750 lines
27 KiB
Python
750 lines
27 KiB
Python
"""MnemosyneArchive — core archive class.
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The living holographic archive. Stores entries, maintains links,
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and provides query interfaces for retrieving connected knowledge.
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"""
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from __future__ import annotations
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import json
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from typing import Optional
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from nexus.mnemosyne.entry import ArchiveEntry, _compute_content_hash
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from nexus.mnemosyne.linker import HolographicLinker
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_EXPORT_VERSION = "1"
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class MnemosyneArchive:
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"""The holographic archive — stores and links entries.
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Phase 1 uses JSON file storage. Phase 2 will integrate with
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MemPalace (ChromaDB) for vector-semantic search.
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"""
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def __init__(self, archive_path: Optional[Path] = None):
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self.path = archive_path or Path.home() / ".hermes" / "mnemosyne" / "archive.json"
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self.path.parent.mkdir(parents=True, exist_ok=True)
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self.linker = HolographicLinker()
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self._entries: dict[str, ArchiveEntry] = {}
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self._load()
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def _load(self):
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if self.path.exists():
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try:
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with open(self.path) as f:
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data = json.load(f)
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for entry_data in data.get("entries", []):
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entry = ArchiveEntry.from_dict(entry_data)
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self._entries[entry.id] = entry
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except (json.JSONDecodeError, KeyError):
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pass # Start fresh on corrupt data
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def _save(self):
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data = {
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"entries": [e.to_dict() for e in self._entries.values()],
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"count": len(self._entries),
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}
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with open(self.path, "w") as f:
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json.dump(data, f, indent=2)
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def find_duplicate(self, entry: ArchiveEntry) -> Optional[ArchiveEntry]:
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"""Return an existing entry with the same content hash, or None."""
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for existing in self._entries.values():
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if existing.content_hash == entry.content_hash and existing.id != entry.id:
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return existing
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return None
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def add(self, entry: ArchiveEntry, auto_link: bool = True) -> ArchiveEntry:
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"""Add an entry to the archive. Auto-links to related entries.
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If an entry with the same content hash already exists, returns the
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existing entry without creating a duplicate.
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"""
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duplicate = self.find_duplicate(entry)
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if duplicate is not None:
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return duplicate
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self._entries[entry.id] = entry
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if auto_link:
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self.linker.apply_links(entry, list(self._entries.values()))
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self._save()
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return entry
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def update_entry(
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self,
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entry_id: str,
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title: Optional[str] = None,
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content: Optional[str] = None,
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metadata: Optional[dict] = None,
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auto_link: bool = True,
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) -> ArchiveEntry:
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"""Update title, content, and/or metadata on an existing entry.
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Bumps ``updated_at`` and re-runs auto-linking when content changes.
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Args:
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entry_id: ID of the entry to update.
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title: New title, or None to leave unchanged.
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content: New content, or None to leave unchanged.
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metadata: Dict to merge into existing metadata (replaces keys present).
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auto_link: If True, re-run holographic linker after content change.
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Returns:
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The updated ArchiveEntry.
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Raises:
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KeyError: If entry_id does not exist.
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"""
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entry = self._entries.get(entry_id)
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if entry is None:
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raise KeyError(entry_id)
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content_changed = False
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if title is not None and title != entry.title:
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entry.title = title
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content_changed = True
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if content is not None and content != entry.content:
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entry.content = content
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content_changed = True
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if metadata is not None:
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entry.metadata.update(metadata)
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if content_changed:
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entry.content_hash = _compute_content_hash(entry.title, entry.content)
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entry.updated_at = datetime.now(timezone.utc).isoformat()
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if content_changed and auto_link:
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# Clear old links from this entry and re-run linker
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for other in self._entries.values():
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if entry_id in other.links:
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other.links.remove(entry_id)
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entry.links = []
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self.linker.apply_links(entry, list(self._entries.values()))
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self._save()
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return entry
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def get(self, entry_id: str) -> Optional[ArchiveEntry]:
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return self._entries.get(entry_id)
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def search(self, query: str, limit: int = 10) -> list[ArchiveEntry]:
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"""Simple keyword search across titles and content."""
