weaviate fixes, test now passes

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

@@ -3,6 +3,7 @@
 #
 # 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
 
@@ -36,7 +37,7 @@ class WeaviateIndex(EmbeddingIndex):
             data_objects.append(
                 wvc.data.DataObject(
                     properties={
-                        "chunk_content": chunk,
+                        "chunk_content": chunk.json(),
                     },
                     vector=embeddings[i].tolist(),
                 )
@@ -44,7 +45,9 @@ class WeaviateIndex(EmbeddingIndex):
 
         # Inserting chunks into a prespecified Weaviate collection
         collection = self.client.collections.get(self.collection_name)
-        await collection.data.insert_many(data_objects)
+
+        # TODO: make this async friendly
+        collection.data.insert_many(data_objects)
 
     async def query(self, embedding: NDArray, k: int) -> QueryDocumentsResponse:
         collection = self.client.collections.get(self.collection_name)
@@ -52,13 +55,23 @@ class WeaviateIndex(EmbeddingIndex):
         results = collection.query.near_vector(
             near_vector=embedding.tolist(),
             limit=k,
-            return_meta_data=wvc.query.MetadataQuery(distance=True),
+            return_metadata=wvc.query.MetadataQuery(distance=True),
         )
 
         chunks = []
         scores = []
         for doc in results.objects:
-            chunk = doc.properties["chunk_content"]
+            chunk_json = doc.properties["chunk_content"]
+            try:
+                chunk_dict = json.loads(chunk_json)
+                chunk = Chunk(**chunk_dict)
+            except Exception:
+                import traceback
+
+                traceback.print_exc()
+                print(f"Failed to parse document: {chunk_json}")
+                continue
+
             chunks.append(chunk)
             scores.append(1.0 / doc.metadata.distance)
 
@@ -102,12 +115,12 @@ class WeaviateMemoryAdapter(Memory, NeedsRequestProviderData):
             memory_bank.type == MemoryBankType.vector.value
         ), f"Only vector banks are supported {memory_bank.type}"
 
-        client = await self._get_client()
+        client = self._get_client()
 
         # Create collection if it doesn't exist
         if not client.collections.exists(memory_bank.identifier):
             client.collections.create(
-                name=smemory_bank.identifier,
+                name=memory_bank.identifier,
                 vectorizer_config=wvc.config.Configure.Vectorizer.none(),
                 properties=[
                     wvc.config.Property(
@@ -121,7 +134,7 @@ class WeaviateMemoryAdapter(Memory, NeedsRequestProviderData):
             bank=memory_bank,
             index=WeaviateIndex(client=client, collection_name=memory_bank.identifier),
         )
-        self.cache[bank_id] = index
+        self.cache[memory_bank.identifier] = index
 
     async def _get_and_cache_bank_index(self, bank_id: str) -> Optional[BankWithIndex]:
         if bank_id in self.cache:
@@ -131,7 +144,7 @@ class WeaviateMemoryAdapter(Memory, NeedsRequestProviderData):
         if not bank:
             raise ValueError(f"Bank {bank_id} not found")
 
-        client = await self._get_client()
+        client = self._get_client()
         if not client.collections.exists(bank_id):
             raise ValueError(f"Collection with name `{bank_id}` not found")
 
@@ -146,6 +159,7 @@ class WeaviateMemoryAdapter(Memory, NeedsRequestProviderData):
         self,
         bank_id: str,
         documents: List[MemoryBankDocument],
+        ttl_seconds: Optional[int] = None,
     ) -> None:
         index = await self._get_and_cache_bank_index(bank_id)
         if not index: