Files
LocalAI/core/config/model_config.go
Ettore Di Giacinto 20baec77ab feat(face-recognition): add insightface/onnx backend for 1:1 verify, 1:N identify, embedding, detection, analysis (#9480)
* feat(face-recognition): add insightface backend for 1:1 verify, 1:N identify, embedding, detection, analysis

Adds face recognition as a new first-class capability in LocalAI via the
`insightface` Python backend, with a pluggable two-engine design so
non-commercial (insightface model packs) and commercial-safe
(OpenCV Zoo YuNet + SFace) models share the same gRPC/HTTP surface.

New gRPC RPCs (backend/backend.proto):
  * FaceVerify(FaceVerifyRequest) returns FaceVerifyResponse
  * FaceAnalyze(FaceAnalyzeRequest) returns FaceAnalyzeResponse

Existing Embedding and Detect RPCs are reused (face image in
PredictOptions.Images / DetectOptions.src) for face embedding and
face detection respectively.

New HTTP endpoints under /v1/face/:
  * verify     — 1:1 image pair same-person decision
  * analyze    — per-face age + gender (emotion/race reserved)
  * register   — 1:N enrollment; stores embedding in vector store
  * identify   — 1:N recognition; detect → embed → StoresFind
  * forget     — remove a registered face by opaque ID

Service layer (core/services/facerecognition/) introduces a
`Registry` interface with one in-memory `storeRegistry` impl backed
by LocalAI's existing local-store gRPC vector backend. HTTP handlers
depend on the interface, not on StoresSet/StoresFind directly, so a
persistent PostgreSQL/pgvector implementation can be slotted in via a
single constructor change in core/application (TODO marker in the
package doc).

New usecase flag FLAG_FACE_RECOGNITION; insightface is also wired
into FLAG_DETECTION so /v1/detection works for face bounding boxes.

Gallery (backend/index.yaml) ships three entries:
  * insightface-buffalo-l   — SCRFD-10GF + ArcFace R50 + genderage
                              (~326MB pre-baked; non-commercial research use only)
  * insightface-opencv      — YuNet + SFace (~40MB pre-baked; Apache 2.0)
  * insightface-buffalo-s   — SCRFD-500MF + MBF (runtime download; non-commercial)

Python backend (backend/python/insightface/):
  * engines.py — FaceEngine protocol with InsightFaceEngine and
    OnnxDirectEngine; resolves model paths relative to the backend
    directory so the same gallery config works in docker-scratch and
    in the e2e-backends rootfs-extraction harness.
  * backend.py — gRPC servicer implementing Health, LoadModel, Status,
    Embedding, Detect, FaceVerify, FaceAnalyze.
  * install.sh — pre-bakes buffalo_l + OpenCV YuNet/SFace inside the
    backend directory so first-run is offline-clean (the final scratch
    image only preserves files under /<backend>/).
  * test.py — parametrized unit tests over both engines.

Tests:
  * Registry unit tests (go test -race ./core/services/facerecognition/...)
    — in-memory fake grpc.Backend, table-driven, covers register/
    identify/forget/error paths + concurrent access.
  * tests/e2e-backends/backend_test.go extended with face caps
    (face_detect, face_embed, face_verify, face_analyze); relative
    ordering + configurable verifyCeiling per engine.
  * Makefile targets: test-extra-backend-insightface-buffalo-l,
    -opencv, and the -all aggregate.
  * CI: .github/workflows/test-extra.yml gains tests-insightface-grpc,
    auto-triggered by changes under backend/python/insightface/.

Docs:
  * docs/content/features/face-recognition.md — feature page with
    license table, quickstart (defaults to the commercial-safe model),
    models matrix, API reference, 1:N workflow, storage caveats.
  * Cross-refs in object-detection.md, stores.md, embeddings.md, and
    whats-new.md.
  * Contributor README at backend/python/insightface/README.md.

Verified end-to-end:
  * buffalo_l: 6/6 specs (health, load, face_detect, face_embed,
    face_verify, face_analyze).
  * opencv: 5/5 specs (same minus face_analyze — SFace has no
    demographic head; correctly skipped via BACKEND_TEST_CAPS).

Assisted-by: Claude:claude-opus-4-7

* fix(face-recognition): move engine selection to model gallery, collapse backend entries

The previous commit put engine/model_pack options on backend gallery
entries (`backend/index.yaml`). That was wrong — `GalleryBackend`
(core/gallery/backend_types.go:32) has no `options` field, so the
YAML decoder silently dropped those keys and all three "different
insightface-*" backend entries resolved to the same container image
with no distinguishing configuration.

Correct split:

  * `backend/index.yaml` now has ONE `insightface` backend entry
    shipping the CPU + CUDA 12 container images. The Python backend
    bundles both the non-commercial insightface model packs
    (buffalo_l / buffalo_s) and the commercial-safe OpenCV Zoo
    weights (YuNet + SFace); the active engine is selected at
    LoadModel time via `options: ["engine:..."]`.

  * `gallery/index.yaml` gains three model entries —
    `insightface-buffalo-l`, `insightface-opencv`,
    `insightface-buffalo-s` — each setting the appropriate
    `overrides.backend` + `overrides.options` so installing one
    actually gives the user the intended engine. This matches how
    `rfdetr-base` lives in the model gallery against the `rfdetr`
    backend.

The earlier e2e tests passed despite this bug because the Makefile
targets pass `BACKEND_TEST_OPTIONS` directly to LoadModel via gRPC,
bypassing any gallery resolution entirely. No code changes needed.

Assisted-by: Claude:claude-opus-4-7

* feat(face-recognition): cover all supported models in the gallery + drop weight baking

Follows up on the model-gallery split: adds entries for every model
configuration either engine actually supports, and switches weight
delivery from image-baked to LocalAI's standard gallery mechanism.

Gallery now has seven `insightface-*` model entries (gallery/index.yaml):

  insightface (family)  — non-commercial research use
    • buffalo-l   (326MB)  — SCRFD-10GF + ResNet50 + genderage, default
    • buffalo-m   (313MB)  — SCRFD-2.5GF + ResNet50 + genderage
    • buffalo-s   (159MB)  — SCRFD-500MF + MBF + genderage
    • buffalo-sc  (16MB)   — SCRFD-500MF + MBF, recognition only
                             (no landmarks, no demographics — analyze
                             returns empty attributes)
    • antelopev2  (407MB)  — SCRFD-10GF + ResNet100@Glint360K + genderage

  OpenCV Zoo family — Apache 2.0 commercial-safe
    • opencv       — YuNet + SFace fp32 (~40MB)
    • opencv-int8  — YuNet + SFace int8 (~12MB, ~3x smaller, faster on CPU)

Model weights are no longer baked into the backend image. The image
now ships only the Python runtime + libraries (~275MB content size,
~1.18GB disk vs ~1.21GB when weights were baked). Weights flow through
LocalAI's gallery mechanism:

  * OpenCV variants list `files:` with ONNX URIs + SHA-256, so
    `local-ai models install insightface-opencv` pulls them into the
    models directory exactly like any other gallery-managed model.

