velesdb_memory/

fused_recall.rs

//! [`MemoryService::recall_fused`]: vector recall combined with the graph
//! reach `why()` already walks, re-ranked by [`crate::fusion::fuse`]. Split
//! out of `service.rs` to keep that file under the crate's NLOC budget; a
//! child module of `service`, so it freely uses `MemoryService`'s private
//! fields and methods (`traverse`, `search`, `HUB_FIELD`, …).

use std::collections::HashMap;

use serde_json::Value;

use super::{
    reject_reserved_keys, strip_reserved_keys, MemoryService, Metadata, HUB_FIELD,
    MENTIONS_RELATION,
};
use crate::embedder::Embedder;
use crate::error::MemoryError;
use crate::fusion::{self, Candidate};
use crate::model::{FusionOptions, MemoryEdge, MemoryNode, Recollection};
use crate::rerank::Reranker;
use crate::storage::MemoryStore;

impl<E: Embedder, S: MemoryStore> MemoryService<E, S> {
    /// Fused recall: like [`Self::recall`], but also walks the graph from the
    /// query's top vector hit and folds any fact it reaches (hop ≥ 1) into the
    /// ranking, scored by `opts.graph_boost · graph_weight` on top of its
    /// normalised vector similarity. A fact the graph reaches never displaces
    /// a strong vector hit unless the boosted score genuinely outranks it; a
    /// fact the vector pool ranked low (or missed) can still surface if the
    /// graph connects it. This is the tri-engine ranking measured on
    /// HotpotQA/TimeQA/LoCoMo (`examples/multihop`, `examples/timeqa`,
    /// `examples/locomo`) — [`Self::recall`] stays pure-vector and unchanged,
    /// so existing callers see no behavior shift.
    ///
    /// The graph reach requires a wired graph to find anything: it walks
    /// edges from [`Self::relate`] or the entity hubs
    /// [`Self::remember_extracted`] auto-wires. Entity hubs themselves are
    /// never returned, exactly like [`Self::recall`].
    ///
    /// # Errors
    /// Returns [`MemoryError`] if embedding, vector search, or graph
    /// traversal fails.
    pub fn recall_fused(
        &self,
        query: &str,
        k: usize,
        filter: Option<&Metadata>,
        opts: FusionOptions,
    ) -> Result<Vec<Recollection>, MemoryError> {
        let query = query.trim();
        if query.is_empty() || k == 0 {
            return Ok(Vec::new());
        }
        let opts = opts.sanitized();
        reject_reserved_keys(filter)?;
        let embedding = self.embedder.embed(query)?;
        let pool = self.fused_pool(&embedding, pool_depth(k, opts), filter)?;
        let reached = self.graph_reached(&embedding, filter, opts.hops)?;
        Ok(fusion::fuse(pool, &reached, k, opts.graph_boost))
    }

    /// [`Self::recall_fused`] with the per-candidate score ventilation kept
    /// (normalised vector term, graph weight, fused score) — consumed by the
    /// context compiler's memory bridge, whose provenance records an
    /// explainable `relevance ∈ [0, 1]` per pulled memory. Same pipeline,
    /// same ordering, same numbers as [`Self::recall_fused`]; only the
    /// breakdown is kept instead of dropped.
    ///
    /// # Errors
    /// Returns [`MemoryError`] if embedding, vector search, or graph
    /// traversal fails.
    #[cfg(feature = "context")]
    pub(crate) fn recall_fused_scored(
        &self,
        query: &str,
        k: usize,
        filter: Option<&Metadata>,
        opts: FusionOptions,
    ) -> Result<Vec<fusion::ScoredCandidate>, MemoryError> {
        let query = query.trim();
        if query.is_empty() || k == 0 {
            return Ok(Vec::new());
        }
        let opts = opts.sanitized();
        reject_reserved_keys(filter)?;
        let embedding = self.embedder.embed(query)?;
        let pool = self.fused_pool(&embedding, pool_depth(k, opts), filter)?;
        let reached = self.graph_reached(&embedding, filter, opts.hops)?;
        Ok(fusion::fuse_scored(pool, &reached, k, opts.graph_boost))
    }

    /// [`Self::recall_fused`] paired with the dated-context rendering of its
    /// results: returns the recalled facts and the
    /// [`DatedContext`](crate::DatedContext) built from their `date_field`
    /// metadata (see [`format_dated_context`](crate::format_dated_context)).
    /// Every binding that exposes a "dated recall" (the MCP `recall_fused`
    /// tool's `date_field`, Node/WASM `recallFusedDated`) calls this, so the
    /// "recall then format" pairing lives in exactly one place and can't drift
    /// between surfaces.
    ///
    /// `date_field` can name any caller metadata key, but passing
    /// [`crate::storage::AUTO_DATE_FIELD`] needs zero setup: `remember`
    /// auto-stamps that key on every fact already, so a caller gets a correct
    /// `dated_context` without ever having managed a date field itself.
    ///
    /// # Errors
    /// Returns [`MemoryError`] if the underlying [`Self::recall_fused`] fails.
    pub fn recall_fused_dated(
        &self,
        query: &str,
        k: usize,
        filter: Option<&Metadata>,
        opts: FusionOptions,
        date_field: &str,
    ) -> Result<(Vec<Recollection>, crate::DatedContext), MemoryError> {
        let hits = self.recall_fused(query, k, filter, opts)?;
        let ctx = crate::format_dated_context(&hits, date_field);
        Ok((hits, ctx))
    }

    /// Like [`Self::recall_fused`], but hands the FULL fused-ranked candidate
    /// pool (before the final `k` cutoff) to `reranker` for a second-stage
    /// re-score, then truncates to `k`. Closes the ranking-miss gap the
    /// `LoCoMo` ceiling diagnostic found: a relevant fact can be IN the pool
    /// (recall@64 ≈ 89% on multi-hop) yet outranked out of a tight `k`
    /// (recall@8 ≈ 50%) — a reranker recovers it without widening `k` itself.
    ///
    /// No built-in reranker ships: bring your own (cross-encoder, LLM judge,
    /// …) via [`Reranker`]. Never call this as a default — a reranker can
    /// also *hurt* out-of-distribution conversational queries (measured on
    /// `LoCoMo`), so it is opt-in, one call at a time.
    ///
    /// # Errors
    /// Returns [`MemoryError`] if embedding, vector search, graph traversal,
    /// or `reranker` itself fails.
    pub fn recall_fused_reranked<R: Reranker>(
        &self,
        query: &str,
        k: usize,
        filter: Option<&Metadata>,
        opts: FusionOptions,
        reranker: &R,
    ) -> Result<Vec<Recollection>, MemoryError> {
        let query = query.trim();
        if query.is_empty() || k == 0 {
            return Ok(Vec::new());
        }
        let opts = opts.sanitized();
        reject_reserved_keys(filter)?;
        let embedding = self.embedder.embed(query)?;
        let depth = pool_depth(k, opts);
        let pool = self.fused_pool(&embedding, depth, filter)?;
        let reached = self.graph_reached(&embedding, filter, opts.hops)?;
        let fused = fusion::fuse(pool, &reached, depth, opts.graph_boost);
        let ranked = reranker.rerank(query, fused)?;
        Ok(ranked.into_iter().take(k).collect())
    }

    /// The oversampled vector pool [`Self::recall_fused`] re-ranks. One
    /// batched metadata lookup covers the whole pool (up to hundreds of ids
    /// at the deepest `pool_depth`), not one round trip per hit.
    fn fused_pool(
        &self,
        embedding: &[f32],
        depth: usize,
        filter: Option<&Metadata>,
    ) -> Result<Vec<Candidate>, MemoryError> {
        let hits = self.search(embedding, depth, filter)?;
        let ids: Vec<u64> = hits.iter().map(|(id, _, _)| *id).collect();
        let metadata = self.recall_metadata_batch(&ids)?;
        Ok(hits
            .into_iter()
            .zip(metadata)
            .map(|((id, score, content), metadata)| Candidate {
                recollection: Recollection {
                    id,
                    score,
                    content,
                    metadata,
                },
                vector_score: f64::from(score),
                graph_weight: 0.0,
            })
            .collect())
    }

    /// The caller-supplied metadata for every id in `ids` (reserved system
    /// keys excluded, `None` per-id when it carries none), in the same
    /// order — one batched storage round trip, so a `k`- or pool-sized
    /// result set (here, and in [`MemoryService::recall`]) costs one
    /// metadata lookup, not `k`/`pool_size` of them.
    pub(crate) fn recall_metadata_batch(
        &self,
        ids: &[u64],
    ) -> Result<Vec<Option<Metadata>>, MemoryError> {
        Ok(self
            .store
            .get_metadata_batch(ids)?
            .into_iter()
            .map(strip_reserved_keys)
            .collect())
    }

