LocalAI/docs/content/operations/middleware.md

+++
title = "Middleware: PII filtering and intelligent routing"
weight = 27
toc = true
url = "/features/middleware/"
description = "Per-model PII redaction and policy-based request routing"
tags = ["Routing", "Privacy", "PII", "Middleware", "Advanced"]
categories = ["Features"]
+++

![The request lifecycle: one shared hook chain for auth, model routing, and PII, with decision and event logs](/images/diagrams/middleware-lifecycle.png)

LocalAI ships a request-middleware layer that sits between the HTTP API and
the backend dispatcher. Two subsystems share that layer because they share
the same lifecycle hook: **PII filtering** scans the request body before it
reaches a backend, and the **intelligent router** rewrites `input.Model` so
a single client-facing model name fans out across multiple downstream
targets.

Both are inspected and configured from the same admin page
(`/app/middleware`), backed by the same REST surface (`/api/middleware/*`,
`/api/pii/*`, `/api/router/*`) and the same MCP tools.

## Request lifecycle

```
client ── auth ── route-model ── per-model PII ── backend ── client
                       │              │
                       │              └─── event log
                       └─── decision log
```

The router runs first (it picks the target model so per-model PII has
something to gate on), per-model PII runs next (gated by the resolved
config), and the backend executes. Filtering is **request-side only** -
the request body is scanned and rewritten before forwarding; the response
is not touched (NER over a streamed response is left as a follow-up). Each
subsystem writes to its own admin-visible log: `/api/router/decisions` for
routing, `/api/pii/events` for redaction and block actions.

---

## PII filtering

PII redaction is **NER-based and runs request-side (input)**. It is
**off by default**, flipping to **on for any `cloud-proxy` backend**
because that traffic crosses the network to a third-party provider. Pick a
[default detector](#instance-wide-default-detector) so those models are actually
scanned. Explicit `pii.enabled` in a model's YAML always wins over the
backend default.

Filtering runs on every text-accepting endpoint that has an adapter wired:
`/v1/chat/completions` and `/v1/messages` (chat), `/v1/completions`,
`/v1/embeddings`, `/v1/edits`, and the Ollama `/api/chat`, `/api/generate`
and `/api/embed` endpoints, plus the [MITM proxy]({{< relref "mitm-proxy.md" >}})
request body. Image, audio (TTS/STT), video, rerank, and the realtime
WebSocket are not filtered yet (different prompt-PII semantics; realtime is
not HTTP middleware).

A request's messages are scanned **as one document** (joined in order), so
the NER detector keeps conversational context: whether `4421` is a PIN or
`jdoe_42` is a username is usually decided by the question asked in the
*previous* message, and a bidirectional encoder only sees that context when
the messages share a forward pass. Detected spans are mapped back to the
individual message they fall in, so redaction still rewrites each message
field in place and events carry message-local offsets.

> The earlier regex pattern tier (`pii.patterns`, the built-in pattern
> catalogue, `--pii-config`, the `/api/pii/patterns|test|decide` endpoints)
> and response/streaming-side redaction have been **removed**. Detection is
> now driven entirely by token-classification (NER) models. Legacy keys
> no-op with a startup warning.

### Detector models

A **detector** is a `token_classify` model (e.g. an `openai-privacy-filter`
GGUF) that carries the detection *policy* in a top-level `pii_detection:`
block - defined once, on the model itself:

```yaml
name: privacy-filter-multilingual
backend: privacy-filter
embeddings: true              # TOKEN_CLS pooling
known_usecases:
  - token_classify
pii_detection:
  min_score: 0.5              # drop detections below this confidence
  default_action: mask        # applied to any detected group with no entry
  entity_actions:             # which PII to block vs mask vs allow-log
    PASSWORD: block
    CREDITCARD: block
    EMAIL: mask
```

`mask` rewrites the matched span to `[REDACTED:ner:<GROUP>]` in the request
body before forwarding. `block` returns HTTP 400 (`error.type=pii_blocked`)
without forwarding. `allow` detects and logs (a PIIEvent is still recorded)
but leaves the text unchanged. The entity-group names are whatever the model
emits (the privacy-filter family uses uppercase names like `EMAIL`,
`PASSWORD`, `CREDITCARD`).

### Pattern detector tier

NER is the wrong tool for high-entropy, highly-regular **secrets** - API keys,
tokens, private-key blocks. A trained NER model has no "API key" class, so it
fragments a key into the nearest categories it *does* know and can leave the
secret part exposed. Those secrets are exactly what a regex catches cheaply.