  * insightface packs (upstream distributes .zip archives only, not
    individual ONNX files) auto-download on first LoadModel via
    FaceAnalysis' built-in machinery, rooted at the LocalAI models
    directory so they live alongside everything else — same pattern
    `rfdetr` uses with `inference.get_model()`.

Backend changes (backend/python/insightface/):

  * backend.py — LoadModel propagates `ModelOptions.ModelPath` (the
    LocalAI models directory) to engines via a `_model_dir` hint.
    This replaces the earlier ModelFile-dirname approach; ModelPath
    is the canonical "models directory" variable set by the Go loader
    (pkg/model/initializers.go:144) and is always populated.

  * engines.py::_resolve_model_path — picks up `model_dir` and searches
    it (plus basename-in-model-dir) before falling back to the dev
    script-dir. This is how OnnxDirectEngine finds gallery-downloaded
    YuNet/SFace files by filename only.

  * engines.py::_flatten_insightface_pack — new helper that works
    around an upstream packaging inconsistency: buffalo_l/s/sc zips
    expand flat, but buffalo_m and antelopev2 zips wrap their ONNX
    files in a redundant `<name>/` directory. insightface's own
    loader looks one level too shallow and fails. We call
    `ensure_available()` explicitly, flatten if nested, then hand to
    FaceAnalysis.

  * engines.py::InsightFaceEngine.prepare — root-resolution order now
    includes the `_model_dir` hint so packs download into the LocalAI
    models directory by default.

  * install.sh — no longer pre-downloads any weights. Everything is
    gallery-managed now.

  * smoke.py (new) — parametrized smoke test that iterates over every
    gallery configuration, simulating the LocalAI install flow
    (creates a models dir, fetches OpenCV files with checksum
    verification, lets insightface auto-download its packs), then
    runs detect + embed + verify (+ analyze where supported) through
    the in-process BackendServicer.

  * test.py — OnnxDirectEngineTest no longer hardcodes `/models/opencv/`
    paths; downloads ONNX files to a temp dir at setUpClass time and
    passes ModelPath accordingly.

Registry change (core/services/facerecognition/store_registry.go):

  * `dim=0` in NewStoreRegistry now means "accept whatever dimension
    arrives" — needed because the backend supports 512-d ArcFace/MBF
    and 128-d SFace via the same Registry. A non-zero dim still fails
    fast with ErrDimensionMismatch.

  * core/application plumbs `faceEmbeddingDim = 0`, explaining the
    rationale in the comment.

Backend gallery description updated to reflect that the image carries
no weights — it's just Python + engines.

Smoke-tested all 7 configurations against the rebuilt image (with the
flatten fix applied), exit 0:

    PASS: insightface-buffalo-l    faces=6 dim=512 same-dist=0.000
    PASS: insightface-buffalo-sc   faces=6 dim=512 same-dist=0.000
    PASS: insightface-buffalo-s    faces=6 dim=512 same-dist=0.000
    PASS: insightface-buffalo-m    faces=6 dim=512 same-dist=0.000
    PASS: insightface-antelopev2   faces=6 dim=512 same-dist=0.000
    PASS: insightface-opencv       faces=6 dim=128 same-dist=0.000
    PASS: insightface-opencv-int8  faces=6 dim=128 same-dist=0.000
    7/7 passed

Assisted-by: Claude:claude-opus-4-7

* fix(face-recognition): pre-fetch OpenCV ONNX for e2e target; drop stale pre-baked claim

CI regression from the previous commit: I moved OpenCV Zoo weight
delivery to LocalAI's gallery `files:` mechanism, but the
test-extra-backend-insightface-opencv target was still passing
relative paths `detector_onnx:models/opencv/yunet.onnx` in
BACKEND_TEST_OPTIONS. The e2e suite drives LoadModel directly over
gRPC without going through the gallery, so those relative paths
resolved to nothing and OpenCV's ONNXImporter failed:

    LoadModel failed: Failed to load face engine:
    OpenCV(4.13.0) ... Can't read ONNX file: models/opencv/yunet.onnx

Fix: add an `insightface-opencv-models` prerequisite target that
fetches the two ONNX files (YuNet + SFace) to a deterministic host
cache at /tmp/localai-insightface-opencv-cache/, verifies SHA-256,
and skips the download on re-runs. The opencv test target depends on
it and passes absolute paths in BACKEND_TEST_OPTIONS, so the backend
finds the files via its normal absolute-path resolution branch.

Also refresh the buffalo_l comment: it no longer says "pre-baked"
(nothing is — the pack auto-downloads from upstream's GitHub release
on first LoadModel, same as in CI).

Locally verified: `make test-extra-backend-insightface-opencv` passes
5/5 specs (health, load, face_detect, face_embed, face_verify).

Assisted-by: Claude:claude-opus-4-7

* feat(face-recognition): add POST /v1/face/embed + correct /v1/embeddings docs

The docs promised that /v1/embeddings returns face vectors when you
send an image data-URI. That was never true: /v1/embeddings is
OpenAI-compatible and text-only by contract — its handler goes
through `core/backend/embeddings.go::ModelEmbedding`, which sets
`predictOptions.Embeddings = s` (a string of TEXT to embed) and never
populates `predictOptions.Images[]`. The Python backend's Embedding
gRPC method does handle Images[] (that's how /v1/face/register reaches
it internally via `backend.FaceEmbed`), but the HTTP embeddings
endpoint wasn't wired to populate it.

Rather than overload /v1/embeddings with image-vs-text detection —
messy, and the endpoint is OpenAI-compatible by design — add a
dedicated /v1/face/embed endpoint that wraps `backend.FaceEmbed`
(already used internally by /v1/face/register and /v1/face/identify).

Matches LocalAI's convention of a dedicated path per non-standard flow
(/v1/rerank, /v1/detection, /v1/face/verify etc.).

Response:

    {
      "embedding": [<dim> floats, L2-normed],
      "dim": int,           // 512 for ArcFace R50 / MBF, 128 for SFace
      "model": "<name>"
    }

Live-tested on the opencv engine: returns a 128-d L2-normalized vector
(sum(x^2) = 1.0000). Sentinel in docs updated to note /v1/embeddings
is text-only and point image users at /v1/face/embed instead.

Assisted-by: Claude:claude-opus-4-7

* fix(http): map malformed image input + gRPC status codes to proper 4xx

Image-input failures on LocalAI's single-image endpoints (/v1/detection,
/v1/face/{verify,analyze,embed,register,identify}) have historically
returned 500 — even when the client was the one who sent garbage.
Classic example: you POST an "image" that isn't a URL, isn't a
data-URI, and isn't a valid JPEG/PNG — the server shouldn't claim
that's its fault.

