fix: Update Qdrant support post-refactor (#1022)

# What does this PR do?

I tried running the Qdrant provider and found some bugs. See #1021 for
details. @terrytangyuan wrote there:

> Please feel free to submit your changes in a PR. I fixed similar
issues for pgvector provider. This might be an issue introduced from a
refactoring.

So I am submitting this PR.

Closes #1021

## Test Plan

Here are the highlights for what I did to test this:

References:
-
https://llama-stack.readthedocs.io/en/latest/getting_started/index.html
-
https://github.com/meta-llama/llama-stack-apps/blob/main/examples/agents/rag_with_vector_db.py
-
https://github.com/meta-llama/llama-stack/blob/main/docs/zero_to_hero_guide/README.md#build-configure-and-run-llama-stack

Install and run Qdrant server:

```
podman pull qdrant/qdrant
mkdir qdrant-data
podman run -p 6333:6333 -v $(pwd)/qdrant-data:/qdrant/storage qdrant/qdrant
```

Install and run Llama Stack from the venv-support PR (mainly because I
didn't want to install conda):

```
brew install cmake # Should just need this once

git clone https://github.com/meta-llama/llama-models.git
gh repo clone cdoern/llama-stack
cd llama-stack
gh pr checkout 1018 # This is the checkout that introduces venv support for build/run.  Otherwise you have to use conda.  Eventually this wil be part of main, hopefully.

uv sync --extra dev
uv pip install -e .
source .venv/bin/activate
uv pip install qdrant_client

LLAMA_STACK_DIR=$(pwd) LLAMA_MODELS_DIR=../llama-models llama stack build --template ollama --image-type venv
```
```
edit llama_stack/templates/ollama/run.yaml
```

in that editor under:
```
  vector_io:
```
add:
```
  - provider_id: qdrant
    provider_type: remote::qdrant
    config: {}
```

see
https://github.com/meta-llama/llama-stack/blob/main/llama_stack/providers/remote/vector_io/qdrant/config.py#L14
for config options (but I didn't need any)

```
LLAMA_STACK_DIR=$(pwd) LLAMA_MODELS_DIR=../llama-models llama stack run ollama --image-type venv \
   --port $LLAMA_STACK_PORT \
   --env INFERENCE_MODEL=$INFERENCE_MODEL \
   --env SAFETY_MODEL=$SAFETY_MODEL \
   --env OLLAMA_URL=$OLLAMA_URL
```

Then I tested it out in a notebook.  Key highlights included:

```
qdrant_provider = None
for provider in client.providers.list():
    if provider.api == "vector_io" and provider.provider_id == "qdrant":
        qdrant_provider = provider
qdrant_provider
assert qdrant_provider is not None, "QDrant is not a provider.  You need to edit the run yaml file you use in your `llama stack run` call"

vector_db_id = f"test-vector-db-{uuid.uuid4().hex}"
client.vector_dbs.register(
    vector_db_id=vector_db_id,
    embedding_model="all-MiniLM-L6-v2",
    embedding_dimension=384,
    provider_id=qdrant_provider.provider_id,
)
```

Other than that, I just followed what was in
https://llama-stack.readthedocs.io/en/latest/getting_started/index.html

It would be good to have automated tests for this in the future, but
that would be a big undertaking.

Signed-off-by: Bill Murdock <bmurdock@redhat.com>

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

@@ -12,8 +12,8 @@ from .config import QdrantConfig
 
 
 async def get_adapter_impl(config: QdrantConfig, deps: Dict[Api, ProviderSpec]):
-    from .qdrant import QdrantVectorMemoryAdapter
+    from .qdrant import QdrantVectorDBAdapter
 
-    impl = QdrantVectorMemoryAdapter(config, deps[Api.inference])
+    impl = QdrantVectorDBAdapter(config, deps[Api.inference])
     await impl.initialize()
     return impl

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

@@ -55,7 +55,7 @@ class QdrantIndex(EmbeddingIndex):
 
         points = []
         for i, (chunk, embedding) in enumerate(zip(chunks, embeddings)):
-            chunk_id = f"{chunk.document_id}:chunk-{i}"
+            chunk_id = f"{chunk.metadata['document_id']}:chunk-{i}"
             points.append(
                 PointStruct(
                     id=convert_id(chunk_id),
@@ -93,6 +93,9 @@ class QdrantIndex(EmbeddingIndex):
 
         return QueryChunksResponse(chunks=chunks, scores=scores)
 
+    async def delete(self):
+        await self.client.delete_collection(collection_name=self.collection_name)
+
 
 class QdrantVectorDBAdapter(VectorIO, VectorDBsProtocolPrivate):
     def __init__(self, config: QdrantConfig, inference_api: Api.inference) -> None: