feat(provider): add Chroma built-in embedding requester

Add chromaembed.py using Chroma's DefaultEmbeddingFunction (all-MiniLM-L6-v2)
for local embedding generation via ONNX Runtime. Also simplify seekdbembed.py
and add ndarray-to-list conversion for JSON serialization compatibility.

src/langbot/pkg/provider/modelmgr/requesters/chromaembed.py

@@ -0,0 +1,61 @@
+from __future__ import annotations
+
+import typing
+
+from .. import requester
+
+REQUESTER_NAME: str = 'chroma-embedding'
+
+
+class ChromaEmbedding(requester.ProviderAPIRequester):
+    """Chroma built-in embedding requester.
+
+    Uses chromadb's DefaultEmbeddingFunction (all-MiniLM-L6-v2).
+    The embedding function runs locally using ONNX Runtime.
+    """
+
+    default_config: dict[str, typing.Any] = {
+        'base_url': '',
+    }
+
+    _embedding_function = None
+
+    async def initialize(self):
+        try:
+            from chromadb.utils import embedding_functions
+        except ImportError:
+            raise ImportError('chromadb is not installed. Install it with: pip install chromadb')
+
+        self._embedding_function = embedding_functions.DefaultEmbeddingFunction()
+
+    async def invoke_llm(
+        self,
+        query,
+        model: requester.RuntimeLLMModel,
+        messages: typing.List,
+        funcs: typing.List = None,
+        extra_args: dict[str, typing.Any] = {},
+        remove_think: bool = False,
+    ):
+        raise NotImplementedError('Chroma embedding does not support LLM inference')
+
+    async def invoke_embedding(
+        self,
+        model: requester.RuntimeEmbeddingModel,
+        input_text: typing.List[str],
+        extra_args: dict[str, typing.Any] = {},
+    ) -> typing.List[typing.List[float]]:
+        """Generate embeddings using Chroma's DefaultEmbeddingFunction."""
+        if self._embedding_function is None:
+            await self.initialize()
+
+        try:
+            result = self._embedding_function(input_text)
+            # DefaultEmbeddingFunction returns list of ndarray, convert for JSON
+            if isinstance(result, list):
+                return [item.tolist() if hasattr(item, 'tolist') else item for item in result]
+            return result.tolist() if hasattr(result, 'tolist') else result
+        except Exception as e:
+            from .. import errors
+
+            raise errors.RequesterError(f'Chroma embedding failed: {str(e)}')