A **pattern detector** is a detector model (`backend: pattern`) that matches
secrets with a **restricted regex subset** compiled to Go's RE2 engine -
linear-time, no backtracking, no ReDoS. It runs entirely in-process: no model
download, no backend, zero VRAM. Install the gallery's **`secret-filter`** for a
ready-made set, or define your own:

```yaml
name: secret-filter
backend: pattern
known_usecases: [token_classify]        # so it appears in the detector picker
pii_detection:
  default_action: block                 # a leaked credential shouldn't leave
  builtins:                             # built-in catalogue (enable by name)
    - anthropic_api_key
    - openai_api_key
    - github_token
    - aws_access_key
    - private_key_block
  patterns:                             # operator-defined, restricted subset
    - name: INTERNAL_TOKEN
      match: "tok-[A-Za-z0-9]{32,64}"
      action: block                      # optional per-pattern override
      min_len: 36                        # optional length floor
```

A match is reported under its group (built-in group name, or the pattern
`name`), so `entity_actions` / `default_action` apply exactly as for NER.

**The restricted grammar** (validated at load - an invalid pattern is rejected,
not silently ignored):
- Allowed: literals, character classes `[…]` and `\w \d \s`, alternation,
  anchors `^ $ \b`, and quantifiers `? * + {m,n}`.
- Rejected: `.` (any-char), capturing groups, and `{n,m}` bounds over 4096.
- **Required anchor**: every pattern must contain a fixed literal run of at
  least 3 characters (e.g. `sk-ant-`, `ghp_`, `AKIA`). This admits real key
  shapes but rejects open-ended ones - an email or a bare `\w+` has no such
  anchor and belongs to the [NER tier](#detector-models).

Use both tiers together: reference an NER detector *and* a pattern detector in a
model's `pii.detectors` (or as instance defaults); their hits union, and a
`block` from either rejects the request.

### Consuming models

Any model opts in by enabling PII and referencing one or more detectors -
no per-consumer policy:

```yaml
name: claude-strict
backend: cloud-proxy
proxy:
  mode: passthrough
  provider: anthropic
  upstream_url: https://api.anthropic.com/v1/messages
  api_key_env: ANTHROPIC_API_KEY
pii:
  enabled: true               # default-on for cloud-proxy; explicit for audit
  detectors:
    - privacy-filter-multilingual
  reversible_redactions: true # restore request PII if the model echoes its wrapped token
  reversible_token_prefix: "[REDACTED:" # optional; this is the default
  reversible_token_suffix: "]"          # optional; this is the default
```

`reversible_redactions` enables bijective, request-scoped replacement. Each
masked value is sent to the backend as a stable wrapped token such as
`[REDACTED:EMAIL_001]` instead of a generic redaction marker. If the model includes that token in
its response, LocalAI restores the original value before returning JSON or SSE
to the caller. The substitution map exists only for that request and is never
logged or persisted. Leave the option unset (the default) for irreversible
`[REDACTED:...]` masking.

The prefix and suffix reduce collisions with ordinary model output and can be
customized with `reversible_token_prefix` and `reversible_token_suffix`.
Reversible redactions provide less confidentiality than irreversible masking:
any third party that can observe both the redacted request and restored response
may be able to infer the original values.

Multiple detectors **union** their detections; overlapping spans resolve to
the strongest action (`block` > `mask` > `allow`). A configured detector
that can't be loaded **fails the request closed** (HTTP 503,
`error.type=pii_ner_unavailable`) rather than silently skipping the check.
The same NER path runs on the [MITM proxy]({{< relref "mitm-proxy.md" >}})
request body for intercepted hosts. Reversible response restoration currently
applies to LocalAI API routes; the MITM proxy keeps its own output-redaction
policy.

### Instance-wide default detector

The **Detector models** table on the Middleware → Filtering page lists every
`token_classify` detector model (neural NER models and in-process pattern
matchers alike) and exposes a per-row **Default** toggle. Toggling a detector
on adds it to the instance-wide default detector set - one or more models
applied to any PII-enabled model that names none of its own `pii.detectors`.
It is persisted through `POST /api/settings` and read live, so a change takes
effect on the next request without a restart. A default that names a model no
longer loaded still appears (marked *not loaded*) so it can be toggled off.

The default set can also be supplied out-of-band with the
`LOCALAI_PII_DEFAULT_DETECTORS` environment variable (comma-separated model
names, e.g. `privacy-filter-nemotron,secret-filter`). When set it takes
precedence over the value persisted via the UI (env > file), which is the
right behaviour for immutable container deployments that pin filtering policy
at boot rather than via the admin UI.

This is what makes `cloud-proxy` / MITM redaction work out of the box: those
backends default to PII-enabled but ship no detector list, so without a
default detector the filter runs with nothing to scan. Set one here and
cloud-proxy traffic is scanned with no per-model config.

Resolution precedence (the single decision point is `ResolvePIIPolicy`,
shared by the chat middleware and the MITM listener so both agree):

1. An explicit `pii.enabled` on the model wins - `true` or `false`.
2. Otherwise PII is on if the backend defaults it on (`cloud-proxy`).
3. Detectors are the model's own `pii.detectors`; if it lists none, the
   instance-wide default detector(s) are used.

A model that resolves enabled but ends up with no detector at all (a
cloud-proxy model with no model detectors and no instance default) scans
nothing - set a default detector to close that gap.

