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feat(models): enhance models endpoint with filtering and pagination support
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Vaayne
parent
38076babcf
commit
73380d76df
@ -1,73 +1,129 @@
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import express, { Request, Response } from 'express'
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import { loggerService } from '../../services/LoggerService'
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import { chatCompletionService } from '../services/chat-completion'
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import { ModelsFilterSchema, modelsService } from '../services/models'
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const logger = loggerService.withContext('ApiServerModelsRoutes')
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const router = express.Router()
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const router = express
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.Router()
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/**
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* @swagger
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* /v1/models:
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* get:
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* summary: List available models
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* description: Returns a list of available AI models from all configured providers
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* tags: [Models]
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* responses:
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* 200:
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* description: List of available models
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* content:
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* application/json:
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* schema:
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* type: object
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* properties:
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* object:
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* type: string
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* example: list
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* data:
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* type: array
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* items:
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* $ref: '#/components/schemas/Model'
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* 503:
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* description: Service unavailable
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* content:
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* application/json:
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* schema:
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* $ref: '#/components/schemas/Error'
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*/
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router.get('/', async (_req: Request, res: Response) => {
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try {
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logger.info('Models list request received')
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/**
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* @swagger
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* /v1/models:
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* get:
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* summary: List available models
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* description: Returns a list of available AI models from all configured providers with optional filtering
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* tags: [Models]
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* parameters:
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* - in: query
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* name: provider
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* schema:
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* type: string
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* enum: [openai, anthropic]
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* description: Filter models by provider type
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* - in: query
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* name: offset
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* schema:
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* type: integer
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* minimum: 0
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* default: 0
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* description: Pagination offset
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* - in: query
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* name: limit
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* schema:
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* type: integer
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* minimum: 1
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* description: Maximum number of models to return
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* responses:
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* 200:
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* description: List of available models
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* content:
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* application/json:
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* schema:
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* type: object
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* properties:
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* object:
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* type: string
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* example: list
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* data:
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* type: array
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* items:
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* $ref: '#/components/schemas/Model'
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* total:
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* type: integer
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* description: Total number of models (when using pagination)
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* offset:
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* type: integer
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* description: Current offset (when using pagination)
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* limit:
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* type: integer
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* description: Current limit (when using pagination)
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* 400:
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* description: Invalid query parameters
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* content:
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* application/json:
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* schema:
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* $ref: '#/components/schemas/Error'
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* 503:
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* description: Service unavailable
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* content:
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* application/json:
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* schema:
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* $ref: '#/components/schemas/Error'
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*/
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.get('/', async (req: Request, res: Response) => {
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try {
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logger.info('Models list request received', { query: req.query })
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const models = await chatCompletionService.getModels()
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// Validate query parameters using Zod schema
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const filterResult = ModelsFilterSchema.safeParse(req.query)
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if (models.length === 0) {
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logger.warn(
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'No models available from providers. This may be because no OpenAI providers are configured or enabled.'
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)
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}
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logger.info(`Returning ${models.length} models (OpenAI providers only)`)
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logger.debug(
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'Model IDs:',
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models.map((m) => m.id)
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)
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return res.json({
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object: 'list',
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data: models
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})
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} catch (error: any) {
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logger.error('Error fetching models:', error)
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return res.status(503).json({
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error: {
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message: 'Failed to retrieve models from available providers',
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type: 'service_unavailable',
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code: 'models_unavailable'
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if (!filterResult.success) {
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logger.warn('Invalid query parameters:', filterResult.error.issues)
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return res.status(400).json({
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error: {
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message: 'Invalid query parameters',
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type: 'invalid_request_error',
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code: 'invalid_parameters',
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details: filterResult.error.issues.map((issue) => ({
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field: issue.path.join('.'),
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message: issue.message
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}))
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}
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})
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}
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})
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}
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})
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const filter = filterResult.data
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const response = await modelsService.getModels(filter)
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if (response.data.length === 0) {
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logger.warn(
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'No models available from providers. This may be because no OpenAI/Anthropic providers are configured or enabled.',
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{ filter }
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)
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}
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logger.info(`Returning ${response.data.length} models`, {
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filter,
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total: response.total
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})
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logger.debug(
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'Model IDs:',
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response.data.map((m) => m.id)
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)
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return res.json(response)
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} catch (error: any) {
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logger.error('Error fetching models:', error)
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return res.status(503).json({
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error: {
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message: 'Failed to retrieve models from available providers',
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type: 'service_unavailable',
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code: 'models_unavailable'
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}
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})
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}
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})
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export { router as modelsRoutes }
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@ -2,70 +2,16 @@ import OpenAI from 'openai'
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import { ChatCompletionCreateParams } from 'openai/resources'
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import { loggerService } from '../../services/LoggerService'
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import {
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getProviderByModel,
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getRealProviderModel,
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listAllAvailableModels,
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OpenAICompatibleModel,
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transformModelToOpenAI,
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validateProvider
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} from '../utils'
