Fix Anthropic tokenizer protocol #17115

llama-index-integrations/llms/llama-index-llms-anthropic/llama_index/llms/anthropic/base.py

@@ -210,13 +210,19 @@ def metadata(self) -> LLMMetadata:
 
     @property
     def tokenizer(self) -> Tokenizer:
-        def _count_tokens(text: str) -> int:
-            return self._client.beta.messages.count_tokens(
-                messages=[{"role": "user", "content": text}],
-                model=self.model,
-            ).input_tokens
-
-        return _count_tokens
+        class AnthropicTokenizer:
+            def __init__(self, client, model) -> None:
+                self._client = client
+                self.model = model
+
+            def encode(self, text: str, *args: Any, **kwargs: Any) -> List[int]:
+                count = self._client.beta.messages.count_tokens(
+                    messages=[{"role": "user", "content": text}],
+                    model=self.model,
+                ).input_tokens
+                return [1] * count
+
+        return AnthropicTokenizer(self._client, self.model)
 
     @property
     def _model_kwargs(self) -> Dict[str, Any]:

llama-index-integrations/llms/llama-index-llms-anthropic/tests/test_llms_anthropic.py

@@ -3,6 +3,7 @@
 from llama_index.core.llms import ChatMessage
 import os
 import pytest
+from unittest.mock import MagicMock
 
 
 def test_text_inference_embedding_class():
@@ -166,3 +167,39 @@ async def test_anthropic_through_bedrock_async():
         print(f"Assertion failed: full_response is not a string")
         print(f"Content of full_response: {full_response}")
         raise
+
+
+def test_anthropic_tokenizer():
+    """Test that the Anthropic tokenizer properly implements the Tokenizer protocol."""
+    # Create a mock Messages object that returns a predictable token count
+    mock_messages = MagicMock()
+    mock_messages.count_tokens.return_value.input_tokens = 5
+
+    # Create a mock Beta object that returns our mock messages
+    mock_beta = MagicMock()
+    mock_beta.messages = mock_messages
+
+    # Create a mock client that returns our mock beta
+    mock_client = MagicMock()
+    mock_client.beta = mock_beta
+
+    # Create the Anthropic instance with our mock
+    anthropic_llm = Anthropic(model="claude-3-5-sonnet-20241022")
+    anthropic_llm._client = mock_client
+
+    # Test that tokenizer implements the protocol
+    tokenizer = anthropic_llm.tokenizer
+    assert hasattr(tokenizer, "encode")
+
+    # Test that encode returns a list of integers
+    test_text = "Hello, world!"
+    tokens = tokenizer.encode(test_text)
+    assert isinstance(tokens, list)
+    assert all(isinstance(t, int) for t in tokens)
+    assert len(tokens) == 5  # Should match our mocked token count
+
+    # Verify the mock was called correctly
+    mock_messages.count_tokens.assert_called_once_with(
+        messages=[{"role": "user", "content": test_text}],
+        model="claude-3-5-sonnet-20241022",
+    )