replace prompts with inquirer

helpers/providers/anthropic.ts

@@ -1,6 +1,6 @@
-import prompts from "prompts";
+import inquirer from "inquirer";
 import { ModelConfigParams } from ".";
-import { questionHandlers, toChoice } from "../../questions/utils";
+import { toChoice } from "../../questions/utils";
 
 const MODELS = [
   "claude-3-opus",
@@ -57,41 +57,36 @@ export async function askAnthropicQuestions({
   };
 
   if (!config.apiKey) {
-    const { key } = await prompts(
+    const { key } = await inquirer.prompt([
       {
-        type: "text",
+        type: "input",
         name: "key",
         message:
           "Please provide your Anthropic API key (or leave blank to use ANTHROPIC_API_KEY env variable):",
       },
-      questionHandlers,
-    );
+    ]);
     config.apiKey = key || process.env.ANTHROPIC_API_KEY;
   }
 
   if (askModels) {
-    const { model } = await prompts(
+    const { model } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "model",
         message: "Which LLM model would you like to use?",
         choices: MODELS.map(toChoice),
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     config.model = model;
 
-    const { embeddingModel } = await prompts(
+    const { embeddingModel } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "embeddingModel",
         message: "Which embedding model would you like to use?",
         choices: Object.keys(EMBEDDING_MODELS).map(toChoice),
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     config.embeddingModel = embeddingModel;
     config.dimensions =
       EMBEDDING_MODELS[

helpers/providers/azure.ts

@@ -1,6 +1,5 @@
-import prompts from "prompts";
+import inquirer from "inquirer";
 import { ModelConfigParams, ModelConfigQuestionsParams } from ".";
-import { questionHandlers } from "../../questions/utils";
 
 const ALL_AZURE_OPENAI_CHAT_MODELS: Record<string, { openAIModel: string }> = {
   "gpt-35-turbo": { openAIModel: "gpt-3.5-turbo" },
@@ -67,28 +66,24 @@ export async function askAzureQuestions({
   };
 
   if (askModels) {
-    const { model } = await prompts(
+    const { model } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "model",
         message: "Which LLM model would you like to use?",
         choices: getAvailableModelChoices(),
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     config.model = model;
 
-    const { embeddingModel } = await prompts(
+    const { embeddingModel } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "embeddingModel",
         message: "Which embedding model would you like to use?",
         choices: getAvailableEmbeddingModelChoices(),
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     config.embeddingModel = embeddingModel;
     config.dimensions = getDimensions(embeddingModel);
   }
@@ -98,14 +93,14 @@ export async function askAzureQuestions({
 
 function getAvailableModelChoices() {
   return Object.keys(ALL_AZURE_OPENAI_CHAT_MODELS).map((key) => ({
-    title: key,
+    name: key,
     value: key,
   }));
 }
 
 function getAvailableEmbeddingModelChoices() {
   return Object.keys(ALL_AZURE_OPENAI_EMBEDDING_MODELS).map((key) => ({
-    title: key,
+    name: key,
     value: key,
   }));
 }

helpers/providers/gemini.ts

@@ -1,6 +1,6 @@
-import prompts from "prompts";
+import inquirer from "inquirer";
 import { ModelConfigParams } from ".";
-import { questionHandlers, toChoice } from "../../questions/utils";
+import { toChoice } from "../../questions/utils";
 
 const MODELS = ["gemini-1.5-pro-latest", "gemini-pro", "gemini-pro-vision"];
 type ModelData = {
@@ -41,41 +41,36 @@ export async function askGeminiQuestions({
   };
 
   if (!config.apiKey) {
-    const { key } = await prompts(
+    const { key } = await inquirer.prompt([
       {
-        type: "text",
+        type: "input",
         name: "key",
         message:
           "Please provide your Google API key (or leave blank to use GOOGLE_API_KEY env variable):",
       },
-      questionHandlers,
-    );
+    ]);
     config.apiKey = key || process.env.GOOGLE_API_KEY;
   }
 
   if (askModels) {
-    const { model } = await prompts(
+    const { model } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "model",
         message: "Which LLM model would you like to use?",
         choices: MODELS.map(toChoice),
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     config.model = model;
 
