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b0959d4756
Additive superset of /v1/models that enriches each model entry with the capabilities it supports plus its input/output modalities (text / image / audio / video). Clients that only understand /v1/models are unaffected -- they simply never call the new route. Audio and video *input* are derived from the model's multimodal limits (vLLM limit_mm_per_prompt), which no single usecase FLAG expresses. That gap is exactly why a plain capability list is insufficient and this enriched endpoint exists: an attachment router can now decide whether an image/audio/video file can go to the active model directly, or must be converted/transcribed first. Capability derivation lives in core/config as the single source of truth (ModelConfig.Capabilities / InputModalities / OutputModalities / VisionSupported / ...); the Ollama capability surface now delegates to it instead of keeping a parallel copy. Vision is gated on chat/completion capability so a MediaMarker hydrated onto a non-chat model (e.g. a pure ASR/TTS backend) no longer reports a false vision capability. Read-only listing: no new FLAG_* flag, reuses the existing `models` swagger tag, and intentionally exposes no MCP admin tool (there is nothing to manage conversationally). Assisted-by: Claude:claude-opus-4-8 [Claude Code] Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
104 lines
3.2 KiB
Go
104 lines
3.2 KiB
Go
package ollama
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import (
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"regexp"
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"strings"
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"github.com/mudler/LocalAI/core/config"
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)
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// modelCapabilities maps a LocalAI ModelConfig to the Ollama capability strings
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// (https://github.com/ollama/ollama/blob/main/docs/api.md#show-model-information).
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//
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// Ollama clients use these to decide which models are eligible for a given task
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// (e.g. only allow embedding models in an "embedding model" picker). Returning
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// an empty list makes clients assume "completion" everywhere, which is wrong
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// for embedding/rerank/audio backends — see issue #9760.
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func modelCapabilities(cfg *config.ModelConfig) []string {
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if cfg == nil {
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return nil
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}
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var caps []string
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if cfg.HasUsecases(config.FLAG_EMBEDDINGS) {
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caps = append(caps, "embedding")
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}
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chatCapable := cfg.HasUsecases(config.FLAG_CHAT) || cfg.HasUsecases(config.FLAG_COMPLETION)
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if chatCapable {
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caps = append(caps, "completion")
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}
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if chatCapable && hasVisionSupport(cfg) {
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caps = append(caps, "vision")
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}
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if chatCapable && hasToolSupport(cfg) {
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caps = append(caps, "tools")
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}
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if chatCapable && hasThinkingSupport(cfg) {
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caps = append(caps, "thinking")
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}
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if chatCapable && cfg.TemplateConfig.Completion != "" {
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caps = append(caps, "insert")
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}
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return caps
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}
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// hasVisionSupport reports whether the model can accept image inputs.
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// The detection heuristic is the canonical config.ModelConfig.VisionSupported —
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// kept as a thin wrapper here so the Ollama capability mapping reads cleanly.
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func hasVisionSupport(cfg *config.ModelConfig) bool {
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return cfg.VisionSupported()
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}
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// hasToolSupport reports whether the model is wired up for tool / function
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// calling. Delegates to the canonical config.ModelConfig.ToolSupported.
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func hasToolSupport(cfg *config.ModelConfig) bool {
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return cfg.ToolSupported()
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}
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// hasThinkingSupport reports whether the model has reasoning / thinking enabled.
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// Delegates to the canonical config.ModelConfig.ThinkingSupported.
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func hasThinkingSupport(cfg *config.ModelConfig) bool {
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return cfg.ThinkingSupported()
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}
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// quantRegex matches GGUF-style quantization suffixes (Q4_K_M, Q8_0, IQ3_XS, F16, ...).
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// Matches the convention used by GGUF tooling and what ggml-org/llama.cpp report.
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var quantRegex = regexp.MustCompile(`(?i)(IQ\d+(?:_[A-Z0-9]+)*|Q\d+(?:_[A-Z0-9]+)*|F16|F32|BF16)`)
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// paramSizeRegex matches a parameter-size token surrounded by separators
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// (e.g. "-7B-", "_3b.", ".70B-"). Avoids matching the "7" inside "Qwen3".
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var paramSizeRegex = regexp.MustCompile(`(?i)(?:^|[-_.])(\d+(?:\.\d+)?[BM])(?:[-_.]|$)`)
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// extractQuantizationLevel pulls the quantization tag from the model filename.
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// Returns the uppercased token (e.g. "Q4_K_M") or "" when not present.
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func extractQuantizationLevel(modelFile string) string {
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if modelFile == "" {
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return ""
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}
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base := strings.TrimSuffix(modelFile, ".gguf")
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if m := quantRegex.FindString(base); m != "" {
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return strings.ToUpper(m)
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}
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return ""
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}
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// extractParameterSize pulls the parameter count from the model filename.
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// Returns "" when no recognizable token is present.
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func extractParameterSize(modelFile string) string {
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if modelFile == "" {
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return ""
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}
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base := strings.TrimSuffix(modelFile, ".gguf")
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if m := paramSizeRegex.FindStringSubmatch(base); len(m) > 1 {
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return strings.ToUpper(m[1])
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}
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return ""
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}
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