### Admin page

The `/app/middleware` page (admin role only) has four tabs - **Filtering**,
**Routing**, **MITM Proxy** (see the [MITM doc]({{< relref "mitm-proxy.md" >}})),
and **Events**. The Filtering tab has a **Detector models** table (every
`token_classify` filter model, with the per-row Default toggle above and an
edit link to each detector's config, plus an *Add detector model* button) and
a per-model table listing only the models PII can actually apply to - chat /
completion / embeddings / edit consumers and cloud-proxy models, not
VAD/STT/image models or the detector models themselves. Each row reports the
**effective** `enabled` state as an inline **toggle** - flipping it writes an
explicit `pii.enabled` to that model's YAML (a server-side deep-merge that
preserves `pii.detectors` and every other field), so a cloud-proxy model shown
on by backend default can be turned off, and vice-versa - plus the
resolved detector(s) - with a *(default)* marker when they come from the
instance-wide default rather than the model's YAML - why it is on (`YAML` /
`backend default`), and the recent event count. Detection *policy*
(entity→action, min score) is still edited on each detector model's config
(Models → edit → PII), not globally.

### Analyze / redact API

The same detection pipeline is also exposed as a standalone service, so a
client can scan or sanitise a string **without** routing a full chat request
through it (the inline path above). Two endpoints, both requiring a normal API
key (the `pii_filter` feature - not admin):

- `POST /api/pii/analyze` - detect only. Returns the matched entity spans
  (`entity_type`, `source` `ner`|`pattern`, `start`/`end`, `score`, `action`)
  and a `blocked` flag, **without modifying the text**.
- `POST /api/pii/redact` - apply the configured policy. Returns `redacted_text`
  (with masked spans replaced by `[REDACTED:<id>]`) and `masked`; when a `block`
  action fires it returns `400` with `type: pii_blocked` and the offending
  entities - never a redacted body.

Both take the same request: `text` plus a detector selection - either explicit
detector model names in `detectors`, or a consuming `model` whose **effective**
policy is used: the model's own `pii.detectors`, else the
[instance-wide default detectors](#instance-wide-default-detector), exactly as
the inline filter resolves them. A `model` with PII disabled - or enabled but
with no detector anywhere - is a `400`: the inline filter would scan nothing
for it, and the API says so rather than implying a clean scan. The detection
policy lives on the detector models exactly as for the inline filter. The raw
matched value is never returned (an admin may pass `reveal: true` to include
the audit `hash_prefix`).

`text` is scanned as a single document. To reproduce the inline filter's
conversation-context behaviour for multi-message content, join the messages
with blank lines into one `text` - NER detection quality depends on that
context (a bare `4421` is nothing; after "what are the last four digits of
your card?" it is a PIN).

```bash
# Redact with an explicit pattern/NER detector
curl -sX POST http://localhost:8080/api/pii/redact \
  -H 'Authorization: Bearer $API_KEY' -H 'Content-Type: application/json' \
  -d '{"text":"reach me at jane@acme.io","detectors":["my-ner-model"]}'
# => {"redacted_text":"reach me at [REDACTED:ner:EMAIL]","masked":true,...}

# Analyze using a consuming model's configured detectors
curl -sX POST http://localhost:8080/api/pii/analyze \
  -H 'Authorization: Bearer $API_KEY' -H 'Content-Type: application/json' \
  -d '{"text":"sk-ant-api03-…","model":"gpt-4"}'
# => {"entities":[{"entity_type":"ANTHROPIC_KEY","source":"pattern",...,"action":"block"}],"blocked":true}
```

Calls are audited in the same event log, tagged with an `origin` of
`pii_analyze` / `pii_redact` (the inline filter records `middleware`, the MITM
proxy records `proxy`), so `GET /api/pii/events?origin=pii_redact` shows just
the redact-API rows.

### REST surface

| Method | Path | Auth | Purpose |
|---|---|---|---|
| POST | `/api/pii/analyze` | api key (`pii_filter`) | Detect PII in a string; returns entity spans, no mutation. |
| POST | `/api/pii/redact` | api key (`pii_filter`) | Redact a string per policy; returns `redacted_text` or `400 pii_blocked`. |
| GET | `/api/pii/events` | admin | Recent middleware events - PII redactions, MITM connect/traffic, admission denials. Filterable by `correlation_id`, `user_id`, `pattern_id` (e.g. `ner:EMAIL`), `kind`, `origin`. |
| GET | `/api/middleware/status` | admin | Aggregated dashboard data: per-model PII state + detectors + router status + MITM status + admission status. One round-trip for the UI. |

### MCP tools

The same surface is mirrored through the LocalAI Assistant MCP server:

| Tool | Read/Write | Purpose |
|---|---|---|
| `get_pii_events` | read | Recent redaction / block events with optional filters. |
| `get_middleware_status` | read | Aggregator - the same payload as `GET /api/middleware/status`. |

Detection policy is part of a detector model's config, so it is managed
through the model-config tools (`edit_model_config`), not a dedicated PII
tool.