-    const { embeddingModel } = await prompts(
+    const { embeddingModel } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "embeddingModel",
         message: "Which embedding model would you like to use?",
         choices: Object.keys(EMBEDDING_MODELS).map(toChoice),
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     config.embeddingModel = embeddingModel;
     config.dimensions = EMBEDDING_MODELS[embeddingModel].dimensions;
   }

helpers/providers/groq.ts

@@ -1,6 +1,6 @@
-import prompts from "prompts";
+import inquirer from "inquirer";
 import { ModelConfigParams } from ".";
-import { questionHandlers, toChoice } from "../../questions/utils";
+import { toChoice } from "../../questions/utils";
 
 import got from "got";
 import ora from "ora";
@@ -97,43 +97,38 @@ export async function askGroqQuestions({
   };
 
   if (!config.apiKey) {
-    const { key } = await prompts(
+    const { key } = await inquirer.prompt([
       {
-        type: "text",
+        type: "input",
         name: "key",
         message:
           "Please provide your Groq API key (or leave blank to use GROQ_API_KEY env variable):",
       },
-      questionHandlers,
-    );
+    ]);
     config.apiKey = key || process.env.GROQ_API_KEY;
   }
 
   if (askModels) {
     const modelChoices = await getAvailableModelChoicesGroq(config.apiKey!);
 
-    const { model } = await prompts(
+    const { model } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "model",
         message: "Which LLM model would you like to use?",
         choices: modelChoices,
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     config.model = model;
 
-    const { embeddingModel } = await prompts(
+    const { embeddingModel } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "embeddingModel",
         message: "Which embedding model would you like to use?",
         choices: Object.keys(EMBEDDING_MODELS).map(toChoice),
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     config.embeddingModel = embeddingModel;
     config.dimensions =
       EMBEDDING_MODELS[

helpers/providers/huggingface.ts

@@ -1,6 +1,6 @@
-import prompts from "prompts";
+import inquirer from "inquirer";
 import { ModelConfigParams } from ".";
-import { questionHandlers, toChoice } from "../../questions/utils";
+import { toChoice } from "../../questions/utils";
 
 const MODELS = ["HuggingFaceH4/zephyr-7b-alpha"];
 type ModelData = {
@@ -38,28 +38,24 @@ export async function askHuggingfaceQuestions({
   };
 
   if (askModels) {
-    const { model } = await prompts(
+    const { model } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "model",
         message: "Which Hugging Face model would you like to use?",
         choices: MODELS.map(toChoice),
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     config.model = model;
 
-    const { embeddingModel } = await prompts(
+    const { embeddingModel } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "embeddingModel",
         message: "Which embedding model would you like to use?",
         choices: Object.keys(EMBEDDING_MODELS).map(toChoice),
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     config.embeddingModel = embeddingModel;
     config.dimensions = EMBEDDING_MODELS[embeddingModel].dimensions;
   }

helpers/providers/index.ts

@@ -1,5 +1,4 @@
-import prompts from "prompts";
-import { questionHandlers } from "../../questions/utils";
+import inquirer from "inquirer";
 import { ModelConfig, ModelProvider, TemplateFramework } from "../types";
 import { askAnthropicQuestions } from "./anthropic";
 import { askAzureQuestions } from "./azure";
@@ -29,29 +28,27 @@ export async function askModelConfig({
   let modelProvider: ModelProvider = DEFAULT_MODEL_PROVIDER;
   if (askModels) {
     let choices = [
-      { title: "OpenAI", value: "openai" },
-      { title: "Groq", value: "groq" },
-      { title: "Ollama", value: "ollama" },
-      { title: "Anthropic", value: "anthropic" },
-      { title: "Gemini", value: "gemini" },
-      { title: "Mistral", value: "mistral" },
-      { title: "AzureOpenAI", value: "azure-openai" },
+      { name: "OpenAI", value: "openai" },
+      { name: "Groq", value: "groq" },
+      { name: "Ollama", value: "ollama" },
+      { name: "Anthropic", value: "anthropic" },
+      { name: "Gemini", value: "gemini" },
+      { name: "Mistral", value: "mistral" },
+      { name: "AzureOpenAI", value: "azure-openai" },
     ];
 
     if (framework === "fastapi") {
-      choices.push({ title: "T-Systems", value: "t-systems" });
-      choices.push({ title: "Huggingface", value: "huggingface" });
+      choices.push({ name: "T-Systems", value: "t-systems" });
+      choices.push({ name: "Huggingface", value: "huggingface" });
     }
-    const { provider } = await prompts(
+    const { provider } = await inquirer.prompt([
       {
-        type: "select",
+        type: "list",
         name: "provider",
         message: "Which model provider would you like to use",
         choices: choices,
-        initial: 0,
       },
-      questionHandlers,
-    );
+    ]);
     modelProvider = provider;
   }