Perplexica/src/lib/providers/index.ts
haddadrm 6edac6938c feat: Add LM Studio Support and Thinking Model Panel
LM Studio Integration:
- Added LM Studio provider with OpenAI-compatible API support
- Dynamic model discovery via /v1/models endpoint
- Support for both chat and embeddings models
- Docker-compatible networking configuration

Thinking Model Panel:
- Added collapsible UI panel for model's chain of thought
- Parses responses with <think> tags to separate reasoning
- Maintains backward compatibility with regular responses
- Styled consistently with app theme for light/dark modes
- Preserves all existing message functionality (sources, markdown, etc.)

These improvements enhance the app's compatibility with local LLMs and
provide better visibility into model reasoning processes while maintaining
existing functionality.
2025-01-26 18:18:35 +04:00

52 lines
1.6 KiB
TypeScript

import { loadGroqChatModels } from './groq';
import { loadOllamaChatModels, loadOllamaEmbeddingsModels } from './ollama';
import { loadOpenAIChatModels, loadOpenAIEmbeddingsModels } from './openai';
import { loadAnthropicChatModels } from './anthropic';
import { loadTransformersEmbeddingsModels } from './transformers';
import { loadGeminiChatModels, loadGeminiEmbeddingsModels } from './gemini';
import { loadLMStudioChatModels, loadLMStudioEmbeddingsModels } from './lmstudio';
const chatModelProviders = {
openai: loadOpenAIChatModels,
groq: loadGroqChatModels,
ollama: loadOllamaChatModels,
anthropic: loadAnthropicChatModels,
gemini: loadGeminiChatModels,
lm_studio: loadLMStudioChatModels,
};
const embeddingModelProviders = {
openai: loadOpenAIEmbeddingsModels,
local: loadTransformersEmbeddingsModels,
ollama: loadOllamaEmbeddingsModels,
gemini: loadGeminiEmbeddingsModels,
lm_studio: loadLMStudioEmbeddingsModels,
};
export const getAvailableChatModelProviders = async () => {
const models = {};
for (const provider in chatModelProviders) {
const providerModels = await chatModelProviders[provider]();
if (Object.keys(providerModels).length > 0) {
models[provider] = providerModels;
}
}
models['custom_openai'] = {};
return models;
};
export const getAvailableEmbeddingModelProviders = async () => {
const models = {};
for (const provider in embeddingModelProviders) {
const providerModels = await embeddingModelProviders[provider]();
if (Object.keys(providerModels).length > 0) {
models[provider] = providerModels;
}
}
return models;
};