move validation logic to server

chromadb/api/models/CollectionCommon.py

@@ -27,7 +27,6 @@
     Include,
     Loadable,
     Metadata,
-    Metadatas,
     Document,
     Documents,
     Image,
@@ -47,7 +46,6 @@
     validate_ids,
     validate_include,
     validate_metadata,
-    validate_metadatas,
     validate_embeddings,
     validate_embedding_function,
     validate_n_results,
@@ -182,65 +180,13 @@ def _unpack_embedding_set(
 
     def _validate_embedding_set(
         self,
-        ids: IDs,
-        embeddings: Optional[Embeddings],
-        metadatas: Optional[Metadatas],
         documents: Optional[Documents],
         images: Optional[Images],
-        uris: Optional[URIs],
-        require_embeddings_or_data: bool = True,
     ) -> None:
-        valid_ids = validate_ids(ids)
-        valid_embeddings = (
-            validate_embeddings(embeddings) if embeddings is not None else None
-        )
-        valid_metadatas = (
-            validate_metadatas(metadatas) if metadatas is not None else None
-        )
-
-        # Already validated from being unpacked from OneOrMany data types
-        valid_documents = documents
-        valid_images = images
-        valid_uris = uris
-
-        # Check that one of embeddings or ducuments or images is provided
-        if require_embeddings_or_data:
-            if (
-                valid_embeddings is None
-                and valid_documents is None
-                and valid_images is None
-                and valid_uris is None
-            ):
-                raise ValueError(
-                    "You must provide embeddings, documents, images, or uris."
-                )
-
         # Only one of documents or images can be provided
         if documents is not None and images is not None:
             raise ValueError("You can only provide documents or images, not both.")
 
-        # Check that, if they're provided, the lengths of the arrays match the length of ids
-        if valid_embeddings is not None and len(valid_embeddings) != len(valid_ids):
-            raise ValueError(
-                f"Number of embeddings {len(valid_embeddings)} must match number of ids {len(valid_ids)}"
-            )
-        if valid_metadatas is not None and len(valid_metadatas) != len(valid_ids):
-            raise ValueError(
-                f"Number of metadatas {len(valid_metadatas)} must match number of ids {len(valid_ids)}"
-            )
-        if documents is not None and len(documents) != len(valid_ids):
-            raise ValueError(
-                f"Number of documents {len(documents)} must match number of ids {len(valid_ids)}"
-            )
-        if images is not None and len(images) != len(valid_ids):
-            raise ValueError(
-                f"Number of images {len(images)} must match number of ids {len(valid_ids)}"
-            )
-        if uris is not None and len(uris) != len(valid_ids):
-            raise ValueError(
-                f"Number of uris {len(uris)} must match number of ids {len(valid_ids)}"
-            )
-
     def _compute_embeddings(
         self,
         embeddings: Optional[Embeddings],
@@ -455,12 +401,8 @@ def _process_add_request(
         )
 
         self._validate_embedding_set(
-            unpacked_embedding_set["ids"],
-            normalized_embeddings,
-            unpacked_embedding_set["metadatas"],
             unpacked_embedding_set["documents"],
             unpacked_embedding_set["images"],
-            unpacked_embedding_set["uris"],
         )
 
         prepared_embeddings = self._compute_embeddings(
@@ -519,13 +461,8 @@ def _process_upsert_or_update_request(
         )
 
         self._validate_embedding_set(
-            unpacked_embedding_set["ids"],
-            normalized_embeddings,
-            unpacked_embedding_set["metadatas"],
             unpacked_embedding_set["documents"],
             unpacked_embedding_set["images"],
-            unpacked_embedding_set["uris"],
-            require_embeddings_or_data,
         )
 
         prepared_embeddings = self._compute_embeddings_upsert_or_update_request(

chromadb/api/segment.py

@@ -37,6 +37,9 @@
     validate_where,
     validate_where_document,
     validate_batch,
+    validate_ids,
+    validate_embeddings,
+    validate_metadatas,
 )
 from chromadb.telemetry.product.events import (
     CollectionAddEvent,
@@ -48,7 +51,7 @@
 )
 
