2.0.0-next.1 — unified embeddings interface (opencode V2)

Embedding access is now unified on a single OpenAI-compatible interface.

Migration (breaking — no backward-compatible aliases)

Old	New
embeddingBackend: "python"	embeddingBackend: "builtin"
embeddingBackend: "remote"	embeddingBackend: "endpoint"
remoteApiUrl	embeddingBaseUrl
remoteApiKey	embeddingApiKey

• Default model: nomic-embed-text-v1 → embeddinggemma-2 (multilingual). The index records its
  embedding-space identity and auto-rebuilds on the next session — both are 768-dim, so the change
  is detected by identity, not dimension.
• builtin is self-contained: the bundled python/embed_server.py serves OpenAI-compatible
  POST /v1/embeddings and auto-downloads the model into ~/.opencode-md-memory/models/<model>/onnx
  on first use (MDM_EMBED_MODEL_DIR to override, MDM_EMBED_AUTO_FETCH=0 to disable).
• endpoint: point at any OpenAI-compatible embeddings service — llama.cpp / Ollama / TEI /
  LM Studio / OpenAI — through the same interface.

Changes

• unify embedding backends on one OpenAI-compatible client (builtin / endpoint)
• python/embed_server.py: serves /v1/embeddings; atomic .part download; /health reports the model
• index rebuilds on any model change (including a dimension-preserving swap)
• universal loader: accepts any ONNX text model (sentence_embedding or mean-pool; token_type_ids
  and image/video/audio feature inputs handled when present)

Compatibility

• opencode V2 (@opencode/plugin >=2.0.0 <3) — this line. Pre-release.
• For opencode V1, install the latest tag (1.3.0).