---

## Intelligent routing

A **router model** is a model whose YAML carries a `router:` block. When
a client addresses it (`"model": "smart-router"`), the middleware
classifies the prompt, picks a downstream candidate model, rewrites
`input.Model` to the candidate, and the standard model-resolution path
runs against that resolved target. ACL checks, disabled-state, and
per-model PII all apply to the resolved model - the router does
*model selection only*.

#### Depth-1 invariant

Candidates **must not** themselves be router models. A
`smart-router → claude-strict → cloud-proxy` chain is fine
(`claude-strict` is a regular cloud-proxy model). A
`smart-router → other-router → real-model` chain is rejected at runtime
by the middleware (the dispatcher returns HTTP 500 with a
`depth-1 invariant` error). This keeps the dispatch graph acyclic and
predictable.

#### Fallback

If no candidate's label set covers the active label set from the classifier,
or the classifier errors out, the router uses `cfg.Router.Fallback`.
An empty `fallback` causes the dispatch to fail with HTTP 500 rather
than silently routing somewhere unintended - fail-fast, not
silent-bypass.

### Available classifiers

LocalAI ships four classifier implementations. Pick one with `classifier:`
in the router YAML:

| Classifier | When to use | Underlying primitive |
|---|---|---|
| `score` (default) | Small classifier-tuned LM (Arch-Router-style). Best when label vocabulary is well-covered by next-token continuation. | `Score` gRPC primitive (llama-cpp, vLLM). |
| `colbert` | When label descriptions are abstract or short and a next-token classifier produces flat distributions. Robust on long-form policy descriptions. | rerankers backend in ColBERT mode (e.g. `bge-m3-colbert` from the gallery). |
| `decisions` | Independent, overlapping policy decisions from a native decision model. | Internal SystemOne pipeline via Score; numeric noul P(true). |
| `knn` | When you have (or can generate) labelled example prompts — including outcome-labelled production traffic. Deterministic, auditable, cheapest per request, and the only classifier with an explicit out-of-distribution fallback. | embeddings backend + local-store KNN over a persisted, curated corpus. |

All four share `policies`, `candidates`, and `fallback`. The existing
`score`, `colbert`, and `knn` classifiers also use `classifier_cache_size`. `score` and `colbert` take a
`classifier_model` (+ `activation_threshold`, optional
`embedding_cache`); `knn` instead takes a `knn:` block and a corpus
seeded through the API.

### Native decision models (`decisions`)

Use `classifier: decisions` with an installed native decision model explicitly
configured with `known_usecases: [decisions]` and a backend supporting the Score
RPC. A chat model with ordinary continuation scoring is not a substitute.
The router calls the internal `ModelSystemOne` adapter, never a loopback HTTP
endpoint, NER extractor, or text-generation fallback.

```yaml
name: policy-router
router:
  classifier: decisions
  classifier_model: my-decision-model
  activation_threshold: 0.5
  policies:
    - label: code
      description: Writing or debugging code
    - label: private
      description: Handling private information
  candidates:
    - model: coding-model
      labels: [code]
    - model: private-coding-model
      labels: [code, private]
  fallback: general-model
```

Each policy becomes a separate `noul` question with explicit `false` and `true`
criteria. Stable question IDs map answers back to policy declaration order.
The native numeric `noul` value is **P(true)**; a probabilities map is not
required. `label_scores` are independent values in [0,1], not a distribution
normalized across policies. Every label with probability **>= threshold** is
active, and `score` is the maximum probability. The default threshold is 0.5
(omitted or zero); positive configured thresholds must be finite and <=1.
There is no exclusive-choice mode or top-one rescue. All-below-threshold means
abstention. The first candidate covering all active labels wins; abstention,
malformed answers, native context rejection, and backend errors use the normal
configured fallback. Parent request cancellation returns without selecting a
fallback.

Limits: 1–64 unique nonblank policies, nonblank descriptions, and 64 KiB each
for serialized internal request and raw response. Missing/null/non-numeric,
wrong-type, unknown-ID and out-of-range answers fail classification; numeric
zero is valid. The native engine owns question framing and context enforcement.
The router does not estimate native context from raw JSON token counts or trim
text on that assumption. A request within the byte limit can still exceed the
model's context and fall back.

The native adapter has a separate **process-wide eight-operation ceiling**,
covering healthy calls as well as abandoned work, independently of the general
backend admission limit (default 1024). Saturation fails classification and uses
the configured fallback; it does not queue. Model loading has no cancellable
API: cancellation releases the waiting caller, **not underlying resources**.
Eight stuck loads therefore prevent further native decisions until underlying
work finishes. This bound is a resource-safety measure, not a throughput claim.

The classifier registry invalidates on router configuration, resolved native
model configuration/usecase, or persisted model revision changes. Decisions do
not memoize prompt results or support `embedding_cache`/composite `knn` blocks;
these combinations are rejected. `classifier_cache_size` has no effect on this
classifier. The same central factory serves chat, Anthropic, realtime and the
existing `/router/decide` oracle. Decision traces contain no prompt text.