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query_tokens = set(query.lower().split())
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scored = []
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for entry in self._entries.values():
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text = f"{entry.title} {entry.content} {' '.join(entry.topics)}".lower()
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hits = sum(1 for t in query_tokens if t in text)
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if hits > 0:
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scored.append((hits, entry))
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scored.sort(key=lambda x: x[0], reverse=True)
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return [e for _, e in scored[:limit]]
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def semantic_search(self, query: str, limit: int = 10, threshold: float = 0.05) -> list[ArchiveEntry]:
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"""Semantic search using holographic linker similarity.
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Scores each entry by Jaccard similarity between query tokens and entry
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tokens, then boosts entries with more inbound links (more "holographic").
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Falls back to keyword search if no entries meet the similarity threshold.
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Args:
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query: Natural language query string.
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limit: Maximum number of results to return.
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threshold: Minimum Jaccard similarity to be considered a semantic match.
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Returns:
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List of ArchiveEntry sorted by combined relevance score, descending.
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"""
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query_tokens = HolographicLinker._tokenize(query)
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if not query_tokens:
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return []
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# Count inbound links for each entry (how many entries link TO this one)
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inbound: dict[str, int] = {eid: 0 for eid in self._entries}
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for entry in self._entries.values():
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for linked_id in entry.links:
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if linked_id in inbound:
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inbound[linked_id] += 1
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max_inbound = max(inbound.values(), default=1) or 1
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scored = []
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for entry in self._entries.values():
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entry_tokens = HolographicLinker._tokenize(f"{entry.title} {entry.content} {' '.join(entry.topics)}")
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if not entry_tokens:
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continue
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intersection = query_tokens & entry_tokens
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union = query_tokens | entry_tokens
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jaccard = len(intersection) / len(union)
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if jaccard >= threshold:
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link_boost = inbound[entry.id] / max_inbound * 0.2 # up to 20% boost
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scored.append((jaccard + link_boost, entry))
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if scored:
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scored.sort(key=lambda x: x[0], reverse=True)
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return [e for _, e in scored[:limit]]
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# Graceful fallback to keyword search
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return self.search(query, limit=limit)
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def get_linked(self, entry_id: str, depth: int = 1) -> list[ArchiveEntry]:
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"""Get entries linked to a given entry, up to specified depth."""
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visited = set()
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frontier = {entry_id}
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result = []
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for _ in range(depth):
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next_frontier = set()
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for eid in frontier:
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if eid in visited:
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continue
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visited.add(eid)
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entry = self._entries.get(eid)
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if entry:
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for linked_id in entry.links:
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if linked_id not in visited:
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linked = self._entries.get(linked_id)
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if linked:
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result.append(linked)
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next_frontier.add(linked_id)
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frontier = next_frontier
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return result
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def by_topic(self, topic: str) -> list[ArchiveEntry]:
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"""Get all entries tagged with a topic."""
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topic_lower = topic.lower()
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return [e for e in self._entries.values() if topic_lower in [t.lower() for t in e.topics]]
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def remove(self, entry_id: str) -> bool:
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"""Remove an entry and clean up all bidirectional links.
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Returns True if the entry existed and was removed, False otherwise.
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"""
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if entry_id not in self._entries:
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return False
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# Remove back-links from all other entries
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for other in self._entries.values():
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if entry_id in other.links:
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other.links.remove(entry_id)
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del self._entries[entry_id]
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self._save()
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return True
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def export(
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self,
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query: Optional[str] = None,
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topics: Optional[list[str]] = None,
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) -> dict:
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"""Export a filtered subset of the archive.
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Args:
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query: keyword filter applied to title + content (case-insensitive)
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topics: list of topic tags; entries must match at least one
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Returns a JSON-serialisable dict with an ``entries`` list and metadata.