Two helpers land in core/http/endpoints/localai/images.go and every
single-image handler is switched over:

  * decodeImageInput(s)
      Wraps utils.GetContentURIAsBase64 and turns any failure
      (invalid URL, not a data-URI, download error, etc.) into
      echo.NewHTTPError(400, "invalid image input: ...").

  * mapBackendError(err)
      Inspects the gRPC status on a backend call error and maps:
        INVALID_ARGUMENT     → 400 Bad Request
        NOT_FOUND            → 404 Not Found
        FAILED_PRECONDITION  → 412 Precondition Failed
        Unimplemented        → 501 Not Implemented
      All other codes fall through unchanged (still 500).

Before, my 1×1 PNG error-path test returned:
    HTTP 500 "rpc error: code = InvalidArgument desc = failed to decode one or both images"
After:
    HTTP 400 "failed to decode one or both images"

Scope-limited to the LocalAI single-image endpoints. The multi-modal
paths (middleware/request.go, openresponses/responses.go,
openai/realtime.go) intentionally log-and-skip individual media parts
when decoding fails — different design intent (graceful degradation
of a multi-part message), not a 400-worthy failure. Left untouched.

Live-verified: every error case in /tmp/face_errors.py now returns
4xx with a meaningful message; the "image with no face (1x1 PNG)"
case specifically went from 500 → 400.

Assisted-by: Claude:claude-opus-4-7

* refactor(face-recognition): insightface packs go through gallery files:, drop FaceAnalysis

Follows up on the discovery that LocalAI's gallery `files:` mechanism
handles archives (zip, tar.gz, …) via mholt/archiver/v3 — the rhasspy
piper voices use exactly this pattern. Insightface packs are zip
archives, so we can now deliver them the same way every other
gallery-managed model gets delivered: declaratively, checksum-verified,
through LocalAI's standard download+extract pipeline.

Two changes:

1. Gallery (gallery/index.yaml) — every insightface-* entry gains a
   `files:` list with the pack zip's URI + SHA-256. `local-ai models
   install insightface-buffalo-l` now fetches the zip, verifies the
   hash, and extracts it into the models directory. No more reliance
   on insightface's library-internal `ensure_available()` auto-download
   or its hardcoded `BASE_REPO_URL`.

2. InsightFaceEngine (backend/python/insightface/engines.py) — drops
   the FaceAnalysis wrapper and drives insightface's `model_zoo`
   directly. The ~50 lines FaceAnalysis provides — glob ONNX files,
   route each through `model_zoo.get_model()`, build a
   `{taskname: model}` dict, loop per-face at inference — are
   reimplemented in `InsightFaceEngine`. The actual inference classes
   (RetinaFace, ArcFaceONNX, Attribute, Landmark) are still
   insightface's — we only replicate the glue, so drift risk against
   upstream is minimal.

   Why drop FaceAnalysis: it hard-codes a `<root>/models/<name>/*.onnx`
   layout that doesn't match what LocalAI's zip extraction produces.
   LocalAI unpacks archives flat into `<models_dir>`. Upstream packs
   are inconsistent — buffalo_l/s/sc ship ONNX at the zip root (lands
   at `<models_dir>/*.onnx`), buffalo_m/antelopev2 wrap in a redundant
   `<name>/` dir (lands at `<models_dir>/<name>/*.onnx`). The new
   `_locate_insightface_pack` helper searches both locations plus
   legacy paths and returns whichever has ONNX files. Replaces the
   earlier `_flatten_insightface_pack` helper (which tried to fight
   FaceAnalysis's layout expectations; now we just find the files
   wherever they are).

Net effect for users: install once via LocalAI's managed flow,
weights live alongside every other model, progress shows in the
jobs endpoint, no first-load network call. Same API surface,
cleaner plumbing.

Assisted-by: Claude:claude-opus-4-7

* fix(face-recognition): CI's insightface e2e path needs the pack pre-fetched

The e2e suite drives LoadModel over gRPC without going through LocalAI's
gallery flow, so the engine's `_model_dir` option (normally populated
from ModelPath) is empty. Previously the insightface target relied on
FaceAnalysis auto-download to paper over this, but we dropped
FaceAnalysis in favor of direct model_zoo calls — so the buffalo_l
target started failing at LoadModel with "no insightface pack found".

Mirror the opencv target's pre-fetch pattern: download buffalo_sc.zip
(same SHA as the gallery entry), extract it on the host, and pass
`root:<dir>` so the engine locates the pack without needing
ModelPath. Switched to buffalo_sc (smallest pack, ~16MB) to keep CI
fast; it covers the same insightface engine code path as buffalo_l.

Face analyze cap dropped since buffalo_sc has no age/gender head.

Assisted-by: Claude:claude-opus-4-7[1m]

* feat(face-recognition): surface face-recognition in advertised feature maps

The six /v1/face/* endpoints were missing from every place LocalAI
advertises its feature surface to clients:

  * api_instructions — the machine-readable capability index at
    GET /api/instructions. Added `face-recognition` as a dedicated
    instruction area with an intro that calls out the in-memory
    registry caveat and the /v1/face/embed vs /v1/embeddings split.
  * auth/permissions — added FeatureFaceRecognition constant, routed
    all six face endpoints through it so admins can gate them per-user
    like any other API feature. Default ON (matches the other API
    features).
  * React UI capabilities — CAP_FACE_RECOGNITION symbol mapped to
    FLAG_FACE_RECOGNITION. Declared only for now; the Face page is a
    follow-up (noted in the plan).

Instruction count bumped 9 → 10; test updated.

Assisted-by: Claude:claude-opus-4-7[1m]

* docs(agents): capture advertising-surface steps in the endpoint guide

Before this change, adding a new /v1/* endpoint reliably missed one or
more of: the swagger @Tags annotation, the /api/instructions registry,
the auth RouteFeatureRegistry, and the React UI CAP_* symbol. The
endpoint would work but be invisible to API consumers, admins, and the
UI — and nothing in the existing docs said to look in those places.

Extend .agents/api-endpoints-and-auth.md with a new "Advertising
surfaces" section covering all four surfaces (swagger tags, /api/
instructions, capabilities.js, docs/), and expand the closing checklist
so it's impossible to ship a feature without visiting each one. Hoist a
one-liner reminder into AGENTS.md's Quick Reference so agents skim it
before diving in.

