use qdrant for face recognition example

examples/face_recognition/README.md

@@ -21,7 +21,12 @@ We appreciate a star ⭐ at [CocoIndex Github](https://github.com/cocoindex-io/c
 
 1.  [Install Postgres](https://cocoindex.io/docs/getting_started/installation#-install-postgres) if you don't have one.
 
-2.  dependencies:
+2.  Install Qdrant
+    ```bash
+    docker run -d -p 6334:6334 -p 6333:6333 qdrant/qdrant
+    ```
+
+3.  Install dependencies:
 
     ```bash
     pip install -e .

examples/face_recognition/main.py

@@ -1,12 +1,15 @@
 import cocoindex
-import io
 import dataclasses
 import datetime
-import typing
+import io
+import os
 
 import face_recognition
-from PIL import Image
 import numpy as np
+from PIL import Image
+
+QDRANT_URL = os.getenv("QDRANT_URL", "http://localhost:6334/")
+QDRANT_COLLECTION = "face_embeddings"
 
 
 @dataclasses.dataclass
@@ -90,7 +93,7 @@ def face_recognition_flow(
     flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope
 ) -> None:
     """
-    Define an example flow that embeds files into a vector database.
+    Define an example flow that embeds files into Qdrant vector database.
     """
     data_scope["images"] = flow_builder.add_source(
         cocoindex.sources.LocalFile(path="images", binary=True),
@@ -108,13 +111,14 @@ def face_recognition_flow(
 
             # Collect embeddings
             face_embeddings.collect(
+                id=cocoindex.GeneratedField.UUID,
                 filename=image["filename"],
                 rect=face["rect"],
                 embedding=face["embedding"],
             )
 
     face_embeddings.export(
-        "face_embeddings",
-        cocoindex.targets.Postgres(),
-        primary_key_fields=["filename", "rect"],
+        QDRANT_COLLECTION,
+        cocoindex.targets.Qdrant(collection_name=QDRANT_COLLECTION),
+        primary_key_fields=["id"],
     )

src/llm/vertex_ai.rs

@@ -1 +0,0 @@
-