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"""
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candidates = list(self._entries.values())
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if topics:
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lower_topics = {t.lower() for t in topics}
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candidates = [
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e for e in candidates
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if any(t.lower() in lower_topics for t in e.topics)
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]
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if query:
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query_tokens = set(query.lower().split())
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candidates = [
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e for e in candidates
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if any(
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token in f"{e.title} {e.content} {' '.join(e.topics)}".lower()
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for token in query_tokens
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)
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]
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return {
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"version": _EXPORT_VERSION,
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"filters": {"query": query, "topics": topics},
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"count": len(candidates),
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"entries": [e.to_dict() for e in candidates],
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}
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def topic_counts(self) -> dict[str, int]:
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"""Return a dict mapping topic name → entry count, sorted by count desc."""
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counts: dict[str, int] = {}
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for entry in self._entries.values():
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for topic in entry.topics:
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counts[topic] = counts.get(topic, 0) + 1
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return dict(sorted(counts.items(), key=lambda x: x[1], reverse=True))
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@property
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def count(self) -> int:
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return len(self._entries)
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def graph_data(
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self,
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topic_filter: Optional[str] = None,
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) -> dict:
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"""Export the full connection graph for 3D constellation visualization.
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Returns a dict with:
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- nodes: list of {id, title, topics, source, created_at}
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- edges: list of {source, target, weight} from holographic links
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Args:
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topic_filter: If set, only include entries matching this topic
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and edges between them.
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"""
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entries = list(self._entries.values())
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if topic_filter:
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topic_lower = topic_filter.lower()
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entries = [
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e for e in entries
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if topic_lower in [t.lower() for t in e.topics]
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]
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entry_ids = {e.id for e in entries}
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nodes = [
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{
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"id": e.id,
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"title": e.title,
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"topics": e.topics,
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"source": e.source,
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"created_at": e.created_at,
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}
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for e in entries
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]
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# Build edges from links, dedup (A→B and B→A become one edge)
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seen_edges: set[tuple[str, str]] = set()
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edges = []
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for e in entries:
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for linked_id in e.links:
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if linked_id not in entry_ids:
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continue
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pair = (min(e.id, linked_id), max(e.id, linked_id))
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if pair in seen_edges:
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continue
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seen_edges.add(pair)
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# Compute weight via linker for live similarity score
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linked = self._entries.get(linked_id)
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if linked:
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weight = self.linker.compute_similarity(e, linked)
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edges.append({
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"source": pair[0],
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"target": pair[1],
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"weight": round(weight, 4),
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})
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return {"nodes": nodes, "edges": edges}
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def stats(self) -> dict:
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entries = list(self._entries.values())
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total_links = sum(len(e.links) for e in entries)
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topics: set[str] = set()
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for e in entries:
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topics.update(e.topics)
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# Orphans: entries with no links at all
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orphans = sum(1 for e in entries if len(e.links) == 0)
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# Link density: average links per entry (0 when empty)
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n = len(entries)
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link_density = round(total_links / n, 4) if n else 0.0
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# Age distribution
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timestamps = sorted(e.created_at for e in entries)
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oldest_entry = timestamps[0] if timestamps else None
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newest_entry = timestamps[-1] if timestamps else None
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return {
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"entries": n,
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"total_links": total_links,
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"unique_topics": len(topics),
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"topics": sorted(topics),
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"orphans": orphans,
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"link_density": link_density,
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"oldest_entry": oldest_entry,
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"newest_entry": newest_entry,
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}
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def _build_adjacency(self) -> dict[str, set[str]]:
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"""Build adjacency dict from entry links. Only includes valid references."""
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adj: dict[str, set[str]] = {eid: set() for eid in self._entries}
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for eid, entry in self._entries.items():
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for linked_id in entry.links:
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if linked_id in self._entries and linked_id != eid:
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adj[eid].add(linked_id)
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adj[linked_id].add(eid)
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return adj
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def graph_clusters(self, min_size: int = 1) -> list[dict]:
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"""Find connected component clusters in the holographic graph.
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Uses BFS to discover groups of entries that are reachable from each
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other through their links. Returns clusters sorted by size descending.
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Args:
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min_size: Minimum cluster size to include (filters out isolated entries).