Assisted-by: Claude:claude-opus-4-7[1m]
2026-04-22 21:55:41 +02:00

821 lines
30 KiB
Go

package config
import (
"fmt"
"os"
"regexp"
"slices"
"strings"
"github.com/mudler/LocalAI/core/schema"
"github.com/mudler/LocalAI/pkg/downloader"
"github.com/mudler/LocalAI/pkg/functions"
"github.com/mudler/LocalAI/pkg/reasoning"
"github.com/mudler/cogito"
"gopkg.in/yaml.v3"
)
const (
RAND_SEED = -1
)
// @Description TTS configuration
type TTSConfig struct {
// Voice wav path or id
Voice string `yaml:"voice,omitempty" json:"voice,omitempty"`
AudioPath string `yaml:"audio_path,omitempty" json:"audio_path,omitempty"`
}
// @Description ModelConfig represents a model configuration
type ModelConfig struct {
modelConfigFile string `yaml:"-" json:"-"`
modelTemplate string `yaml:"-" json:"-"`
schema.PredictionOptions `yaml:"parameters,omitempty" json:"parameters,omitempty"`
Name string `yaml:"name,omitempty" json:"name,omitempty"`
F16 *bool `yaml:"f16,omitempty" json:"f16,omitempty"`
Threads *int `yaml:"threads,omitempty" json:"threads,omitempty"`
Debug *bool `yaml:"debug,omitempty" json:"debug,omitempty"`
Roles map[string]string `yaml:"roles,omitempty" json:"roles,omitempty"`
Embeddings *bool `yaml:"embeddings,omitempty" json:"embeddings,omitempty"`
Backend string `yaml:"backend,omitempty" json:"backend,omitempty"`
TemplateConfig TemplateConfig `yaml:"template,omitempty" json:"template,omitempty"`
KnownUsecaseStrings []string `yaml:"known_usecases,omitempty" json:"known_usecases,omitempty"`
KnownUsecases *ModelConfigUsecase `yaml:"-" json:"-"`
Pipeline Pipeline `yaml:"pipeline,omitempty" json:"pipeline,omitempty"`
PromptStrings, InputStrings []string `yaml:"-" json:"-"`
InputToken [][]int `yaml:"-" json:"-"`
functionCallString, functionCallNameString string `yaml:"-" json:"-"`
ResponseFormat string `yaml:"-" json:"-"`
ResponseFormatMap map[string]any `yaml:"-" json:"-"`
// MediaMarker is the runtime-discovered multimodal marker the backend expects
// in the prompt (e.g. "<__media__>" or a random "<__media_<rand>__>" picked by
// llama.cpp). Populated on first successful ModelMetadata call. Empty until
// then — callers must fall back to templates.DefaultMultiMediaMarker.
MediaMarker string `yaml:"-" json:"-"`
FunctionsConfig functions.FunctionsConfig `yaml:"function,omitempty" json:"function,omitempty"`
ReasoningConfig reasoning.Config `yaml:"reasoning,omitempty" json:"reasoning,omitempty"`
FeatureFlag FeatureFlag `yaml:"feature_flags,omitempty" json:"feature_flags,omitempty"` // Feature Flag registry. We move fast, and features may break on a per model/backend basis. Registry for (usually temporary) flags that indicate aborting something early.
// LLM configs (GPT4ALL, Llama.cpp, ...)
LLMConfig `yaml:",inline" json:",inline"`
// Diffusers
Diffusers Diffusers `yaml:"diffusers,omitempty" json:"diffusers,omitempty"`
Step int `yaml:"step,omitempty" json:"step,omitempty"`
// GRPC Options
GRPC GRPC `yaml:"grpc,omitempty" json:"grpc,omitempty"`
// TTS specifics
TTSConfig `yaml:"tts,omitempty" json:"tts,omitempty"`
// CUDA
// Explicitly enable CUDA or not (some backends might need it)
CUDA bool `yaml:"cuda,omitempty" json:"cuda,omitempty"`
DownloadFiles []File `yaml:"download_files,omitempty" json:"download_files,omitempty"`
Description string `yaml:"description,omitempty" json:"description,omitempty"`
Usage string `yaml:"usage,omitempty" json:"usage,omitempty"`
Disabled *bool `yaml:"disabled,omitempty" json:"disabled,omitempty"`
Pinned *bool `yaml:"pinned,omitempty" json:"pinned,omitempty"`
Options []string `yaml:"options,omitempty" json:"options,omitempty"`
Overrides []string `yaml:"overrides,omitempty" json:"overrides,omitempty"`
MCP MCPConfig `yaml:"mcp,omitempty" json:"mcp,omitempty"`
Agent AgentConfig `yaml:"agent,omitempty" json:"agent,omitempty"`
}
// @Description MCP configuration
type MCPConfig struct {
Servers string `yaml:"remote,omitempty" json:"remote,omitempty"`
Stdio string `yaml:"stdio,omitempty" json:"stdio,omitempty"`
}
// @Description Agent configuration
type AgentConfig struct {
MaxAttempts int `yaml:"max_attempts,omitempty" json:"max_attempts,omitempty"`
MaxIterations int `yaml:"max_iterations,omitempty" json:"max_iterations,omitempty"`
EnableReasoning bool `yaml:"enable_reasoning,omitempty" json:"enable_reasoning,omitempty"`
EnablePlanning bool `yaml:"enable_planning,omitempty" json:"enable_planning,omitempty"`
EnableMCPPrompts bool `yaml:"enable_mcp_prompts,omitempty" json:"enable_mcp_prompts,omitempty"`
EnablePlanReEvaluator bool `yaml:"enable_plan_re_evaluator,omitempty" json:"enable_plan_re_evaluator,omitempty"`
DisableSinkState bool `yaml:"disable_sink_state,omitempty" json:"disable_sink_state,omitempty"`
LoopDetection int `yaml:"loop_detection,omitempty" json:"loop_detection,omitempty"`
MaxAdjustmentAttempts int `yaml:"max_adjustment_attempts,omitempty" json:"max_adjustment_attempts,omitempty"`
ForceReasoningTool bool `yaml:"force_reasoning_tool,omitempty" json:"force_reasoning_tool,omitempty"`
}
// HasMCPServers returns true if any MCP servers (remote or stdio) are configured.
func (c MCPConfig) HasMCPServers() bool {
return c.Servers != "" || c.Stdio != ""
}
func (c *MCPConfig) MCPConfigFromYAML() (MCPGenericConfig[MCPRemoteServers], MCPGenericConfig[MCPSTDIOServers], error) {
var remote MCPGenericConfig[MCPRemoteServers]
var stdio MCPGenericConfig[MCPSTDIOServers]
if err := yaml.Unmarshal([]byte(c.Servers), &remote); err != nil {
return remote, stdio, err
}
if err := yaml.Unmarshal([]byte(c.Stdio), &stdio); err != nil {
return remote, stdio, err
}
return remote, stdio, nil
}
