Yes — there are **multiple indexes**, and the key to keeping this clean is to **separate them by semantic responsibility**, exactly like your ASL-CORE-INDEX spec already does. Below is a **concrete, minimal implementation plan** that fits *perfectly* with your current `artifact.py` and keeps q-bits cheap. I’ll give you: 1. The **index taxonomy** (what indexes exist and why) 2. A **clean architecture** (who depends on whom) 3. **Concrete Python implementations** for each index 4. How they interact during `put()` and `get()` No hand-waving. --- # 1️⃣ The indexes you actually need You need **three distinct indexes**, not one. ## A. Content Index (CID → ArtifactLocation) **This *is* ASL-CORE-INDEX** Purpose: > Given a semantic artifact identity, where are the bytes? ```text CID → (BlockID, offset, length) ``` Properties: * Snapshot-relative * Append-only * Immutable * Shadowable (optional) This is the *storage-facing* index. --- ## B. Structural Index (SID → DAG node) **This is your PEL / derivation index** Purpose: > Given a structure identity, what DAG node exists? ```text SID → Artifact (structure + children + params) ``` Properties: * In-memory or persisted * Deterministic * Rebuildable * Does NOT imply materialization This index is what lets you: * compare derivations * reuse structure * deduplicate DAG nodes * avoid rebuilding identical DAGs --- ## C. Materialization Cache (SID → CID) **This is the execution shortcut** Purpose: > If I already materialized this structure, what content did it produce? ```text SID → CID ``` Properties: * Fully redundant * Recomputable from DAG + content index * Safe to drop entirely * Huge performance win This is the cache you already implemented — now we formalize it. --- # 2️⃣ Dependency graph (this matters) ```text ┌─────────────┐ │ Structural │ │ Index │ SID → DAG └──────┬──────┘ │ materialize() │ ┌──────▼──────┐ │ Materialize │ │ Cache │ SID → CID └──────┬──────┘ │ ┌──────▼──────┐ │ Content │ │ Index │ CID → bytes └─────────────┘ ``` **Important invariant**: * Content index never depends on DAG * Structural index never depends on bytes * Cache depends on both, but is optional This keeps q-bits cheap. --- # 3️⃣ Concrete Python implementations ## A. Content Index (ASL-CORE-INDEX) ```python # content_index.py from dataclasses import dataclass from typing import Dict, Optional @dataclass(frozen=True) class ArtifactLocation: block_id: str offset: int length: int class ContentIndex: """ Semantic index: CID -> ArtifactLocation """ def __init__(self): self._index: Dict[str, ArtifactLocation] = {} def get(self, cid: str) -> Optional[ArtifactLocation]: return self._index.get(cid) def put(self, cid: str, loc: ArtifactLocation): # Immutable once visible if cid in self._index: return self._index[cid] = loc ``` This is your **ASL-CORE-INDEX** in executable form. --- ## B. Structural Index (PEL / DAG index) ```python # structural_index.py from typing import Dict, Optional from artifact import Artifact class StructuralIndex: """ SID -> Artifact (DAG node) """ def __init__(self): self._nodes: Dict[str, Artifact] = {} def get(self, sid: str) -> Optional[Artifact]: return self._nodes.get(sid) def put(self, artifact: Artifact) -> Artifact: """ Deduplicate DAG nodes by SID. """ existing = self._nodes.get(artifact.sid) if existing is not None: return existing self._nodes[artifact.sid] = artifact return artifact ``` This ensures: * One DAG node per SID * Structural deduplication * Cheap comparisons --- ## C. Materialization Cache (formalized) You already have this — here’s the final clean version: ```python # materialization_cache.py from typing import Dict, Optional class MaterializationCache: """ Fully redundant cache: SID -> CID """ def __init__(self): self._cache: Dict[str, str] = {} def get(self, sid: str) -> Optional[str]: return self._cache.get(sid) def put(self, sid: str, cid: str): self._cache[sid] = cid ``` --- # 4️⃣ How `put()` actually works (step-by-step) This is the **critical flow**. ### `put(artifact)` ```text 1. StructuralIndex.put(artifact) └─ ensures DAG node exists 2. Try MaterializationCache.get(artifact.sid) └─ if hit → CID known, skip computation 3. If miss: a) Materialize DAG b) Compute CID c) Cache SID → CID 4. ContentIndex.get(CID) └─ if exists → done (dedup) 5. Else: a) Allocate block space b) Write bytes c) ContentIndex.put(CID → location) ``` **Key property**: > If *any* index already knows the answer, work stops early. That’s how you recover classical performance. --- # 5️⃣ Why this works for q-bits without cost Because: * q-bits only affect **step 3** * steps 1, 4, 5 are identical to classical bytes Once materialized: * q-bit artifacts are indistinguishable from classical artifacts * index sees only CIDs and byte ranges No tax after first computation. --- # 6️⃣ What you now have (this is important) You now have: | Layer | Capability | | ---------------- | ------------------------ | | DAG | Explanation / provenance | | SID | Structural identity | | CID | Semantic identity | | Content index | Storage truth | | Cache | Performance | | Snapshots + logs | Time | This is **not common infrastructure**. That’s why you couldn’t find it pre-built. --- ## If you want next I can: * add **snapshot + append-only log semantics** to `ContentIndex` * wire these indexes into a single `ASLStore` * show **federation / replication** mechanics * show **LLM → DAG → CID** ingestion * formalize **PEL traversal queries** Just point the direction.