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How to generate an OpenAPI/Swagger spec with Gnostic

In this tutorial, we’ll start with an OpenAPI document and end with a fully functional gRPC server. We’ll also create a RESTful gateway to the gRPC server, and create SDKs in multiple languages.

You might rightfully ask why we would want to do all of this, and there’s no better way to illustrate a need than by starting with a real example that is entirely plausible and definitely not made up.

Picture this: You’re tasked with setting up an underground bar in the far reaches of the galaxy. Interstellar travelers from far and wide look forward to drinks they’ve never even dreamt of.

Because this is the far future and, obviously, everyone is still using gRPC, we better set up a gRPC server that can handle billions of requests from light years away. gRPC helps us keep throughput high and latency low—both essential elements of an interstellar API. However, even civilizations that have mastered the Dyson Sphere know better than to use gRPC in the browser and other clients. So we’ll need to create an HTTP server to complement our gRPC server. Our users will need SDKs to query our endpoints.

There’s no AI in the Laniakea Supercluster of galaxies who’d be willing to code all of this token by token, so let’s help them generate as much of this as possible.

We’re working with enough acronyms to make our heads spin faster than the neutron star and the space jokes aren’t helping.

Let’s break this down step by step and park the science fiction for the moment. Here’s what we’ll do:

  1. Create an OpenAPI document describing our API.
  2. Set up a development environment using Docker and dev containers.
  3. Install a handful of dependencies.
  4. Use Gnostic to generate a binary protocol buffer description of our API.
  5. Use the Gnostic gRPC plugin to generate an annotated protocol buffer description of our API.
  6. Transcode that description to create a gRPC API.
  7. Create our server logic as a Go package.
  8. Generate a gRPC gateway to handle HTTP requests and pass these to our server.
  9. Use Speakeasy to create SDKs in Python and TypeScript.
  10. And finally, test all of this by requesting some spectacular drinks.

Example gRPC and REST API Server Repository

The source code for our complete example is available in the Speakeasy gRPC and REST example repository.

This repository already contains all the generated code we’ll cover in this tutorial. You can clone it and follow along with the tutorial, or use it as a reference to build your own gRPC and REST API server.

Creating an OpenAPI Document to Describe an API

As a start, and for the sake of shipping our server, we’ll create an API with only two endpoints.

The first endpoint is createDrink: Create a new drink based on the provided ingredients and return the drink’s name, description, recipe, and possibly a photo.

Our second endpoint is getDrink: Create a new drink based only on the drink’s name. Return a list of ingredients with quantities, a recipe, and a photo.

Creating the OpenAPI Document

Let’s take a detailed tour of our API by exploring bar.yaml.

OpenAPI Version

We’ll start by creating an OpenAPI 3.0.0 document. Gnostic, unfortunately, only supports OpenAPI 3.0.0, so we’ll have to make sure our document is compliant.

openapi: 3.0.0

API Information

Next, we’ll create an info object that describes our API:

info:
  title: Intergalactic Bar API
  version: 1.0.0
  description: "An API for a cosmic bar that serves drinks from across the galaxy."

Server Configuration

Now let’s define the servers where our API will be hosted:

servers:
  - url: http://localhost:8080
    description: Local server

Endpoints

Let’s define the endpoints for our API:

Create Drink Endpoint

The /create-drink endpoint accepts a POST request with the ingredients:

paths:
  /create-drink:
    post:
      summary: Create a new drink based on ingredients
      operationId: createDrink
      description: "Supply a list of ingredients and get back a new drink recipe."
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/IngredientsRequest'

The endpoint returns a drink response:

      responses:
        200:
          description: "Successfully created a new drink"
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/DrinkResponse'
        400:
          description: "Invalid request"
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/error'

Get Drink Endpoint

The /get-drink endpoint accepts a POST request with the drink name:

  /get-drink:
    post:
      summary: Get a drink recipe by name
      operationId: getDrink
      description: "Supply a drink name and get back its recipe."
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/DrinkNameRequest'