// @Description MCP generic configuration
type MCPGenericConfig[T any] struct {
Servers T `yaml:"mcpServers,omitempty" json:"mcpServers,omitempty"`
}
type MCPRemoteServers map[string]MCPRemoteServer
type MCPSTDIOServers map[string]MCPSTDIOServer
// @Description MCP remote server configuration
type MCPRemoteServer struct {
URL string `json:"url,omitempty"`
Token string `json:"token,omitempty"`
}
// @Description MCP STDIO server configuration
type MCPSTDIOServer struct {
Args []string `json:"args,omitempty"`
Env map[string]string `json:"env,omitempty"`
Command string `json:"command,omitempty"`
}
// @Description Pipeline defines other models to use for audio-to-audio
type Pipeline struct {
TTS string `yaml:"tts,omitempty" json:"tts,omitempty"`
LLM string `yaml:"llm,omitempty" json:"llm,omitempty"`
Transcription string `yaml:"transcription,omitempty" json:"transcription,omitempty"`
VAD string `yaml:"vad,omitempty" json:"vad,omitempty"`
}
// @Description File configuration for model downloads
type File struct {
Filename string `yaml:"filename,omitempty" json:"filename,omitempty"`
SHA256 string `yaml:"sha256,omitempty" json:"sha256,omitempty"`
URI downloader.URI `yaml:"uri,omitempty" json:"uri,omitempty"`
}
type FeatureFlag map[string]*bool
func (ff FeatureFlag) Enabled(s string) bool {
if v, exists := ff[s]; exists && v != nil {
return *v
}
return false
}
// @Description GRPC configuration
type GRPC struct {
Attempts int `yaml:"attempts,omitempty" json:"attempts,omitempty"`
AttemptsSleepTime int `yaml:"attempts_sleep_time,omitempty" json:"attempts_sleep_time,omitempty"`
}
// @Description Diffusers configuration
type Diffusers struct {
CUDA bool `yaml:"cuda,omitempty" json:"cuda,omitempty"`
PipelineType string `yaml:"pipeline_type,omitempty" json:"pipeline_type,omitempty"`
SchedulerType string `yaml:"scheduler_type,omitempty" json:"scheduler_type,omitempty"`
EnableParameters string `yaml:"enable_parameters,omitempty" json:"enable_parameters,omitempty"` // A list of comma separated parameters to specify
IMG2IMG bool `yaml:"img2img,omitempty" json:"img2img,omitempty"` // Image to Image Diffuser
ClipSkip int `yaml:"clip_skip,omitempty" json:"clip_skip,omitempty"` // Skip every N frames
ClipModel string `yaml:"clip_model,omitempty" json:"clip_model,omitempty"` // Clip model to use
ClipSubFolder string `yaml:"clip_subfolder,omitempty" json:"clip_subfolder,omitempty"` // Subfolder to use for clip model
ControlNet string `yaml:"control_net,omitempty" json:"control_net,omitempty"`
}
// @Description LLMConfig is a struct that holds the configuration that are generic for most of the LLM backends.
type LLMConfig struct {
SystemPrompt string `yaml:"system_prompt,omitempty" json:"system_prompt,omitempty"`
TensorSplit string `yaml:"tensor_split,omitempty" json:"tensor_split,omitempty"`
MainGPU string `yaml:"main_gpu,omitempty" json:"main_gpu,omitempty"`
RMSNormEps float32 `yaml:"rms_norm_eps,omitempty" json:"rms_norm_eps,omitempty"`
NGQA int32 `yaml:"ngqa,omitempty" json:"ngqa,omitempty"`
PromptCachePath string `yaml:"prompt_cache_path,omitempty" json:"prompt_cache_path,omitempty"`
PromptCacheAll bool `yaml:"prompt_cache_all,omitempty" json:"prompt_cache_all,omitempty"`
PromptCacheRO bool `yaml:"prompt_cache_ro,omitempty" json:"prompt_cache_ro,omitempty"`
MirostatETA *float64 `yaml:"mirostat_eta,omitempty" json:"mirostat_eta,omitempty"`
MirostatTAU *float64 `yaml:"mirostat_tau,omitempty" json:"mirostat_tau,omitempty"`
Mirostat *int `yaml:"mirostat,omitempty" json:"mirostat,omitempty"`
NGPULayers *int `yaml:"gpu_layers,omitempty" json:"gpu_layers,omitempty"`
MMap *bool `yaml:"mmap,omitempty" json:"mmap,omitempty"`
MMlock *bool `yaml:"mmlock,omitempty" json:"mmlock,omitempty"`
LowVRAM *bool `yaml:"low_vram,omitempty" json:"low_vram,omitempty"`
Reranking *bool `yaml:"reranking,omitempty" json:"reranking,omitempty"`
Grammar string `yaml:"grammar,omitempty" json:"grammar,omitempty"`
StopWords []string `yaml:"stopwords,omitempty" json:"stopwords,omitempty"`
Cutstrings []string `yaml:"cutstrings,omitempty" json:"cutstrings,omitempty"`
ExtractRegex []string `yaml:"extract_regex,omitempty" json:"extract_regex,omitempty"`
TrimSpace []string `yaml:"trimspace,omitempty" json:"trimspace,omitempty"`
TrimSuffix []string `yaml:"trimsuffix,omitempty" json:"trimsuffix,omitempty"`
ContextSize *int `yaml:"context_size,omitempty" json:"context_size,omitempty"`
NUMA bool `yaml:"numa,omitempty" json:"numa,omitempty"`
LoraAdapter string `yaml:"lora_adapter,omitempty" json:"lora_adapter,omitempty"`
LoraBase string `yaml:"lora_base,omitempty" json:"lora_base,omitempty"`
LoraAdapters []string `yaml:"lora_adapters,omitempty" json:"lora_adapters,omitempty"`
LoraScales []float32 `yaml:"lora_scales,omitempty" json:"lora_scales,omitempty"`
LoraScale float32 `yaml:"lora_scale,omitempty" json:"lora_scale,omitempty"`
NoMulMatQ bool `yaml:"no_mulmatq,omitempty" json:"no_mulmatq,omitempty"`
DraftModel string `yaml:"draft_model,omitempty" json:"draft_model,omitempty"`
NDraft int32 `yaml:"n_draft,omitempty" json:"n_draft,omitempty"`
Quantization string `yaml:"quantization,omitempty" json:"quantization,omitempty"`
LoadFormat string `yaml:"load_format,omitempty" json:"load_format,omitempty"`
GPUMemoryUtilization float32 `yaml:"gpu_memory_utilization,omitempty" json:"gpu_memory_utilization,omitempty"` // vLLM
TrustRemoteCode bool `yaml:"trust_remote_code,omitempty" json:"trust_remote_code,omitempty"` // vLLM
EnforceEager bool `yaml:"enforce_eager,omitempty" json:"enforce_eager,omitempty"` // vLLM
SwapSpace int `yaml:"swap_space,omitempty" json:"swap_space,omitempty"` // vLLM
