Genkit MCP

// Copyright 2025 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. // // SPDX-License-Identifier: Apache-2.0 package main import ( "context" "log" "cloud.google.com/go/bigquery" "github.com/firebase/genkit/go/ai" "github.com/firebase/genkit/go/genkit" "github.com/firebase/genkit/go/plugins/googlegenai" "github.com/firebase/genkit/go/plugins/vertexai/vectorsearch" ) // menuItem is the data model for an item on the menu. type menuItem struct { Title string `json:"title" jsonschema_description:"The name of the menu item"` Description string `json:"description" jsonschema_description:"Details including ingredients and preparation"` Price float64 `json:"price" jsonschema_description:"Price in dollars"` } // menuQuestionInput is a question about the menu. type menuQuestionInput struct { Question string `json:"question"` } // answerOutput is an answer to a question. type answerOutput struct { Answer string `json:"answer"` } // dataMenuQuestionInput is a question about the menu, // where the menu is provided in the JSON data. type dataMenuQuestionInput struct { MenuData []*menuItem `json:"menuData"` Question string `json:"question"` } // textMenuQuestionInput is for a question about the menu, // where the menu is provided as unstructured text. type textMenuQuestionInput struct { MenuText string `json:"menuText"` Question string `json:"question"` } type VectorsearchConfig struct { ProjectID string `json:"projectId"` Location string `json:"location"` IndexID string `json:"indexId"` IndexEndpointID string `json:"indexEndpointId"` DeployedIndexID string `json:"deployedIndexId"` ProjectNumber string `json:"projectNumber"` PublicDomainName string `json:"publicDomainName"` Embedder ai.Embedder `json:"embedder"` NeighborsCount int `json:"neighborsCount,omitempty"` DocumentIndexer vectorsearch.DocumentIndexer DocumentRetriever vectorsearch.DocumentRetriever } func main() { ctx := context.Background() vectorsearchPlugin := &vectorsearch.VertexAIVectorSearch{ ProjectID: "${GOOGLE_CLOUD_PROJECT_ID}", // Replace with your Google Cloud project ID Location: "${GOOGLE_CLOUD_PROJECT_LOCATION}", // Replace with your desired location } g := genkit.Init(ctx, genkit.WithPlugins(&googlegenai.VertexAI{ ProjectID: vectorsearchPlugin.ProjectID, Location: vectorsearchPlugin.Location, }, vectorsearchPlugin)) model := googlegenai.VertexAIModel(g, "gemini-2.0-flash") // Create a BigQuery client. bqClient, err := bigquery.NewClient(ctx, vectorsearchPlugin.ProjectID) if err != nil { log.Fatalf("failed to create BigQuery client: %v", err) } // Create the BigQuery Document Indexer and Retriever. datasetID := "${BIGQUERY_DATASET_ID}" // Replace with your BigQuery dataset ID. tableID := "${BIGQUERY_TABLE_ID}" // Replace with your BigQuery table ID. documentIndexer := vectorsearch.GetBigQueryDocumentIndexer(bqClient, datasetID, tableID) documentRetriever := vectorsearch.GetBigQueryDocumentRetriever(bqClient, datasetID, tableID) // Define Vectorsearch parameters. vectorsearchParams := &VectorsearchConfig{ ProjectID: vectorsearchPlugin.ProjectID, Location: vectorsearchPlugin.Location, IndexID: "${VECTOR_SEARCH_INDEX_ID}", // Replace with your index ID IndexEndpointID: "${VECTOR_SEARCH_INDEX_ENDPOINT_ID}", // Replace with your index endpoint ID DeployedIndexID: "${VECTOR_SEARCH_DEPLOYED_INDEX_ID}", // Replace with your deployed index ID ProjectNumber: "${GOOGLE_CLOUD_PROJECT_NUMBER}", // Replace with your Google Cloud project number PublicDomainName: "${VECTOR_SEARCH_PUBLIC_DOMAIN_NAME}", // Replace with your public domain name Embedder: googlegenai.VertexAIEmbedder(g, "text-embedding-004"), // Replace with your desired embedder NeighborsCount: 10, // Number of neighbors to retrieve DocumentIndexer: documentIndexer, DocumentRetriever: documentRetriever, } // Define the retriever for vector search. retriever, err := vectorsearch.DefineRetriever(ctx, g, vectorsearch.Config{ IndexID: vectorsearchParams.IndexID, // Replace with your index ID }, nil) if err != nil { log.Fatal(err) } if err := menu(ctx, g, retriever, model, vectorsearchParams); err != nil { log.Fatal(err) } <-ctx.Done() }