@genkit-ai/googleai
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Genkit AI framework plugin for Google AI APIs, including Gemini APIs.
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TypeScript
import { ModelReference, z, EmbedderReference, Genkit, EmbedderAction } from 'genkit';
import { GenkitPlugin } from 'genkit/plugin';
import { GeminiConfigSchema } from './gemini.js';
/**
* Copyright 2024 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
interface PluginOptions {
/**
* Provide the API key to use to authenticate with the Gemini API. By
* default, an API key must be provided explicitly here or through the
* `GEMINI_API_KEY` or `GOOGLE_API_KEY` environment variables.
*
* If `false` is explicitly passed, the plugin will be configured to
* expect an `apiKey` option to be provided to the model config at
* call time.
**/
apiKey?: string | false;
apiVersion?: string | string[];
baseUrl?: string;
models?: (ModelReference</** @ignore */ typeof GeminiConfigSchema> | string)[];
experimental_debugTraces?: boolean;
}
/**
* Google Gemini Developer API plugin.
*/
declare function googleAI(options?: PluginOptions): GenkitPlugin;
/**
* Copyright 2024 Google LLC
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
declare const TaskTypeSchema: z.ZodEnum<["RETRIEVAL_DOCUMENT", "RETRIEVAL_QUERY", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING"]>;
type TaskType = z.infer<typeof TaskTypeSchema>;
declare const GeminiEmbeddingConfigSchema: z.ZodObject<{
/** Override the API key provided at plugin initialization. */
apiKey: z.ZodOptional<z.ZodString>;
/**
* The `task_type` parameter is defined as the intended downstream application to help the model
* produce better quality embeddings.
**/
taskType: z.ZodOptional<z.ZodEnum<["RETRIEVAL_DOCUMENT", "RETRIEVAL_QUERY", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING"]>>;
title: z.ZodOptional<z.ZodString>;
version: z.ZodOptional<z.ZodString>;
/**
* The `outputDimensionality` parameter allows you to specify the dimensionality of the embedding output.
* By default, the model generates embeddings with 768 dimensions. Models such as
* `text-embedding-004`, `text-embedding-005`, and `text-multilingual-embedding-002`
* allow the output dimensionality to be adjusted between 1 and 768.
* By selecting a smaller output dimensionality, users can save memory and storage space, leading to more efficient computations.
**/
outputDimensionality: z.ZodOptional<z.ZodNumber>;
}, "strip", z.ZodTypeAny, {
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
version?: string | undefined;
outputDimensionality?: number | undefined;
}, {
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
version?: string | undefined;
outputDimensionality?: number | undefined;
}>;
type GeminiEmbeddingConfig = z.infer<typeof GeminiEmbeddingConfigSchema>;
declare const textEmbeddingGecko001: EmbedderReference<z.ZodObject<{
/** Override the API key provided at plugin initialization. */
apiKey: z.ZodOptional<z.ZodString>;
/**
* The `task_type` parameter is defined as the intended downstream application to help the model
* produce better quality embeddings.
**/
taskType: z.ZodOptional<z.ZodEnum<["RETRIEVAL_DOCUMENT", "RETRIEVAL_QUERY", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING"]>>;
title: z.ZodOptional<z.ZodString>;
version: z.ZodOptional<z.ZodString>;
/**
* The `outputDimensionality` parameter allows you to specify the dimensionality of the embedding output.
* By default, the model generates embeddings with 768 dimensions. Models such as
* `text-embedding-004`, `text-embedding-005`, and `text-multilingual-embedding-002`
* allow the output dimensionality to be adjusted between 1 and 768.
* By selecting a smaller output dimensionality, users can save memory and storage space, leading to more efficient computations.
**/
outputDimensionality: z.ZodOptional<z.ZodNumber>;
}, "strip", z.ZodTypeAny, {
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
version?: string | undefined;
outputDimensionality?: number | undefined;
}, {
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
version?: string | undefined;
outputDimensionality?: number | undefined;
}>>;
declare const textEmbedding004: EmbedderReference<z.ZodObject<{
/** Override the API key provided at plugin initialization. */
apiKey: z.ZodOptional<z.ZodString>;
/**
* The `task_type` parameter is defined as the intended downstream application to help the model
* produce better quality embeddings.
