Hypothesis proximity update

hypothesis-proximity-update

Goal:

• Update hypothesis proximity state by invoking the canonical embedding bridge for one hypothesis.

Inputs:

• optional existing state/PROXIMITY_GRAPH.json
• target hypothesis_id
• run-frozen embedding provider settings from state/RESOLVED_RUN_CONFIG.json

Outputs:

• updated state/proximity_receipts/<hypothesis_id>.json
• updated state/PROXIMITY_STATUS.json
• updated in-memory ProximityGraphContract
• updated state/PROXIMITY_GRAPH.json when the provider returns a valid embedding
• updated state/PIPELINE_STATE.json
• updated state/CURRENT_STAGE.json

Context Loading:

• Open skills/shared-references/schema-index.md.
• Read packages/agent_contracts/state.py and confirm the exact ProximityGraphContract shape before writing state/PROXIMITY_GRAPH.json.
• Read packages/agent_contracts/proximity.py and confirm the proximity receipt and status schemas before interpreting provider outcomes.
• Read packages/agent_contracts/pipeline_runtime.py before updating state/PIPELINE_STATE.json or state/CURRENT_STAGE.json.
• Load the current proximity graph if it exists. If it does not exist yet, start from an empty ProximityGraphContract.
• Treat embedding generation as a bridge/tool concern. Host agents must not generate, infer, paste, or hand-write numeric embeddings in prompt output.
• Do not skip this skill merely because no embedding vector is already present in the execution context.

Execution Contract:

• This skill is deterministic and must not call an LLM.
• Use from tools import sync_pipeline_stage_artifacts as the canonical paired write surface for entering the Proximity substage.
• On entry, call tools.sync_pipeline_stage_artifacts(run_dir, current_phase="Proximity", current_skill="hypothesis-proximity-update").
• Use from tools import update_hypothesis_proximity as the stable invocation surface.
• The exported bridge is implemented in packages/agent_mechanics/hypothesis_embedding.py.
• The canonical hypothesis-to-embedding text formatter is implemented in packages/agent_mechanics/hypothesis_embedding_text.py.
• The helper signature is update_hypothesis_proximity(run_dir, hypothesis_id, config=None, provider=None) -> ProximityEmbeddingReceiptContract.
• The bridge formats the hypothesis text, reads the run-frozen proximity provider settings from state/RESOLVED_RUN_CONFIG.json unless an explicit test config is passed, calls the configured provider when enabled, records a proximity receipt/status, and updates state/PROXIMITY_GRAPH.json only when a valid embedding is returned.
• Provider-specific backends such as openai_compatible and gemini must remain behind tools.update_hypothesis_proximity(...); host agents must not call provider SDKs directly or paste provider-returned vectors into artifacts.
• The lower-level graph helper remains tools.update_proximity_graph(...), implemented in packages/agent_mechanics/proximity_update.py; host agents must not call it with prompt-fabricated vectors.
• This skill must not hand-write embeddings, infer embeddings from hypothesis prose, or fabricate placeholder numeric vectors.
• If the provider is disabled, unavailable, invalid, or fails, preserve the bridge-written receipt/status, do not fabricate a graph update, and return control to ranking with receipt-gated fallback semantics.

Execution Steps:

1. Open skills/shared-references/schema-index.md, then read packages/agent_contracts/state.py, packages/agent_contracts/proximity.py, and packages/agent_contracts/pipeline_runtime.py before writing proximity or run-level stage artifacts.
2. Before running the bridge, call tools.sync_pipeline_stage_artifacts(run_dir, current_phase="Proximity", current_skill="hypothesis-proximity-update").
3. Confirm the target hypothesis_id identifies an existing canonical hypotheses/<id>/HYPOTHESIS.json artifact.
4. Call tools.update_hypothesis_proximity(run_dir, hypothesis_id) exactly once for the target hypothesis.
5. Inspect the returned ProximityEmbeddingReceiptContract and the persisted state/proximity_receipts/<hypothesis_id>.json.
6. If the receipt status is succeeded, validate the updated state/PROXIMITY_GRAPH.json.
7. If the receipt status is skipped_disabled, skipped_provider_unavailable, failed_provider_error, or failed_invalid_embedding, preserve the receipt/status and return control to ranking. Do not infer or fabricate placeholder embeddings from text, IDs, or manual feature heuristics.
8. Validate the updated receipt/status artifacts before declaring completion.

Artifact Rules:

• state/PROXIMITY_GRAPH.json must remain the canonical persisted graph artifact.
• state/proximity_receipts/<hypothesis_id>.json is the canonical per-hypothesis embedding bridge receipt.
• state/PROXIMITY_STATUS.json is the canonical run-level proximity bridge status artifact.
• Similarity edges must stay symmetric across both hypothesis IDs.
• Each persisted graph embedding should include metadata for provider, model, dimensions, input text hash, and embedding config hash.
• If the graph already contains the target hypothesis ID with the same embedding input hash and embedding config hash, the bridge may confirm the existing state through a succeeded receipt with graph_updated=false; do not duplicate entries.
• If the graph already contains the target hypothesis ID with different or missing input/config metadata, preserve the bridge receipt and do not overwrite the existing embedding in prompt code.
• Placeholder embeddings, manually engineered stand-in vectors, or prose-derived pseudo-embeddings must never be written into the canonical graph artifact.

Completion Rule:

• This skill is complete only when tools.update_hypothesis_proximity(run_dir, hypothesis_id) has returned a persisted receipt and either state/PROXIMITY_GRAPH.json validates after a succeeded receipt or the receipt/status explicitly records why the graph was not updated.