 import chromadb.types as t
-from typing import Optional, Sequence, Generator, List, cast, Set, Dict
+from typing import Optional, Sequence, Generator, List, cast, Set, Dict, Tuple, Any
 from overrides import override
 from uuid import UUID, uuid4
 import time
@@ -340,10 +343,17 @@ def _add(
         self._quota.static_check(metadatas, documents, embeddings, str(collection_id))
         coll = self._get_collection(collection_id)
         self._manager.hint_use_collection(collection_id, t.Operation.ADD)
-        validate_batch(
-            (ids, embeddings, metadatas, documents, uris),
-            {"max_batch_size": self.get_max_batch_size()},
+
+        self._validate_embedding_record_set(
+            collection=coll,
+            ids=ids,
+            embeddings=embeddings,
+            documents=documents,
+            uris=uris,
+            metadatas=metadatas,
+            require_embeddings_or_data=True,
         )
+
         records_to_submit = list(
             _records(
                 t.Operation.ADD,
@@ -354,7 +364,6 @@ def _add(
                 uris=uris,
             )
         )
-        self._validate_embedding_record_set(coll, records_to_submit)
         self._producer.submit_embeddings(collection_id, records_to_submit)
 
         self._product_telemetry_client.capture(
@@ -382,10 +391,17 @@ def _update(
         self._quota.static_check(metadatas, documents, embeddings, str(collection_id))
         coll = self._get_collection(collection_id)
         self._manager.hint_use_collection(collection_id, t.Operation.UPDATE)
-        validate_batch(
-            (ids, embeddings, metadatas, documents, uris),
-            {"max_batch_size": self.get_max_batch_size()},
+
+        self._validate_embedding_record_set(
+            collection=coll,
+            ids=ids,
+            embeddings=embeddings,
+            documents=documents,
+            uris=uris,
+            metadatas=metadatas,
+            require_embeddings_or_data=False,
         )
+
         records_to_submit = list(
             _records(
                 t.Operation.UPDATE,
@@ -396,7 +412,6 @@ def _update(
                 uris=uris,
             )
         )
-        self._validate_embedding_record_set(coll, records_to_submit)
         self._producer.submit_embeddings(collection_id, records_to_submit)
 
         self._product_telemetry_client.capture(
@@ -426,10 +441,17 @@ def _upsert(
         self._quota.static_check(metadatas, documents, embeddings, str(collection_id))
         coll = self._get_collection(collection_id)
         self._manager.hint_use_collection(collection_id, t.Operation.UPSERT)
-        validate_batch(
-            (ids, embeddings, metadatas, documents, uris),
-            {"max_batch_size": self.get_max_batch_size()},
+
+        self._validate_embedding_record_set(
+            collection=coll,
+            ids=ids,
+            embeddings=embeddings,
+            documents=documents,
+            uris=uris,
+            metadatas=metadatas,
+            require_embeddings_or_data=True,
         )
+
         records_to_submit = list(
             _records(
                 t.Operation.UPSERT,
@@ -440,7 +462,6 @@ def _upsert(
                 uris=uris,
             )
         )
-        self._validate_embedding_record_set(coll, records_to_submit)
         self._producer.submit_embeddings(collection_id, records_to_submit)
 
         return True
@@ -606,10 +627,12 @@ def _delete(
         if len(ids_to_delete) == 0:
             return []
 
+        self._validate_embedding_record_set(
+            collection=coll, ids=ids_to_delete, require_embeddings_or_data=False
+        )
         records_to_submit = list(
             _records(operation=t.Operation.DELETE, ids=ids_to_delete)
         )
-        self._validate_embedding_record_set(coll, records_to_submit)
         self._producer.submit_embeddings(collection_id, records_to_submit)
 
