feat(providers): separate embedding providers, add custom-openai provider
This commit is contained in:
parent
c710f4f88c
commit
9f45ecb98d
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@ -8,7 +8,7 @@ import {
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} from '../config';
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import logger from '../utils/logger';
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export const getAvailableProviders = async () => {
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export const getAvailableChatModelProviders = async () => {
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const openAIApiKey = getOpenaiApiKey();
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const groqApiKey = getGroqApiKey();
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const ollamaEndpoint = getOllamaApiEndpoint();
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@ -33,10 +33,6 @@ export const getAvailableProviders = async () => {
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modelName: 'gpt-4-turbo',
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temperature: 0.7,
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}),
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embeddings: new OpenAIEmbeddings({
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openAIApiKey,
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modelName: 'text-embedding-3-large',
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}),
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};
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} catch (err) {
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logger.error(`Error loading OpenAI models: ${err}`);
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@ -86,10 +82,6 @@ export const getAvailableProviders = async () => {
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baseURL: 'https://api.groq.com/openai/v1',
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},
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),
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embeddings: new OpenAIEmbeddings({
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openAIApiKey: openAIApiKey,
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modelName: 'text-embedding-3-large',
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}),
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};
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} catch (err) {
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logger.error(`Error loading Groq models: ${err}`);
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@ -110,17 +102,56 @@ export const getAvailableProviders = async () => {
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});
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return acc;
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}, {});
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if (Object.keys(models['ollama']).length > 0) {
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models['ollama']['embeddings'] = new OllamaEmbeddings({
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baseUrl: ollamaEndpoint,
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model: models['ollama'][Object.keys(models['ollama'])[0]].model,
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});
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}
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} catch (err) {
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logger.error(`Error loading Ollama models: ${err}`);
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}
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}
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models['custom_openai'] = {};
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return models;
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};
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export const getAvailableEmbeddingModelProviders = async () => {
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const openAIApiKey = getOpenaiApiKey();
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const ollamaEndpoint = getOllamaApiEndpoint();
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const models = {};
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if (openAIApiKey) {
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try {
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models['openai'] = {
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'Text embedding 3 small': new OpenAIEmbeddings({
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openAIApiKey,
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modelName: 'text-embedding-3-small',
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}),
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'Text embedding 3 large': new OpenAIEmbeddings({
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openAIApiKey,
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modelName: 'text-embedding-3-large',
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}),
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};
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} catch (err) {
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logger.error(`Error loading OpenAI embeddings: ${err}`);
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}
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}
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if (ollamaEndpoint) {
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try {
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const response = await fetch(`${ollamaEndpoint}/api/tags`);
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const { models: ollamaModels } = (await response.json()) as any;
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models['ollama'] = ollamaModels.reduce((acc, model) => {
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acc[model.model] = new OllamaEmbeddings({
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baseUrl: ollamaEndpoint,
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model: model.model,
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});
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return acc;
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}, {});
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} catch (err) {
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logger.error(`Error loading Ollama embeddings: ${err}`);
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}
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}
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return models;
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};
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@ -1,5 +1,8 @@
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import express from 'express';
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import { getAvailableProviders } from '../lib/providers';
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import {
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getAvailableChatModelProviders,
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getAvailableEmbeddingModelProviders,
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} from '../lib/providers';
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import {
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getGroqApiKey,
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getOllamaApiEndpoint,
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@ -12,16 +15,24 @@ const router = express.Router();
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router.get('/', async (_, res) => {
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const config = {};
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const providers = await getAvailableProviders();
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const [chatModelProviders, embeddingModelProviders] = await Promise.all([
