Nomad changes

docs/snippets/python/plugins/embedding_backend.md

@@ -0,0 +1,44 @@
+```python title="Python"
+from kreuzberg import register_embedding_backend, EmbeddingConfig, embed_texts
+from sentence_transformers import SentenceTransformer
+
+# Wrap an already-loaded embedder (e.g. sentence-transformers, llama-cpp-python,
+# or a tuned ONNX session) so kreuzberg can call back into it during chunking
+# and standalone embed requests.
+class MyEmbedder:
+    def __init__(self):
+        self._model = SentenceTransformer("BAAI/bge-base-en-v1.5")
+
+    # Plugin trait hooks
+    def name(self) -> str:
+        return "my-embedder"
+
+    def version(self) -> str:
+        return "1.0.0"
+
+    def initialize(self) -> None:
+        # Optional warm-up; runs once at registration before dimensions() is cached.
+        pass
+
+    def shutdown(self) -> None:
+        pass
+
+    # EmbeddingBackend hooks
+    def dimensions(self) -> int:
+        # Captured once at registration; the dispatcher uses this for shape validation.
+        return self._model.get_sentence_embedding_dimension()
+
+    def embed(self, texts: list[str]) -> list[list[float]]:
+        return self._model.encode(texts).tolist()
+
+
+# Register once at startup. Reference by name in config.
+register_embedding_backend(MyEmbedder())
+
+config: EmbeddingConfig = {
+    "model": {"type": "plugin", "name": "my-embedder"},
+    # Optional: bound the wait on a hung backend (default: 60s; None disables)
+    "max_embed_duration_secs": 30,
+}
+vectors = embed_texts(["Hello, world!", "Second text"], config)
+```