### The Score classifier

The `score` classifier works like this:

1. Build a Qwen/ChatML system prompt that lists every policy label with
   its description and primes the model to emit a label as the assistant
   turn.
2. Ask the classifier model to **score every policy label** as the
   first-token(s) continuation. This uses the `Score` gRPC primitive
   (`backend.proto::Score`), which returns per-candidate log-probabilities
   length-normalized so candidates of unequal token length stay
   comparable.
3. Softmax the length-normalized log-probabilities into a probability
   distribution over labels.
4. Threshold the distribution: every label whose probability passes
   `activation_threshold` joins the **active label set**.
5. Pick the FIRST candidate whose `Labels` is a superset of the active
   set. Admins order candidates smallest → largest so a single-label
   query routes to the smallest capable model, while a query that
   activates multiple labels falls to a candidate that covers them all.

This is the Arch-Router approach extended for multi-label. The
distribution carries more signal than the argmax - reading off the
spread lets one prompt activate multiple policies and route to a model
capable of all of them.

#### Recommended classifier model

[Arch-Router-1.5B](https://huggingface.co/katanemo/Arch-Router-1.5B) is
the canonical choice. It's a Qwen-2.5-1.5B-Instruct base trained
specifically on routing-policy continuation, so the ChatML system-prompt
+ label-continuation pattern produces well-separated label probabilities
without prompt tuning. The Q4_K_M GGUF runs on CPU, GPU, and Intel SYCL.

The classifier model must support the `Score` gRPC primitive (today: the
llama-cpp and vLLM backends) and use the ChatML chat template. Any small
ChatML instruct model works under those constraints, but expect flatter
probability distributions which translate to a higher
`activation_threshold` to keep noise out of the active label set.

On llama-cpp, scoring rides the server's task queue alongside
generation and embeddings, so the classifier may share a model config
with `chat`/`completion`/`embeddings` - a dedicated scorer model is no
longer required. Repeated calls with the same prompt also reuse the
prompt's KV cache across candidates.

### The Colbert classifier

The `colbert` classifier reranks each policy *description* against the
prompt via the rerankers backend and activates the labels whose
relevance scores clear `activation_threshold` (default 0.5 for
reranker-style scores in [0, 1]).

```yaml
router:
  classifier: colbert
  classifier_model: bge-m3-colbert  # gallery entry; loads BAAI/bge-m3 in ColBERT mode
  activation_threshold: 0.5
  policies:
    - label: code-generation
      description: writing, debugging, reading, or explaining code
    - label: casual-chat
      description: small talk, greetings, jokes
  candidates: [...]
```

The reranker scores the *description* (natural English) rather than
asking a small LM to score the *label* as a next-token continuation,
so it tends to be more robust when policy labels are abstract slugs
(`compliance-review`, `tier-2-support`). The trade-off is one
reranker round-trip per request - bge-m3 in ColBERT mode is fast
enough on GPU that this is comparable to the Score path for most
workloads. The `embedding_cache` block applies identically.

The reranker model's `type:` (in the model YAML) selects which
underlying scoring head loads - `colbert` for late-interaction MaxSim,
`cross-encoder` for cross-attention scoring. The classifier itself is
indifferent; pick the head that fits your latency / quality budget.

### The KNN classifier

The `knn` classifier routes by **similarity-weighted vote over a
curated corpus of labelled example prompts**. Where `score` and
`colbert` ask a model's opinion per request, `knn` consults recorded
experience: each corpus entry is an example prompt plus the policy
labels it should activate. It needs no classifier model — just an
embedding model and a seeded corpus.

```yaml
router:
  classifier: knn
  fallback: gpt-4o-proxy          # used whenever the prompt is unlike all corpus entries
  knn:
    embedding_model: nomic-embed-text-v1.5
    # embedding_revision: "2026-07" # bump for remote/in-place weight changes LocalAI cannot identify
    k: 3                          # neighbours that vote (default 3)
    similarity_threshold: 0.80    # the epistemic gate (default 0.80)
    vote_threshold: 0.5           # weighted vote share a label needs (default 0.5)
    # store_name: router-corpus-smart-router   # default "router-corpus-<router>"
  policies:
    - label: code-generation
      description: writing or debugging code
    - label: casual-chat
      description: small talk
  candidates:
    - model: qwen3-0.6b
      labels: [casual-chat]
    - model: qwen-coder
      labels: [code-generation, casual-chat]
```

For each request:

1. Embed the prompt with `knn.embedding_model`.
2. Fetch the `k` nearest corpus entries (cosine similarity).
3. **Epistemic gate**: entries below `similarity_threshold` cannot
   vote. If none clears it, the classifier activates **no** labels and
   the router uses `fallback` — a prompt unlike all labelled
   experience is treated as *undecidable*, not guessed. The decision
   log records `nearest_similarity` so you can see how far away the
   closest labelled example was.
4. Each surviving neighbour votes for its labels, weighted by its
   similarity; every label whose vote share clears `vote_threshold`
   joins the active set. Candidate matching then proceeds exactly as
   for the other classifiers. With `k: 1` this degenerates to
   "nearest example's labels".

