diff --git a/examples/hybrid_search.py b/examples/hybrid_search.py
new file mode 100644
index 0000000..d51dbe5
--- /dev/null
+++ b/examples/hybrid_search.py
@@ -0,0 +1,116 @@
+import turbopuffer as tpuf
+
+
+# Converts a search result set to a dict of ID -> rank
+def results_to_ranks(results):
+    return {item.id: rank for rank, item in enumerate(results, start=1)}
+
+# Fuses two search result sets together into one
+# Uses reciprocal rank fusion
+def rank_fusion(bm25_results, vector_results, k=60):
+    # Compute the ranks of all docs
+    bm25_ranks = results_to_ranks(bm25_results)
+    vector_ranks = results_to_ranks(vector_results)
+
+    print("bm25_ranks:", bm25_ranks)
+    print("vector ranks", vector_ranks)
+
+    # Get all the unique document IDs
+    doc_ids = set(bm25_ranks.keys()).union(set(vector_ranks.keys()))
+
+    # Calculate the RRF score for each document
+    scores = {}
+    for doc_id in doc_ids:
+        score = 0.0
+        if doc_id in bm25_ranks:
+            score += 1.0 / (k + bm25_ranks[doc_id])
+        if doc_id in vector_ranks:
+            score += 1.0 / (k + vector_ranks[doc_id])
+        scores[doc_id] = score
+
+    # Fuse the results together and return
+    fused_results = sorted(scores.items(), key=lambda item: item[1], reverse=True)
+    return [{"id": doc_id, "score": score} for doc_id, score in fused_results]
+
+
+def main():
+    ns = tpuf.Namespace("tpuf_python_hybrid_search")
+    try:
+        ns.delete_all()  # For cleaning up from previous runs
+    except tpuf.NotFoundError:
+        pass
+
+    # Upsert 10 documents, containing both vectors and text
+    # Whale facts from: https://www.natgeokids.com/uk/discover/animals/sea-life/10-blue-whale-facts/
+    ns.upsert(
+        {
+            "ids": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
+            "vectors": [
+                [0.1, 0.1],
+                [0.2, 0.2],
+                [0.3, 0.3],
+                [0.4, 0.4],
+                [0.5, 0.5],
+                [0.6, 0.6],
+                [0.7, 0.7],
+                [0.8, 0.8],
+                [0.9, 0.9],
+                [1.0, 1.0],
+            ],
+            "attributes": {
+                "text": [
+                    "blue whales can grow to over 30m long",
+                    "pretty much everything about the blue whale is massive",
+                    "blue whales can be found in all oceans, except for the arctic",
+                    "blue whales eat tiny shrimp-like crustaceans called krill",
+                    "to communicate with each other, blue whales make a series of super loud vocal sounds",
+                    "blah",
+                    "blah",
+                    "blah",
+                    "blah",
+                    "blah",
+                ]
+            },
+            "schema": {
+                "text": {
+                    "type": "?string",
+                    "bm25": {
+                        "language": "english",
+                        "stemming": True,
+                        "case_sensitive": False,
+                        "remove_stopwords": True,
+                    },
+                },
+            },
+            "distance_metric": "euclidean_squared",
+        }
+    )
+    print("upsert documents successfully")
+    print("")
+
+    # First, do query w/ BM25 full-text search
+    bm25_results = ns.query({"top_k": 10, "rank_by": ("text", "BM25", "blue whale")})
+    print(
+        "search results for 'blue whale':",
+        [(item.id, item.dist) for item in bm25_results],
+    )
+    print("")
+
+    # Now, we also do a query with a vector
+    vector_results = ns.query(
+        top_k=10, vector=[1.0, 1.0], distance_metric="euclidean_squared"
+    )
+    print(
+        "search results for [1.0, 1.0]:",
+        [(item.id, item.dist) for item in vector_results],
+    )
+    print("")
+
+    # Fuse the results with Reciprocal rank fusion (RRF)
+    fused_results = rank_fusion(bm25_results, vector_results)
+    print("")
+    print("fused results:", fused_results)
+
+
+if __name__ == "__main__":
+    main()