    /// Facts the graph reaches (hop ≥ 1) from the query's top vector seed,
    /// entity hubs excluded, each weighted by [`Self::reach_weight`]: a link
    /// through a rare, specific entity hub promotes harder than one through a
    /// generic mega-hub whose connections carry little signal — the idf lever
    /// validated on `HotpotQA` (+5.0pp both-facts-complete) and `LoCoMo` (turns
    /// the graph net-positive on multi-hop, no regression elsewhere).
    ///
    /// `filter` is re-checked against every reached fact's own metadata, not
    /// just the seed: the graph walk is otherwise filter-blind, so a fact
    /// outside the caller's scope (e.g. a different tenant/project) could
    /// leak in just by being graph-connected to the seed.
    fn graph_reached(
        &self,
        embedding: &[f32],
        filter: Option<&Metadata>,
        hops: usize,
    ) -> Result<Vec<Candidate>, MemoryError> {
        let seeds = self.search(embedding, 1, filter)?;
        let Some((seed_id, _score, seed_content)) = seeds.into_iter().next() else {
            return Ok(Vec::new());
        };
        let explanation = self.traverse(seed_id, seed_content, hops)?;
        let nodes: Vec<&MemoryNode> = explanation.nodes.iter().filter(|n| n.hop != 0).collect();
        let ids: Vec<u64> = nodes.iter().map(|n| n.id).collect();
        let raw_payloads = self.store.get_metadata_batch(&ids)?;
        let mentions_by_target = index_mentions_edges(&explanation.edges);

        let mut idf_cache: HashMap<u64, f64> = HashMap::new();
        let mut reached = Vec::new();
        for (node, raw) in nodes.into_iter().zip(raw_payloads) {
            if let Some(candidate) =
                self.reached_candidate(node, raw, &mentions_by_target, filter, &mut idf_cache)?
            {
                reached.push(candidate);
            }
        }
        Ok(reached)
    }

    /// The graph-reached candidate for `node` given its already-fetched raw
    /// payload `raw`, or `None` when it's an entity hub (internal
    /// scaffolding, never a caller fact) or outside `filter`'s scope — split
    /// out of [`Self::graph_reached`] to keep that loop's complexity within
    /// budget. `raw` is fetched once, batched across the whole traversal, by
    /// the caller — not per node — and serves both the hub check and the
    /// returned candidate's metadata: the hub flag lives under the reserved
    /// `_veles_hub` key, so it must be checked before `strip_reserved_keys`
    /// removes it for the caller-facing metadata. `idf_cache` memoizes
    /// [`Self::entity_idf`] per hub across the whole traversal (siblings
    /// under the same hub would otherwise recompute an identical value once
    /// per fact).
    fn reached_candidate(
        &self,
        node: &MemoryNode,
        raw: Option<Metadata>,
        mentions_by_target: &HashMap<u64, Vec<u64>>,
        filter: Option<&Metadata>,
        idf_cache: &mut HashMap<u64, f64>,
    ) -> Result<Option<Candidate>, MemoryError> {
        if raw
            .as_ref()
            .is_some_and(|meta| meta.get(HUB_FIELD) == Some(&Value::Bool(true)))
        {
            return Ok(None);
        }
        let metadata = strip_reserved_keys(raw);
        if !matches_filter(metadata.as_ref(), filter) {
            return Ok(None);
        }
        let weight = self.reach_weight(node.id, mentions_by_target, idf_cache)?;
        Ok(Some(Candidate {
            recollection: Recollection {
                id: node.id,
                score: 0.0,
                content: node.content.clone(),
                metadata,
            },
            vector_score: 0.0,
            graph_weight: weight,
        }))
    }

    /// The strength of the link(s) that reached `fact_id`: the maximum
    /// entity-idf ([`Self::entity_idf`]) over every hub `mentions_by_target`
    /// lists for it, or a flat `1.0` when it was reached through a direct
    /// (non-hub) [`Self::relate`] edge instead — idf has nothing to weight
    /// there, so the original flat signal is kept.
    fn reach_weight(
        &self,
        fact_id: u64,
        mentions_by_target: &HashMap<u64, Vec<u64>>,
        idf_cache: &mut HashMap<u64, f64>,
    ) -> Result<f64, MemoryError> {
        let Some(hub_ids) = mentions_by_target.get(&fact_id) else {
            return Ok(1.0);
        };
        let mut weight: Option<f64> = None;
        for &hub_id in hub_ids {
            let idf = self.cached_entity_idf(hub_id, idf_cache)?;
            weight = Some(weight.map_or(idf, |w: f64| w.max(idf)));
        }
        Ok(weight.unwrap_or(1.0))
    }