MaxModelLen int `yaml:"max_model_len,omitempty" json:"max_model_len,omitempty"` // vLLM
TensorParallelSize int `yaml:"tensor_parallel_size,omitempty" json:"tensor_parallel_size,omitempty"` // vLLM
DisableLogStatus bool `yaml:"disable_log_stats,omitempty" json:"disable_log_stats,omitempty"` // vLLM
DType string `yaml:"dtype,omitempty" json:"dtype,omitempty"` // vLLM
LimitMMPerPrompt LimitMMPerPrompt `yaml:"limit_mm_per_prompt,omitempty" json:"limit_mm_per_prompt,omitempty"` // vLLM
MMProj string `yaml:"mmproj,omitempty" json:"mmproj,omitempty"`
FlashAttention *string `yaml:"flash_attention,omitempty" json:"flash_attention,omitempty"`
NoKVOffloading bool `yaml:"no_kv_offloading,omitempty" json:"no_kv_offloading,omitempty"`
CacheTypeK string `yaml:"cache_type_k,omitempty" json:"cache_type_k,omitempty"`
CacheTypeV string `yaml:"cache_type_v,omitempty" json:"cache_type_v,omitempty"`
RopeScaling string `yaml:"rope_scaling,omitempty" json:"rope_scaling,omitempty"`
ModelType string `yaml:"type,omitempty" json:"type,omitempty"`
YarnExtFactor float32 `yaml:"yarn_ext_factor,omitempty" json:"yarn_ext_factor,omitempty"`
YarnAttnFactor float32 `yaml:"yarn_attn_factor,omitempty" json:"yarn_attn_factor,omitempty"`
YarnBetaFast float32 `yaml:"yarn_beta_fast,omitempty" json:"yarn_beta_fast,omitempty"`
YarnBetaSlow float32 `yaml:"yarn_beta_slow,omitempty" json:"yarn_beta_slow,omitempty"`
CFGScale float32 `yaml:"cfg_scale,omitempty" json:"cfg_scale,omitempty"` // Classifier-Free Guidance Scale
}
// @Description LimitMMPerPrompt is a struct that holds the configuration for the limit-mm-per-prompt config in vLLM
type LimitMMPerPrompt struct {
LimitImagePerPrompt int `yaml:"image,omitempty" json:"image,omitempty"`
LimitVideoPerPrompt int `yaml:"video,omitempty" json:"video,omitempty"`
LimitAudioPerPrompt int `yaml:"audio,omitempty" json:"audio,omitempty"`
}
// @Description TemplateConfig is a struct that holds the configuration of the templating system
type TemplateConfig struct {
// Chat is the template used in the chat completion endpoint
Chat string `yaml:"chat,omitempty" json:"chat,omitempty"`
// ChatMessage is the template used for chat messages
ChatMessage string `yaml:"chat_message,omitempty" json:"chat_message,omitempty"`
// Completion is the template used for completion requests
Completion string `yaml:"completion,omitempty" json:"completion,omitempty"`
// Edit is the template used for edit completion requests
Edit string `yaml:"edit,omitempty" json:"edit,omitempty"`
// Functions is the template used when tools are present in the client requests
Functions string `yaml:"function,omitempty" json:"function,omitempty"`
// UseTokenizerTemplate is a flag that indicates if the tokenizer template should be used.
// Note: this is mostly consumed for backends such as vllm and transformers
// that can use the tokenizers specified in the JSON config files of the models
UseTokenizerTemplate bool `yaml:"use_tokenizer_template,omitempty" json:"use_tokenizer_template,omitempty"`
// JoinChatMessagesByCharacter is a string that will be used to join chat messages together.
// It defaults to \n
JoinChatMessagesByCharacter *string `yaml:"join_chat_messages_by_character,omitempty" json:"join_chat_messages_by_character,omitempty"`
Multimodal string `yaml:"multimodal,omitempty" json:"multimodal,omitempty"`
ReplyPrefix string `yaml:"reply_prefix,omitempty" json:"reply_prefix,omitempty"`
}
func (c *ModelConfig) syncKnownUsecasesFromString() {
c.KnownUsecases = GetUsecasesFromYAML(c.KnownUsecaseStrings)
// Make sure the usecases are valid, we rewrite with what we identified
c.KnownUsecaseStrings = []string{}
for k, usecase := range GetAllModelConfigUsecases() {
if c.HasUsecases(usecase) {
c.KnownUsecaseStrings = append(c.KnownUsecaseStrings, k)
}
}
}
func (c *ModelConfig) UnmarshalYAML(value *yaml.Node) error {
type BCAlias ModelConfig
var aux BCAlias
if err := value.Decode(&aux); err != nil {
return err
}
mc := ModelConfig(aux)
*c = mc
c.syncKnownUsecasesFromString()
return nil
}
func (c *ModelConfig) SetFunctionCallString(s string) {
c.functionCallString = s
}
func (c *ModelConfig) SetFunctionCallNameString(s string) {
c.functionCallNameString = s
}
func (c *ModelConfig) ShouldUseFunctions() bool {
return ((c.functionCallString != "none" || c.functionCallString == "") || c.ShouldCallSpecificFunction())
}
func (c *ModelConfig) ShouldCallSpecificFunction() bool {
return len(c.functionCallNameString) > 0
}
// MMProjFileName returns the filename of the MMProj file
// If the MMProj is a URL, it will return the MD5 of the URL which is the filename
func (c *ModelConfig) MMProjFileName() string {
uri := downloader.URI(c.MMProj)
if uri.LooksLikeURL() {
f, _ := uri.FilenameFromUrl()
return f
}
return c.MMProj
}
func (c *ModelConfig) IsMMProjURL() bool {
uri := downloader.URI(c.MMProj)
return uri.LooksLikeURL()
}
func (c *ModelConfig) IsModelURL() bool {
uri := downloader.URI(c.Model)
return uri.LooksLikeURL()
}
// ModelFileName returns the filename of the model
// If the model is a URL, it will return the MD5 of the URL which is the filename
func (c *ModelConfig) ModelFileName() string {
uri := downloader.URI(c.Model)
if uri.LooksLikeURL() {
f, _ := uri.FilenameFromUrl()
return f
}
return c.Model
}
func (c *ModelConfig) FunctionToCall() string {
if c.functionCallNameString != "" &&
c.functionCallNameString != "none" && c.functionCallNameString != "auto" {
return c.functionCallNameString
}
return c.functionCallString
}
func (cfg *ModelConfig) SetDefaults(opts ...ConfigLoaderOption) {
lo := &LoadOptions{}
