Retrieval and Recall

This guide explains how Mnemosyne retrieves relevant memories from the knowledge graph, how scoring works, and how to tune retrieval parameters.

How Recall Works

When you call Mnemosyne.recall/3, the retrieval pipeline runs through four stages:

1. Mode Classification

The query is classified into one of four retrieval modes using an LLM call:

2. Tag Generation

The pipeline generates retrieval tags -- short concept phrases that capture the query's key aspects. These tags are embedded alongside the query itself, giving the retrieval system multiple vectors to match against.

3. Candidate Scoring (Hop 0)

The graph backend scores all nodes of the target types against both the query embedding and tag embeddings. Each node's relevance is the maximum of:

• Cosine similarity with the query vector
• Maximum cosine similarity across all tag vectors

This raw relevance is then combined with node metadata through the value function:

score = relevance * recency_factor * frequency_factor * reward_factor

Candidates below the per-type threshold are filtered out, and only the top-k per type survive.

4. Multi-Hop Traversal

After the initial candidates are found, the pipeline expands through routing nodes (Tags for semantic mode, Intents for procedural mode) to discover related knowledge:

Candidate semantic node
  --> linked Tag nodes
    --> other semantic nodes linked to those Tags
      --> score and merge with initial candidates

This runs for up to max_hops iterations (default: 2), each time expanding the candidate set through routing nodes and re-ranking.

For episodic mode, an additional provenance expansion step follows Source nodes back to their originating episodes, applying a decay factor (0.5) to the parent's score.

Value Function

The default value function (Mnemosyne.ValueFunction.Default) scores nodes using four multiplicative factors:

Relevance

Cosine similarity between the query/tag embeddings and the node's embedding. This is the primary signal.

Recency Factor

Exponential decay based on hours since last access:

recency = exp(-lambda * hours_since_last_access)

Default lambda: 0.01. At this rate, a node accessed 24 hours ago retains ~78% of its recency score; after a week, ~19%.

Frequency Factor

Saturating function of access count:

frequency = max(base_floor, access_count / (access_count + k))

Default k: 5, base_floor: 0.3. A node accessed once scores 0.3; five times scores 0.5; twenty times scores 0.8.

Reward Factor

Sigmoid of average cumulative reward:

reward = 1 / (1 + exp(-beta * avg_reward))

Default beta: 1.0. Nodes from high-reward trajectories score higher. Nodes with no reward history score 1.0 (neutral).

Tuning Parameters

Value function parameters are configured per node type in Mnemosyne.Config:

config = %Mnemosyne.Config{
  llm: %{model: "gpt-4o-mini", opts: %{}},
  embedding: %{model: "text-embedding-3-small", opts: %{}},
  value_function: %{
    module: Mnemosyne.ValueFunction.Default,
    params: %{
      semantic: %{threshold: 0.1, top_k: 30, lambda: 0.005, k: 10, base_floor: 0.2, beta: 1.5},
      procedural: %{threshold: 0.8, top_k: 10, lambda: 0.01, k: 5, base_floor: 0.3, beta: 1.0},
      episodic: %{threshold: 0.0, top_k: 30, lambda: 0.01, k: 5, base_floor: 0.3, beta: 1.0}
    }
  }
}

Parameter Reference

Default Thresholds by Type

Procedural nodes have a high threshold (0.8) because vague procedural matches tend to be unhelpful. Semantic and episodic nodes use 0.0 to let the value function handle ranking.

These defaults can be overridden per node type via extraction profiles, which merge domain-specific parameter tweaks on top of config-level settings.

Context-Aware Recall

When recalling during an active session, use recall_in_context/4:

{:ok, memories} = Mnemosyne.recall_in_context("my-repo", session_id, "What patterns apply?")

This augments the query with the session's current goal and recent steps, producing more targeted results. If the session is not found, it falls back to a plain recall/3.

Custom Value Functions

Implement the Mnemosyne.ValueFunction behaviour to create your own scoring logic:

defmodule MyApp.ValueFunction do
  @behaviour Mnemosyne.ValueFunction

  @impl true
  def score(relevance, node, metadata, params) do
    # Your scoring logic here
    # node gives you access to the node struct and type
    # metadata is a NodeMetadata struct (or nil)
    # params are the per-type config values
    relevance
  end
end

Register it in your config:

value_function: %{
  module: MyApp.ValueFunction,
  params: %{...}
}

Result Structure

recall/3 runs the retrieval pipeline and then passes the candidates through a reasoning step that synthesizes them into typed summaries using LLM calls. The final result is a RecallResult struct:

%Mnemosyne.Pipeline.RecallResult{
  reasoned: %Mnemosyne.Pipeline.Reasoning.ReasonedMemory{
    episodic: "The user previously planned a trip to Kyoto...",
    semantic: "The user prefers 2-week trips and travels in March...",
    procedural: "When discussing destinations, first confirm travel dates..."
  },
  touched_nodes: [
    %Mnemosyne.Pipeline.Retrieval.TouchedNode{
      id: "sem_42", type: :semantic, score: 0.92, phase: :initial, hop: 0
    },
    %Mnemosyne.Pipeline.Retrieval.TouchedNode{
      id: "ep_7", type: :episodic, score: 0.85, phase: :multi_hop, hop: 1
    }
  ],
  trace: %Mnemosyne.Notifier.Trace.Recall{
    mode: :mixed,
    tags: ["travel", "preferences"],
    candidate_count: 5,
    phase_timings: %{hop_0: 1200, multi_hop: 3400, ...}
  }
}

The reasoned field contains natural-language summaries per memory type (nil when no candidates of that type were found). The reasoning step runs in parallel across the three types.

The touched_nodes field lists every node that contributed to the result, sorted by score. Each entry carries the node's origin phase (:initial, :multi_hop, :refinement, or :provenance) and hop number. When config.trace_verbosity is :detailed, the full node struct is included in the node field; at :summary (the default) it is nil.

The trace field provides execution metadata: classified mode, generated tags, per-phase timings, candidate counts per hop, and composite scores per node.

Next Steps

• Core Concepts - understand the knowledge graph structure
• Extraction Profiles - domain-specific tuning for extraction and retrieval
• Graph Maintenance - consolidation and decay
• Custom Backends - implement your own graph backend with optimized queries