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.
Overview
Bundle sampling, steering, constraints, and verification into a reusable configuration.
policy.run()
Runs generation with a policy configuration applied.
Inline policy configuration. Specify any combination of sampling, steering, constraints, and verification settings.
ID of a previously saved policy. Mutually exclusive with policy.
Maximum tokens to generate.
If true, includes step-by-step trace data in the response.
output, accepted (bool), score, latency_ms, tokens, and optionally trace.
import mechanex as mx
mx.set_key("ax_your_key_here")
result = mx.policy.run(
prompt="What is the capital of France?",
policy={
"sampling": {"method": "greedy"},
"steering": {"preset": "truthfulness", "strength": 1.2},
},
)
print(result["output"])
Save & Reuse
# Save a policy for reuse
policy_id = mx.policy.save({
"name": "strict-json-output",
"sampling": {"method": "guided-generation"},
"constraints": {"json_schema": {"type": "object", "properties": {"answer": {"type": "string"}}}},
})
# Run with the saved policy
result = mx.policy.run("Extract the key fact.", policy_id=policy_id)
# List all saved policies
policies = mx.policy.list()
# Retrieve a specific policy
policy = mx.policy.get(policy_id)
Compare & Evaluate
Test multiple policies against the same prompt to find the best configuration:
# Compare two policies side-by-side
comparison = mx.policy.compare(
prompt="Explain quantum computing.",
policies=[
{"sampling": {"method": "greedy"}},
{"sampling": {"method": "top-p"}, "steering": {"preset": "brevity"}},
],
)
# Batch evaluation across multiple prompts
evaluation = mx.policy.evaluate(
prompts=["What is 2+2?", "Name the largest planet.", "Define entropy."],
policy_id=policy_id,
)
print(f"Success rate: {evaluation['success_rate']}")
Policy Structure
A policy bundles five configuration sections:
policy = {
"name": "my-policy",
"sampling": {
"method": "guided-generation",
"temperature": 0.3,
# top_k, top_p, min_p, ads_subset_size, ads_beta, etc.
},
"steering": {
"enabled": True,
"preset": "truthfulness", # or vector_id for a custom vector
"strength": 1.2,
},
"constraints": {
"json_schema": {"type": "object", "properties": {"answer": {"type": "string"}}},
"regex_pattern": None, # regex alternative to json_schema
"grammar": None, # grammar alternative
"required_fields": [], # fields that must appear in JSON output
"forbidden_terms": [], # terms that must not appear in output
},
"verifiers": {
"enabled": ["json_schema", "syntax"],
"repair_on_failure": True,
"max_retries": 1,
},
"optimization": {
"best_of_n": 1,
"retry_on_failure": 1,
"confidence_triggered_regeneration": False,
"confidence_threshold": 0.5,
},
}
Verifiers
Verifiers validate generated output and optionally repair failures by retrying. Enable them by name in the verifiers.enabled list:
| Verifier | Validates |
|---|
syntax | Output is syntactically valid (parseable) |
json_schema | Output conforms to the constraints.json_schema |
regex | Output matches constraints.regex_pattern |
code_compile | Generated code compiles without errors |
unit_tests | Generated code passes provided unit tests |
factuality | Output is factually consistent with the prompt |
tool_args | Tool call arguments match the expected schema |
result = mx.policy.run(
prompt="Extract the key facts as JSON.",
policy={
"sampling": {"method": "guided-generation"},
"constraints": {
"json_schema": {"type": "object", "properties": {"facts": {"type": "array"}}},
},
"verifiers": {
"enabled": ["json_schema", "syntax"],
"repair_on_failure": True,
"max_retries": 2,
},
},
)
print(result["accepted"]) # True if all verifiers passed
result = mx.policy.run(
prompt="Write a Python function that reverses a string.",
policy={
"verifiers": {
"enabled": ["code_compile", "unit_tests"],
"code_language": "python",
"code_unit_tests": [
"assert reverse_string('hello') == 'olleh'",
"assert reverse_string('') == ''",
],
"unit_test_timeout_ms": 2000,
},
},
)
Optimization
| Parameter | Default | Description |
|---|
best_of_n | 1 | Generate N candidates and return the best |
retry_on_failure | 1 | Retry count when verifiers fail |
confidence_triggered_regeneration | false | Regenerate if model confidence falls below threshold |
confidence_threshold | 0.5 | Minimum confidence score to accept output |
Auto-Tune
Automatically search for the best policy parameters:
best = mx.policy.auto_tune(
prompts=["Generate a JSON response for...", "Extract the key facts from..."],
base_policy={"sampling": {"method": "guided-generation"}},
max_trials=10,
)
print(best["best_policy"])
Presets
Use built-in preset builders for common configurations:
# Strict JSON extraction
policy = mx.policy.strict_json_extraction(schema=my_schema)
# Fast tool routing
policy = mx.policy.fast_tool_router()
# Diverse chatbot
policy = mx.policy.diverse_chatbot()
# Ensemble voting across models
policy = mx.policy.ensemble_vote(models=["model-a", "model-b"])
# Steering preset
policy = mx.policy.steering_preset(preset="truthfulness", sampling_method="greedy")
# Publish a preset
preset_id = mx.policy.publish_preset(name="My Config", policy=policy, visibility="public")
# Browse and clone
presets = mx.policy.list_presets(include_public=True)
cloned_id = mx.policy.clone_preset(preset_id)
