Update Hybrid Search with Reranking tutorial (#2274)

* Update for Cloud Inference and data ingestion * Fix link * Review feedback * Make code snippets testable * Add C# code snippets * Add Go code snippets * Add Java code snippets * Add Rust code snippets * Add TS code snippets * Move CSV streaming/parsing to separate function
2026-10-09 21:08:31 +02:00 · 2026-04-22 11:03:05 +02:00
parent bc0efe70b0
commit 434a451c2b
68 changed files with 3911 additions and 247 deletions
@@ -0,0 +1,159 @@
+# @hide-start
+# mypy: disable-error-code="arg-type"
+# @hide-end
+# @block-start client-connection
+from qdrant_client import QdrantClient
+
+client = QdrantClient(
+    url="https://xyz-example.eu-central.aws.cloud.qdrant.io:6333",
+    api_key="<your-api-key>",
+    cloud_inference=True,
+)
+# @block-end client-connection
+
+# @block-start define-models
+dense_embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
+sparse_embedding_model = "qdrant/bm25"
+late_interaction_embedding_model = "answerdotai/answerai-colbert-small-v1"
+# @block-end define-models
+
+# @block-start create-collection
+from qdrant_client.models import Distance, VectorParams, models
+
+collection_name = "hybrid-search"
+
+if client.collection_exists(collection_name=collection_name):
+    client.delete_collection(collection_name=collection_name)
+
+client.create_collection(
+    collection_name,
+    vectors_config={
+        "dense": models.VectorParams(
+            size=384,
+            distance=models.Distance.COSINE,
+        ),
+        "multi": models.VectorParams(
+            size=96,
+            distance=models.Distance.COSINE,
+            multivector_config=models.MultiVectorConfig(
+                comparator=models.MultiVectorComparator.MAX_SIM,
+            ),
+            hnsw_config=models.HnswConfigDiff(m=0)  #  Disable HNSW for reranking
+        ),
+    },
+    sparse_vectors_config={
+        "sparse": models.SparseVectorParams(modifier=models.Modifier.IDF)
+    }
+)
+# @block-end create-collection
+
+# @block-start parse-csv
+import csv
+import urllib.request
+
+def parse_csv(url):
+    with urllib.request.urlopen(url) as response:
+        reader = csv.DictReader(line.decode('utf-8') for line in response)
+        yield from reader
+# @block-end parse-csv
+
+# @block-start ingest-data
+from qdrant_client.models import Document, PointStruct
+
+csv_url = 'https://raw.githubusercontent.com/qdrant/examples/refs/heads/master/sci-fi-books/top_100_scifi_books_full.csv'
+
+points = (
+    PointStruct(
+        id=idx,
+        vector={
+            "dense": Document(text=row['Description'], model=dense_embedding_model),
+            "sparse": Document(text=row['Description'], model=sparse_embedding_model),
+            "multi": Document(text=row['Description'], model=late_interaction_embedding_model),
+        },
+        payload={"title": row['Title'], "author": row['Author'], "description": row['Description']}
+    )
+    for idx, row in enumerate(parse_csv(csv_url))
+)
+client.upload_points(
+    collection_name=collection_name,
+    points=points,
+    batch_size=25
+)
+# @block-end ingest-data
+
+# @block-start dense-retrieval
+import pprint
+
+query = "time travel"
+
+results = client.query_points(
+    collection_name,
+    query=models.Document(text=query, model=dense_embedding_model),
+    using="dense",
+    limit=10,
+)
+
+pprint.pp(results.points)
+# @block-end dense-retrieval
+
+# @block-start sparse-retrieval
+results = client.query_points(
+    collection_name,
+    query=models.Document(text=query, model=sparse_embedding_model),
+    using="sparse",
+    limit=10,
+)
+
+pprint.pp(results.points)
+# @block-end sparse-retrieval
+
+# @block-start hybrid-search
+prefetch = [
+    models.Prefetch(
+        query=models.Document(text=query, model=dense_embedding_model),
+        using="dense",
+        limit=20,
+    ),
+    models.Prefetch(
+        query=models.Document(text=query, model=sparse_embedding_model),
+        using="sparse",
+        limit=20,
+    ),
+]
+
+results = client.query_points(
+    collection_name,
+    prefetch=prefetch,
+    query=models.FusionQuery(fusion=models.Fusion.RRF),
+    with_payload=True,
+    limit=10,
+)
+
+pprint.pp(results.points)
+# @block-end hybrid-search
+
+# @block-start rerank
+prefetch = [
+    models.Prefetch(
+        query=models.Document(text=query, model=dense_embedding_model),
+        using="dense",
+        limit=20,
+    ),
+    models.Prefetch(
+        query=models.Document(text=query, model=sparse_embedding_model),
+        using="sparse",
+        limit=20,
+    ),
+]
+
+results = client.query_points(
+    collection_name,
+    prefetch=prefetch,
+    query=models.Document(text=query, model=late_interaction_embedding_model),
+    using="multi",
+    with_payload=True,
+    limit=10,
+)
+
+pprint.pp(results.points)
+# @block-end rerank