# Bounded Authority Multiplier in ai-memory: How It Affects Retrieval Relevance

> Discover the bounded authority multiplier in ai-memory. Learn how it re-weights search results using canonical metadata to boost authoritative knowledge and improve retrieval relevance.

- Repository: [Fabio Akita/ai-memory](https://github.com/akitaonrails/ai-memory)
- Tags: deep-dive
- Published: 2026-09-06

---

**The bounded authority multiplier is a post-fusion scoring adjustment in ai-memory that re-weights search results based on canonical page metadata—such as kind, tier, `pinned` status, and explicit tags—to prioritize authoritative project knowledge over transient content.**

The **bounded authority multiplier** is a core ranking mechanism in [ai-memory](https://github.com/akitaonrails/ai-memory) (akitaonrails/ai-memory), an open-source knowledge retrieval system designed to surface trustworthy project documentation. It operates as a policy-driven boost that adjusts fused relevance scores after combining full-text, lexical, and vector similarity streams, ensuring that canonical rules and procedures rank higher than ephemeral notes.

## How the Bounded Authority Multiplier Works

The retrieval pipeline in ai-memory fuses multiple relevance streams—including **FTS5** full-text search, lexical-entity matches, link-neighbour RRF (Reciprocal Rank Fusion), and optional vector similarity—into a single candidate score. The bounded authority multiplier is applied **after** these streams are combined but **before** the final result truncation.

This multiplier re-weights each candidate page according to its **canonical authority**, derived from front-matter metadata. The effect is non-linear: high-authority pages move up in the result list (receiving a lower rank value), while low-authority pages are pushed down, even when raw semantic or text scores are comparable.

### Authority Factors That Drive the Multiplier

Four primary metadata fields influence the bounded authority multiplier:

- **Page kind** – Pages categorized as rules, decisions, procedures, or gotchas receive higher authority boosts than narrative or transient content.
- **Tier** – The knowledge tier (working, episodic, semantic, procedural) determines base authority; procedural tiers outrank ephemeral working notes.
- **`pinned` flag** – Pinned pages receive the strongest authority multiplier and are exempt from temporal decay, ensuring persistent visibility.
- **Explicit tags** – Front-matter tags prefixed with `+` increase authority, while `-` tags decrease it, allowing fine-grained policy control.

## Implementation in the ai-memory Source Code

The architecture specification in [`docs/ARCHITECTURE.md`](https://github.com/akitaonrails/ai-memory/blob/main/docs/ARCHITECTURE.md) introduces the multiplier as a mechanism that "adjusts relevance using canonical page kind, tier, `pinned`, and explicit positive/negative front-matter tags" (lines 120–124).

The actual implementation resides in [`crates/ai-memory-store/src/lib.rs`](https://github.com/akitaonrails/ai-memory/blob/main/crates/ai-memory-store/src/lib.rs) around line 6890, where the final rank calculation multiplies the fused score by the authority factor. The authority factor itself is computed in [`crates/ai-memory-store/src/reader.rs`](https://github.com/akitaonrails/ai-memory/blob/main/crates/ai-memory-store/src/reader.rs), which parses page metadata to derive the composite authority score used during the final ranking phase.

## Impact on Search Result Ranking

The bounded authority multiplier ensures that **trustworthy, policy-level knowledge surfaces first**. When two pages have similar FTS5 or vector similarity scores, the one with higher canonical authority—such as a pinned procedural rule versus an episodic working note—will achieve a lower (better) rank value.

This design preserves the ability to retrieve less-authoritative content when needed, but prevents transient or test-level evidence from dominating results when canonical documentation exists.

## Query Examples

When executing a `memory_query`, the returned results automatically incorporate the bounded authority multiplier.

```rust
use ai_memory_store::MemoryStore;

#[tokio::main]
async fn main() -> anyhow::Result<()> {
    // Initialise the store pointing at the default data directory
    let store = MemoryStore::open_default().await?;

    // Perform a query – results are re-ranked by the bounded authority multiplier
    let results = store
        .memory_query("how to reset a project")
        .await?
        .take(10);

    for page in results {
        println!(
            "Score: {:.3} – Title: {} – Authority: {}",
            page.rank,              // lower = more relevant after multiplier
            page.title,
            page.authority_factor,  // value contributed by the multiplier
        );
    }

    Ok(())
}

```

For command-line usage, the CLI applies the same backend logic:

```bash

# Returns top 10 results with authority-weighted ranking

$ ai-memory query "how to reset a project" --limit 10

```

In both cases, the `rank` field reflects the fused score after the bounded authority multiplier has been applied.

## Summary

- The **bounded authority multiplier** is a post-fusion ranking adjustment in ai-memory that applies after combining FTS5, lexical, and vector streams.
- It re-weights results based on **canonical authority metadata**: page kind, tier, `pinned` status, and explicit `+`/`-` tags.
- **Pinned pages** receive the maximum authority boost and are immune to decay.
- The implementation spans [`docs/ARCHITECTURE.md`](https://github.com/akitaonrails/ai-memory/blob/main/docs/ARCHITECTURE.md) (design), [`crates/ai-memory-store/src/lib.rs`](https://github.com/akitaonrails/ai-memory/blob/main/crates/ai-memory-store/src/lib.rs) (application at line ~6890), and [`crates/ai-memory-store/src/reader.rs`](https://github.com/akitaonrails/ai-memory/blob/main/crates/ai-memory-store/src/reader.rs) (calculation).
- This mechanism ensures **authoritative project knowledge** surfaces above transient content without sacrificing recall.

## Frequently Asked Questions

### When is the bounded authority multiplier applied during query execution?

The multiplier is applied **after** the fusion of relevance streams (FTS5, lexical-entity, link-neighbour RRF, and vector similarity) but **before** the final truncation of results. This intermediate stage allows the system to re-rank candidates based on policy-driven metadata before returning the top-k results to the user.

### Which page metadata fields affect the authority multiplier?

Four fields determine the multiplier value: **page kind** (rule, decision, procedure, gotcha), **tier** (working, episodic, semantic, procedural), the **`pinned` boolean flag**, and **explicit tags** (`+` for positive authority, `-` for negative). These are defined in page front-matter and parsed during indexing.

### How does the `pinned` flag interact with the authority multiplier?

Pages marked with `pinned: true` receive the **strongest authority multiplier** available in the system. Additionally, pinned pages are exempt from temporal decay algorithms, meaning their authority remains constant over time, ensuring persistent high-ranking placement for critical project knowledge.

### Where in the source code is the bounded authority multiplier implemented?

The design is documented in [`docs/ARCHITECTURE.md`](https://github.com/akitaonrails/ai-memory/blob/main/docs/ARCHITECTURE.md) at lines 120–124. The application logic resides in [`crates/ai-memory-store/src/lib.rs`](https://github.com/akitaonrails/ai-memory/blob/main/crates/ai-memory-store/src/lib.rs) around line 6890, where the fused rank is multiplied by the authority factor. The underlying calculation of the authority factor from metadata occurs in [`crates/ai-memory-store/src/reader.rs`](https://github.com/akitaonrails/ai-memory/blob/main/crates/ai-memory-store/src/reader.rs).