@genkit-ai/vertexai/lib/vectorsearch/vector_search/retrievers.mjs

Genkit AI framework plugin for Google Cloud Vertex AI APIs including Gemini APIs, Imagen, and more.

import {
  retrieverRef
} from "genkit";
import { queryPublicEndpoint } from "./query_public_endpoint.mjs";
import {
  VertexAIVectorRetrieverOptionsSchema
} from "./types.mjs";
import { getProjectNumber } from "./utils.mjs";
const DEFAULT_K = 10;
function vertexAiRetrievers(ai, params) {
  const vectorSearchOptions = params.pluginOptions.vectorSearchOptions;
  const defaultEmbedder = params.defaultEmbedder;
  const retrievers = [];
  if (!vectorSearchOptions || vectorSearchOptions.length === 0) {
    return retrievers;
  }
  for (const vectorSearchOption of vectorSearchOptions) {
    const { documentRetriever, indexId, publicDomainName } = vectorSearchOption;
    const embedderOptions = vectorSearchOption.embedderOptions;
    const retriever = ai.defineRetriever(
      {
        name: `vertexai/${indexId}`,
        configSchema: VertexAIVectorRetrieverOptionsSchema.optional()
      },
      async (content, options) => {
        const embedderReference = vectorSearchOption.embedder ?? defaultEmbedder;
        if (!embedderReference) {
          throw new Error(
            "Embedder reference is required to define Vertex AI retriever"
          );
        }
        const queryEmbedding = (await ai.embed({
          embedder: embedderReference,
          options: embedderOptions,
          content
        }))[0].embedding;
        const accessToken = await params.authClient.getAccessToken();
        if (!accessToken) {
          throw new Error(
            "Error generating access token when defining Vertex AI retriever"
          );
        }
        const projectId = params.pluginOptions.projectId;
        if (!projectId) {
          throw new Error(
            "Project ID is required to define Vertex AI retriever"
          );
        }
        const projectNumber = await getProjectNumber(projectId);
        const location = params.pluginOptions.location;
        if (!location) {
          throw new Error("Location is required to define Vertex AI retriever");
        }
        const res = await queryPublicEndpoint({
          featureVector: queryEmbedding,
          neighborCount: options?.k || DEFAULT_K,
          accessToken,
          projectId,
          location,
          publicDomainName,
          projectNumber,
          indexEndpointId: vectorSearchOption.indexEndpointId,
          deployedIndexId: vectorSearchOption.deployedIndexId,
          restricts: content.metadata?.restricts,
          numericRestricts: content.metadata?.numericRestricts
        });
        const nearestNeighbors = res.nearestNeighbors;
        const queryRes = nearestNeighbors ? nearestNeighbors[0] : null;
        const neighbors = queryRes ? queryRes.neighbors : null;
        if (!neighbors) {
          return { documents: [] };
        }
        const documents = await documentRetriever(neighbors, options);
        return { documents };
      }
    );
    retrievers.push(retriever);
  }
  return retrievers;
}
const vertexAiRetrieverRef = (params) => {
  return retrieverRef({
    name: `vertexai/${params.indexId}`,
    info: {
      label: params.displayName ?? `ertex AI - ${params.indexId}`
    },
    configSchema: VertexAIVectorRetrieverOptionsSchema.optional()
  });
};
export {
  vertexAiRetrieverRef,
  vertexAiRetrievers
};
//# sourceMappingURL=retrievers.mjs.map