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Returns:
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List of dicts with keys: cluster_id, size, entries, topics, density
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"""
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adj = self._build_adjacency()
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visited: set[str] = set()
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clusters: list[dict] = []
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cluster_id = 0
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for eid in self._entries:
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if eid in visited:
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continue
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# BFS from this entry
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component: list[str] = []
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queue = [eid]
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while queue:
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current = queue.pop(0)
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if current in visited:
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continue
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visited.add(current)
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component.append(current)
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for neighbor in adj.get(current, set()):
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if neighbor not in visited:
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queue.append(neighbor)
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# Single-entry clusters are orphans
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if len(component) < min_size:
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continue
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# Collect topics from cluster entries
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cluster_topics: dict[str, int] = {}
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internal_edges = 0
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for cid in component:
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entry = self._entries[cid]
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for t in entry.topics:
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cluster_topics[t] = cluster_topics.get(t, 0) + 1
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internal_edges += len(adj.get(cid, set()))
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internal_edges //= 2 # undirected, counted twice
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# Density: actual edges / possible edges
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n = len(component)
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max_edges = n * (n - 1) // 2
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density = round(internal_edges / max_edges, 4) if max_edges > 0 else 0.0
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# Top topics by frequency
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top_topics = sorted(cluster_topics.items(), key=lambda x: x[1], reverse=True)[:5]
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clusters.append({
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"cluster_id": cluster_id,
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"size": n,
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"entries": component,
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"top_topics": [t for t, _ in top_topics],
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"internal_edges": internal_edges,
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"density": density,
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})
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cluster_id += 1
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clusters.sort(key=lambda c: c["size"], reverse=True)
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return clusters
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def hub_entries(self, limit: int = 10) -> list[dict]:
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"""Find the most connected entries (highest degree centrality).
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These are the "hubs" of the holographic graph — entries that bridge
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many topics and attract many links.
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Args:
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limit: Maximum number of hubs to return.
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Returns:
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List of dicts with keys: entry, degree, inbound, outbound, topics
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"""
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adj = self._build_adjacency()
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inbound: dict[str, int] = {eid: 0 for eid in self._entries}
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for entry in self._entries.values():
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for lid in entry.links:
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if lid in inbound:
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inbound[lid] += 1
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hubs = []
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for eid, entry in self._entries.items():
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degree = len(adj.get(eid, set()))
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if degree == 0:
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continue
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hubs.append({
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"entry": entry,
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"degree": degree,
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"inbound": inbound.get(eid, 0),
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"outbound": len(entry.links),
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"topics": entry.topics,
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})