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import { getProviderByModel, getRealProviderModel, validateProvider } from '../utils'
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const logger = loggerService.withContext('ChatCompletionService')
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export interface ModelData extends OpenAICompatibleModel {
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provider_id: string
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model_id: string
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name: string
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}
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export interface ValidationResult {
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isValid: boolean
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errors: string[]
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}
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export class ChatCompletionService {
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async getModels(): Promise<ModelData[]> {
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try {
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logger.info('Getting available models from providers')
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const models = await listAllAvailableModels()
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// Use Map to deduplicate models by their full ID (provider:model_id)
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const uniqueModels = new Map<string, ModelData>()
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for (const model of models) {
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const openAIModel = transformModelToOpenAI(model)
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const fullModelId = openAIModel.id // This is already in format "provider:model_id"
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// Only add if not already present (first occurrence wins)
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if (!uniqueModels.has(fullModelId)) {
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uniqueModels.set(fullModelId, {
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...openAIModel,
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provider_id: model.provider,
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model_id: model.id,
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name: model.name
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})
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} else {
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logger.debug(`Skipping duplicate model: ${fullModelId}`)
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}
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}
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const modelData = Array.from(uniqueModels.values())
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logger.info(`Successfully retrieved ${modelData.length} unique models from ${models.length} total models`)
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if (models.length > modelData.length) {
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logger.debug(`Filtered out ${models.length - modelData.length} duplicate models`)
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}
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return modelData
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} catch (error: any) {
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logger.error('Error getting models:', error)
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return []
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}
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}
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validateRequest(request: ChatCompletionCreateParams): ValidationResult {
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const errors: string[] = []
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@ -98,17 +44,6 @@ export class ChatCompletionService {
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}
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// Validate optional parameters
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if (request.temperature !== undefined) {
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if (typeof request.temperature !== 'number' || request.temperature < 0 || request.temperature > 2) {
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errors.push('Temperature must be a number between 0 and 2')
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}
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}
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if (request.max_tokens !== undefined) {
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if (typeof request.max_tokens !== 'number' || request.max_tokens < 1) {
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errors.push('max_tokens must be a positive number')
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}
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}
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return {
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isValid: errors.length === 0,
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112
src/main/apiServer/services/models.ts
Normal file
112
src/main/apiServer/services/models.ts
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@ -0,0 +1,112 @@
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import { z } from 'zod'
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import { loggerService } from '../../services/LoggerService'
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import { getAvailableProviders, listAllAvailableModels, OpenAICompatibleModel, transformModelToOpenAI } from '../utils'
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const logger = loggerService.withContext('ModelsService')
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// Zod schema for models filtering
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export const ModelsFilterSchema = z.object({
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provider: z.enum(['openai', 'anthropic']).optional(),
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offset: z.coerce.number().min(0).default(0).optional(),
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limit: z.coerce.number().min(1).optional()
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})
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export type ModelsFilter = z.infer<typeof ModelsFilterSchema>
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export interface ModelsResponse {
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object: 'list'
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data: OpenAICompatibleModel[]
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total?: number
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offset?: number
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limit?: number
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}
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export class ModelsService {
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async getModels(filter?: ModelsFilter): Promise<ModelsResponse> {
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try {
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logger.info('Getting available models from providers', { filter })
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const models = await listAllAvailableModels()
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const providers = await getAvailableProviders()
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// Use Map to deduplicate models by their full ID (provider:model_id)
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const uniqueModels = new Map<string, OpenAICompatibleModel>()
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for (const model of models) {
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const openAIModel = transformModelToOpenAI(model)
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const fullModelId = openAIModel.id // This is already in format "provider:model_id"
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// Only add if not already present (first occurrence wins)
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if (!uniqueModels.has(fullModelId)) {
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uniqueModels.set(fullModelId, {
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...openAIModel,
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name: model.name
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})
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} else {
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logger.debug(`Skipping duplicate model: ${fullModelId}`)
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}
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}
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let modelData = Array.from(uniqueModels.values())
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// Apply filters
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if (filter?.provider) {
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const providerType = filter.provider
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modelData = modelData.filter((model) => {
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// Find the provider for this model and check its type
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const provider = providers.find((p) => p.id === model.provider)
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return provider && provider.type === providerType
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})
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logger.debug(`Filtered by provider type '${providerType}': ${modelData.length} models`)
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}
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const total = modelData.length
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// Apply pagination
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const offset = filter?.offset || 0
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const limit = filter?.limit
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if (limit !== undefined) {
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modelData = modelData.slice(offset, offset + limit)
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logger.debug(
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`Applied pagination: offset=${offset}, limit=${limit}, showing ${modelData.length} of ${total} models`
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)
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} else if (offset > 0) {
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modelData = modelData.slice(offset)
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logger.debug(`Applied offset: offset=${offset}, showing ${modelData.length} of ${total} models`)
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}
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logger.info(`Successfully retrieved ${modelData.length} models from ${models.length} total models`)
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if (models.length > total) {
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logger.debug(`Filtered out ${models.length - total} models after deduplication and filtering`)
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}
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const response: ModelsResponse = {
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object: 'list',
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data: modelData
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}
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// Add pagination metadata if applicable
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if (filter?.limit !== undefined || filter?.offset !== undefined) {
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response.total = total
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response.offset = offset
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if (filter?.limit !== undefined) {
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response.limit = filter.limit
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}
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}
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return response
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} catch (error: any) {
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logger.error('Error getting models:', error)
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return {
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object: 'list',
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data: []
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}
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}
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}
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}
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// Export singleton instance
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export const modelsService = new ModelsService()
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@ -9,6 +9,7 @@ export interface OpenAICompatibleModel {
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id: string
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object: 'model'
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created: number
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name: string
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owned_by: string
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provider?: string
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provider_model_id?: string
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@ -185,6 +186,7 @@ export function transformModelToOpenAI(model: Model): OpenAICompatibleModel {
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return {
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id: `${model.provider}:${model.id}`,
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object: 'model',
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name: model.name,
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created: Math.floor(Date.now() / 1000),
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owned_by: model.owned_by || model.provider,
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provider: model.provider,
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