Memory Retrieval Overview

🔍 Retrieval Stack Overview

Spector features a unified 4-layer hybrid retrieval stack that acts as the candidate generation and reranking stages of the Recall Pathway. Rather than relying on simple semantic vector matches, Spector combines lexical precision, learned sparse expansions, deep late-interaction reranking, and multi-layer graph traversal in a single cohesive pass.

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🏗️ The Recall Pathway Flow

Spector coordinates query execution by retrieving candidates in parallel from the first three layers, merging them, reranking them, and finally traversing the cognitive graph to discover associated memories:

flowchart TD
    Query["👤 Input Query"] --> Dense["🧠 Layer 1: Dense Similarity<br/>(HNSW Vector Index)"]
    Query --> Lexical["📝 Layer 2: Lexical Matching<br/>(BM25 Index)"]
    Query --> Sparse["📈 Layer 3: Learned Sparse<br/>(SPLADE Expansion Index)"]

    Dense --> RRF["🧬 Reciprocal Rank Fusion (RRF)<br/>(First-Stage Merge)"]
    Lexical --> RRF
    Sparse --> RRF

    RRF --> Rerank["🚀 Layer 4: ColBERT Reranker<br/>(Token-Level MaxSim Rerank)"]
    
    Rerank --> Graph["🔗 Cognitive Graph Traversal<br/>(Hebbian, Entity, Temporal, Episode)"]
    
    Graph --> Final["✨ Final Recalled Memories"]

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Layer	Primary Purpose	Best For
Layer 1: Dense Similarity	Semantic similarity retrieval	Conceptual matching, synonyms
Layer 2: Lexical BM25	Exact term and keyword matching	Code snippets, IDs, specific terms
Layer 3: Sparse (SPLADE)	Learned sparse / neural term expansion	Capturing keyword intent without exact matches
Layer 4: ColBERT v2	High-precision candidate reranking	Complex reasoning queries, grounding context

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🎛️ Retrieval Modes

You can control which layers of the retrieval stack are active during memory recall by configuring the retrieval mode:

Mode	Active Layers	Description
HYBRID (Default)	BM25 + Dense Vector	Combines lexical exact-matches with semantic vector recall, merged via RRF.
KEYWORD_ONLY	BM25 only	Bypasses dense vector computations. Best for specific error codes or names.
VECTOR_ONLY	Dense Vector only	Pure semantic similarity search.
SPLADE	SPLADE only	Uses learned sparse representations with term expansions only.
SPLADE_HYBRID	SPLADE + Dense Vector	Combines semantic recall with neural term expansion, bypassing BM25.
LI_LSR	Li-LSR only	Fast, inference-free learned sparse retrieval utilizing precomputed tables.
COLBERT_RERANK	BM25 + Vector + ColBERT Reranker	Runs first-stage hybrid search, then reranks candidates using ColBERT v2.
FULL_STACK	All Layers (Vector + BM25 + SPLADE + Colbert)	Maximum retrieval quality. Merges all first-stage signals and reranks with ColBERT.

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🧬 Reciprocal Rank Fusion (RRF)

When multiple first-stage layers are active (e.g., in HYBRID or FULL_STACK), Spector merges their ranked lists using Reciprocal Rank Fusion (RRF).

The RRF score for a candidate document $d$ is calculated as:

[RRF(d) = \sum_{m \in M} \frac{1}{k + r_m(d)}]

Where:

• $M$ is the set of active retrieval modes (e.g., Vector, BM25, SPLADE).
• $r_m(d)$ is the rank of document $d$ in retrieval mode $m$ (1-indexed). If the document is not retrieved by mode $m$, $r_m(d) = \infty$.
• $k$ is a constant smoothing parameter (defaults to $60$).

RRF ensures that documents ranked highly across multiple retrieval strategies are elevated to the top of the final candidate list, regardless of the scale differences in their raw scores.

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🔗 Graph Traversal & Expansion

Once the retrieval stack generates and reranks the top candidate memories, Spector passes them to the Cognitive Graph Traversal stage. This phase discovers hidden connections and adds related context that first-stage similarity cannot find:

1. Hebbian Co-activation: Traverses strong co-activation edges to pull in memories frequently recalled together (spreading activation).
2. Entity-Relationship Graph: BFS traversal of extracted entities (e.g., people, projects, concepts) and their relationships to recall related structured facts.
3. Temporal Causal Chains: Follows chronological links forward and backward to reconstruct the event context of a conversation or activity.
4. Event-Episode (Hyperedge) Graph: Links groups of entities and actions belonging to a single temporal episode.

For a deep dive into graph parameters, thresholds, and spreading activation decay formulas, see the 4-Layer Cognitive Graph documentation.