**/
taskType: z.ZodOptional<z.ZodEnum<["RETRIEVAL_DOCUMENT", "RETRIEVAL_QUERY", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING"]>>;
title: z.ZodOptional<z.ZodString>;
version: z.ZodOptional<z.ZodString>;
/**
* The `outputDimensionality` parameter allows you to specify the dimensionality of the embedding output.
* By default, the model generates embeddings with 768 dimensions. Models such as
* `text-embedding-004`, `text-embedding-005`, and `text-multilingual-embedding-002`
* allow the output dimensionality to be adjusted between 1 and 768.
* By selecting a smaller output dimensionality, users can save memory and storage space, leading to more efficient computations.
**/
outputDimensionality: z.ZodOptional<z.ZodNumber>;
}, "strip", z.ZodTypeAny, {
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
version?: string | undefined;
outputDimensionality?: number | undefined;
}, {
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
version?: string | undefined;
outputDimensionality?: number | undefined;
}>>;
declare const SUPPORTED_MODELS: {
'embedding-001': EmbedderReference<z.ZodObject<{
/** Override the API key provided at plugin initialization. */
apiKey: z.ZodOptional<z.ZodString>;
/**
* The `task_type` parameter is defined as the intended downstream application to help the model
* produce better quality embeddings.
**/
taskType: z.ZodOptional<z.ZodEnum<["RETRIEVAL_DOCUMENT", "RETRIEVAL_QUERY", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING"]>>;
title: z.ZodOptional<z.ZodString>;
version: z.ZodOptional<z.ZodString>;
/**
* The `outputDimensionality` parameter allows you to specify the dimensionality of the embedding output.
* By default, the model generates embeddings with 768 dimensions. Models such as
* `text-embedding-004`, `text-embedding-005`, and `text-multilingual-embedding-002`
* allow the output dimensionality to be adjusted between 1 and 768.
* By selecting a smaller output dimensionality, users can save memory and storage space, leading to more efficient computations.
**/
outputDimensionality: z.ZodOptional<z.ZodNumber>;
}, "strip", z.ZodTypeAny, {
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
version?: string | undefined;
outputDimensionality?: number | undefined;
}, {
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
version?: string | undefined;
outputDimensionality?: number | undefined;
}>>;
'text-embedding-004': EmbedderReference<z.ZodObject<{
/** Override the API key provided at plugin initialization. */
apiKey: z.ZodOptional<z.ZodString>;
/**
* The `task_type` parameter is defined as the intended downstream application to help the model
* produce better quality embeddings.
**/
taskType: z.ZodOptional<z.ZodEnum<["RETRIEVAL_DOCUMENT", "RETRIEVAL_QUERY", "SEMANTIC_SIMILARITY", "CLASSIFICATION", "CLUSTERING"]>>;
title: z.ZodOptional<z.ZodString>;
version: z.ZodOptional<z.ZodString>;
/**
* The `outputDimensionality` parameter allows you to specify the dimensionality of the embedding output.
* By default, the model generates embeddings with 768 dimensions. Models such as
* `text-embedding-004`, `text-embedding-005`, and `text-multilingual-embedding-002`
* allow the output dimensionality to be adjusted between 1 and 768.
* By selecting a smaller output dimensionality, users can save memory and storage space, leading to more efficient computations.
**/
outputDimensionality: z.ZodOptional<z.ZodNumber>;
}, "strip", z.ZodTypeAny, {
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
version?: string | undefined;
outputDimensionality?: number | undefined;
}, {
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
version?: string | undefined;
outputDimensionality?: number | undefined;
}>>;
};
declare function defineGoogleAIEmbedder(ai: Genkit, name: string, pluginOptions: PluginOptions): EmbedderAction<any>;
export { GeminiEmbeddingConfigSchema as G, type PluginOptions as P, SUPPORTED_MODELS as S, TaskTypeSchema as T, textEmbeddingGecko001 as a, type TaskType as b, type GeminiEmbeddingConfig as c, defineGoogleAIEmbedder as d, googleAI as g, textEmbedding004 as t };