anthropic fix

backend/onyx/tools/utils.py

@@ -1,11 +1,13 @@
 import json
 
+import litellm
 from sqlalchemy.orm import Session
 
 from onyx.configs.app_configs import AZURE_DALLE_API_KEY
 from onyx.db.connector import check_connectors_exist
 from onyx.db.document import check_docs_exist
 from onyx.db.models import LLMProvider
+from onyx.llm.llm_provider_options import ANTHROPIC_PROVIDER_NAME
 from onyx.llm.utils import find_model_obj
 from onyx.llm.utils import get_model_map
 from onyx.natural_language_processing.utils import BaseTokenizer
@@ -20,7 +22,20 @@ def explicit_tool_calling_supported(model_provider: str, model_name: str) -> boo
         model_name=model_name,
     )
 
-    return model_obj.get("supports_function_calling", False) if model_obj else False
+    model_supports = (
+        model_obj.get("supports_function_calling", False) if model_obj else False
+    )
+    # Anthropic models support tool calling, but
+    # a) will raise an error if you provide any tool messages and don't provide a list of tools.
+    # b) will send text before and after generating tool calls.
+    # We don't want to provide that list of tools because our UI doesn't support sequential
+    # tool calling yet for (a) and just looks bad for (b), so for now we just treat anthropic
+    # models as non-tool-calling.
+    return (
+        model_supports
+        and model_provider != ANTHROPIC_PROVIDER_NAME
+        and model_name not in litellm.anthropic_models
+    )
 
 
 def compute_tool_tokens(tool: Tool, llm_tokenizer: BaseTokenizer) -> int:

backend/tests/unit/onyx/tools/test_utils.py

@@ -0,0 +1,87 @@
+from unittest.mock import MagicMock
+from unittest.mock import patch
+
+import pytest
+
+from onyx.llm.llm_provider_options import ANTHROPIC_PROVIDER_NAME
+from onyx.tools.utils import explicit_tool_calling_supported
+
+
+@pytest.mark.parametrize(
+    "model_provider, model_name, mock_model_supports_fc, mock_litellm_anthropic_models, expected_result",
+    [
+        # === Anthropic Scenarios (expected False due to override) ===
+        # Provider is Anthropic, base model claims FC support
+        (ANTHROPIC_PROVIDER_NAME, "claude-3-opus-20240229", True, [], False),
+        # Model name in litellm.anthropic_models, base model claims FC support
+        (
+            "another-provider",
+            "claude-3-haiku-20240307",
+            True,
+            ["claude-3-haiku-20240307"],
+            False,
+        ),
+        # Both provider is Anthropic AND model name in litellm.anthropic_models, base model claims FC support
+        (
+            ANTHROPIC_PROVIDER_NAME,
+            "claude-3-sonnet-20240229",
+            True,
+            ["claude-3-sonnet-20240229"],
+            False,
+        ),
+        # === Anthropic Scenarios (expected False due to base support being False) ===
+        # Provider is Anthropic, base model does NOT claim FC support
+        (ANTHROPIC_PROVIDER_NAME, "claude-2.1", False, [], False),
+        # === Non-Anthropic Scenarios ===
+        # Non-Anthropic provider, base model claims FC support -> True
+        ("openai", "gpt-4o", True, [], True),
+        # Non-Anthropic provider, base model does NOT claim FC support -> False
+        ("openai", "gpt-3.5-turbo-instruct", False, [], False),
+        # Non-Anthropic provider, model name happens to be in litellm list (should still be True if provider isn't Anthropic)
+        (
+            "yet-another-provider",
+            "model-also-in-anthropic-list",
+            True,
+            ["model-also-in-anthropic-list"],
+            False,
+        ),
+        # Control for the above: Non-Anthropic provider, model NOT in litellm list, supports FC -> True
+        (
+            "yet-another-provider",
+            "some-other-model",
+            True,
+            ["model-NOT-this-one"],
+            True,
+        ),
+    ],
+)
+@patch("onyx.tools.utils.find_model_obj")
+@patch("onyx.tools.utils.litellm")
+def test_explicit_tool_calling_supported(
+    mock_litellm: MagicMock,
+    mock_find_model_obj: MagicMock,
+    model_provider: str,
+    model_name: str,
+    mock_model_supports_fc: bool,
+    mock_litellm_anthropic_models: list[str],
+    expected_result: bool,
+) -> None:
+    """
+    Anthropic models support tool calling, but
+    a) will raise an error if you provide any tool messages and don't provide a list of tools.
+    b) will send text before and after generating tool calls.
+    We don't want to provide that list of tools because our UI doesn't support sequential
+    tool calling yet for (a) and just looks bad for (b), so for now we just treat anthropic
+    models as non-tool-calling.
+    """
+    mock_find_model_obj.return_value = {
+        "supports_function_calling": mock_model_supports_fc
+    }
+    mock_litellm.anthropic_models = mock_litellm_anthropic_models
+
+    # get_model_map is called inside explicit_tool_calling_supported before find_model_obj,
+    # but its return value doesn't affect the mocked find_model_obj.
+    # So, no need to mock get_model_map separately if find_model_obj is fully mocked.
+
+    actual_result = explicit_tool_calling_supported(model_provider, model_name)
+    assert actual_result == expected_result