The numeric configuration is bounded so one request cannot allocate an
unbounded neighbour result and the cosine-weighted vote remains meaningful:

| Field | Accepted values |
|-------|-----------------|
| `k` | `0` for the default (`3`), otherwise `1` through `1024` |
| `similarity_threshold` | `0` for the default (`0.80`), otherwise greater than `0` and at most `1` |
| `vote_threshold` | `0` for the default (`0.5`), otherwise greater than `0` and at most `1` |

Negative, non-finite, and above-range values are rejected when the model
configuration is loaded. The `k` cap bounds the store priority queue and
returned neighbour arrays. Similarity is restricted to non-negative cosine
weights because negative weights would make vote totals and shares invalid;
vote share itself is necessarily between zero and one.

Every knn decision (in the decision log and the `/api/router/decide`
response) also carries `neighbors` — the `k` retrieved corpus entries
by descending similarity, **including** ones below the gate, each as
`{id, similarity, labels}`. The `id` is the entry's content hash (the
first 8 bytes of the SHA-256 of its text, hex-encoded): stable across
reseeds and re-embeds, and text-free — whoever seeded the corpus can
recompute text→id on their own copy to group decisions by corpus
region (e.g. for external per-region reliability accounting) without
corpus text ever leaving the server.

#### Seeding and curating the corpus (API-only)

Corpus entries may contain example user content, so they are managed
exclusively through the admin API — the UI never sends or displays
them, and no endpoint returns entry texts (inspection is label counts
only):

```bash
# Seed labelled exemplars (embedded server-side; indexed immediately)
curl -X POST http://localhost:8080/api/router/smart-router/corpus \
  -H "Content-Type: application/json" \
  -d '{"entries": [
        {"text": "why does this segfault when I free the buffer twice", "labels": ["code-generation"]},
        {"text": "hey hows it going", "labels": ["casual-chat"]}
      ]}'

# Inspect — counts only, never texts
curl http://localhost:8080/api/router/smart-router/corpus/stats

# Wipe (file + live index); reseed afterwards
curl -X DELETE http://localhost:8080/api/router/smart-router/corpus
```

Entry labels must be declared in `policies` (same invariant as
candidate labels). Empty and duplicate labels are rejected, and
duplicate texts are skipped rather than double-weighted. Label your exemplars
with *outcomes*, not topics,
when routing for difficulty: an entry recording "the small model
handled prompts like this" is exactly as useful as one recording that
it failed — grade a sample of production traffic against your
candidates and seed both.

#### Persistence

The corpus is persisted as one JSONL file per router under
`<data path>/router-corpus/` (text, labels, vector, embedding-model name,
and embedding fingerprint) — **the file is the source of truth** and
survives restarts; the local-store index is rebuilt from it at classifier
build time without re-embedding. Before each KNN lookup, LocalAI checks a stored
vector against the live index. If the store restarts empty after eviction or
an idle timeout, LocalAI restores the index from the file without re-embedding.
A synchronization error fails the lookup.

The fingerprint follows the effective
embedding-model config and local artifact identity, so changing the model or
replacing its local weights re-embeds the corpus on the next process load.
For remote embedding services whose weights can change invisibly, bump
`knn.embedding_revision` explicitly.

If an embedding fingerprint changes after that corpus is already present in
the live in-memory index, LocalAI fails the classifier build instead of
querying mixed embedding spaces. Restart LocalAI to rebuild the empty live
index and re-embed the persisted entries.

#### Tuning notes

- **`similarity_threshold` is the safety knob.** Too low and the
  router confidently extrapolates from unrelated exemplars; too high
  and everything falls back. Watch `nearest_similarity` in the
  decision log: fallback rows clustering just under the threshold mean
  the corpus needs entries near that traffic (or the gate is too
  tight).
- **`k` trades robustness for corpus density**: `k: 3` tolerates one
  mislabelled neighbour; raise it only when every label region has
  several exemplars. The maximum is `1024`.
- **`embedding_cache` is ignored** for `knn` (with a warning) — the
  classifier is already an embedding KNN lookup; wrapping it in
  another would embed twice for no additional information.

