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