remove a duplicate collection of tools

Author: HendrikStrobelt

mellea/backends/litellm.py

@@ -237,37 +237,6 @@ def _generate_from_chat_context_standard(
         else:
             response_format = {"type": "text"}
 
-        # Append tool call information if applicable.
-        tools: dict[str, Callable] = dict()
-        if tool_calls:
-            if format:
-                FancyLogger.get_logger().warning(
-                    f"Tool calling typically uses constrained generation, but you have specified a `format` in your generate call. NB: tool calling is superseded by format; we will NOT call tools for your request: {action}"
-                )
-            else:
-                if isinstance(action, Component) and isinstance(
-                    action.format_for_llm(), TemplateRepresentation
-                ):
-                    tools = get_tools_from_action(action)
-
-                model_options_tools = model_opts.get(ModelOption.TOOLS, None)
-                if model_options_tools is not None:
-                    assert isinstance(model_options_tools, dict)
-                    for fn_name in model_options_tools:
-                        # invariant re: relationship between the model_options set of tools and the TemplateRepresentation set of tools
-                        assert fn_name not in tools.keys(), (
-                            f"Cannot add tool {fn_name} because that tool was already defined in the TemplateRepresentation for the action."
-                        )
-                        # type checking because ModelOptions is an untyped dict and the calling convention for tools isn't clearly documented at our abstraction boundaries.
-                        assert type(fn_name) is str, (
-                            "When providing a `ModelOption.TOOLS` parameter to `model_options`, always used the type Dict[str, Callable] where `str` is the function name and the callable is the function."
-                        )
-                        assert callable(model_options_tools[fn_name]), (
-                            "When providing a `ModelOption.TOOLS` parameter to `model_options`, always used the type Dict[str, Callable] where `str` is the function name and the callable is the function."
-                        )
-                        # Add the model_options tool to the existing set of tools.
-                        tools[fn_name] = model_options_tools[fn_name]
-
         thinking = model_opts.get(ModelOption.THINKING, None)
         if type(thinking) is bool and thinking:
             # OpenAI uses strings for its reasoning levels.