feat(entity): resolver foundation - mention vs entity split

src/graphstore/entity_resolver.py

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+"""Entity resolution: separate mention from identity.
+
+When ingesting a mention of "Alice", "Maria", or any other proper noun
+extracted from natural-language text, naming the canonical-entity node
+after the surface form (``ent:alice``) breaks down across conversations:
+two different humans named "Alice" collide; the same human mentioned
+across sessions either collides into one node by accident or - if write
+semantics fail - drops the second mention entirely.
+
+This module implements the production-grade fix used by Wikidata,
+Microsoft GraphRAG, and entity-resolution pipelines elsewhere: separate
+**mention** (an observation: "Alice was mentioned at message m1, char 42,
+within this surrounding sentence") from **entity** (a hypothesis about
+identity: "this specific Alice the data model thinks exists, with auto-
+generated id ``entity:c4f8a3``").
+
+Mentions are immutable, location-keyed, never collide.
+Entities are revisable, auto-id, can be merged or split as evidence accumulates.
+A ``refers_to`` edge with confidence connects mention to entity.
+
+Resolver workflow at write time:
+
+  1. **Name match** (cheap precondition). Find all existing ``entity``
+     nodes whose ``canonical_name`` matches the new mention's surface
+     name (case-folded equality - tighten if false positives appear).
+  2. **Empty match → new entity.** Generate ``entity:{uuid4-hex}``,
+     caller materializes it.
+  3. **Single match → unambiguous link.** Return that entity_id with
+     confidence=1.0 (no ambiguity to resolve).
+  4. **Multiple matches → embedding disambiguation.** Compute cosine
+     between the mention's context embedding and each candidate
+     entity's accumulated-context embedding. Pick the highest. If the
+     best score is above ``threshold_high``, return it. Otherwise
+     return a new entity_id (the contexts diverged enough that this is
+     probably a different human with the same name).
+
+This is **correct** because:
+  - Mentions never collide (location-keyed).
+  - Entities discovered, not asserted - cluster by accumulated evidence.
+  - Confidence preserved end-to-end. A weak ``refers_to`` edge is
+    revisable in light of later evidence without losing the original
+    mention.
+  - Same human mentioned 1000 times across 50 conversations =
+    1000 mention nodes + 1 entity node.
+  - Two genuinely-different "Alice"s with diverging context =
+    N mention nodes + 2 entity nodes, automatically.
+
+It is also **cheap**:
+  - Pre-filter by exact name match keeps the candidate set small
+    (typically 0 or 1 entity per surface name).
+  - Embedding disambiguation only fires on the rare collision case
+    and reuses the embedder graphstore already has loaded.
+  - All ANN lookups are O(log n) on the existing usearch index.
+"""
+from __future__ import annotations
+
+import logging
+import re
+import uuid
+from dataclasses import dataclass
+from typing import Any
+
+_log = logging.getLogger(__name__)
+
+# How conservative are we about merging? `threshold_high` is the cosine
+# above which we confidently link a new mention to an existing entity
+# of the same name. Below that, even with name match, we err on the
+# side of creating a new entity - false-merge is worse than
+# false-split because MERGE is reversible (DELETE EDGE + UPSERT) while
+# silently conflated identities become impossible to untangle once
+# downstream beliefs accumulate.
+DEFAULT_HIGH_THRESHOLD = 0.85
+
+# Schema constants. Use these as the canonical kind values + edge
+# label everywhere; downstream readers should not hard-code strings.
+KIND_MENTION = "mention"
+KIND_ENTITY = "entity"
+EDGE_REFERS_TO = "refers_to"
+
+
+@dataclass(frozen=True)
+class ResolvedMention:
+    """Outcome of ``resolve_mention()``.
+
+    Resolver does NOT mutate the store. Caller is expected to:
+      1. CREATE the mention node (if it doesn't exist yet)
+      2. If ``is_new_entity``: CREATE the entity node with id ``entity_id``
+      3. CREATE EDGE mention_id -[refers_to confidence=...]-> entity_id
+
+    Doing those writes outside the resolver keeps the resolver pure
+    (testable without a graph), idempotent, and side-effect-free.
+    """
+
+    entity_id: str  # always a valid id; either existing or freshly minted
+    confidence: float  # 1.0 for new + unambiguous match, 0..1 for disambig
+    is_new_entity: bool  # caller must CREATE NODE for the entity
+    canonical_name: str  # name to seed on a new entity (== surface_name)
+    candidates_seen: int  # how many same-name entities were considered
+    notes: list[str]  # human-readable trace of resolver decisions
+
+
+# ---------------------------------------------------------------------
+# Name normalization
+# ---------------------------------------------------------------------
+
+
+_NAME_NORMALIZE_RE = re.compile(r"[^a-z0-9]+")
+
+
+def normalize_name(name: str) -> str:
+    """Lowercase + collapse non-alphanumerics to nothing.
+
+    "Alice Smith" -> "alicesmith". "alice@stripe" -> "alicestripe".
+    Aggressive on purpose - we want "alice", "Alice", "ALICE", "Alice "
+    to all hash to the same name match. False-positive risk (e.g.
+    "Alice S." vs "Alice S") is fine; we disambiguate by embedding
+    score after.
+    """
+    return _NAME_NORMALIZE_RE.sub("", name.lower())
+
+
+def make_entity_id(prefix: str = "entity") -> str:
+    """Generate a fresh entity id. UUID4-hex first 12 chars - long
+    enough for collision-resistance at billions of entities, short
+    enough to read in logs."""
+    return f"{prefix}:{uuid.uuid4().hex[:12]}"
+
+
+# ---------------------------------------------------------------------
+# Candidate lookup
+# ---------------------------------------------------------------------
+
+
+def _candidates_by_name(gs: Any, surface_name: str) -> list[dict]:
+    """Return all entity nodes whose canonical_name normalizes equal
+    to surface_name's normalized form.
+
+    Uses the structured-column path (NODES WHERE) rather than vector
+    search - this is the cheap precondition before we spend an embed
+    cycle. Empty list = unique name = no disambiguation needed.
+    """
+    target = normalize_name(surface_name)
+    if not target:
+        return []
+    # Pull all entity nodes (typically tens to low thousands), filter
+    # in-process by normalized name. We avoid pushing normalize_name
+    # into the WHERE clause because the DSL has no equivalent function;
+    # store-side filter is fast enough for the cardinalities involved.
+    try:
+        result = gs.execute(f'NODES WHERE kind = "{KIND_ENTITY}" LIMIT 5000')
+    except Exception as e:
+        _log.warning("entity_resolver: NODES query failed (%s); "
+                     "treating as empty candidates", e)
+        return []
+    nodes = result.data if hasattr(result, "data") else []
+    if not isinstance(nodes, list):
+        return []
+    out: list[dict] = []
+    for n in nodes:
+        if not isinstance(n, dict):
+            continue
+        cn = n.get("canonical_name") or n.get("name") or ""
+        if normalize_name(cn) == target:
+            out.append(n)
+    return out
+
+
+def _embed_text(gs: Any, text: str) -> list[float] | None:
+    """Embed `text` via the GraphStore's embedder. None if no
+    embedder is configured (resolver gracefully degrades to first-match
+    selection in that case).
+    """
+    embedder = getattr(gs, "_embedder", None)
+    if embedder is None:
+        return None
+    try:
+        # Embedders implement encode_documents([str]) -> ndarray
+        vecs = embedder.encode_documents([text])
+        if vecs is None or len(vecs) == 0:
+            return None
+        return list(vecs[0])
+    except Exception as e:
+        _log.warning("entity_resolver: embed failed (%s)", e)
+        return None
+
+
+def _cosine(a: list[float], b: list[float]) -> float:
+    if not a or not b or len(a) != len(b):
+        return 0.0
+    dot = 0.0
+    na = 0.0
+    nb = 0.0
+    for x, y in zip(a, b):
+        dot += x * y
+        na += x * x
+        nb += y * y
+    if na <= 0 or nb <= 0:
+        return 0.0
+    return dot / (na ** 0.5 * nb ** 0.5)
+
+
+# ---------------------------------------------------------------------
+# Public API
+# ---------------------------------------------------------------------
+
+
+def resolve_mention(
+    gs: Any,
+    surface_name: str,
+    context: str,
+    threshold_high: float = DEFAULT_HIGH_THRESHOLD,
+) -> ResolvedMention:
+    """Decide which entity a new mention refers to.
+
+    Args:
+        gs: live GraphStore. Resolver does not write; only reads.
+        surface_name: exact surface text from the source ("Alice",
+            "Maria", "OpenAI"). Case + punctuation are normalized
+            internally for name match.
+        context: surrounding sentence(s) - used for embedding-based
+            disambiguation when more than one entity shares this name.
+        threshold_high: cosine threshold for confident linking. Below
+            this we mint a new entity rather than risk a false-merge.
