Local AI Run
GitHub

Referencia API

Compatible con OpenAI. Reemplazo directo, sin cambios de código.

Una vez iniciado el motor, los tres servicios compatibles con OpenAI están expuestos. Configura base_url al puerto correspondiente; api_key puede ser cualquier cadena.

Resumen de servicios

Service Port Endpoint
VLM8080POST /v1/chat/completions
Embedding8081POST /v1/embeddings
STT8082POST /inference
HealthallGET /health

1. Visión-Lenguaje (VLM)

💡 Gemma 4 es un thinking model: escribe razonamiento antes del content. Usa max_tokens >= 256 para evitar truncación.

Python (openai SDK)

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8080/v1", api_key="not-needed")

# Text only
resp = client.chat.completions.create(
    model="gemma-4-e4b",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=256,
)

# With image
import base64
img_b64 = base64.b64encode(open("photo.jpg", "rb").read()).decode()
resp = client.chat.completions.create(
    model="gemma-4-e4b",
    messages=[{
        "role": "user",
        "content": [
            {"type": "text", "text": "Describe this photo"},
            {"type": "image_url",
             "image_url": {"url": f"data:image/jpeg;base64,{img_b64}"}},
        ],
    }],
    max_tokens=256,
)

curl

curl http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma-4-e4b",
    "messages": [{"role": "user", "content": "Hello"}],
    "max_tokens": 256
  }'

JavaScript

const resp = await fetch("http://127.0.0.1:8080/v1/chat/completions", {
  method: "POST",
  headers: { "Content-Type": "application/json" },
  body: JSON.stringify({
    model: "gemma-4-e4b",
    messages: [{ role: "user", content: "Hello" }],
    max_tokens: 256,
  }),
});
const data = await resp.json();

2. Embeddings de texto

Python

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8081/v1", api_key="not-needed")

# Single
resp = client.embeddings.create(model="bge-base-zh", input="Sample text")
vec = resp.data[0].embedding   # list[float], len == 768

# Batch
texts = ["Text 1", "Text 2", "Text 3"]
resp = client.embeddings.create(model="bge-base-zh", input=texts)
vectors = [d.embedding for d in resp.data]   # 3 x 768

curl

curl http://127.0.0.1:8081/v1/embeddings \
  -H "Content-Type: application/json" \
  -d '{"model":"bge-base-zh","input":"Sample text"}'

3. Voz a texto (STT)

ℹ️ whisper.cpp usa multipart nativo, NO la API de Audio de OpenAI.

curl

curl http://127.0.0.1:8082/inference \
  -F "file=@audio.wav" \
  -F "language=zh" \
  -F "response_format=json"

# Response: {"text": "transcribed text..."}

Python (requests)

import requests

with open("audio.wav", "rb") as f:
    r = requests.post(
        "http://127.0.0.1:8082/inference",
        files={"file": f},
        data={"language": "zh", "response_format": "json"},
    )
print(r.json())

Limitaciones