@genkit-ai/googleai
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Genkit AI framework plugin for Google AI APIs, including Gemini APIs.
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text/typescript
import { ModelReference, z, EmbedderReference, Genkit, EmbedderAction } from 'genkit';
import { GenkitPlugin } from 'genkit/plugin';
import { GeminiConfigSchema, GeminiVersionString } from './gemini.mjs';
/**
* 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 googleAIPlugin(options?: PluginOptions): GenkitPlugin;
type GoogleAIPlugin = {
(params?: PluginOptions): GenkitPlugin;
model(name: GeminiVersionString, config?: z.infer<typeof GeminiConfigSchema>): ModelReference<typeof GeminiConfigSchema>;
embedder(name: string, config?: GeminiEmbeddingConfig): EmbedderReference<typeof GeminiEmbeddingConfigSchema>;
};
/**
* Google Gemini Developer API plugin.
*/
declare const googleAI: GoogleAIPlugin;
/**
* 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, {
version?: string | undefined;
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
outputDimensionality?: number | undefined;
}, {
version?: string | undefined;
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: 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, {
version?: string | undefined;
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
outputDimensionality?: number | undefined;
}, {
version?: string | undefined;
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: 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, {
version?: string | undefined;
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
outputDimensionality?: number | undefined;
}, {
version?: string | undefined;
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: 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, {
version?: string | undefined;
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
outputDimensionality?: number | undefined;
}, {
version?: string | undefined;
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: 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, {
version?: string | undefined;
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
outputDimensionality?: number | undefined;
}, {
version?: string | undefined;
apiKey?: string | undefined;
taskType?: "RETRIEVAL_DOCUMENT" | "RETRIEVAL_QUERY" | "SEMANTIC_SIMILARITY" | "CLASSIFICATION" | "CLUSTERING" | undefined;
title?: string | undefined;
outputDimensionality?: number | undefined;
}>>;
};
declare function defineGoogleAIEmbedder(ai: Genkit, name: string, pluginOptions: PluginOptions): EmbedderAction<any>;
export { type GoogleAIPlugin as G, type PluginOptions as P, SUPPORTED_MODELS as S, TaskTypeSchema as T, textEmbeddingGecko001 as a, googleAIPlugin as b, type TaskType as c, GeminiEmbeddingConfigSchema as d, type GeminiEmbeddingConfig as e, defineGoogleAIEmbedder as f, googleAI as g, textEmbedding004 as t };