### YAML reference

```yaml
name: smart-router
known_usecases:
  - chat
router:
  # `score` (Arch-Router-style next-token scoring), `colbert` (rerank
  # policy descriptions), or `knn` (vote over a labelled corpus).
  # See "Available classifiers" above.
  classifier: score

  # A model loaded by LocalAI that supports the Score gRPC primitive
  # (llama-cpp and vLLM ship implementations). Arch-Router-1.5B is the
  # canonical choice.
  classifier_model: arch-router-1.5b

  # Bounded LRU keyed on (case-folded, whitespace-trimmed) prompt - prompts
  # repeat in agent loops; the cache amortises the classifier round-trip
  # across them. 0 here means "use the default" (1024); the cache cannot be
  # disabled from YAML today.
  classifier_cache_size: 256

  # Softmax probability floor a label must clear to join the active label set.
  # 0 = use the package default (0.15). 0.40 is a better empirical
  # starting point on Arch-Router-1.5B - see the tuning note below.
  activation_threshold: 0.40

  # Used when no candidate covers the active label set, or the classifier
  # itself errors. Empty here = fail-fast with HTTP 500.
  fallback: qwen3-0.6b

  # The label vocabulary. Descriptions are fed verbatim into the
  # classifier's system prompt - short, action-oriented sentences work
  # best ("writing or debugging code", "small talk").
  policies:
    - label: code-generation
      description: writing, debugging, reading, or explaining code in any programming language
    - label: casual-chat
      description: small talk, greetings, jokes, or general conversation with no specific task
    - label: math-reasoning
      description: arithmetic, equations, percentage calculations, or step-by-step word problems

  # Routing table - order matters (smallest → largest). See "Score
  # classifier" above for the matching rule.
  candidates:
    - model: qwen3-0.6b
      labels: [casual-chat]
    - model: qwen_qwen3.5-2b
      labels: [code-generation, casual-chat, math-reasoning]
```

### Tuning `activation_threshold`

The threshold is the single knob you'll want to tune per
(classifier-model, policy-set) pair. On Arch-Router-1.5B with the
three-policy setup above, sweeping the threshold over a hand-labeled
30-prompt corpus produced:

| Threshold | Label-set accuracy | End-to-end routing accuracy |
|---:|---:|---:|
| 0.15 (package default) | 30% | 73% |
| 0.30 | 57% | 87% |
| **0.40** | **60%** | **90%** |
| 0.45 | 67% | 97% |
| 0.50 | 67% | 97% |

The classifier's argmax matches the dominant label 93% of the time on
this corpus - what the threshold controls is how much secondary-label
noise leaks into the active label set. Low thresholds push single-label
queries to multi-label-capable (larger) candidates unnecessarily; 0.40
keeps the dominant label dominant without losing genuine compound
activations.

Re-tune per (classifier-model, policy-set) pair. The `/api/score`
endpoint (see below) is the convenient probe - it returns the raw
length-normalized log-probabilities so you can sweep thresholds offline
without driving real chat completions.

### Embedding cache (L2)

Classification is the most expensive thing the middleware does. The
score classifier already memo-caches verbatim repeats (case- and
whitespace-folded prompt → decision); the **embedding cache** is the
L2 tier that catches *semantically similar* prompts - "How do I exit
vim?" and "i need to quit vim" can share a decision instead of running
the classifier twice.

Pairs naturally with a larger / slower classifier model: the steady-state
cost on cache hits collapses to one embedding round-trip plus a KNN
search, both well under 100ms with `nomic-embed-text-v1.5` + local-store.

#### Configuration

Add an `embedding_cache:` block to a router model:

```yaml
router:
  classifier: score
  classifier_model: arch-router-1.5b
  policies: [...]
  candidates: [...]

  embedding_cache:
    embedding_model: nomic-embed-text-v1.5    # any loaded embedding model
    similarity_threshold: 0.80                # cosine sim floor for a hit (default 0.80)
    confidence_threshold: 0.60                # min top-label prob to cache a decision (default 0.60)
    # store_name: router-cache-smart-router   # optional override; defaults to "router-cache-<router>"
```

Omit the block entirely to disable. The cache adds two new failure modes
(embedder unavailable, store unavailable) - both fall through to the
inner classifier so routing keeps working.

#### How it works

For each request:

1. Embed the probe prompt via the configured `embedding_model`.
2. KNN top-1 against the per-router local-store collection.
3. If similarity ≥ `similarity_threshold`, return the cached decision
   (`Cached=true`, `CacheSimilarity=<sim>` in the decision log).
4. Miss → run the inner classifier. If `decision.score >= confidence_threshold`,
   insert `(embedding, decision)` into the store. Low-confidence
   decisions are deliberately skipped so they can't poison future
   paraphrases.

The local-store collection is named `router-cache-<router-model-name>` by
default — each router gets its own collection so two routers can't
cross-contaminate. The collection is **in-memory only**: local-store
keeps no on-disk artefact, so the embedding cache starts empty on every
restart and re-learns from live traffic. (The KNN classifier's corpus
does NOT have this limitation — its corpus file is the source of truth
and re-indexes on startup; see "The KNN classifier" above.)

#### Tuning notes

- **Similarity threshold**: 0.80 is the package default - re-tune
  per (embedding model, corpus). The histogram on the Routing tab
  shows where the cosine distribution actually sits; pick a
  threshold above the cross-intent cluster and below the paraphrase
  cluster.
- **Confidence threshold**: 0.60 corresponds roughly to "the
  classifier is committed to a top label." Don't lower this - caching
  unsure decisions propagates the uncertainty.
- **Cache flush**: invalidates automatically when the router YAML
  changes (the classifier cache is fingerprinted by `yaml.Marshal`),
  but the underlying local-store collection still holds the old
  payloads. Manual flush via local-store admin or by renaming
  `store_name` if you need a hard reset.
- **Latency budget**: an embedding round-trip (typically 30-80ms for
  small embedding models) plus KNN search (~5ms) is added to every
  *miss* on top of the classifier latency. Cache hits skip the
  classifier entirely. Break-even is around 7-10% hit rate; agent
  loops with repeated phrasing easily exceed this.

