Class Inference

Generates embeddings for the provided inputs using the specified model and (optional) parameters.

Example

import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();

const inputs = ['Who created the first computer?'];
const model = 'multilingual-e5-large';
const parameters = {
  inputType: 'passage',
  truncate: 'END',
};
const embeddings = await pc.inference.embed(model, inputs, parameters);
console.log(embeddings);
// {
//   model: 'multilingual-e5-large',
//   vectorType: 'dense',
//   data: [ { values: [Array], vectorType: 'dense' } ],
//   usage: { totalTokens: 10 }
// }
```

@param model - The model to use for generating embeddings.
@param inputs - A list of items to generate embeddings for.
@param params - A dictionary of parameters to use when generating embeddings.
@returns A promise that resolves to {@link EmbeddingsList}.
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Get the information for a model hosted by Pinecone.

Example

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.
@returns A promise that resolves to {@link ModelInfo}.
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List available models hosted by Pinecone.

Example

import { 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.
@returns A promise that resolves to {@link ModelInfoList}.
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Rerank documents against a query with a reranking model. Each document is ranked in descending relevance order against the query provided.

Example

import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const rerankingModel = 'bge-reranker-v2-m3';
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(
  rerankingModel,
  myQuery,
  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 rerankOptions = {
  topN: 3,
  returnDocuments: false,
  rankFields: ['body'],
  parameters: {
    inputType: 'passage',
    truncate: 'END',
  },
};

// Option 2 response
const response = await pc.inference.rerank(
  rerankingModel,
  myQuery,
  myDocsObjs,
  rerankOptions
);
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 model - (Required) The model to use for reranking. Currently, the only available model is "[bge-reranker-v2-m3](https://docs.pinecone.io/models/bge-reranker-v2-m3)"}.
@param query - (Required) The query to rerank documents against.
@param documents - (Required) An array of documents to rerank. The array can either be an array of strings or
an array of objects.
@param options - (Optional) Additional options to send with the reranking request. See {@link RerankOptions} for more details.
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