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Overview

Mechanex exposes three lower-level modules for understanding model structure and how context affects answers:
  • mx.model for computation graph metadata
  • mx.attribution for attribution patching scores
  • mx.raag for retrieval-augmented answer generation
mx.model.get_graph() is backed by the current API contract. mx.attribution and mx.raag are exported by the SDK but are not present in the current OpenAPI fixture, so treat them as experimental wrappers until the backend contract includes their endpoints. They also check that local model state exists before posting; use them after mx.load(...) or mx.set_local_model(...). 
import mechanex as mx

mx.set_key("ax_your_key_here")

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Model Graph

Use mx.model.get_graph() to retrieve the model computation graph from the /graph endpoint. 
graph = mx.model.get_graph()
for node in graph:
    print(node.get("name"))
mx.model.graph() is an alias for get_graph(). mx.model.get_paths() is deprecated and now derives names from the graph response.

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Attribution

Load or attach a local model before using attribution: 
mx.load("gpt2-small")
Use mx.attribution.compute_scores() to compare a clean prompt against a corrupted prompt and return attribution-patching scores when the backend endpoint is available. 
scores = mx.attribution.compute_scores(
    clean_prompt="The capital of France is Paris.",
    corrupted_prompt="The capital of France is London.",
    target_module_paths=["blocks.6.hook_resid_post"],
)
target_module_paths is optional. When omitted, Mechanex sends an empty path list and lets the backend choose the applicable target set.

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RAAG

Use RAAG after loading or attaching local model state, as with attribution. Use mx.raag.generate() for retrieval-augmented answer generation when the backend endpoint is available. Pass question entries plus either inline documents or a Pinecone index name. 
result = mx.raag.generate(
    qa_entries=[
        {
            "question": "Which controls affect runtime steering?",
            "answer": "Steering vector ID and steering strength.",
        }
    ],
    docs=[
        {
            "id": "runtime-controls",
            "text": "Runtime requests can include steering vectors, policies, and behavior monitors.",
        }
    ],
)

result = mx.raag.generate(
    qa_entries=[{"question": "What changed in this release?"}],
    pinecone_index_name="release-notes",
)