### Admin page

The `/app/middleware` page has a **Routing** tab listing every router
model's classifier, policies, candidates, and fallback. The **Events**
tab shows the decision log - one row per classified request with
correlation ID, requested model, served model, classifier name, active
labels, top-label score, and latency.

Routing decisions are stored in an in-process ring buffer (default
capacity 5,000). The decision log is for audit and tuning - the
canonical usage log lives in `/api/usage` and correlates by request ID.

### REST surface

| Method | Path | Auth | Purpose |
|---|---|---|---|
| GET | `/api/router/status` | any | Router configuration: each router model's classifier, policies, candidates. |
| GET | `/api/router/decisions` | admin | Decision log with optional filters (`correlation_id`, `user_id`, `router_model`, `limit`). |
| POST | `/api/router/{name}/corpus` | admin | Seed the KNN corpus with labelled exemplars: `{"entries": [{"text": "...", "labels": ["..."]}]}`. Embedded server-side, persisted, indexed immediately. |
| GET | `/api/router/{name}/corpus/stats` | admin | KNN corpus size and per-label counts. Counts only — entry texts are never returned. |
| DELETE | `/api/router/{name}/corpus` | admin | Wipe the KNN corpus (file + live index). |
| POST | `/api/score` | admin | Direct access to the `Score` gRPC primitive — useful for offline threshold tuning. Body: `{"model": "<classifier-model>", "prompt": "<chatml-prompt>", "candidates": ["label-a", ...], "length_normalize": true}`. The llama-cpp and vLLM backends implement Score; other backends return `UNIMPLEMENTED`. |

### MCP tools

| Tool | Read/Write | Purpose |
|---|---|---|
| `get_router_decisions` | read | Recent decision log with optional filters. |
| `get_middleware_status` | read | Includes the router section listing configured router models. |
| `get_router_corpus_stats` | read | KNN corpus size and per-label counts (never texts). |
| `seed_router_corpus` | write | Add labelled exemplars to a KNN router's corpus. |
| `clear_router_corpus` | write | Wipe a KNN router's corpus. |

Mutating the rest of the routing config — adding a candidate, changing
the classifier model — goes through the model-config surface
(`edit_model_config` / `PATCH /api/models/config-json/:name`); reload
with `POST /models/reload` to pick up YAML edits without restarting.

### Operational notes

- **Reload after YAML edits.** The router configs are loaded at startup
  and cached. `POST /models/reload` re-reads from disk; the next request
  rebuilds the classifier from the new config (the classifier cache is
  fingerprinted by `yaml.Marshal(RouterConfig)` so it invalidates
  automatically).
- **Classifier latency** on Arch-Router-1.5B Q4_K_M is ~500ms steady
  for 3 policies on Intel SYCL. The score primitive re-decodes the full
  prompt for every candidate today (the KV cache is cleared between
  candidates); the prompt-KV-sharing optimization is on the perf TODO
  list in `backend/cpp/llama-cpp/grpc-server.cpp::Score`. Until then,
  `classifier_cache_size` is the highest-leverage knob for repeat-query
  workloads (agent loops).
- **Decision log size**: 5,000-entry ring buffer per process. The
  log is in-process and not persisted - pair with the usage log for
  long-horizon audit.

---

## Related features

- [Cloud passthrough proxy]({{< relref "cloud-proxy.md" >}}) - combine
  the router with `proxy-*` backends to send simple prompts to local
  models and complex ones to cloud providers.
- [MITM proxy]({{< relref "mitm-proxy.md" >}}) - apply the same PII
  filter to Claude Code, Codex CLI, and any HTTPS client without
  LocalAI holding their API keys.
- [Authentication]({{< relref "authentication.md" >}}) - admin role is
  required for mutating endpoints and the `/app/middleware` page; in
  no-auth single-user mode the synthetic local user has admin role
  automatically.

### Creating a Decisions router in the UI

In **Middleware → Routing → Create routing model**, select **Decisions (native
probabilities)** under Classifier. The Classifier Model picker lists installed,
enabled native decision models explicitly declaring `known_usecases: [decisions]`
on a backend supporting Score, including `llama-cpp` and `vllm-cpp`. NER-only
models and routing dispatchers are not eligible. Selecting a model saves its exact
configured name; it does not install weights.

Decisions needs no ChatML template and returns independent label probabilities,
not an exclusive choice. Start with an activation threshold of **0.5** (zero uses
the Decisions default of 0.5). Changing classifiers clears the dependent model
selection but preserves your threshold, including the template's initial 0.40;
set it deliberately before saving. Existing saved selections reopen unchanged.