[WIP] Configurable distance_metric:

- Configurable distance_metric enabled for PGVector.
- Added plumbing to support configuring more distance metrics for each vector provider.

llama_stack/providers/remote/vector_io/chroma/chroma.py

@@ -45,10 +45,14 @@ async def maybe_await(result):
 
 
 class ChromaIndex(EmbeddingIndex):
-    def __init__(self, client: ChromaClientType, collection, kvstore: KVStore | None = None):
+    def __init__(
+        self, client: ChromaClientType, collection, kvstore: KVStore | None = None, distance_metric: str = "COSINE"
+    ):
         self.client = client
         self.collection = collection
         self.kvstore = kvstore
+        self._check_distance_metric_support(distance_metric)
+        self.distance_metric = distance_metric
 
     async def initialize(self):
         pass
@@ -102,6 +106,22 @@ class ChromaIndex(EmbeddingIndex):
         ids = [f"{chunk.document_id}:{chunk.chunk_id}" for chunk in chunks_for_deletion]
         await maybe_await(self.collection.delete(ids=ids))
 
+    def _check_distance_metric_support(self, distance_metric: str) -> None:
+        """Check if the distance metric is supported by Chroma.
+
+        Args:
+            distance_metric: The distance metric to check
+
+        Raises:
+            NotImplementedError: If the distance metric is not supported yet
+        """
+        if distance_metric != "COSINE":
+            # TODO: Implement support for other distance metrics in Chroma
+            raise NotImplementedError(
+                f"Distance metric '{distance_metric}' is not yet supported by the Chroma provider. "
+                f"Currently only 'COSINE' is supported. Please use 'COSINE' or switch to a different provider."
+            )
+
     async def query_hybrid(
         self,
         embedding: NDArray,
@@ -157,8 +177,9 @@ class ChromaVectorIOAdapter(OpenAIVectorStoreMixin, VectorIO, VectorStoresProtoc
                 name=vector_store.identifier, metadata={"vector_store": vector_store.model_dump_json()}
             )
         )
+        distance_metric = vector_store.distance_metric or "COSINE"
         self.cache[vector_store.identifier] = VectorStoreWithIndex(
-            vector_store, ChromaIndex(self.client, collection), self.inference_api
+            vector_store, ChromaIndex(self.client, collection, distance_metric=distance_metric), self.inference_api
         )
 
     async def unregister_vector_store(self, vector_store_id: str) -> None: