Axelered AI

Quick Start

Get up and running with Axelered in minutes.

This guide walks you through the complete lifecycle of a document processing task: from setting up your secure environment to extracting structured data from multiple files.

 ┌──────────────┐      ┌──────────────────┐      ┌──────────────────┐      ┌───────────────────┐
 │ Authenticate │ ───► │ Create Workspace │ ───► │ Register Config  │ ───► │ Process Files     │
 │ (API Key)    │      │ (Project Sandbox)│      │ (LLM Blueprint)  │      │ (Upload & Extract)│
 └──────────────┘      └──────────────────┘      └──────────────────┘      └───────────────────┘

Step 1: Create a Workspace

A workspace is a secure sandbox. All your configurations, documents, and processing runs are isolated within this boundary.

curl -X POST "https://api.axelered.com/v1/w" \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Production Environment"
  }'

Note the id in the response (e.g., 018f8e02-4b2a...); you will use it as {workspace_id} in all subsequent calls.


Step 2: Register an LLM Configuration

Instead of sending your API keys on every request, register a Blueprint. This template stores your model preferences and credentials securely.

curl -X POST "https://api.axelered.com/v1/w/{workspace_id}/config/models" \
  -H "Authorization: Bearer <your_api_key>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "My GPT-4o Engine",
    "config": {
      "provider": "openai",
      "model": "gpt-4o",
      "apiKey": "sk-proj-...",
      "temperature": 0.0
    }
  }'

Note the id returned here; you will reference it as llmId during processing.


Step 3: Upload & Extract Data

Now, trigger a stateless batch run. We will upload two invoice files and extract their totals using the configuration we just created.

curl -X POST "https://api.axelered.com/v1/w/{workspace_id}/process" \
  -H "Authorization: Bearer <your_api_key>" \
  -F "files=@/path/to/invoice_a.pdf" \
  -F "files=@/path/to/invoice_b.pdf" \
  -F "config={
    \"extractionConfig\": {
      \"llmId\": \"{your_llm_config_id}\",
      \"jsonSchema\": {
        \"type\": \"object\",
        \"properties\": {
          \"totalAmount\": { \"type\": \"number\" },
          \"vendorName\": { \"type\": \"string\" }
        },
        \"required\": [\"totalAmount\"]
      }
    }
  }"

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