches with pastrami',
},
{
title: 'Lemon Turkey',
body: 'A lemon brined Turkey with apple sausage stuffing is a classic Thanksgiving main',
},
{
title: 'Thanksgiving',
body: 'My favorite Thanksgiving dish is pumpkin pie',
},
{ title: 'Protein Sources', body: 'Turkey is a great source of protein' },
];
// Option 2: Options object declaring which custom key to rerank on
// Note: If no custom key is passed via `rankFields`, each doc must contain a `text` key, and that will act as the default)
const response = await pc.inference.rerank({
model: 'bge-reranker-v2-m3',
query: myQuery,
documents: myDocsObjs,
topN: 3,
returnDocuments: false,
rankFields: ['body'],
parameters: {
inputType: 'passage',
truncate: 'END',
},
});
console.log(response);
// {
// model: 'bge-reranker-v2-m3',
// data: [
// { index: 1, score: 0.5633179, document: undefined },
// { index: 2, score: 0.02013874, document: undefined },
// { index: 3, score: 0.00035419367, document: undefined },
// ],
// usage: { rerankUnits: 1 }
//}
```
@param options - The {@link RerankOptions} for the reranking operation.
@throws {@link Errors.PineconeArgumentError} when arguments passed to the method fail a runtime validation.
Generates embeddings for the provided inputs using the specified model and (optional) parameters.