The endpoint returns a drink recipe:

      responses:
        200:
          description: "Successfully retrieved the drink recipe"
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/DrinkRecipeResponse'
        400:
          description: "Invalid request"
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/error'
        404:
          description: "Drink not found"
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/error'

Component Schemas

Let’s define the schemas for our components:

Ingredients Request

components:
  schemas:
    IngredientsRequest:
      type: object
      description: "A request to create a new drink based on a list of ingredients."
      required:
        - ingredients_request
      properties:
        ingredients_request:
          type: object
          required:
            - ingredients
          properties:
            ingredients:
              type: array
              items:
                type: string
              description: "A list of ingredients to include in the drink."

Drink Response

    DrinkResponse:
      type: object
      description: "A response containing a new drink recipe."
      required:
        - name
        - description
        - recipe
      properties:
        name:
          type: string
          description: "The name of the drink."
        description:
          type: string
          description: "A description of the drink."
        recipe:
          type: string
          description: "Instructions on how to make the drink."
        photo:
          type: string
          description: "A URL to a photo of the drink."

Drink Name Request

    DrinkNameRequest:
      type: object
      description: "A request to get a drink recipe by name."
      required:
        - drink_name_request
      properties:
        drink_name_request:
          type: object
          required:
            - name
          properties:
            name:
              type: string
              description: "The name of the drink."

Drink Recipe Response

    DrinkRecipeResponse:
      type: object
      description: "A response containing a drink recipe."
      required:
        - ingredients
        - recipe
      properties:
        ingredients:
          type: array
          items:
            $ref: '#/components/schemas/IngredientQuantity'
          description: "A list of ingredients with quantities."
        recipe:
          type: string
          description: "Instructions on how to make the drink."
        photo:
          type: string
          description: "A URL to a photo of the drink."

Ingredient Quantity

    IngredientQuantity:
      type: object
      description: "An ingredient with a quantity."
      required:
        - name
        - quantity
      properties:
        name:
          type: string
          description: "The name of the ingredient."
        quantity:
          type: string
          description: "The quantity of the ingredient."

Error

    error:
      type: object
      description: "An error response."
      required:
        - code
        - message
      properties:
        code:
          type: integer
          description: "The error code."
        message:
          type: string
          description: "The error message."

Setting Up the Development Environment

Here, we’ll take an opinionated approach to setting up a development environment. We’ll use Docker and dev containers in VS Code to ensure that everyone has the same environment and avoid any issues with dependencies.

We’ll start by creating a Dockerfile in the .devcontainer directory. Our development container is based on mcr.microsoft.com/devcontainers/go from the Microsoft Dev Container Images repository:

FROM mcr.microsoft.com/devcontainers/go:1.22-bookworm
 
RUN curl -o- https://raw.githubusercontent.com/speakeasy-api/speakeasy/main/install.sh | bash
 
RUN go install github.com/google/gnostic@v0.7.0 && \
    go install github.com/google/gnostic-grpc@latest && \
    go install github.com/protocolbuffers/protobuf-go/cmd/protoc-gen-go@v1.32.0 && \
    go install github.com/bufbuild/buf/cmd/buf@v1.30.0 && \
    go install google.golang.org/grpc/cmd/protoc-gen-go-grpc@v1.3.0 && \
    go install github.com/fullstorydev/grpcurl/cmd/grpcurl@latest && \
    go install github.com/grpc-ecosystem/grpc-gateway/v2/protoc-gen-grpc-gateway@v2.18.0
 
COPY . /app
WORKDIR /app
 
RUN go mod tidy
 
EXPOSE 50051
 
CMD ["go", "run", "main.go"]

In this Dockerfile:

  1. We install Speakeasy using the install.sh script from the Speakeasy repository
  2. We install all necessary tools:
    • Gnostic
    • Gnostic gRPC plugin
    • Go protocol buffer compiler
    • Buf
    • gRPC Go plugin
    • gRPCurl
    • gRPC gateway plugin
  3. We copy our project files into the container and set the working directory to /app
  4. We run go mod tidy to ensure all dependencies are up to date
  5. We expose port 50051 for the gRPC server and set the command to run our server

