Moondream 3 Preview [Query] | Large Language Models

fal-ai/moondream3-preview/query

Moondream 3 is a vision language model that brings frontier-level visual reasoning with native object detection, pointing, and OCR capabilities to real-world applications requiring fast, inexpensive inference at scale.

Inference

Commercial use

Schema

LLMs

Playground API

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Run Query

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Please check the migration guide for more information.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/moondream3-preview/query", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/moondream-3-preview/query_in.jpg",
    prompt: "List the safety measures taken by this worker in a JSON array under `safety_measures` key"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("fal-ai/moondream3-preview/query", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/example_inputs/moondream-3-preview/query_in.jpg",
    prompt: "List the safety measures taken by this worker in a JSON array under `safety_measures` key"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("fal-ai/moondream3-preview/query", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("fal-ai/moondream3-preview/query", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

image_url string* required

URL of the image to be processed

Max width: 7000px, Max height: 7000px, Timeout: 20.0s

prompt string* required

Query to be asked in the image

reasoning boolean

Whether to include detailed reasoning behind the answer Default value: true

temperature float

Sampling temperature to use, between 0 and 1. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic. If not set, defaults to 0.

top_p float

Nucleus sampling probability mass to use, between 0 and 1.

{
  "image_url": "https://storage.googleapis.com/falserverless/example_inputs/moondream-3-preview/query_in.jpg",
  "prompt": "List the safety measures taken by this worker in a JSON array under `safety_measures` key",
  "reasoning": true
}

Output#

finish_reason string* required

Reason for finishing the output generation

usage_info UsageInfo* required

Usage information for the request

output string* required

Answer to the query about the image

reasoning string

Detailed reasoning behind the answer, if enabled

{
  "finish_reason": "stop",
  "usage_info": {
    "output_tokens": 23,
    "decode_time_ms": 811.5944429300725,
    "input_tokens": 737,
    "ttft_ms": 91.87838807702065,
    "prefill_time_ms": 54.45315001998097
  },
  "output": "{\n  \"safety_measures\": [\n    \"Red hard hat\",\n    \"Safety glasses\"\n  ]\n}",
  "reasoning": "The worker is wearing a red hard hat for head protection and safety glasses for eye protection."
}

Other types#

Object#

x_min float* required

Left boundary of detection box in normalized format (0 to 1)

y_min float* required

Top boundary of detection box in normalized format (0 to 1)

x_max float* required

Right boundary of detection box in normalized format (0 to 1)

y_max float* required

Bottom boundary of detection box in normalized format (0 to 1)

UsageInfo#

input_tokens integer* required

Number of input tokens processed

output_tokens integer* required

Number of output tokens generated

prefill_time_ms float* required

Time taken for prefill in milliseconds

decode_time_ms float* required

Time taken for decoding in milliseconds

ttft_ms float* required

Time to first token in milliseconds

Point#

x float* required

X coordinate of the point in normalized format (0 to 1)

y float* required

Y coordinate of the point in normalized format (0 to 1)

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

Object#

UsageInfo#

Point#

Related Models

Moondream 3 Preview [Query] Large Language Models

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

Object#

UsageInfo#

Point#

Related Models