Extend ColBERT support to non-standard BERT backbones (#34170)

Signed-off-by: Ilya Boytsov <ilya.boytsov@aleph-alpha.com>

examples/pooling/score/colbert_rerank_online.py

@@ -1,15 +1,27 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
-Example of using ColBERT late interaction model for reranking.
+Example of using ColBERT late interaction models for reranking and scoring.
 
 ColBERT (Contextualized Late Interaction over BERT) uses per-token embeddings
 and MaxSim scoring for document reranking, providing better accuracy than
 single-vector models while being more efficient than cross-encoders.
 
-Start the server with:
+vLLM supports ColBERT with multiple encoder backbones. Start the server
+with one of the following:
+
+    # BERT backbone (works out of the box)
     vllm serve answerdotai/answerai-colbert-small-v1
 
+    # ModernBERT backbone
+    vllm serve lightonai/GTE-ModernColBERT-v1 \
+        --hf-overrides '{"architectures": ["ColBERTModernBertModel"]}'
+
+    # Jina XLM-RoBERTa backbone
+    vllm serve jinaai/jina-colbert-v2 \
+        --hf-overrides '{"architectures": ["ColBERTJinaRobertaModel"]}' \
+        --trust-remote-code
+
 Then run this script:
     python colbert_rerank_online.py
 """
@@ -18,39 +30,62 @@ import json
 
 import requests
 
-url = "http://127.0.0.1:8000/rerank"
+# Change this to match the model you started the server with
+MODEL = "answerdotai/answerai-colbert-small-v1"
+BASE_URL = "http://127.0.0.1:8000"
 
 headers = {"accept": "application/json", "Content-Type": "application/json"}
 
-data = {
-    "model": "answerdotai/answerai-colbert-small-v1",
-    "query": "What is machine learning?",
-    "documents": [
-        "Machine learning is a subset of artificial intelligence.",
-        "Python is a programming language.",
-        "Deep learning uses neural networks for complex tasks.",
-        "The weather today is sunny.",
-    ],
-}
+documents = [
+    "Machine learning is a subset of artificial intelligence.",
+    "Python is a programming language.",
+    "Deep learning uses neural networks for complex tasks.",
+    "The weather today is sunny.",
+]
+
+
+def rerank_example():
+    """Use the /rerank endpoint to rank documents by query relevance."""
+    print("=== Rerank Example ===")
+
+    data = {
+        "model": MODEL,
+        "query": "What is machine learning?",
+        "documents": documents,
+    }
+
+    response = requests.post(f"{BASE_URL}/rerank", headers=headers, json=data)
+    result = response.json()
+    print(json.dumps(result, indent=2))
+
+    print("\nRanked documents (most relevant first):")
+    for item in result["results"]:
+        doc_idx = item["index"]
+        score = item["relevance_score"]
+        print(f"  Score {score:.4f}: {documents[doc_idx]}")
+
+
+def score_example():
+    """Use the /score endpoint for pairwise query-document scoring."""
+    print("\n=== Score Example ===")
+
+    data = {
+        "model": MODEL,
+        "text_1": "What is machine learning?",
+        "text_2": [
+            "Machine learning is a subset of AI.",
+            "The weather is sunny.",
+        ],
+    }
+
+    response = requests.post(f"{BASE_URL}/score", headers=headers, json=data)
+    result = response.json()
+    print(json.dumps(result, indent=2))
 
 
 def main():
-    response = requests.post(url, headers=headers, json=data)
-
-    if response.status_code == 200:
-        print("ColBERT Rerank Request successful!")
-        result = response.json()
-        print(json.dumps(result, indent=2))
-
-        # Show ranked results
-        print("\nRanked documents (most relevant first):")
-        for item in result["results"]:
-            doc_idx = item["index"]
-            score = item["relevance_score"]
-            print(f"  Score {score:.4f}: {data['documents'][doc_idx]}")
-    else:
-        print(f"Request failed with status code: {response.status_code}")
-        print(response.text)
+    rerank_example()
+    score_example()
 
 
 if __name__ == "__main__":