lo.Apply(opts...)
ctx := lo.ctxSize
threads := lo.threads
f16 := lo.f16
debug := lo.debug
// Apply model-family-specific inference defaults before generic fallbacks.
// This ensures gallery-installed and runtime-loaded models get optimal parameters.
ApplyInferenceDefaults(cfg, cfg.Name, cfg.Model)
// https://github.com/ggerganov/llama.cpp/blob/75cd4c77292034ecec587ecb401366f57338f7c0/common/sampling.h#L22
defaultTopP := 0.95
defaultTopK := 40
defaultMinP := 0.0
defaultTemp := 0.9
// https://github.com/mudler/LocalAI/issues/2780
defaultMirostat := 0
defaultMirostatTAU := 5.0
defaultMirostatETA := 0.1
defaultTypicalP := 1.0
defaultTFZ := 1.0
defaultZero := 0
trueV := true
falseV := false
if cfg.Seed == nil {
// random number generator seed
defaultSeed := RAND_SEED
cfg.Seed = &defaultSeed
}
if cfg.TopK == nil {
cfg.TopK = &defaultTopK
}
if cfg.MinP == nil {
cfg.MinP = &defaultMinP
}
if cfg.TypicalP == nil {
cfg.TypicalP = &defaultTypicalP
}
if cfg.TFZ == nil {
cfg.TFZ = &defaultTFZ
}
if cfg.MMap == nil {
// MMap is enabled by default
// Only exception is for Intel GPUs
if os.Getenv("XPU") != "" {
cfg.MMap = &falseV
} else {
cfg.MMap = &trueV
}
}
if cfg.MMlock == nil {
// MMlock is disabled by default
cfg.MMlock = &falseV
}
if cfg.TopP == nil {
cfg.TopP = &defaultTopP
}
if cfg.Temperature == nil {
cfg.Temperature = &defaultTemp
}
if cfg.Maxtokens == nil {
cfg.Maxtokens = &defaultZero
}
if cfg.Mirostat == nil {
cfg.Mirostat = &defaultMirostat
}
if cfg.MirostatETA == nil {
cfg.MirostatETA = &defaultMirostatETA
}
if cfg.MirostatTAU == nil {
cfg.MirostatTAU = &defaultMirostatTAU
}
if cfg.LowVRAM == nil {
cfg.LowVRAM = &falseV
}
if cfg.Embeddings == nil {
cfg.Embeddings = &falseV
}
if cfg.Reranking == nil {
cfg.Reranking = &falseV
}
if threads == 0 {
// Threads can't be 0
threads = 4
}
if cfg.Threads == nil {
cfg.Threads = &threads
}
if cfg.F16 == nil {
cfg.F16 = &f16
}
if cfg.Debug == nil {
cfg.Debug = &falseV
}
if debug {
cfg.Debug = &trueV
}
// If a context size was provided via LoadOptions, apply it before hooks so they
// don't override it with their own defaults.
if ctx != 0 && cfg.ContextSize == nil {
cfg.ContextSize = &ctx
}
runBackendHooks(cfg, lo.modelPath)
cfg.syncKnownUsecasesFromString()
}
func (c *ModelConfig) Validate() (bool, error) {
downloadedFileNames := []string{}
for _, f := range c.DownloadFiles {
downloadedFileNames = append(downloadedFileNames, f.Filename)
}
validationTargets := []string{c.Backend, c.Model, c.MMProj}
validationTargets = append(validationTargets, downloadedFileNames...)
// Simple validation to make sure the model can be correctly loaded
for _, n := range validationTargets {
if n == "" {
continue
}
if strings.HasPrefix(n, string(os.PathSeparator)) ||
strings.Contains(n, "..") {
return false, fmt.Errorf("invalid file path: %s", n)
}
}
if c.Backend != "" {
// a regex that checks that is a string name with no special characters, except '-' and '_'
re := regexp.MustCompile(`^[a-zA-Z0-9-_]+$`)
if !re.MatchString(c.Backend) {
return false, fmt.Errorf("invalid backend name: %s", c.Backend)
}
}
// Validate MCP configuration if present
if c.MCP.Servers != "" || c.MCP.Stdio != "" {
if _, _, err := c.MCP.MCPConfigFromYAML(); err != nil {
return false, fmt.Errorf("invalid MCP configuration: %w", err)
}
}
return true, nil
}
func (c *ModelConfig) HasTemplate() bool {
return c.TemplateConfig.Completion != "" || c.TemplateConfig.Edit != "" || c.TemplateConfig.Chat != "" || c.TemplateConfig.ChatMessage != "" || c.TemplateConfig.UseTokenizerTemplate
}
func (c *ModelConfig) GetModelConfigFile() string {
return c.modelConfigFile
}
// GetModelTemplate returns the model's chat template if available
func (c *ModelConfig) GetModelTemplate() string {
return c.modelTemplate
}
// IsDisabled returns true if the model is disabled
func (c *ModelConfig) IsDisabled() bool {
return c.Disabled != nil && *c.Disabled
}
// IsPinned returns true if the model is pinned (excluded from idle unloading and eviction)
func (c *ModelConfig) IsPinned() bool {
return c.Pinned != nil && *c.Pinned
}
type ModelConfigUsecase int
const (
FLAG_ANY ModelConfigUsecase = 0b000000000000
FLAG_CHAT ModelConfigUsecase = 0b000000000001
FLAG_COMPLETION ModelConfigUsecase = 0b000000000010
FLAG_EDIT ModelConfigUsecase = 0b000000000100
FLAG_EMBEDDINGS ModelConfigUsecase = 0b000000001000
FLAG_RERANK ModelConfigUsecase = 0b000000010000
FLAG_IMAGE ModelConfigUsecase = 0b000000100000
FLAG_TRANSCRIPT ModelConfigUsecase = 0b000001000000
FLAG_TTS ModelConfigUsecase = 0b000010000000
FLAG_SOUND_GENERATION ModelConfigUsecase = 0b000100000000
FLAG_TOKENIZE ModelConfigUsecase = 0b001000000000
FLAG_VAD ModelConfigUsecase = 0b010000000000
FLAG_VIDEO ModelConfigUsecase = 0b100000000000
FLAG_DETECTION ModelConfigUsecase = 0b1000000000000
FLAG_FACE_RECOGNITION ModelConfigUsecase = 0b10000000000000
// Common Subsets
FLAG_LLM ModelConfigUsecase = FLAG_CHAT | FLAG_COMPLETION | FLAG_EDIT
)
func GetAllModelConfigUsecases() map[string]ModelConfigUsecase {
return map[string]ModelConfigUsecase{
// Note: FLAG_ANY is intentionally excluded from this map
// because it's 0 and would always match in HasUsecases checks
"FLAG_CHAT": FLAG_CHAT,
"FLAG_COMPLETION": FLAG_COMPLETION,
"FLAG_EDIT": FLAG_EDIT,
"FLAG_EMBEDDINGS": FLAG_EMBEDDINGS,
"FLAG_RERANK": FLAG_RERANK,
"FLAG_IMAGE": FLAG_IMAGE,
"FLAG_TRANSCRIPT": FLAG_TRANSCRIPT,
"FLAG_TTS": FLAG_TTS,
"FLAG_SOUND_GENERATION": FLAG_SOUND_GENERATION,
"FLAG_TOKENIZE": FLAG_TOKENIZE,
"FLAG_VAD": FLAG_VAD,
"FLAG_LLM": FLAG_LLM,
"FLAG_VIDEO": FLAG_VIDEO,
"FLAG_DETECTION": FLAG_DETECTION,
