GitHub - Stream29/ProxyAsLocalModel: Proxy remote LLM API as Ollama and LM Studio, for using them in JetBrains AI Assistant

https://github.com/Stream29/ProxyAsLocalModel • May 17, 2025 11:08

Extracto

Proxy remote LLM API as Ollama and LM Studio, for using them in JetBrains AI Assistant - Stream29/ProxyAsLocalModel

Resumen

Resumen Principal

ProxyAsLocalModel es una innovadora aplicación diseñada para extender la funcionalidad del Asistente de IA de JetBrains, permitiendo a los usuarios utilizar sus tokens de API de Modelos de Lenguaje Grandes (LLM) remotos (como OpenAI, Claude, Gemini y Qwen) al emular las APIs de modelos locales de LM Studio y Ollama. La creación de esta herramienta surge de la necesidad de superar las limitaciones de cuota del plan gratuito de JetBrains y la incompatibilidad directa con APIs de terceros. El proyecto destaca por su solución técnica para la compilación a imagen nativa de GraalVM, que inicialmente se vio obstaculizada por el uso de SDKs Java oficiales con características dinámicas. Esto llevó a una reescritura del cliente para la API de completado de chat en streaming utilizando Ktor y kotlinx.serialization, aprovechando sus propiedades no-reflex y funcionales. Esta elección tecnológica resulta en una aplicación multiplataforma, de inicio rápido y con menor consumo de memoria, distribuida eficientemente. El proxy soporta una amplia gama de proveedores de LLM y se enfoca exclusivamente en la API de completado de chat en streaming, con una configuración flexible y con recarga en caliente.

Elementos Clave

Puente entre APIs Remotas y Locales para JetBrains AI Assistant: La función principal de ProxyAsLocalModel es actuar como un intermediario crucial, permitiendo que las APIs de modelos de lenguaje grandes (LLM) remotos (incluyendo OpenAI, Claude, Gemini, Dash

Contenido

ProxyAsLocalModel

Proxy remote LLM API as Local model. Especially works for using custom LLM in JetBrains AI Assistant.

Powered by Ktor and kotlinx.serialization. Thanks to their no-reflex features.

Story of this project

Currently, JetBrains AI Assistant provides a free plan with very limited quotes. I tried out and my quote ran out quickly.

I already bought other LLM API tokens, such like Gemini and Qwen. So I started to think of using them in AI Assistant. Unfortunately, only local models from LM Studio and Ollama are supported. So I started to work on this proxy application that proxy third party LLM API as LM Studio and Ollama API so that I can use them in my JetBrains IDEs.

This is Just a simple task, so I started to use the official SDKs as clients and write a simple Ktor server that provides endpoints as LM Studio and Ollama. The problem appears when I try to distribute it as a GraalVM native image. The official Java SDKS uses too many dynamic features, making it hard to compile into a native image, even with a tracing agent. So I decided to implement a simple client of streaming chat completion API by myself with Ktor and kotlinx.serialization which are both no-reflex, functional and DSL styled.

As you can see, this application is distributed as a fat runnable jar and a GraalVM native image, which makes it cross-platform and fast to start.

The development of this application gives me confidence in Kotlin/Ktor/kotlinx.serialization. The Kotlin world uses more functional programming and less reflexion, which makes it more suitable for GraalVM native image, with faster startup and less memory usage.

Currently supported

Proxy from: OpenAI, Claude, DashScope(Alibaba Qwen), Gemini, Deepseek, Mistral, SiliconFlow.

Proxy as: LM Studio, Ollama.

Streaming chat completion API only.

How to use

This application is a proxy server, distributed as a fat runnable jar and a GraalVM native image (Windows x64).

Run the application, and you will see a help message:

2025-05-02 10:43:53 INFO  Help - It looks that you are starting the program for the first time here.
2025-05-02 10:43:53 INFO  Help - A default config file is created at your_path\config.yml with schema annotation.
2025-05-02 10:43:53 INFO  Config - Config file watcher started at your_path\config.yml
2025-05-02 10:43:53 INFO  LM Studio Server - LM Studio Server started at 1234
2025-05-02 10:43:53 INFO  Ollama Server - Ollama Server started at 11434
2025-05-02 10:43:53 INFO  Model List - Model list loaded with: []

Then you can edit the config file to set up your proxy server.

Config file

This config file is automatically hot-reloaded when you change it. Only the influenced parts of the server will be updated.

When first generating the config file, it will be created with schema annotations. This will bring completion and check in your editor.

Example config file

# $schema: https://github.com/Stream29/ProxyAsLocalModel/raw/master/config_v3.schema.json
lmStudio:
  port: 1234 # This is default value
  enabled: true # This is default value
  host: 0.0.0.0 # This is default value
  path: /your/path # Will be add before the original endpoints, default value is empty
ollama:
  port: 11434 # This is default value
  enabled: true # This is default value
  host: 0.0.0.0 # This is default value
  path: /your/path # Will be add before the original endpoints, default value is empty
client:
  socketTimeout: 1919810 # Long.MAX_VALUE is default value, in milliseconds
  connectionTimeout: 1919810 # Long.MAX_VALUE is default value, in milliseconds
  requestTimeout: 1919810 # Long.MAX_VALUE is default value, in milliseconds
  retry: 3 # This is default value
  delayBeforeRetry: 1000 # This is default value, in milliseconds

apiProviders:
  OpenAI:
    type: OpenAi
    baseUrl: https://api.openai.com/v1
    apiKey: <your_api_key>
    modelList:
      - gpt-4o
  Claude:
    type: Claude
    apiKey: <your_api_key>
    modelList:
      - claude-3-7-sonnet
  Qwen:
    type: DashScope
    apiKey: <your_api_key>
    modelList: # This is default value
      - qwen-max
      - qwen-plus
      - qwen-turbo
      - qwen-long
  DeepSeek:
    type: DeepSeek
    apiKey: <your_api_key>
    modelList: # This is default value
      - deepseek-chat
      - deepseek-reasoner
  Mistral:
    type: Mistral
    apiKey: <your_api_key>
    modelList: # This is default value
      - codestral-latest
      - mistral-large
  SiliconFlow:
    type: SiliconFlow
    apiKey: <your_api_key>
    modelList:
      - Qwen/Qwen3-235B-A22B
      - Pro/deepseek-ai/DeepSeek-V3
      - THUDM/GLM-4-32B-0414
  OpenRouter:
    type: OpenRouter
    apiKey: <your_api_key>
    modelList:
      - openai/gpt-4o
  Gemini:
    type: Gemini
    apiKey: <your_api_key>
    modelList:
      - gemini-2.5-flash-preview-04-17