         self._product_telemetry_client.capture(
@@ -799,16 +822,62 @@ def get_max_batch_size(self) -> int:
         "SegmentAPI._validate_embedding_record_set", OpenTelemetryGranularity.ALL
     )
     def _validate_embedding_record_set(
-        self, collection: t.Collection, records: List[t.OperationRecord]
+        self,
+        ids: IDs,
+        collection: t.Collection,
+        require_embeddings_or_data: bool,
+        embeddings: Optional[Embeddings] = None,
+        documents: Optional[Documents] = None,
+        uris: Optional[URIs] = None,
+        metadatas: Optional[Metadatas] = None,
     ) -> None:
-        """Validate the dimension of an embedding record before submitting it to the system."""
+        validate_batch(
+            (ids, embeddings, metadatas, documents, uris),
+            {"max_batch_size": self.get_max_batch_size()},
+        )
+
         add_attributes_to_current_span({"collection_id": str(collection["id"])})
-        for record in records:
-            if record["embedding"]:
-                self._validate_dimension(
-                    collection, len(record["embedding"]), update=True
+
+        validate_ids(ids)
+        validate_embeddings(embeddings) if embeddings is not None else None
+        validate_metadatas(metadatas) if metadatas is not None else None
+
+        if (
+            require_embeddings_or_data
+            and embeddings is None
+            and documents is None
+            and uris is None
+        ):
+            raise ValueError("You must provide embeddings, documents, or uris.")
+
+        entities: List[Tuple[Any, str]] = [
+            (embeddings, "embeddings"),
+            (metadatas, "metadatas"),
+            (documents, "documents"),
+            (uris, "uris"),
+        ]
+
+        n_ids = len(ids)
+        for entity in entities:
+            if entity[0] is None:
+                continue
+
+            name = entity[1]
+            n = len(entity[0])
+
+            if n != len(ids):
+                raise ValueError(
+                    f"Number of {name} ({n}) does not match number of ids ({n_ids})"
                 )
 
+        if embeddings is None:
+            return
+
+        """Validate the dimension of an embedding record before submitting it to the system."""
+        for embedding in embeddings:
+            if embedding:
+                self._validate_dimension(collection, len(embedding), update=True)
+
     # This method is intentionally left untraced because otherwise it can emit thousands of spans for requests containing many embeddings.
     def _validate_dimension(
         self, collection: t.Collection, dim: int, update: bool

chromadb/test/test_api.py

@@ -706,15 +706,15 @@ def test_metadata_update_get_int_float(client):
 def test_metadata_validation_add(client):
     client.reset()
     collection = client.create_collection("test_metadata_validation")
-    with pytest.raises(ValueError, match="metadata"):
+    with pytest.raises(Exception, match="metadata"):
         collection.add(**bad_metadata_records)
 
 
 def test_metadata_validation_update(client):
     client.reset()
     collection = client.create_collection("test_metadata_validation")
     collection.add(**metadata_records)
-    with pytest.raises(ValueError, match="metadata"):
+    with pytest.raises(Exception, match="metadata"):
         collection.update(ids=["id1"], metadatas={"value": {"nested": "5"}})
 
 
@@ -1203,17 +1203,17 @@ def test_invalid_id(client):
     client.reset()
     collection = client.create_collection("test_invalid_id")
     # Add with non-string id
-    with pytest.raises(ValueError) as e:
+    with pytest.raises(Exception) as e:
         collection.add(embeddings=[0, 0, 0], ids=[1], metadatas=[{}])
     assert "ID" in str(e.value)
 
     # Get with non-list id
-    with pytest.raises(ValueError) as e:
+    with pytest.raises(Exception) as e:
         collection.get(ids=1)
     assert "ID" in str(e.value)
 
     # Delete with malformed ids
-    with pytest.raises(ValueError) as e:
+    with pytest.raises(Exception) as e:
         collection.delete(ids=["valid", 0])
     assert "ID" in str(e.value)
 
@@ -1548,12 +1548,12 @@ def test_invalid_embeddings(client):
         "embeddings": [["0", "0", "0"], ["1.2", "2.24", "3.2"]],
         "ids": ["id1", "id2"],
     }
-    with pytest.raises(ValueError) as e:
+    with pytest.raises(Exception) as e:
         collection.add(**invalid_records)
     assert "embedding" in str(e.value)
 
     # Query with invalid embeddings
-    with pytest.raises(ValueError) as e:
+    with pytest.raises(Exception) as e:
         collection.query(
             query_embeddings=[["1.1", "2.3", "3.2"]],
             n_results=1,
@@ -1565,7 +1565,7 @@ def test_invalid_embeddings(client):
         "embeddings": [[[0], [0], [0]], [[1.2], [2.24], [3.2]]],
         "ids": ["id1", "id2"],
     }
-    with pytest.raises(ValueError) as e:
+    with pytest.raises(Exception) as e:
         collection.update(**invalid_records)
     assert "embedding" in str(e.value)
 
@@ -1574,7 +1574,7 @@ def test_invalid_embeddings(client):
         "embeddings": [[[1.1, 2.3, 3.2]], [[1.2, 2.24, 3.2]]],
         "ids": ["id1", "id2"],
     }
-    with pytest.raises(ValueError) as e:
+    with pytest.raises(Exception) as e:
         collection.upsert(**invalid_records)
     assert "embedding" in str(e.value)