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replace prompts with inquirer #489

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replace prompts with inquirer #489

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8ae4b4d
replace prompts with inquirer
marcusschiesser Jan 21, 2025
0a1a36c
fix parsing
leehuwuj Jan 21, 2025
df7ac7f
simplify changes
leehuwuj Jan 21, 2025
411fb6f
revert changes
leehuwuj Jan 22, 2025
3a10379
revert changes
leehuwuj Jan 22, 2025
7d8195c
add group separator
leehuwuj Jan 22, 2025
75fa673
Merge remote-tracking branch 'origin' into ms/use-inquirer
leehuwuj Jan 22, 2025
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43 changes: 24 additions & 19 deletions helpers/providers/anthropic.ts
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -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",
Expand Down Expand Up @@ -56,42 +56,47 @@ export async function askAnthropicQuestions({
},
};

if (!config.apiKey) {
const { key } = await prompts(
if (!config.apiKey && !process.env.CI) {
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):",
message: askModels
? "Please provide your Anthropic API key (or leave blank to use ANTHROPIC_API_KEY env variable):"
: "Please provide your Anthropic API key (leave blank to skip):",
validate: (value: string) => {
if (askModels && !value) {
if (process.env.ANTHROPIC_API_KEY) {
return true;
}
return "ANTHROPIC_API_KEY env variable is not set - key is required";
}
return true;
},
},
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[
Expand Down
47 changes: 33 additions & 14 deletions helpers/providers/azure.ts
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Open in desktop
Original file line number Diff line number Diff line change
@@ -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" },
Expand Down Expand Up @@ -66,29 +65,49 @@ export async function askAzureQuestions({
},
};

if (!config.apiKey) {
const { key } = await inquirer.prompt([
{
type: "input",
name: "key",
message:
"Please provide your Azure OpenAI API key (or leave blank to use AZURE_OPENAI_API_KEY env variable):",
},
]);
config.apiKey = key || process.env.AZURE_OPENAI_API_KEY;
}

if (!config.endpoint) {
const { endpoint } = await inquirer.prompt([
{
type: "input",
name: "endpoint",
message:
"Please provide your Azure OpenAI endpoint (or leave blank to use AZURE_OPENAI_ENDPOINT env variable):",
},
]);
config.endpoint = endpoint || process.env.AZURE_OPENAI_ENDPOINT;
}

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);
}
Expand All @@ -98,14 +117,14 @@ 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,
}));
}
Expand Down
41 changes: 23 additions & 18 deletions helpers/providers/gemini.ts
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -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 = {
Expand Down Expand Up @@ -41,41 +41,46 @@ 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):",
message: askModels
? "Please provide your Google API key (or leave blank to use GOOGLE_API_KEY env variable):"
: "Please provide your Google API key (leave blank to skip):",
validate: (value: string) => {
if (askModels && !value) {
if (process.env.GOOGLE_API_KEY) {
return true;
}
return "GOOGLE_API_KEY env variable is not set - key is required";
}
return true;
},
},
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;
}
Expand Down
41 changes: 23 additions & 18 deletions helpers/providers/groq.ts
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -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";
Expand Down Expand Up @@ -97,43 +97,48 @@ 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):",
message: askModels
? "Please provide your Groq API key (or leave blank to use GROQ_API_KEY env variable):"
: "Please provide your Groq API key (leave blank to skip):",
validate: (value: string) => {
if (askModels && !value) {
if (process.env.GROQ_API_KEY) {
return true;
}
return "GROQ_API_KEY env variable is not set - key is required";
}
return true;
},
},
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[
Expand Down
34 changes: 21 additions & 13 deletions helpers/providers/huggingface.ts
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -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 = {
Expand Down Expand Up @@ -37,29 +37,37 @@ export async function askHuggingfaceQuestions({
},
};

if (!config.apiKey) {
const { key } = await inquirer.prompt([
{
type: "input",
name: "key",
message:
"Please provide your Huggingface API key (or leave blank to use HF_API_KEY env variable):",
},
]);
config.apiKey = key || process.env.HF_API_KEY;
}

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?",
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;
}
Expand Down
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