feat(server): pluggable AI providers - any open-standard or commercial AI

Lets the owner point transcription and summarisation at any AI: an open-standard
endpoint (OpenAI-compatible / local faster-whisper / Ollama) or a commercial API
(OpenAI, Anthropic, Gemini). Config-driven, self-hostable, no lock-in.

What changed:
- server/app/providers/: provider layer.
  - base.py: Transcriber/Summariser protocols + shared summary prompt + tolerant JSON
    parser (uniform Summary shape across providers).
  - summary.py: OpenAICompatibleSummariser (any /chat/completions - OpenAI, Groq,
    OpenRouter, LocalAI, LM Studio, vLLM, Ollama /v1) and AnthropicSummariser (Claude
    via the official anthropic SDK; Messages API has no OpenAI-compatible endpoint).
  - transcription.py: OpenAICompatibleTranscriber (/audio/transcriptions - OpenAI,
    Groq, self-hosted whisper server) and LocalWhisperTranscriber (faster-whisper,
    execution wired in M5).
  - factory.py: builds the configured providers with per-provider defaults
    (anthropic -> claude-opus-4-8, openai_compatible -> gpt-4o-mini, ollama -> llama3.1).
- config.py + .env.example: transcription_provider / llm_provider selectors + base_url,
  key, model settings; local faster-whisper and Ollama kept as the self-hosted defaults.
- main.py: /health now reports the resolved provider names (no secrets).
- requirements.txt: httpx drives all HTTP providers; anthropic + faster-whisper are
  optional, only for their respective providers.
- docs/ai-providers.md: config recipes for OpenAI, Groq, Anthropic, Gemini, LocalAI,
  LM Studio, Ollama, self-hosted whisper.
- state/: DECISIONS, ARCHITECTURE, TODO updated.

Why:
- The user asked to connect the device to any open standard AI or commercial one; this
  is also the core differentiator vs Plaud's locked cloud.

Notes:
- Anthropic provider uses the official SDK and defaults to claude-opus-4-8 (per the
  claude-api guidance). AI deps are optional per chosen provider. Modules byte-compile
  cleanly; end-to-end wiring into the ingest pipeline lands with M5.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

server/app/providers/base.py

@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: GPL-3.0-only
+"""Provider interfaces plus the shared summary prompt and a tolerant JSON parser.
+
+Keeping the prompt and parser here means every LLM provider produces the same Summary
+shape regardless of whether it is OpenAI-compatible, Anthropic, or a local model.
+"""
+from __future__ import annotations
+
+import json
+import re
+from typing import Protocol
+
+from ..models import Summary, Transcript
+
+SUMMARY_SYSTEM = (
+    "You are a meeting-notes assistant. Given a transcript, produce a concise overview, "
+    "the key points, and any action items. Respond with ONLY a JSON object of the form "
+    '{"overview": string, "key_points": [string], "action_items": [string]} and nothing '
+    "else."
+)
+
+
+def summary_user_prompt(transcript_text: str) -> str:
+    return f"Transcript:\n\n{transcript_text}\n\nProduce the JSON summary now."
+
+
+def parse_summary(recording_id: str, model: str, raw: str) -> Summary:
+    """Parse an LLM reply into a Summary, tolerating prose or code-fences around the JSON."""
+    data: dict = {}
+    match = re.search(r"\{.*\}", raw, re.DOTALL)
+    if match:
+        try:
+            data = json.loads(match.group(0))
+        except json.JSONDecodeError:
+            data = {}
+    return Summary(
+        recording_id=recording_id,
+        model=model,
+        overview=str(data.get("overview") or raw.strip()[:1000]),
+        key_points=[str(x) for x in (data.get("key_points") or [])],
+        action_items=[str(x) for x in (data.get("action_items") or [])],
+    )
+
+
+class Transcriber(Protocol):
+    name: str
+
+    def transcribe(self, audio_path: str, language: str | None = None) -> Transcript: ...
+
+
+class Summariser(Protocol):
+    name: str
+
+    def summarise(self, recording_id: str, transcript_text: str) -> Summary: ...