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Documentation Index

Fetch the complete documentation index at: https://docs.axioniclabs.ai/llms.txt

Use this file to discover all available pages before exploring further.

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Prerequisites

  • A trained model with Ready status on the Models page
  • An Axionic API key (default key created with your account, find it in Settings > API Keys)

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Quick Test with curl

curl -X POST https://api.axioniclabs.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-api-key: ax_your_key_here" \
  -d '{
    "model": "your-model-name",
    "messages": [
      {"role": "user", "content": "Hello, what can you do?"}
    ],
    "max_tokens": 256
  }'

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Using the Python SDK

pip install mechanex

import mechanex as mx

mx.set_key("ax_your_key_here")
mx.set_model("your-model-name")

response = mx.generation.generate(
    prompt="Explain how tool calling works.",
    max_tokens=256,
)
print(response["output"])

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Using the OpenAI Python Client

from openai import OpenAI

client = OpenAI(
    api_key="ax_your_key_here",
    base_url="https://api.axioniclabs.ai/v1",
)

response = client.chat.completions.create(
    model="your-model-name",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=256,
)
print(response.choices[0].message.content)

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Structured Outputs

If you are coming from OpenAI’s Structured Outputs guide, the main difference is that Axionic does not currently expose OpenAI’s response_format field on /v1/chat/completions. Use one of these Axionic-native paths instead:
  • pass an inline policy or saved policy_id on /v1/chat/completions or /v1/completions when you want to stay inside an OpenAI SDK client
  • call /sampling/generate with method: "guided-generation" when you want direct JSON-schema, regex, or grammar constraints
Guided Generation is model/runtime-dependent. If the target runtime does not support that decoding strategy, Axionic can fall back to a standard decoding path. Test your schema on the exact model you plan to deploy.

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OpenAI client with an inline policy

Pass the policy through extra_body: 
import json
from openai import OpenAI

client = OpenAI(
    api_key="ax_your_key_here",
    base_url="https://api.axioniclabs.ai/v1",
)

response = client.chat.completions.create(
    model="your-model-name",
    messages=[
        {
            "role": "system",
            "content": "Extract the message into a typed JSON object.",
        },
        {
            "role": "user",
            "content": "Relayer outage on Osmosis, confidence 0.82",
        },
    ],
    extra_body={
        "policy": {
            "sampling": {
                "method": "guided-generation",
                "temperature": 0.2,
                "top_p": 0.9,
            },
            "constraints": {
                "json_mode": True,
                "json_schema": {
                    "type": "object",
                    "properties": {
                        "topic": {"type": "string"},
                        "confidence": {"type": "number"},
                    },
                    "required": ["topic", "confidence"],
                    "additionalProperties": False,
                },
            },
            "verifiers": {
                "enabled": ["syntax", "json_schema"],
                "repair_on_failure": True,
            },
        }
    },
)

data = json.loads(response.choices[0].message.content)
print(data["topic"], data["confidence"])
The response still comes back as text in response.choices[0].message.content, so parse it yourself with json.loads(...). See Reusable inference policies for the full policy shape.

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Direct guided-generation request

This is the same constraint path used by Spectra’s Optimization page when you choose Guided Generation: 
curl -X POST https://api.axioniclabs.ai/sampling/generate \
  -H "Content-Type: application/json" \
  -H "x-api-key: ax_your_key_here" \
  -d '{
    "prompt": "Extract the primary topic and confidence from: relayer outage on Osmosis, confidence 0.82",
    "method": "guided-generation",
    "max_new_tokens": 120,
    "params": {
      "temperature": 0.2
    },
    "json_schema": {
      "type": "object",
      "properties": {
        "topic": {"type": "string"},
        "confidence": {"type": "number"}
      },
      "required": ["topic", "confidence"],
      "additionalProperties": false
    }
  }'
For non-JSON constraints, replace json_schema with either regex_pattern or grammar. Use regex_pattern when you need hard matching. Use grammar only for lightweight format guidance; it is not documented as full OpenAI-style grammar-constrained decoding.

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Schema guidance and current limitations

  • keep the root schema as a JSON object; the current runtime validation path expects object-shaped JSON responses
  • add additionalProperties: false when you want to reject extra keys instead of only validating required ones
  • unlike OpenAI’s response_format flow, optional fields are allowed here as long as you leave them out of required
  • OpenAI-style fields such as response_format on requests, plus message.parsed and message.refusal on responses, are not part of Axionic’s chat surface today
  • if you need acceptance flags, scores, or trace data instead of just the generated text, use the policy APIs rather than the OpenAI-compatible chat response
OpenAI’s Structured Outputs guide recommends object-rooted schemas, clear field descriptions, and explicit required lists. Those are good practices here as well, but the transport is different: use Axionic policy or /sampling/generate fields rather than OpenAI’s response_format.

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Applying Steering Vectors

Pass steering parameters via extra_body: 
response = client.chat.completions.create(
    model="your-model-name",
    messages=[{"role": "user", "content": "Describe a sunset."}],
    extra_body={
        "steering_vector_id": "sv_abc123",
        "steering_strength": 1.0,
    },
)
Or with curl: 
curl -X POST https://api.axioniclabs.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-api-key: ax_your_key_here" \
  -d '{
    "model": "your-model-name",
    "messages": [{"role": "user", "content": "Describe a sunset."}],
    "max_tokens": 256,
    "steering_vector_id": "sv_abc123",
    "steering_strength": 1.0
  }'