Perplexica/src/lib/providers/index.ts

195 lines
5.4 KiB
TypeScript

import { Embeddings } from '@langchain/core/embeddings';
import { BaseChatModel } from '@langchain/core/language_models/chat_models';
import {
loadOpenAIChatModels,
loadOpenAIEmbeddingModels,
PROVIDER_INFO as OpenAIInfo,
PROVIDER_INFO,
} from './openai';
import {
getCustomOpenaiApiKey,
getCustomOpenaiApiUrl,
getCustomOpenaiModelName,
getHiddenModels,
} from '../config';
import { ChatOpenAI } from '@langchain/openai';
import {
loadOllamaChatModels,
loadOllamaEmbeddingModels,
PROVIDER_INFO as OllamaInfo,
} from './ollama';
import { loadGroqChatModels, PROVIDER_INFO as GroqInfo } from './groq';
import {
loadAnthropicChatModels,
PROVIDER_INFO as AnthropicInfo,
} from './anthropic';
import {
loadGeminiChatModels,
loadGeminiEmbeddingModels,
PROVIDER_INFO as GeminiInfo,
} from './gemini';
import {
loadTransformersEmbeddingsModels,
PROVIDER_INFO as TransformersInfo,
} from './transformers';
import {
loadDeepseekChatModels,
PROVIDER_INFO as DeepseekInfo,
} from './deepseek';
import {
loadLMStudioChatModels,
loadLMStudioEmbeddingsModels,
PROVIDER_INFO as LMStudioInfo,
} from './lmstudio';
export const PROVIDER_METADATA = {
openai: OpenAIInfo,
ollama: OllamaInfo,
groq: GroqInfo,
anthropic: AnthropicInfo,
gemini: GeminiInfo,
transformers: TransformersInfo,
deepseek: DeepseekInfo,
lmstudio: LMStudioInfo,
custom_openai: {
key: 'custom_openai',
displayName: 'Custom OpenAI',
},
};
export interface ChatModel {
displayName: string;
model: BaseChatModel;
}
export interface EmbeddingModel {
displayName: string;
model: Embeddings;
}
export const chatModelProviders: Record<
string,
() => Promise<Record<string, ChatModel>>
> = {
openai: loadOpenAIChatModels,
ollama: loadOllamaChatModels,
groq: loadGroqChatModels,
anthropic: loadAnthropicChatModels,
gemini: loadGeminiChatModels,
deepseek: loadDeepseekChatModels,
lmstudio: loadLMStudioChatModels,
};
export const embeddingModelProviders: Record<
string,
() => Promise<Record<string, EmbeddingModel>>
> = {
openai: loadOpenAIEmbeddingModels,
ollama: loadOllamaEmbeddingModels,
gemini: loadGeminiEmbeddingModels,
transformers: loadTransformersEmbeddingsModels,
lmstudio: loadLMStudioEmbeddingsModels,
};
export const getAvailableChatModelProviders = async (
options: { includeHidden?: boolean } = {}
) => {
const { includeHidden = false } = options;
const models: Record<string, Record<string, ChatModel>> = {};
for (const provider in chatModelProviders) {
const providerModels = await chatModelProviders[provider]();
if (Object.keys(providerModels).length > 0) {
// Sort models alphabetically by their keys
const sortedModels: Record<string, ChatModel> = {};
Object.keys(providerModels)
.sort()
.forEach((key) => {
sortedModels[key] = providerModels[key];
});
models[provider] = sortedModels;
}
}
const customOpenAiApiKey = getCustomOpenaiApiKey();
const customOpenAiApiUrl = getCustomOpenaiApiUrl();
const customOpenAiModelName = getCustomOpenaiModelName();
// Only add custom_openai provider if all required fields are configured
if (customOpenAiApiKey && customOpenAiApiUrl && customOpenAiModelName) {
models['custom_openai'] = {
[customOpenAiModelName]: {
displayName: customOpenAiModelName,
model: new ChatOpenAI({
openAIApiKey: customOpenAiApiKey,
modelName: customOpenAiModelName,
// temperature: 0.7,
configuration: {
baseURL: customOpenAiApiUrl,
},
}) as unknown as BaseChatModel,
},
};
}
// Filter out hidden models if includeHidden is false
if (!includeHidden) {
const hiddenModels = getHiddenModels();
if (hiddenModels.length > 0) {
for (const provider in models) {
for (const modelKey in models[provider]) {
if (hiddenModels.includes(modelKey)) {
delete models[provider][modelKey];
}
}
// Remove provider if all models are hidden
if (Object.keys(models[provider]).length === 0) {
delete models[provider];
}
}
}
}
return models;
};
export const getAvailableEmbeddingModelProviders = async (
options: { includeHidden?: boolean } = {}
) => {
const { includeHidden = false } = options;
const models: Record<string, Record<string, EmbeddingModel>> = {};
for (const provider in embeddingModelProviders) {
const providerModels = await embeddingModelProviders[provider]();
if (Object.keys(providerModels).length > 0) {
// Sort embedding models alphabetically by their keys
const sortedModels: Record<string, EmbeddingModel> = {};
Object.keys(providerModels)
.sort()
.forEach((key) => {
sortedModels[key] = providerModels[key];
});
models[provider] = sortedModels;
}
}
// Filter out hidden models if includeHidden is false
if (!includeHidden) {
const hiddenModels = getHiddenModels();
if (hiddenModels.length > 0) {
for (const provider in models) {
for (const modelKey in models[provider]) {
if (hiddenModels.includes(modelKey)) {
delete models[provider][modelKey];
}
}
// Remove provider if all models are hidden
if (Object.keys(models[provider]).length === 0) {
delete models[provider];
}
}
}
}
return models;
};