rebase issues

Signed-off-by: Daniele Martinoli <dmartino@redhat.com>

docs/source/providers/vector_io/qdrant.md

@@ -3,7 +3,7 @@ orphan: true
 ---
 # Qdrant
 
-[Qdrant](https://qdrant.tech/documentation/) is a inline and remote vector database provider for Llama Stack. It 
+[Qdrant](https://qdrant.tech/documentation/) is a inline and remote vector database provider for Llama Stack. It
 allows you to store and query vectors directly in memory.
 That means you'll get fast and efficient vector retrieval.
 

llama_stack/providers/inline/vector_io/qdrant/__init__.py

@@ -11,7 +11,7 @@ from llama_stack.providers.datatypes import Api, ProviderSpec
 from .config import QdrantVectorIOConfig
 
 
-async def get_provider_impl(config: QdrantVectorIOConfig, deps: Dict[Api, ProviderSpec]):
+async def get_adapter_impl(config: QdrantVectorIOConfig, deps: Dict[Api, ProviderSpec]):
     from llama_stack.providers.remote.vector_io.qdrant.qdrant import QdrantVectorIOAdapter
 
     impl = QdrantVectorIOAdapter(config, deps[Api.inference])

llama_stack/providers/inline/vector_io/qdrant/config.py

@@ -5,6 +5,8 @@
 # the root directory of this source tree.
 
 
+from typing import Any, Dict
+
 from pydantic import BaseModel
 
 from llama_stack.schema_utils import json_schema_type
@@ -15,7 +17,7 @@ class QdrantVectorIOConfig(BaseModel):
     path: str
 
     @classmethod
-    def sample_run_config(cls, __distro_dir__: str) -> dict[str, any]:
+    def sample_run_config(cls, __distro_dir__: str) -> Dict[str, Any]:
         return {
             "path": "${env.QDRANT_PATH:~/.llama/" + __distro_dir__ + "}/" + "qdrant.db",
         }

llama_stack/providers/registry/vector_io.py

@@ -92,16 +92,6 @@ def available_providers() -> List[ProviderSpec]:
             ),
             api_dependencies=[Api.inference],
         ),
-        remote_provider_spec(
-            api=Api.vector_io,
-            adapter=AdapterSpec(
-                adapter_type="sample",
-                pip_packages=[],
-                module="llama_stack.providers.remote.vector_io.sample",
-                config_class="llama_stack.providers.remote.vector_io.sample.SampleVectorIOConfig",
-            ),
-            api_dependencies=[],
-        ),
         InlineProviderSpec(
             api=Api.vector_io,
             provider_type="inline::qdrant",

llama_stack/providers/remote/vector_io/qdrant/qdrant.py

@@ -103,13 +103,17 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
     def __init__(
         self, config: Union[RemoteQdrantVectorIOConfig, InlineQdrantVectorIOConfig], inference_api: Api.inference
     ) -> None:
+        # TODO: this is required otherwise the precommit hook fails with
+        # _init__.py:17: error: Cannot instantiate abstract class "QdrantVectorIOAdapter" with abstract attribute "vector_db_store"  [abstract]
+        # Will investigate further and remove it once fixed
+        self.vector_db_store = None
         self.config = config
-        self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
+        self.client: AsyncQdrantClient = None
         self.cache = {}
         self.inference_api = inference_api
 
     async def initialize(self) -> None:
-        pass
+        self.client = AsyncQdrantClient(**self.config.model_dump(exclude_none=True))
 
     async def shutdown(self) -> None:
         self.client.close()
@@ -126,6 +130,11 @@ class QdrantVectorIOAdapter(VectorIO, VectorDBsProtocolPrivate):
 
         self.cache[vector_db.identifier] = index
 
+    async def unregister_vector_db(self, vector_db_id: str) -> None:
+        if vector_db_id in self.cache:
+            await self.cache[vector_db_id].index.delete()
+            del self.cache[vector_db_id]
+
     async def _get_and_cache_vector_db_index(self, vector_db_id: str) -> Optional[VectorDBWithIndex]:
         if vector_db_id in self.cache:
             return self.cache[vector_db_id]