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getAvailableChatModelProviders(),
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getAvailableEmbeddingModelProviders(),
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]);
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for (const provider in providers) {
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delete providers[provider]['embeddings'];
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config['chatModelProviders'] = {};
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config['embeddingModelProviders'] = {};
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for (const provider in chatModelProviders) {
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config['chatModelProviders'][provider] = Object.keys(
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chatModelProviders[provider],
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);
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}
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config['providers'] = {};
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for (const provider in providers) {
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config['providers'][provider] = Object.keys(providers[provider]);
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for (const provider in embeddingModelProviders) {
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config['embeddingModelProviders'][provider] = Object.keys(
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embeddingModelProviders[provider],
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);
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}
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config['openaiApiKey'] = getOpenaiApiKey();
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@ -1,7 +1,7 @@
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import express from 'express';
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import handleImageSearch from '../agents/imageSearchAgent';
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import { BaseChatModel } from '@langchain/core/language_models/chat_models';
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import { getAvailableProviders } from '../lib/providers';
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import { getAvailableChatModelProviders } from '../lib/providers';
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import { HumanMessage, AIMessage } from '@langchain/core/messages';
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import logger from '../utils/logger';
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@ -19,7 +19,7 @@ router.post('/', async (req, res) => {
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}
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});
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const chatModels = await getAvailableProviders();
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const chatModels = await getAvailableChatModelProviders();
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const provider = chat_model_provider || Object.keys(chatModels)[0];
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const chatModel = chat_model || Object.keys(chatModels[provider])[0];
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@ -1,14 +1,20 @@
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import express from 'express';
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import logger from '../utils/logger';
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import { getAvailableProviders } from '../lib/providers';
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import {
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getAvailableChatModelProviders,
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getAvailableEmbeddingModelProviders,
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} from '../lib/providers';
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const router = express.Router();
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router.get('/', async (req, res) => {
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try {
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const providers = await getAvailableProviders();
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const [chatModelProviders, embeddingModelProviders] = await Promise.all([
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getAvailableChatModelProviders(),
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getAvailableEmbeddingModelProviders(),
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]);
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res.status(200).json({ providers });
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res.status(200).json({ chatModelProviders, embeddingModelProviders });
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} catch (err) {
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res.status(500).json({ message: 'An error has occurred.' });
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logger.error(err.message);
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@ -1,6 +1,6 @@
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import express from 'express';
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import { BaseChatModel } from '@langchain/core/language_models/chat_models';
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import { getAvailableProviders } from '../lib/providers';
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import { getAvailableChatModelProviders } from '../lib/providers';
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import { HumanMessage, AIMessage } from '@langchain/core/messages';
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import logger from '../utils/logger';
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import handleVideoSearch from '../agents/videoSearchAgent';
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@ -19,7 +19,7 @@ router.post('/', async (req, res) => {
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}
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});
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const chatModels = await getAvailableProviders();
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const chatModels = await getAvailableChatModelProviders();
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const provider = chat_model_provider || Object.keys(chatModels)[0];
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const chatModel = chat_model || Object.keys(chatModels[provider])[0];
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@ -1,10 +1,14 @@
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import { WebSocket } from 'ws';
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import { handleMessage } from './messageHandler';
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import { getAvailableProviders } from '../lib/providers';
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import {
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getAvailableEmbeddingModelProviders,
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getAvailableChatModelProviders,
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} from '../lib/providers';
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import { BaseChatModel } from '@langchain/core/language_models/chat_models';
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import type { Embeddings } from '@langchain/core/embeddings';
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import type { IncomingMessage } from 'http';
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import logger from '../utils/logger';
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import { ChatOpenAI } from '@langchain/openai';
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export const handleConnection = async (
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ws: WebSocket,
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@ -13,18 +17,53 @@ export const handleConnection = async (
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const searchParams = new URL(request.url, `http://${request.headers.host}`)
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.searchParams;
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const models = await getAvailableProviders();
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const provider =
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searchParams.get('chatModelProvider') || Object.keys(models)[0];
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const [chatModelProviders, embeddingModelProviders] = await Promise.all([
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getAvailableChatModelProviders(),
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getAvailableEmbeddingModelProviders(),
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]);
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const chatModelProvider =
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searchParams.get('chatModelProvider') || Object.keys(chatModelProviders)[0];
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const chatModel =
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searchParams.get('chatModel') || Object.keys(models[provider])[0];
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searchParams.get('chatModel') ||
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Object.keys(chatModelProviders[chatModelProvider])[0];
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const embeddingModelProvider =
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searchParams.get('embeddingModelProvider') ||
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Object.keys(embeddingModelProviders)[0];
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const embeddingModel =
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searchParams.get('embeddingModel') ||
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Object.keys(embeddingModelProviders[embeddingModelProvider])[0];
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let llm: BaseChatModel | undefined;
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let embeddings: Embeddings | undefined;
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if (models[provider] && models[provider][chatModel]) {
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llm = models[provider][chatModel] as BaseChatModel | undefined;
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embeddings = models[provider].embeddings as Embeddings | undefined;
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if (
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chatModelProviders[chatModelProvider] &&
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chatModelProviders[chatModelProvider][chatModel] &&
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chatModelProvider != 'custom_openai'
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) {
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llm = chatModelProviders[chatModelProvider][chatModel] as
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| BaseChatModel
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| undefined;
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} else if (chatModelProvider == 'custom_openai') {
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llm = new ChatOpenAI({
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modelName: chatModel,
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openAIApiKey: searchParams.get('openAIApiKey'),
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temperature: 0.7,
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configuration: {
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baseURL: searchParams.get('openAIBaseURL'),
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},
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});
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}
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if (
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embeddingModelProviders[embeddingModelProvider] &&
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embeddingModelProviders[embeddingModelProvider][embeddingModel]
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) {
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embeddings = embeddingModelProviders[embeddingModelProvider][
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embeddingModel
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] as Embeddings | undefined;
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}
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if (!llm || !embeddings) {
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@ -22,11 +22,23 @@ const useSocket = (url: string) => {
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const connectWs = async () => {
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let chatModel = localStorage.getItem('chatModel');
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let chatModelProvider = localStorage.getItem('chatModelProvider');
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let embeddingModel = localStorage.getItem('embeddingModel');
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let embeddingModelProvider = localStorage.getItem(
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'embeddingModelProvider',
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);
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if (!chatModel || !chatModelProvider) {
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const chatModelProviders = await fetch(
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if (
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!chatModel ||
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!chatModelProvider ||
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!embeddingModel ||
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!embeddingModelProvider
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) {
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const providers = await fetch(
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`${process.env.NEXT_PUBLIC_API_URL}/models`,
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).then(async (res) => (await res.json())['providers']);
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).then(async (res) => await res.json());
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const chatModelProviders = providers.chatModelProviders;
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const embeddingModelProviders = providers.embeddingModelProviders;
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if (
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!chatModelProviders ||
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@ -34,16 +46,52 @@ const useSocket = (url: string) => {
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)
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return console.error('No chat models available');
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if (
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!embeddingModelProviders ||
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Object.keys(embeddingModelProviders).length === 0
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)
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return console.error('No embedding models available');
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chatModelProvider = Object.keys(chatModelProviders)[0];
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chatModel = Object.keys(chatModelProviders[chatModelProvider])[0];
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embeddingModelProvider = Object.keys(embeddingModelProviders)[0];
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embeddingModel = Object.keys(
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embeddingModelProviders[embeddingModelProvider],
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)[0];