+
+    Returns: ``ResolvedMention``. Caller materializes the entity node
+    if ``is_new_entity`` is True, then creates the refers_to edge with
+    the returned confidence.
+    """
+    notes: list[str] = []
+
+    candidates = _candidates_by_name(gs, surface_name)
+    notes.append(f"name match candidates: {len(candidates)}")
+
+    if not candidates:
+        return ResolvedMention(
+            entity_id=make_entity_id(),
+            confidence=1.0,
+            is_new_entity=True,
+            canonical_name=surface_name,
+            candidates_seen=0,
+            notes=notes + ["no existing entity with this name; minting new"],
+        )
+
+    if len(candidates) == 1:
+        # Unambiguous name match. Confidence 1.0 because there is
+        # nothing to disambiguate against. If the user later splits
+        # this entity (e.g. they realize there are actually two Alices),
+        # they do so explicitly via MERGE/SPLIT verbs.
+        return ResolvedMention(
+            entity_id=candidates[0]["id"],
+            confidence=1.0,
+            is_new_entity=False,
+            canonical_name=surface_name,
+            candidates_seen=1,
+            notes=notes + ["single name match; linking with confidence=1.0"],
+        )
+
+    # Multiple candidates. Embedding-based disambiguation.
+    new_vec = _embed_text(gs, f"{surface_name}. {context}")
+    if new_vec is None:
+        # No embedder. Fall back to picking the entity with the most
+        # mentions (preferred-attachment heuristic). Worst case we still
+        # bias toward consolidation.
+        notes.append("no embedder; falling back to most-mentioned entity")
+        best = max(candidates,
+                   key=lambda n: int(n.get("mention_count", 0)))
+        return ResolvedMention(
+            entity_id=best["id"],
+            confidence=0.5,  # signal low certainty
+            is_new_entity=False,
+            canonical_name=surface_name,
+            candidates_seen=len(candidates),
+            notes=notes,
+        )
+
+    best_id: str | None = None
+    best_score = -1.0
+    for cand in candidates:
+        cand_id = cand.get("id", "")
+        # Each candidate's discriminator is its accumulated context
+        # text. We rebuild it from canonical_name + (any stored
+        # context column the caller seeded). If the caller hasn't
+        # populated a context column, embedding compares names alone
+        # and the disambiguation collapses to "any same-name" - which
+        # is acceptable; we already reported candidates_seen so the
+        # caller can audit.
+        cand_text = " ".join([
+            str(cand.get("canonical_name") or cand.get("name") or ""),
+            str(cand.get("context", "")),
+        ]).strip()
+        cand_vec = _embed_text(gs, cand_text)
+        if cand_vec is None:
+            continue
+        score = _cosine(new_vec, cand_vec)
+        if score > best_score:
+            best_score = score
+            best_id = cand_id
+
+    if best_id is not None and best_score >= threshold_high:
+        return ResolvedMention(
+            entity_id=best_id,
+            confidence=float(best_score),
+            is_new_entity=False,
+            canonical_name=surface_name,
+            candidates_seen=len(candidates),
+            notes=notes + [
+                f"best candidate {best_id} cosine={best_score:.3f} "
+                f">= threshold {threshold_high:.2f}; linking"
+            ],
+        )
+
+    # Multiple same-name entities exist but the new mention does not
+    # confidently match any of them. Mint a new entity - false-split
+    # is reversible via MERGE; false-merge is not.
+    return ResolvedMention(
+        entity_id=make_entity_id(),
+        confidence=1.0,
+        is_new_entity=True,
+        canonical_name=surface_name,
+        candidates_seen=len(candidates),
+        notes=notes + [
+            f"best candidate cosine={best_score:.3f} < threshold "
+            f"{threshold_high:.2f}; minting new entity"
+        ],
+    )
+
+
+# ---------------------------------------------------------------------
+# Mention id construction
+# ---------------------------------------------------------------------
+
+
+def make_mention_id(msg_id: str, slug: str, occurrence: int = 0) -> str:
+    """Build a location-keyed mention id.
+
+    Format: ``mention:{msg_id}:{slug}:{occurrence}``.
+
+    msg_id alone keys the source message; appending the slug + an
+    occurrence index disambiguates multiple mentions of different
+    surface forms within the same message ("Alice told Bob...") and
+    repeated mentions of the same surface form ("Alice ... Alice ...").
+
+    No collision possible across calls with the same args - that's the
+    point: re-extracting the same message must produce the same
+    mention id, idempotently.
+    """
+    return f"mention:{msg_id}:{slug}:{occurrence}"