Dev Container Configuration

Next, we’ll create a devcontainer.json file in our .devcontainer directory to configure our development container:

{
  "name": "Go",
  "dockerComposeFile": [
    "../docker-compose.yaml",
    "docker-compose.yaml"
  ],
  "service": "app",
  "workspaceFolder": "/app",
  "shutdownAction": "stopCompose"
}

For help with the devcontainer.json file, check out the official documentation .

Docker Compose

We’ll also create a docker-compose.yaml file in our .devcontainer directory to define our development container:

version: "3"
services:
  app:
    build:
      context: .
      dockerfile: .devcontainer/Dockerfile
    volumes:
      - ..:/workspaces:cached
    command: /bin/sh -c "while sleep 1000; do :; done"

This Docker Compose file defines a service called app that uses the Dockerfile in the .devcontainer directory. It also mounts the current directory into the container at /app and overrides the default command to start the server.

We use /bin/sh -c "while sleep 1000; do :; done" as the default command to keep the container running while we work on our server. This means we can start the server manually when we’re ready.

Starting the Development Container

For this step, you’ll need to have Docker  and the Dev Containers extension  for VS Code installed.

Start the Docker engine and open the project in VS Code. Press F1 to open the command palette, then select Dev Containers: Reopen in Container. This will build the development container and open a new VS Code window inside it.

This step might take a while the first time you run it, as it needs to download the base image and install all the dependencies.

Interacting With the Development Container

Once the development container is running, you can interact with it using the terminal in VS Code. You can run commands as you would in a regular terminal, such as go run main.go to start the server later on.

When we refer to running commands in the development container, we mean running them in the dev container terminal in VS Code.

If you think something isn’t working as expected, try restarting the development container by running Dev Containers: Rebuild Container from the command palette. You may also restart Docker to ensure everything is working as expected.

Dependencies for Generating a gRPC Server Using Gnostic

To generate a gRPC server using Gnostic, we added the following dependencies to our development container:

  • Gnostic : A compiler for APIs described by the OpenAPI 3.0.0 specification.
  • Gnostic gRPC plugin : A plugin for Gnostic that generates gRPC service definitions.
  • Go protocol buffer compiler : A plugin for the protocol buffer compiler that generates Go code.
  • Buf : A tool for managing protocol buffer files.
  • gRPC Go plugin : A plugin for the protocol buffer compiler that generates Go gRPC code.
  • gRPCurl : A command-line tool for interacting with gRPC servers. We’ll use this to test our gRPC server.
  • gRPC gateway plugin : A plugin for the protocol buffer compiler that generates a reverse proxy server to translate RESTful HTTP and JSON requests to gRPC.

Generating a gRPC Server Using Gnostic

To generate a gRPC server using Gnostic, we need to follow these steps:

  1. Compile the API definition to a binary protocol buffer file using Gnostic.
  2. Create an API definition in a .proto file.
  3. Generate a gRPC service definition using the Gnostic gRPC plugin.
  4. Generate Go code using the Go protocol buffer compiler and the gRPC Go plugin.

We’ve already completed step 1 by creating the bar.yaml file. Now we’ll compile the API definition to a binary protocol buffer file using Gnostic.

Compiling the API Definition to a Binary Protocol Buffer File

This step is necessary because we’ve found that the Go protocol buffer compiler and the gRPC Go plugin require a binary protocol buffer file as input. To compile the API definition to a binary protocol buffer file, we’ll use the following command:

gnostic --pb-out=. bar.yaml

This command compiles the API definition in bar.yaml to a binary protocol buffer file named bar.pb.

Creating an API Definition in a .proto File

Next, we’ll create an API definition in a .proto file. We’ll use the bar.pb file generated in the previous step as input. To accomplish this, we’ll use the following command:

gnostic-grpc -input bar.pb -output .

