MCP Terminal Server

// Copyright 2024 Google LLC // SPDX-License-Identifier: Apache-2.0 // [START main] package main import ( "context" "errors" "fmt" "log" // Import Genkit and the Google AI plugin "github.com/firebase/genkit/go/ai" "github.com/firebase/genkit/go/genkit" "github.com/firebase/genkit/go/plugins/googleai" ) func main() { ctx := context.Background() g, err := genkit.New(nil) if err != nil { log.Fatal(err) } // Initialize the Google AI plugin. When you pass nil for the // Config parameter, the Google AI plugin will get the API key from the // GOOGLE_GENAI_API_KEY environment variable, which is the recommended // practice. if err := googleai.Init(ctx, g, nil); err != nil { log.Fatal(err) } // Define a simple flow that prompts an LLM to generate menu suggestions. genkit.DefineFlow(g, "menuSuggestionFlow", func(ctx context.Context, input string) (string, error) { // The Google AI API provides access to several generative models. Here, // we specify gemini-1.5-flash. m := googleai.Model(g, "gemini-1.5-flash") if m == nil { return "", errors.New("menuSuggestionFlow: failed to find model") } // Construct a request and send it to the model API (Google AI). resp, err := genkit.Generate(ctx, g, ai.WithModel(m), ai.WithConfig(&ai.GenerationCommonConfig{Temperature: 1}), ai.WithTextPrompt(fmt.Sprintf(`Suggest an item for the menu of a %s themed restaurant`, input))) if err != nil { return "", err } // Handle the response from the model API. In this sample, we just // convert it to a string. but more complicated flows might coerce the // response into structured output or chain the response into another // LLM call. text := resp.Text() return text, nil }) // Initialize Genkit and start a flow server. This call must come last, // after all of your plug-in configuration and flow definitions. When you // pass a nil configuration to Init, Genkit starts a local flow server, // which you can interact with using the developer UI. if err := g.Start(ctx, nil); err != nil { log.Fatal(err) } } // [END main]