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localStorage.setItem('chatModel', chatModel!);
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localStorage.setItem('chatModelProvider', chatModelProvider);
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localStorage.setItem('embeddingModel', embeddingModel!);
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localStorage.setItem(
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'embeddingModelProvider',
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embeddingModelProvider,
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);
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}
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const ws = new WebSocket(
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`${url}?chatModel=${chatModel}&chatModelProvider=${chatModelProvider}`,
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);
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const wsURL = new URL(url);
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const searchParams = new URLSearchParams({});
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searchParams.append('chatModel', chatModel!);
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searchParams.append('chatModelProvider', chatModelProvider);
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if (chatModelProvider === 'custom_openai') {
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searchParams.append(
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'openAIApiKey',
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localStorage.getItem('openAIApiKey')!,
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);
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searchParams.append(
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'openAIBaseURL',
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localStorage.getItem('openAIBaseURL')!,
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);
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}
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searchParams.append('embeddingModel', embeddingModel!);
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searchParams.append('embeddingModelProvider', embeddingModelProvider);
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wsURL.search = searchParams.toString();
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const ws = new WebSocket(wsURL.toString());
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ws.onopen = () => {
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console.log('[DEBUG] open');
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setWs(ws);
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@ -3,7 +3,10 @@ import { CloudUpload, RefreshCcw, RefreshCw } from 'lucide-react';
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import React, { Fragment, useEffect, useState } from 'react';
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interface SettingsType {
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providers: {
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chatModelProviders: {
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[key: string]: string[];
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};
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embeddingModelProviders: {
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[key: string]: string[];
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};
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openaiApiKey: string;
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@ -25,6 +28,17 @@ const SettingsDialog = ({
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const [selectedChatModel, setSelectedChatModel] = useState<string | null>(
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null,
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);
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const [selectedEmbeddingModelProvider, setSelectedEmbeddingModelProvider] =
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useState<string | null>(null);
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const [selectedEmbeddingModel, setSelectedEmbeddingModel] = useState<
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string | null
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>(null);
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const [customOpenAIApiKey, setCustomOpenAIApiKey] = useState<string | null>(
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null,
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);
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const [customOpenAIBaseURL, setCustomOpenAIBaseURL] = useState<string | null>(
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null,
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);
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const [isLoading, setIsLoading] = useState(false);
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const [isUpdating, setIsUpdating] = useState(false);
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@ -46,6 +60,12 @@ const SettingsDialog = ({
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useEffect(() => {
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setSelectedChatModelProvider(localStorage.getItem('chatModelProvider'));
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setSelectedChatModel(localStorage.getItem('chatModel'));
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setSelectedEmbeddingModelProvider(
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localStorage.getItem('embeddingModelProvider'),
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);
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setSelectedEmbeddingModel(localStorage.getItem('embeddingModel'));
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setCustomOpenAIApiKey(localStorage.getItem('openAIApiKey'));
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setCustomOpenAIBaseURL(localStorage.getItem('openAIBaseUrl'));
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}, []);
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const handleSubmit = async () => {
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@ -62,6 +82,13 @@ const SettingsDialog = ({
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localStorage.setItem('chatModelProvider', selectedChatModelProvider!);
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localStorage.setItem('chatModel', selectedChatModel!);
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localStorage.setItem(
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'embeddingModelProvider',
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selectedEmbeddingModelProvider!,
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);
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localStorage.setItem('embeddingModel', selectedEmbeddingModel!);
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localStorage.setItem('openAIApiKey', customOpenAIApiKey!);
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localStorage.setItem('openAIBaseURL', customOpenAIBaseURL!);
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} catch (err) {
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console.log(err);
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} finally {
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|
@ -107,7 +134,7 @@ const SettingsDialog = ({
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</Dialog.Title>
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{config && !isLoading && (
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<div className="flex flex-col space-y-4 mt-6">
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{config.providers && (
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{config.chatModelProviders && (
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<div className="flex flex-col space-y-1">
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<p className="text-white/70 text-sm">
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Chat model Provider
