Skip to main content
Cognis uses a hybrid search pipeline that combines vector similarity and keyword matching for accurate retrieval. This is the same pipeline architecture used in the hosted Cognis service.

​
Pipeline Overview

  1. Query embedding — Generate 768D (full) and 256D (shortlist) embeddings via Gemini 2
  2. Matryoshka vector search — 256D shortlist pass narrows to 200 candidates, then 768D reranks for precision
  3. BM25 keyword search — SQLite FTS5 with Porter stemming for exact-match queries
  4. Immediate recall — 256D search over raw session messages for very recent context
  5. RRF fusion — Reciprocal Rank Fusion combines signals: 70% vector + 30% BM25
  6. Recency boost — Exponential decay (0.25 * exp(-age / 120s)) weights recent memories higher
  7. Temporal boosting — For time-based queries (“last week”), blends RRF (60%) with temporal relevance (40%)
  8. Deduplication — Content-level dedup and query echo filtering

​
Why Hybrid?

Vector search excels at semantic similarity (“Where does she work?” matches “Alice is employed at Google”) but misses exact terms. BM25 catches keyword matches that vectors may rank lower. RRF fusion gets the best of both without needing manual score calibration.

​
Matryoshka Embeddings

Cognis uses Gemini 2’s Matryoshka embeddings, which produce useful representations at multiple dimensions from a single embedding call:
  • 256D — Fast candidate shortlisting. Used for the first-pass filter and immediate recall
  • 768D — Full precision reranking. Used to re-score the shortlisted candidates
This two-stage approach is faster than a single 768D search over the full collection while maintaining accuracy.

​
RRF Fusion

Reciprocal Rank Fusion combines ranked lists without needing comparable scores: 
RRF_score = vector_weight * (1 / (k + vector_rank)) * (k + 1)
           + bm25_weight * (1 / (k + bm25_rank)) * (k + 1)
Default weights:
  • vector_weight = 0.70
  • bm25_weight = 0.30
  • k = 10
These defaults were tuned from ablation studies on the LoCoMo benchmark. You can adjust them in CognisConfig: 
from cognis import CognisConfig

config = CognisConfig(
    vector_weight=0.60,    # Less vector emphasis
    bm25_weight=0.40,      # More keyword emphasis
    rrf_k=15,              # Smoother rank distribution
)

​
Recency Boost

Recent memories get a score boost using exponential decay: 
recency_boost = 0.25 * exp(-age_seconds / 120.0)
A memory created 2 minutes ago gets a ~0.09 boost; one created 10 minutes ago gets ~0.002. Configurable via recency_boost_weight and recency_half_life_seconds.

​
Temporal Query Detection

When the query contains time references (“last week”, “yesterday”, “in March”), Cognis detects this and adjusts scoring: 
final_score = 0.6 * rrf_score + 0.4 * temporal_relevance + recency_boost
This ensures “what did I say yesterday?” surfaces memories from the right time window, not just the most semantically similar ones.

​
Search Scoping

  • Extracted memories are searched globally across all sessions for the owner/agent
  • Immediate recall (raw messages) is scoped to the current session only
  • Both are merged, deduplicated, and scored together

​
Tuning Tips

GoalConfig Change
More keyword-sensitiveIncrease bm25_weight, decrease vector_weight
Stricter resultsIncrease similarity_threshold (default: 0.3)
More candidatesIncrease shortlist_size (default: 200)
Less recency biasDecrease recency_boost_weight or increase recency_half_life_seconds