feat: Qdrant Vector index support (#221)

This PR adds support for Qdrant - https://qdrant.tech/ to be used as a vector memory.

I've unit-tested the methods to confirm that they work as intended.

To run Qdrant

```
docker run -p 6333:6333 qdrant/qdrant
```

llama_stack/providers/tests/memory/provider_config_example.yaml

@@ -15,6 +15,11 @@ providers:
   - provider_id: test-weaviate
     provider_type: remote::weaviate
     config: {}
+  - provider_id: test-qdrant
+    provider_type: remote::qdrant
+    config:
+      host: localhost
+      port: 6333
 # if a provider needs private keys from the client, they use the
 # "get_request_provider_data" function (see distribution/request_headers.py)
 # this is a place to provide such data.

llama_stack/providers/tests/memory/test_memory.py

@@ -144,10 +144,11 @@ async def test_query_documents(memory_settings, sample_documents):
 
     # Test case 5: Query with threshold on similarity score
     query5 = "quantum computing"  # Not directly related to any document
-    params5 = {"score_threshold": 0.5}
+    params5 = {"score_threshold": 0.2}
     response5 = await memory_impl.query_documents("test_bank", query5, params5)
     assert_valid_response(response5)
-    assert all(score >= 0.5 for score in response5.scores)
+    print("The scores are:", response5.scores)
+    assert all(score >= 0.2 for score in response5.scores)
 
 
 def assert_valid_response(response: QueryDocumentsResponse):