This command generates a .proto file named bar.proto that contains the gRPC service definition:

syntax = "proto3";
 
package bar;
 
import "google/api/annotations.proto";
 
import "google/protobuf/descriptor.proto";
 
import "google/protobuf/empty.proto";
 
message IngredientsRequest {
  repeated string ingredients = 1;
}
 
message DrinkResponse {
  string name = 1;
 
  string description = 2;
 
  string recipe = 3;
 
  string photo = 4;
}
 
message DrinkNameRequest {
  string name = 1;
}
 
message DrinkRecipeResponse {
  repeated IngredientQuantity ingredients = 1;
 
  string recipe = 2;
 
  string photo = 3;
}
 
message IngredientQuantity {
  string name = 1;
 
  string quantity = 2;
}
 
message Error {
  int32 code = 1;
 
  string message = 2;
}
 
//CreateDrinkParameters holds parameters to CreateDrink
message CreateDrinkRequest {
  IngredientsRequest ingredients_request = 1;
}
 
//GetDrinkParameters holds parameters to GetDrink
message GetDrinkRequest {
  DrinkNameRequest drink_name_request = 1;
}
 
service Bar {
  rpc CreateDrink ( CreateDrinkRequest ) returns ( DrinkResponse ) {
    option (google.api.http) = { post:"/create-drink" body:"ingredients_request"  };
  }
 
  rpc GetDrink ( GetDrinkRequest ) returns ( DrinkRecipeResponse ) {
    option (google.api.http) = { post:"/get-drink" body:"drink_name_request"  };
  }
}

Generating Go Code Using Buf and the gRPC Go Plugin

To set up Buf, we need to run the following command:

buf mod init

This command initializes a new Buf module in the current directory.

Update the buf.yaml file created in the project root with the following content:

version: v1
deps:
  - buf.build/googleapis/googleapis

We depend on googleapis because the gRPC Go plugin requires it.

Configuring Buf for Code Generation

Next, create a buf.gen.yaml file in the project root with the following content:

version: v1
managed:
  enabled: true
  go_package_prefix:
    default: github.com/speakeasy-api/grpc-rest-service/bar
    except:
      - buf.build/googleapis/googleapis
plugins:
  - name: go
    out: .
    opt: paths=source_relative
  - name: go-grpc
    out: .
    opt: paths=source_relative,require_unimplemented_servers=false
  - name: grpc-gateway
    out: .
    opt: paths=source_relative

In this configuration:

  1. We enable managed mode, which helps Buf manage the generated code
  2. We set the go_package_prefix to github.com/speakeasy-api/grpc-rest-service/bar, which determines the package name in the generated Go code
  3. We configure three plugins:
    • go: Generates Go code for the protocol buffers
    • go-grpc: Generates Go code for the gRPC service
    • grpc-gateway: Generates Go code for the gRPC gateway, which translates RESTful HTTP and JSON requests to gRPC

Update Buf’s dependencies using the following command:

buf mod update

Now we can generate Go code using the following command:

buf generate

This command generates Go code in the bar directory. The generated code includes the gRPC service definition and the gRPC gateway definition.

Implementing the gRPC Server

To implement the gRPC server, we created one big main.go with our endpoint implementations and business logic all in one. This will make a lot of people very angry and is widely regarded as a bad move. (🫡 Douglas Adams )

To make things slightly more confusing, we’re including OpenAI API calls alongside our focus on OpenAPI. The similarities in the names are purely coincidental.

The main.go file is too large to include here, but you can find it in the root of the example project.

Optional: Adding Your OpenAI API Key

If you want to use the OpenAI API to generate drink recipes, you’ll need to add your OpenAI API key to the project. Without this key, the server will return placeholder data for the drink recipes.

Copy the .env.template file to a new file named .env:

cp .env.template .env

Open the .env file and add your OpenAI API key:

OPENAI_API_KEY=your-openai-api-key

This file is included in the .gitignore file, so it won’t be checked into version control.

Rebuilding the Docker Image

We’ve installed a bunch of new dependencies and generated a lot of new code. To make sure everything is working as expected, we need to rebuild our Docker image.

This also runs go mod tidy to ensure all dependencies are up to date, which can take a while.

In VS Code, press F1 to open the command palette, then select Dev Containers: Rebuild Container. This will rebuild the development container and install all the dependencies.

Running the gRPC Server

To run the gRPC server, we need to start the development container and run the following command:

go run main.go

This command starts the gRPC server on port 50051 and the gRPC gateway on port 8080.

Testing the gRPC Server

To test the gRPC server, we’ll use the grpcurl command-line tool.

First, open a new terminal in VS Code by pressing ⌃⇧`.

This next step will use OpenAI credits, so make sure you have credits available before running the command.

Now run the following command to send a request to the gRPC server:

grpcurl -plaintext -d '{"drink_name_request": {"name": "Pan Galactic Gargle Blaster"}}' localhost:50051 bar.Bar/GetDrink

This command sends a request to the GetDrink endpoint with the name field set to Pan Galactic Gargle Blaster.

The response should look something like this:

{
  "ingredients": [
    {
      "name": "Ol' Janx Spirit",
      "quantity": "1 oz"
    },
    {
      "name": "Water from the seas of Santraginus V",
      "quantity": "0.5 oz"
    },
    {
      "name": "Arcturan Mega-gin",
      "quantity": "1 oz"
    },
    {
      "name": "Fallian marsh gas",
      "quantity": "A gentle bubble"
    },
    {
      "name": "Quantum hyper-mint extract",
      "quantity": "1 teaspoon"
    },
    {
      "name": "Zap powder",
      "quantity": "A pinch"
    },
    {
      "name": "Algolian Suntiger tooth extract",
      "quantity": "1 drop"
    },
    {
      "name": "Galaxy-wide famous Olives",
      "quantity": "1 olive"
    }
  ],
  "recipe": "In a cosmic shaker, mix Ol' Janx Spirit, Arcturan Mega-gin, and water from Santraginus V. Gently add fallian marsh gas to create a mystery bubble. Stir in quantum hyper-mint extract and a pinch of zap powder with a molecular stirrer (mind the speed, or you'll end up in another dimension). Carefully add a single drop of Algolian Suntiger tooth extract, ensuring not to evaporate your mixing vessel. Serve in a glass forged from comets' ice, garnished with a galaxy-wide famous Olive. Be sure to have your propulsion system set to the nearest recovery planet because after one sip, you'll need it.",
  "photo": "https://example.com/photo.jpg"
}

The photo URL returned by OpenAI is only valid for an hour, so be sure to open it in a browser to view it.

You may find that the terminal output escapes the JSON response, breaking the photo URL. You can use the following command to unescape the JSON response:

grpcurl -plaintext -d '{"drink_name_request": {"name": "Pan Galactic Gargle Blaster"}}' localhost:50051 bar.Bar/GetDrink | sed 's/%3A/:/g; s/%2F/\//g; s/%3D/=/g; s/%3F/?/g; s/%26/\&/g' | jq

This command uses sed to unescape the JSON response and jq to format the JSON response.

Generating SDKs for the gRPC Gateway

To generate a TypeScript SDK for the gRPC gateway, we run the following command:

speakeasy quickstart

Follow the onscreen prompts to provide the necessary configuration details for your new SDK such as the name, schema location and output path. Enter bar.yaml when prompted for the OpenAPI document location and select TypeScript when prompted for which language you would like to generate.

To generate a Python SDK for the gRPC gateway, we run the following command:

speakeasy quickstart

Follow the onscreen prompts to provide the necessary configuration details for your new SDK such as the name, schema location and output path. Enter bar.yaml when prompted for the OpenAPI document location and select Python when prompted for which language you would like to generate.

Now we have generated SDKs for the gRPC gateway in both TypeScript and Python.

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