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|
@ -116,36 +143,47 @@ const SettingsDialog = ({
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onChange={(e) => {
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setSelectedChatModelProvider(e.target.value);
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setSelectedChatModel(
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config.providers[e.target.value][0],
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config.chatModelProviders[e.target.value][0],
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);
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}}
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className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
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>
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{Object.keys(config.providers).map((provider) => (
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<option
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key={provider}
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value={provider}
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selected={provider === selectedChatModelProvider}
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>
|
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{provider.charAt(0).toUpperCase() +
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provider.slice(1)}
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</option>
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))}
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{Object.keys(config.chatModelProviders).map(
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(provider) => (
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<option
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key={provider}
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value={provider}
|
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selected={
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provider === selectedChatModelProvider
|
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}
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>
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{provider.charAt(0).toUpperCase() +
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provider.slice(1)}
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</option>
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),
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)}
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</select>
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</div>
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)}
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{selectedChatModelProvider && (
|
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<div className="flex flex-col space-y-1">
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<p className="text-white/70 text-sm">Chat Model</p>
|
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<select
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onChange={(e) => setSelectedChatModel(e.target.value)}
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className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
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>
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{config.providers[selectedChatModelProvider] ? (
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config.providers[selectedChatModelProvider].length >
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0 ? (
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config.providers[selectedChatModelProvider].map(
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(model) => (
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{selectedChatModelProvider &&
|
||||
selectedChatModelProvider != 'custom_openai' && (
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-white/70 text-sm">Chat Model</p>
|
||||
<select
|
||||
onChange={(e) =>
|
||||
setSelectedChatModel(e.target.value)
|
||||
}
|
||||
className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
|
||||
>
|
||||
{config.chatModelProviders[
|
||||
selectedChatModelProvider
|
||||
] ? (
|
||||
config.chatModelProviders[
|
||||
selectedChatModelProvider
|
||||
].length > 0 ? (
|
||||
config.chatModelProviders[
|
||||
selectedChatModelProvider
|
||||
].map((model) => (
|
||||
<option
|
||||
key={model}
|
||||
value={model}
|
||||
|
@ -153,11 +191,126 @@ const SettingsDialog = ({
|
|||
>
|
||||
{model}
|
||||
</option>
|
||||
),
|
||||
))
|
||||
) : (
|
||||
<option value="" disabled selected>
|
||||
No models available
|
||||
</option>
|
||||
)
|
||||
) : (
|
||||
<option value="" disabled selected>
|
||||
No models available
|
||||
Invalid provider, please check backend logs
|
||||
</option>
|
||||
)}
|
||||
</select>
|
||||
</div>
|
||||
)}
|
||||
{selectedChatModelProvider &&
|
||||
selectedChatModelProvider === 'custom_openai' && (
|
||||
<>
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-white/70 text-sm">Model name</p>
|
||||
<input
|
||||
type="text"
|
||||
placeholder="Model name"
|
||||
defaultValue={selectedChatModel!}
|
||||
onChange={(e) =>
|
||||
setSelectedChatModel(e.target.value)
|
||||
}
|
||||
className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
|
||||
/>
|
||||
</div>
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-white/70 text-sm">
|
||||
Custom OpenAI API Key (optional)
|
||||
</p>
|
||||
<input
|
||||
type="text"
|
||||
placeholder="Custom OpenAI API Key"
|
||||
defaultValue={customOpenAIApiKey!}
|
||||
onChange={(e) =>
|
||||
setCustomOpenAIApiKey(e.target.value)
|
||||
}
|
||||
className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
|
||||
/>
|
||||
</div>
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-white/70 text-sm">
|
||||
Custom OpenAI Base URL
|
||||
</p>
|
||||
<input
|
||||
type="text"
|
||||
placeholder="Custom OpenAI Base URL"
|
||||
defaultValue={customOpenAIBaseURL!}
|
||||
onChange={(e) =>
|
||||
setCustomOpenAIBaseURL(e.target.value)
|
||||
}
|
||||
className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
|
||||
/>
|
||||
</div>
|
||||
</>
|
||||
)}
|
||||
{/* Embedding models */}
|
||||
{config.chatModelProviders && (
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-white/70 text-sm">
|
||||
Embedding model Provider
|
||||
</p>
|
||||
<select
|
||||
onChange={(e) => {
|
||||
setSelectedEmbeddingModelProvider(e.target.value);
|
||||
setSelectedEmbeddingModel(
|
||||
config.embeddingModelProviders[e.target.value][0],
|
||||
);
|
||||
}}
|
||||
className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
|
||||
>
|
||||
{Object.keys(config.embeddingModelProviders).map(
|
||||
(provider) => (
|
||||
<option
|
||||
key={provider}
|
||||
value={provider}
|
||||
selected={
|
||||
provider === selectedEmbeddingModelProvider
|
||||
}
|
||||
>
|
||||
{provider.charAt(0).toUpperCase() +
|
||||
provider.slice(1)}
|
||||
</option>
|
||||
),
|
||||
)}
|
||||
</select>
|
||||
</div>
|
||||
)}
|
||||
{selectedEmbeddingModelProvider && (
|
||||
<div className="flex flex-col space-y-1">
|
||||
<p className="text-white/70 text-sm">Embedding Model</p>
|
||||
<select
|
||||
onChange={(e) =>
|
||||
setSelectedEmbeddingModel(e.target.value)
|
||||
}
|
||||
className="bg-[#111111] px-3 py-2 flex items-center overflow-hidden border border-[#1C1C1C] text-white rounded-lg text-sm"
|
||||
>
|
||||
{config.embeddingModelProviders[
|
||||
selectedEmbeddingModelProvider
|
||||
] ? (
|
||||
config.embeddingModelProviders[
|
||||
selectedEmbeddingModelProvider
|
||||
].length > 0 ? (
|
||||
config.embeddingModelProviders[
|
||||
selectedEmbeddingModelProvider
|
||||
].map((model) => (
|
||||
<option
|
||||
key={model}
|
||||
value={model}
|
||||
selected={model === selectedEmbeddingModel}
|
||||
>
|
||||
{model}
|
||||
</option>
|
||||
))
|
||||
) : (
|
||||
<option value="" disabled selected>
|
||||
No embedding models available
|
||||
</option>
|
||||
)
|
||||
) : (
|
||||
|
|
Loading…
Reference in New Issue