    /// [`Self::entity_idf`], memoized in `cache` for the lifetime of one
    /// [`Self::graph_reached`] call — sibling facts under the same hub would
    /// otherwise each pay a fresh `relations`+`count` store round trip for an
    /// identical value.
    fn cached_entity_idf(
        &self,
        hub_id: u64,
        cache: &mut HashMap<u64, f64>,
    ) -> Result<f64, MemoryError> {
        if let Some(&idf) = cache.get(&hub_id) {
            return Ok(idf);
        }
        let idf = self.entity_idf(hub_id)?;
        cache.insert(hub_id, idf);
        Ok(idf)
    }

    /// Normalised inverse document frequency of hub `hub_id`, in `[0, 1]`:
    /// `1` when it links a single fact (maximally specific), trending to `0`
    /// as it links ever more (a generic mega-hub whose links carry little
    /// answer signal). Mirrors the `LoCoMo` harness formula
    /// (`examples/locomo/ingest.rs`), using the store's total memory count
    /// (facts + hubs) as a corpus-size proxy.
    fn entity_idf(&self, hub_id: u64) -> Result<f64, MemoryError> {
        let degree = self.store.relations(hub_id)?.len();
        let n = self.store.count();
        if degree == 0 || n <= 1 {
            return Ok(0.0);
        }
        #[allow(clippy::cast_precision_loss)] // corpus/degree sizes are far below f64's exact range
        let (n, d) = (n as f64, degree as f64);
        Ok((n / d).ln() / n.ln())
    }
}

/// Index of `mentions` edges by target, built once per [`MemoryService::graph_reached`]
/// call: `fact_id -> [hub_id, ...]`. [`MemoryService::reach_weight`] used to
/// rescan every edge in the traversal for each reached node, making
/// `graph_reached` quadratic in the number of facts a hub mentions (a single
/// entity accumulating history is the product's nominal use case, not an edge
/// case). This index turns that per-node scan into an O(1) lookup, so the
/// whole pass over `edges` costs O(edges) once instead of O(edges) per node.
fn index_mentions_edges(edges: &[MemoryEdge]) -> HashMap<u64, Vec<u64>> {
    let mut index: HashMap<u64, Vec<u64>> = HashMap::new();
    for edge in edges {
        if edge.relation == MENTIONS_RELATION {
            index.entry(edge.to).or_default().push(edge.from);
        }
    }
    index
}

/// True when `filter` is absent, or every key in it matches `metadata`
/// exactly — the same "all filter keys must match" semantics
/// [`MemoryService::search`]'s vector-side filtering applies, now also
/// enforced on graph-reached facts so a caller-scoped `recall_fused` can't
/// leak a fact outside that scope just because it's graph-connected to the
/// seed.
fn matches_filter(metadata: Option<&Metadata>, filter: Option<&Metadata>) -> bool {
    let Some(filter) = filter else {
        return true;
    };
    // Mirrors velesdb-core's `payload_matches`: an empty (not absent) filter
    // matches everything, including a metadata-less fact — `Some({})` from a
    // caller (e.g. a JS `recallFused(q, k, {})`) must behave exactly like
    // `None`, not like "reject anything without metadata".
    if filter.is_empty() {
        return true;
    }
    let Some(metadata) = metadata else {
        return false;
    };
    filter.iter().all(|(k, v)| metadata.get(k) == Some(v))
}

/// The oversampled candidate pool depth for a `k`-sized fused recall:
/// `opts.pool` if the caller set one (floored at 1), else the proven default
/// ([`fusion::pool_size`]) — either way, capped at
/// [`crate::limits::MAX_RECALL_LIMIT`], the same `DoS` ceiling `k`/`hops`
/// carry. Both bounds live here, not at each binding's FFI boundary:
/// - the floor of 1 stops an explicit `pool` of 0 (a binding now exposes the
///   knob: `options={"pool": 0}` in Python) from oversampling *zero* candidates
///   and returning nothing. A caller can still deliberately narrow the pool
///   below the default (e.g. `pool: 1` to admit only the top vector hit — the
///   documented behavior fusion's tests pin); the floor only rules out the
///   degenerate empty-set case, it does not force a minimum recall depth.
/// - the cap bounds the default too: `k.saturating_mul(8)` exceeds the limit
///   well before `k` itself does, so even a caller who never touches `pool` is
///   bounded.
fn pool_depth(k: usize, opts: FusionOptions) -> usize {
    let depth = opts.pool.map_or_else(|| fusion::pool_size(k), |p| p.max(1));
    crate::limits::clamp_recall_limit(depth)
}