zeph 0.22.4

# Audio and Vision

Zeph supports audio transcription and image input across all channels.

## Audio Input

Pipeline: Audio attachment → STT provider → Transcribed text → Agent loop

### Configuration

Enable the `stt` feature flag:

```bash
cargo build --release --features stt
```

```toml
[llm.stt]
provider = "whisper"
model = "whisper-1"
```

When `base_url` is omitted, the provider uses the OpenAI API key from the `openai` `[[llm.providers]]` entry or `ZEPH_OPENAI_API_KEY`. Set `base_url` to point at any OpenAI-compatible server (no API key required for local servers). The `language` field accepts an [ISO-639-1](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) code (e.g. `ru`, `en`, `de`) or `auto` for automatic detection.

Environment variable overrides: `ZEPH_STT_PROVIDER`, `ZEPH_STT_MODEL`, `ZEPH_STT_LANGUAGE`, `ZEPH_STT_BASE_URL`.

### Backends

| Backend | Provider | Feature | Description |
|---------|----------|---------|-------------|
| OpenAI Whisper API | `whisper` | `stt` | Cloud-based transcription |
| OpenAI-compatible server | `whisper` | `stt` | Any local server with `/v1/audio/transcriptions` |
| Local Whisper | `candle-whisper` | `candle` | Fully offline via candle |

### Local Whisper Server (whisper.cpp)

The recommended setup for local speech-to-text. Uses Metal acceleration on Apple Silicon and handles all audio formats (including Telegram OGG/Opus) server-side.

**Install and run:**

```bash
brew install whisper-cpp

# Download a model
curl -L -o ~/.cache/whisper/ggml-large-v3.bin \
  https://huggingface.co/ggerganov/whisper.cpp/resolve/main/ggml-large-v3.bin

# Start the server
whisper-server \
  --model ~/.cache/whisper/ggml-large-v3.bin \
  --host 127.0.0.1 --port 8080 \
  --inference-path "/v1/audio/transcriptions" \
  --convert
```

**Configure Zeph:**

```toml
[llm.stt]
provider = "whisper"
model = "large-v3"
base_url = "http://127.0.0.1:8080/v1"
language = "en"   # ISO-639-1 code or "auto"
```

| Model | Parameters | Disk | Notes |
|-------|------------|------|-------|
| `ggml-tiny` | 39M | ~75 MB | Fastest, lower accuracy |
| `ggml-base` | 74M | ~142 MB | Good balance |
| `ggml-small` | 244M | ~466 MB | Better accuracy |
| `ggml-large-v3` | 1.5B | ~2.9 GB | Best accuracy |

### Local Whisper (Candle)

```bash
cargo build --release --features candle   # CPU
cargo build --release --features metal    # macOS Metal GPU
cargo build --release --features cuda     # NVIDIA GPU
```

```toml
[llm.stt]
provider = "candle-whisper"
model = "openai/whisper-tiny"
```

| Model | Parameters | Disk |
|-------|------------|------|
| `openai/whisper-tiny` | 39M | ~150 MB |
| `openai/whisper-base` | 74M | ~290 MB |
| `openai/whisper-small` | 244M | ~950 MB |

Models are downloaded from HuggingFace on first use. Device auto-detection: Metal → CUDA → CPU.

### Channel Support

- **Telegram**: voice notes and audio files downloaded automatically
- **Slack**: audio uploads detected, downloaded via `url_private_download` (25 MB limit, `.slack.com` host validation). Requires `files:read` OAuth scope
- **CLI/TUI**: no audio input mechanism

### Limits

- 5-minute audio duration guard (candle backend)
- 25 MB file size limit
- No streaming transcription — entire file processed in one pass
- One audio attachment per message

## Image Input

Pipeline: Image attachment → MessagePart::Image → LLM provider (base64) → Response

### Provider Support

| Provider | Vision | Notes |
|----------|--------|-------|
| Claude | Yes | Anthropic image content block |
| OpenAI | Conditional | Auto-detected from the model name, or set explicitly via `vision` |
| Compatible | Conditional | Model-name auto-detection rarely applies (third-party names); set `vision` explicitly |
| Ollama | Conditional | Only when `vision_model` is configured, or the main model is confirmed vision-capable |
| Candle | No | Text-only |

### Ollama Vision Model

Ollama models vary widely in vision support — a text-only model (e.g. `qwen3:8b`) cannot
process images, so Zeph never assumes vision support for Ollama: it checks the configured
model's capabilities via `/api/show` at startup, and drops incoming images (with a warning)
for any model that isn't confirmed vision-capable.

Route image requests to a dedicated vision model while keeping a smaller text model for
regular queries:

```toml
[llm]
model = "mistral:7b"
vision_model = "llava:13b"
```

Alternatively, set `model` directly to a vision-capable model (e.g. `llava:13b`,
`qwen2.5vl`, `llama3.2-vision`) and Zeph detects the capability automatically — no
`vision_model` override needed.

### OpenAI / Compatible Vision Override

`type = "openai"` and `type = "compatible"` providers auto-detect vision support from a
built-in model-name prefix table (`gpt-4o`, `gpt-4-turbo`, `gpt-4.1`, `gpt-5`, `o`+digit
reasoning models — all match; `gpt-3.5*` and anything unrecognised do not). Unlike Ollama,
there is no standardized capability-discovery endpoint for arbitrary `OpenAI`-compatible
servers, so this table is the only automatic signal available, and it fails safe to `false`
for any model name it cannot identify — which is the common case for third-party
`compatible` endpoints (Together AI, local vLLM, etc.), since their model names carry no
`OpenAI` naming convention.

Set `vision` explicitly to override the auto-detected value in either direction:

```toml
[[llm.providers]]
name = "local-vlm"
type = "compatible"
base_url = "http://localhost:8000/v1"
model = "llava-onevision"
vision = true   # the prefix table cannot recognise this model name
```

```toml
[[llm.providers]]
type = "openai"
model = "gpt-4o"
vision = false  # force images off even for a recognised vision model
```

### Sending Images

- **CLI/TUI**: `/image /path/to/screenshot.png What is shown in this image?`
- **Telegram**: send a photo directly; the caption becomes the prompt

### Limits

- 20 MB maximum image size
- One image per message
- No image generation (input only)