Weaviate "should" work (i.e., is code-complete) but not tested

llama_stack/providers/adapters/memory/weaviate/weaviate.py

@@ -4,7 +4,6 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-import json
 from typing import Any, Dict, List, Optional
 
 import weaviate
@@ -23,9 +22,9 @@ from .config import WeaviateConfig, WeaviateRequestProviderData
 
 
 class WeaviateIndex(EmbeddingIndex):
-    def __init__(self, client: weaviate.Client, collection: str):
+    def __init__(self, client: weaviate.Client, collection_name: str):
         self.client = client
-        self.collection = collection
+        self.collection_name = collection_name
 
     async def add_chunks(self, chunks: List[Chunk], embeddings: NDArray):
         assert len(chunks) == len(
@@ -44,17 +43,13 @@ class WeaviateIndex(EmbeddingIndex):
             )
 
         # Inserting chunks into a prespecified Weaviate collection
-        assert self.collection is not None, "Collection name must be specified"
-        my_collection = self.client.collections.get(self.collection)
-
-        await my_collection.data.insert_many(data_objects)
+        collection = self.client.collections.get(self.collection_name)
+        await collection.data.insert_many(data_objects)
 
     async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
-        assert self.collection is not None, "Collection name must be specified"
+        collection = self.client.collections.get(self.collection_name)
 
-        my_collection = self.client.collections.get(self.collection)
-
-        results = my_collection.query.near_vector(
+        results = collection.query.near_vector(
             near_vector=embedding.tolist(),
             limit=k,
             return_meta_data=wvc.query.MetadataQuery(distance=True),
@@ -63,17 +58,10 @@ class WeaviateIndex(EmbeddingIndex):
         chunks = []
         scores = []
         for doc in results.objects:
-            try:
             chunk = doc.properties["chunk_content"]
             chunks.append(chunk)
             scores.append(1.0 / doc.metadata.distance)
 
-            except Exception as e:
-                import traceback
-
-                traceback.print_exc()
-                print(f"Failed to parse document: {e}")
-
         return QueryDocumentsResponse(chunks=chunks, scores=scores)
 
 
@@ -131,7 +119,7 @@ class WeaviateMemoryAdapter(Memory, NeedsRequestProviderData):
 
         index = BankWithIndex(
             bank=memory_bank,
-            index=WeaviateIndex(client=client, collection=memory_bank.identifier),
+            index=WeaviateIndex(client=client, collection_name=memory_bank.identifier),
         )
         self.cache[bank_id] = index
 
@@ -144,20 +132,16 @@ class WeaviateMemoryAdapter(Memory, NeedsRequestProviderData):
             raise ValueError(f"Bank {bank_id} not found")
 
         client = await self._get_client()
-        collections = await client.collections.list_all().keys()
+        if not client.collections.exists(bank_id):
+            raise ValueError(f"Collection with name `{bank_id}` not found")
 
-        for collection in collections:
-            if collection == bank_id:
-                bank = MemoryBank(**json.loads(collection.metadata["bank"]))
         index = BankWithIndex(
             bank=bank,
-                    index=WeaviateIndex(self.client, collection),
+            index=WeaviateIndex(client=client, collection_name=bank_id),
         )
         self.cache[bank_id] = index
         return index
 
-        return None
-
     async def insert_documents(
         self,
         bank_id: str,