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hubs.sort(key=lambda h: h["degree"], reverse=True)
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return hubs[:limit]
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def bridge_entries(self) -> list[dict]:
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"""Find articulation points — entries whose removal would split a cluster.
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These are "bridge" entries in the holographic graph. Removing them
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disconnects members that were previously reachable through the bridge.
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Uses Tarjan's algorithm for finding articulation points.
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Returns:
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List of dicts with keys: entry, cluster_size, bridges_between
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"""
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adj = self._build_adjacency()
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# Find clusters first
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clusters = self.graph_clusters(min_size=3)
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if not clusters:
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return []
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# For each cluster, run Tarjan's algorithm
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bridges: list[dict] = []
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for cluster in clusters:
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members = set(cluster["entries"])
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if len(members) < 3:
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continue
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# Build subgraph adjacency
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sub_adj = {eid: adj[eid] & members for eid in members}
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# Tarjan's DFS for articulation points
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discovery: dict[str, int] = {}
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low: dict[str, int] = {}
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parent: dict[str, Optional[str]] = {}
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ap: set[str] = set()
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timer = [0]
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def dfs(u: str):
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children = 0
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discovery[u] = low[u] = timer[0]
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timer[0] += 1
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for v in sub_adj[u]:
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if v not in discovery:
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children += 1
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parent[v] = u
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dfs(v)
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low[u] = min(low[u], low[v])
|
|
|
|
# u is AP if: root with 2+ children, or non-root with low[v] >= disc[u]
|
|
if parent.get(u) is None and children > 1:
|
|
ap.add(u)
|
|
if parent.get(u) is not None and low[v] >= discovery[u]:
|
|
ap.add(u)
|
|
elif v != parent.get(u):
|
|
low[u] = min(low[u], discovery[v])
|
|
|
|
for eid in members:
|
|
if eid not in discovery:
|
|
parent[eid] = None
|
|
dfs(eid)
|
|
|
|
# For each articulation point, estimate what it bridges
|
|
for ap_id in ap:
|
|
ap_entry = self._entries[ap_id]
|
|
# Remove it temporarily and count resulting components
|
|
temp_adj = {k: v.copy() for k, v in sub_adj.items()}
|
|
del temp_adj[ap_id]
|
|
for k in temp_adj:
|
|
temp_adj[k].discard(ap_id)
|
|
|
|
# BFS count components after removal
|
|
temp_visited: set[str] = set()
|
|
component_count = 0
|
|
for mid in members:
|
|
if mid == ap_id or mid in temp_visited:
|
|
continue
|
|
component_count += 1
|
|
queue = [mid]
|
|
while queue:
|
|
cur = queue.pop(0)
|
|
if cur in temp_visited:
|
|
continue
|
|
temp_visited.add(cur)
|
|
for nb in temp_adj.get(cur, set()):
|
|
if nb not in temp_visited:
|
|
queue.append(nb)
|
|
|
|
if component_count > 1:
|
|
bridges.append({
|
|
"entry": ap_entry,
|
|
"cluster_size": cluster["size"],
|
|
"components_after_removal": component_count,
|
|
"topics": ap_entry.topics,
|
|
})
|
|
|
|
bridges.sort(key=lambda b: b["components_after_removal"], reverse=True)
|
|
return bridges
|
|
|
|
def add_tags(self, entry_id: str, tags: list[str]) -> ArchiveEntry:
|
|
"""Add new tags to an existing entry (deduplicates, case-preserving).
|
|
|
|
Args:
|
|
entry_id: ID of the entry to update.
|
|
tags: Tags to add. Already-present tags (case-insensitive) are skipped.
|
|
|
|
Returns:
|
|
The updated ArchiveEntry.
|
|
|
|
Raises:
|
|
KeyError: If entry_id does not exist.
|
|
"""
|
|
entry = self._entries.get(entry_id)
|
|
if entry is None:
|
|
raise KeyError(entry_id)
|
|
existing_lower = {t.lower() for t in entry.topics}
|
|
for tag in tags:
|
|
if tag.lower() not in existing_lower:
|
|
entry.topics.append(tag)
|
|
existing_lower.add(tag.lower())
|
|
self._save()
|
|
return entry
|
|
|
|
def remove_tags(self, entry_id: str, tags: list[str]) -> ArchiveEntry:
|
|
"""Remove specific tags from an existing entry (case-insensitive match).
|
|
|
|
Args:
|
|
entry_id: ID of the entry to update.
|
|
tags: Tags to remove. Tags not present are silently ignored.
|
|
|
|
Returns:
|
|
The updated ArchiveEntry.
|
|
|
|
Raises:
|
|
KeyError: If entry_id does not exist.
|
|
"""
|
|
entry = self._entries.get(entry_id)
|
|
if entry is None:
|
|
raise KeyError(entry_id)
|
|
remove_lower = {t.lower() for t in tags}
|
|
entry.topics = [t for t in entry.topics if t.lower() not in remove_lower]
|
|
self._save()
|
|
return entry
|
|
|
|
def retag(self, entry_id: str, tags: list[str]) -> ArchiveEntry:
|
|
"""Replace all tags on an existing entry (deduplicates new list).
|
|
|
|
Args:
|
|
entry_id: ID of the entry to update.
|
|
tags: New tag list. Duplicates (case-insensitive) are collapsed.
|
|
|
|
Returns:
|
|
The updated ArchiveEntry.
|
|
|
|
Raises:
|
|
KeyError: If entry_id does not exist.
|
|
"""
|
|
entry = self._entries.get(entry_id)
|
|
if entry is None:
|
|
raise KeyError(entry_id)
|
|
seen: set[str] = set()
|
|
deduped: list[str] = []
|
|
for tag in tags:
|
|
if tag.lower() not in seen:
|
|
seen.add(tag.lower())
|
|
deduped.append(tag)
|
|
entry.topics = deduped
|
|
self._save()
|
|
return entry
|
|
|
|
@staticmethod
|
|
def _parse_dt(dt_str: str) -> datetime:
|
|
"""Parse an ISO datetime string. Assumes UTC if no timezone is specified."""
|
|
dt = datetime.fromisoformat(dt_str)
|
|
if dt.tzinfo is None:
|
|
dt = dt.replace(tzinfo=timezone.utc)
|
|
return dt
|
|
|
|
def by_date_range(self, start: str, end: str) -> list[ArchiveEntry]:
|
|
"""Return entries whose ``created_at`` falls within [start, end] (inclusive).
|
|
|
|
Args:
|
|
start: ISO datetime string for the range start (e.g. "2024-01-01" or
|
|
"2024-01-01T00:00:00Z"). Timezone-naive strings are treated as UTC.
|
|
end: ISO datetime string for the range end. Timezone-naive strings are
|
|
treated as UTC.
|
|
|
|
Returns:
|
|
List of ArchiveEntry sorted by ``created_at`` ascending.
|
|
"""
|
|
start_dt = self._parse_dt(start)
|
|
end_dt = self._parse_dt(end)
|
|
results = []
|
|
for entry in self._entries.values():
|
|
entry_dt = self._parse_dt(entry.created_at)
|
|
if start_dt <= entry_dt <= end_dt:
|
|
results.append(entry)
|
|
results.sort(key=lambda e: e.created_at)
|
|
return results
|
|
|
|
def temporal_neighbors(self, entry_id: str, window_days: int = 7) -> list[ArchiveEntry]:
|
|
"""Return entries created within ``window_days`` of a given entry.
|
|
|
|
The reference entry itself is excluded from results.
|
|
|
|
Args:
|
|
entry_id: ID of the anchor entry.
|
|
window_days: Number of days around the anchor's ``created_at`` to search.
|
|
|
|
Returns:
|
|
List of ArchiveEntry sorted by ``created_at`` ascending.
|
|
|
|
Raises:
|
|
KeyError: If ``entry_id`` does not exist in the archive.
|
|
"""
|
|
anchor = self._entries.get(entry_id)
|
|
if anchor is None:
|
|
raise KeyError(entry_id)
|
|
anchor_dt = self._parse_dt(anchor.created_at)
|
|
delta = timedelta(days=window_days)
|
|
window_start = anchor_dt - delta
|
|
window_end = anchor_dt + delta
|
|
results = []
|
|
for entry in self._entries.values():
|
|
if entry.id == entry_id:
|
|
continue
|
|
entry_dt = self._parse_dt(entry.created_at)
|
|
if window_start <= entry_dt <= window_end:
|
|
results.append(entry)
|
|
results.sort(key=lambda e: e.created_at)
|
|
return results
|
|
|
|
def rebuild_links(self, threshold: Optional[float] = None) -> int:
|
|
"""Recompute all links from scratch.
|
|
|
|
Clears existing links and re-applies the holographic linker to every
|
|
entry pair. Useful after bulk ingestion or threshold changes.
|
|
|
|
Args:
|
|
threshold: Override the linker's default similarity threshold.
|
|
|
|
Returns:
|
|
Total number of links created.
|
|
"""
|
|
if threshold is not None:
|
|
old_threshold = self.linker.threshold
|
|
self.linker.threshold = threshold
|
|
|
|
# Clear all links
|
|
for entry in self._entries.values():
|
|
entry.links = []
|
|
|
|
entries = list(self._entries.values())
|
|
total_links = 0
|
|
|
|
# Re-link each entry against all others
|
|
for entry in entries:
|
|
candidates = [e for e in entries if e.id != entry.id]
|
|
new_links = self.linker.apply_links(entry, candidates)
|
|
total_links += new_links
|
|
|
|
if threshold is not None:
|
|
self.linker.threshold = old_threshold
|
|
|
|
self._save()
|
|
return total_links
|