"FLAG_FACE_RECOGNITION": FLAG_FACE_RECOGNITION,
}
}
func stringToFlag(s string) string {
return "FLAG_" + strings.ToUpper(s)
}
func GetUsecasesFromYAML(input []string) *ModelConfigUsecase {
if len(input) == 0 {
return nil
}
result := FLAG_ANY
flags := GetAllModelConfigUsecases()
for _, str := range input {
for _, flag := range []string{stringToFlag(str), str} {
f, exists := flags[flag]
if exists {
result |= f
}
}
}
return &result
}
// HasUsecases examines a ModelConfig and determines which endpoints have a chance of success.
func (c *ModelConfig) HasUsecases(u ModelConfigUsecase) bool {
if (c.KnownUsecases != nil) && ((u & *c.KnownUsecases) == u) {
return true
}
return c.GuessUsecases(u)
}
// GuessUsecases is a **heuristic based** function, as the backend in question may not be loaded yet, and the config may not record what it's useful at.
// In its current state, this function should ideally check for properties of the config like templates, rather than the direct backend name checks for the lower half.
// This avoids the maintenance burden of updating this list for each new backend - but unfortunately, that's the best option for some services currently.
func (c *ModelConfig) GuessUsecases(u ModelConfigUsecase) bool {
// Backends that are clearly not text-generation
nonTextGenBackends := []string{
"whisper", "piper", "kokoro",
"diffusers", "stablediffusion", "stablediffusion-ggml",
"rerankers", "silero-vad", "rfdetr", "insightface",
"transformers-musicgen", "ace-step", "acestep-cpp",
}
if (u & FLAG_CHAT) == FLAG_CHAT {
if c.TemplateConfig.Chat == "" && c.TemplateConfig.ChatMessage == "" && !c.TemplateConfig.UseTokenizerTemplate {
return false
}
if slices.Contains(nonTextGenBackends, c.Backend) {
return false
}
if c.Embeddings != nil && *c.Embeddings {
return false
}
}
if (u & FLAG_COMPLETION) == FLAG_COMPLETION {
if c.TemplateConfig.Completion == "" {
return false
}
if slices.Contains(nonTextGenBackends, c.Backend) {
return false
}
}
if (u & FLAG_EDIT) == FLAG_EDIT {
if c.TemplateConfig.Edit == "" {
return false
}
}
if (u & FLAG_EMBEDDINGS) == FLAG_EMBEDDINGS {
if c.Embeddings == nil || !*c.Embeddings {
return false
}
}
if (u & FLAG_IMAGE) == FLAG_IMAGE {
imageBackends := []string{"diffusers", "stablediffusion", "stablediffusion-ggml"}
if !slices.Contains(imageBackends, c.Backend) {
return false
}
if c.Backend == "diffusers" && c.Diffusers.PipelineType == "" {
return false
}
}
if (u & FLAG_VIDEO) == FLAG_VIDEO {
videoBackends := []string{"diffusers", "stablediffusion", "vllm-omni"}
if !slices.Contains(videoBackends, c.Backend) {
return false
}
if c.Backend == "diffusers" && c.Diffusers.PipelineType == "" {
return false
}
}
if (u & FLAG_RERANK) == FLAG_RERANK {
if c.Backend != "rerankers" && (c.Reranking == nil || !*c.Reranking) {
return false
}
}
if (u & FLAG_TRANSCRIPT) == FLAG_TRANSCRIPT {
if c.Backend != "whisper" {
return false
}
// whisper models with vad_only option are VAD, not transcription
if slices.Contains(c.Options, "vad_only") {
return false
}
}
if (u & FLAG_TTS) == FLAG_TTS {
ttsBackends := []string{"piper", "transformers-musicgen", "kokoro"}
if !slices.Contains(ttsBackends, c.Backend) {
return false
}
}
if (u & FLAG_DETECTION) == FLAG_DETECTION {
detectionBackends := []string{"rfdetr", "sam3-cpp", "insightface"}
if !slices.Contains(detectionBackends, c.Backend) {
return false
}
}
if (u & FLAG_FACE_RECOGNITION) == FLAG_FACE_RECOGNITION {
faceBackends := []string{"insightface"}
if !slices.Contains(faceBackends, c.Backend) {
return false
}
}
if (u & FLAG_SOUND_GENERATION) == FLAG_SOUND_GENERATION {
soundGenBackends := []string{"transformers-musicgen", "ace-step", "acestep-cpp", "mock-backend"}
if !slices.Contains(soundGenBackends, c.Backend) {
return false
}
}
if (u & FLAG_TOKENIZE) == FLAG_TOKENIZE {
tokenizeCapableBackends := []string{"llama.cpp", "rwkv"}
if !slices.Contains(tokenizeCapableBackends, c.Backend) {
return false
}
}
if (u & FLAG_VAD) == FLAG_VAD {
if c.Backend != "silero-vad" && !(c.Backend == "whisper" && slices.Contains(c.Options, "vad_only")) {
return false
}
}
return true
}
// BuildCogitoOptions generates cogito options from the model configuration
// It accepts a context, MCP sessions, and optional callback functions for status, reasoning, tool calls, and tool results
func (c *ModelConfig) BuildCogitoOptions() []cogito.Option {
cogitoOpts := []cogito.Option{
cogito.WithIterations(3), // default to 3 iterations
cogito.WithMaxAttempts(3), // default to 3 attempts
cogito.WithForceReasoning(),
}
// Apply agent configuration options
if c.Agent.EnableReasoning {
cogitoOpts = append(cogitoOpts, cogito.WithForceReasoning())
}
if c.Agent.EnablePlanning {
cogitoOpts = append(cogitoOpts, cogito.EnableAutoPlan)
}
if c.Agent.EnableMCPPrompts {
cogitoOpts = append(cogitoOpts, cogito.EnableMCPPrompts)
}
if c.Agent.EnablePlanReEvaluator {
cogitoOpts = append(cogitoOpts, cogito.EnableAutoPlanReEvaluator)
}
if c.Agent.MaxIterations != 0 {
cogitoOpts = append(cogitoOpts, cogito.WithIterations(c.Agent.MaxIterations))
}
if c.Agent.MaxAttempts != 0 {
cogitoOpts = append(cogitoOpts, cogito.WithMaxAttempts(c.Agent.MaxAttempts))
}
if c.Agent.DisableSinkState {
cogitoOpts = append(cogitoOpts, cogito.DisableSinkState)
}
if c.Agent.LoopDetection != 0 {
cogitoOpts = append(cogitoOpts, cogito.WithLoopDetection(c.Agent.LoopDetection))
}
if c.Agent.MaxAdjustmentAttempts != 0 {
cogitoOpts = append(cogitoOpts, cogito.WithMaxAdjustmentAttempts(c.Agent.MaxAdjustmentAttempts))
}
if c.Agent.ForceReasoningTool {
cogitoOpts = append(cogitoOpts, cogito.WithForceReasoningTool())
}
return cogitoOpts
}