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| import { | |
| type CoreMessage, | |
| type LanguageModelV1, | |
| embed, | |
| generateText, | |
| streamText, | |
| } from "ai"; | |
| import { openai } from "@ai-sdk/openai"; | |
| import { logger } from "~/services/logger.service"; | |
| import { createOllama, type OllamaProvider } from "ollama-ai-provider"; | |
| import { anthropic } from "@ai-sdk/anthropic"; | |
| import { google } from "@ai-sdk/google"; | |
| export async function makeModelCall( | |
| stream: boolean, | |
| messages: CoreMessage[], | |
| onFinish: (text: string, model: string) => void, | |
| options?: any, | |
| ) { | |
| let modelInstance; | |
| const model = process.env.MODEL as any; | |
| const ollamaUrl = process.env.OLLAMA_URL; | |
| let ollama: OllamaProvider | undefined; | |
| if (ollamaUrl) { | |
| ollama = createOllama({ | |
| baseURL: ollamaUrl, | |
| }); | |
| } | |
| switch (model) { | |
| case "gpt-4.1-2025-04-14": | |
| case "gpt-4.1-mini-2025-04-14": | |
| case "gpt-4.1-nano-2025-04-14": | |
| modelInstance = openai(model, { ...options }); | |
| break; | |
| case "claude-3-7-sonnet-20250219": | |
| case "claude-3-opus-20240229": | |
| case "claude-3-5-haiku-20241022": | |
| modelInstance = anthropic(model, { ...options }); | |
| break; | |
| case "gemini-2.5-flash-preview-04-17": | |
| case "gemini-2.5-pro-preview-03-25": | |
| case "gemini-2.0-flash": | |
| case "gemini-2.0-flash-lite": | |
| modelInstance = google(model, { ...options }); | |
| break; | |
| default: | |
| if (ollama) { | |
| modelInstance = ollama(model); | |
| } | |
| logger.warn(`Unsupported model type: ${model}`); | |
| break; | |
| } | |
| if (stream) { | |
| return streamText({ | |
| model: modelInstance as LanguageModelV1, | |
| messages, | |
| onFinish: async ({ text }) => { | |
| onFinish(text, model); | |
| }, | |
| }); | |
| } | |
| const { text } = await generateText({ | |
| model: modelInstance as LanguageModelV1, | |
| messages, | |
| }); | |
| onFinish(text, model); | |
| return text; | |
| } | |
| export async function getEmbedding(text: string) { | |
| const ollamaUrl = process.env.OLLAMA_URL; | |
| if (!ollamaUrl) { | |
| // Use OpenAI embedding model when explicitly requested | |
| const { embedding } = await embed({ | |
| model: openai.embedding("text-embedding-3-small"), | |
| value: text, | |
| }); | |
| return embedding; | |
| } | |
| // Default to using Ollama | |
| const model = process.env.EMBEDDING_MODEL; | |
| const ollama = createOllama({ | |
| baseURL: ollamaUrl, | |
| }); | |
| const { embedding } = await embed({ | |
| model: ollama.embedding(model as string), | |
| value: text, | |
| }); | |
| return embedding; | |
| } | |