Model Request

from PIL import Image
from vlmrun.client import VLMRun
from vlmrun.client.types import GenerationConfig, AgentSkill

client = VLMRun(api_key="<VLMRUN_API_KEY>")

response = client.image.generate(
    images=[Image.open("photo.jpg")],
    model="vlm-1",
    config=GenerationConfig(
        skills=[AgentSkill(skill_name="invoice-extraction", version="latest")]
    )
)

import { VlmRun } from "vlmrun";

const client = new VlmRun({ apiKey: "<VLMRUN_API_KEY>" });

const response = await client.image.generate({
    images: ["photo.jpg"],
    model: "vlm-1",
    config: {
        skills: [{ skillName: "invoice-extraction", version: "latest" }],
    },
});

curl -X POST https://api.vlm.run/v1/image/generate \
  -H "Authorization: Bearer <VLMRUN_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vlm-1",
    "images": ["<base64-encoded-image>"],
    "config": {
      "skills": [{"skill_name": "invoice-extraction", "version": "latest"}]
    }
  }'

from pathlib import Path
from vlmrun.client import VLMRun
from vlmrun.client.types import GenerationConfig, AgentSkill

client = VLMRun(api_key="<VLMRUN_API_KEY>")

response = client.document.generate(
    file=Path("invoice.pdf"),
    model="vlm-1",
    config=GenerationConfig(
        skills=[AgentSkill(skill_name="invoice-extraction", version="latest")]
    ),
)

import { VlmRun } from "vlmrun";

const client = new VlmRun({ apiKey: "<VLMRUN_API_KEY>" });

const fileResponse = await client.files.upload({ filePath: "invoice.pdf" });
const response = await client.document.generate({
    fileId: fileResponse.id,
    model: "vlm-1",
    config: {
        skills: [{ skillName: "invoice-extraction", version: "latest" }],
    },
});

curl -X POST https://api.vlm.run/v1/document/generate \
  -H "Authorization: Bearer <VLMRUN_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vlm-1",
    "file_id": "<file-id>",
    "config": {
      "skills": [{"skill_name": "invoice-extraction", "version": "latest"}]
    }
  }'

from pathlib import Path
from vlmrun.client import VLMRun
from vlmrun.client.types import GenerationConfig, AgentSkill

client = VLMRun(api_key="<VLMRUN_API_KEY>")

response = client.video.generate(
    file=Path("recording.mp4"),
    model="vlm-1",
    config=GenerationConfig(
        skills=[AgentSkill(skill_name="meeting-notes", version="latest")]
    ),
    batch=True,
)

import { VlmRun } from "vlmrun";

const client = new VlmRun({ apiKey: "<VLMRUN_API_KEY>" });

const fileResponse = await client.files.upload({ filePath: "recording.mp4" });
const response = await client.video.generate({
    fileId: fileResponse.id,
    model: "vlm-1",
    config: {
        skills: [{ skillName: "meeting-notes", version: "latest" }],
    },
    batch: true,
});

curl -X POST https://api.vlm.run/v1/video/generate \
  -H "Authorization: Bearer <VLMRUN_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "vlm-1",
    "file_id": "<file-id>",
    "batch": true,
    "config": {
      "skills": [{"skill_name": "meeting-notes", "version": "latest"}]
    }
  }'