refactor(tool-call): normalize wire shapes at LiteLLM boundary

docs/docs/api/primitives/Tool.md

@@ -11,7 +11,7 @@
             - description
             - extract_custom_type_from_annotation
             - format
-            - format_as_litellm_function_call
+            - format_as_litellm_tool_definition
             - from_langchain
             - from_mcp_tool
             - is_streamable

docs/docs/api/primitives/ToolCalls.md

@@ -9,7 +9,6 @@
             - description
             - extract_custom_type_from_annotation
             - format
-            - from_dict_list
             - is_streamable
             - parse_lm_response
             - parse_stream_chunk

dspy/adapters/base.py

@@ -1,8 +1,6 @@
 import logging
 from typing import Any, get_origin
 
-import json_repair
-
 from dspy.adapters.types import History, Type
 from dspy.adapters.types.base_type import split_message_content_for_custom_types
 from dspy.adapters.types.reasoning import Reasoning
@@ -85,7 +83,7 @@ def _call_preprocess(
                 tools = inputs[tool_call_input_field_name]
                 tools = tools if isinstance(tools, list) else [tools]
 
-                lm_tools = [tool.format_as_litellm_function_call() for tool in tools]
+                lm_tools = [tool.format_as_litellm_tool_definition(model_type=lm.model_type) for tool in tools]
 
                 lm_kwargs["tools"] = lm_tools
 
@@ -148,14 +146,7 @@ def _call_postprocess(
                 )
 
             if tool_calls and tool_call_output_field_name:
-                tool_calls = [
-                    {
-                        "name": v["function"]["name"],
-                        "args": json_repair.loads(v["function"]["arguments"]),
-                    }
-                    for v in tool_calls
-                ]
-                value[tool_call_output_field_name] = ToolCalls.from_dict_list(tool_calls)
+                value[tool_call_output_field_name] = ToolCalls(tool_calls=list(tool_calls))
 
             # Parse custom types that does not rely on the `Adapter.parse()` method
             for name, field in original_signature.output_fields.items():

dspy/adapters/two_step_adapter.py

@@ -1,7 +1,5 @@
 from typing import Any
 
-import json_repair
-
 from dspy.adapters.base import Adapter
 from dspy.adapters.chat_adapter import ChatAdapter
 from dspy.adapters.types import ToolCalls
@@ -145,14 +143,7 @@ async def acall(
                 raise ValueError(f"Failed to parse response from the original completion: {output}") from e
 
             if tool_calls and tool_call_output_field_name:
-                tool_calls = [
-                    {
-                        "name": v["function"]["name"],
-                        "args": json_repair.loads(v["function"]["arguments"]),
-                    }
-                    for v in tool_calls
-                ]
-                value[tool_call_output_field_name] = ToolCalls.from_dict_list(tool_calls)
+                value[tool_call_output_field_name] = ToolCalls(tool_calls=list(tool_calls))
 
             if output_logprobs is not None:
                 value["logprobs"] = output_logprobs

dspy/adapters/types/base_type.py

@@ -5,11 +5,10 @@
 import json_repair
 import pydantic
 
-from dspy.clients.base_lm import BaseLM
-
 if TYPE_CHECKING:
     from litellm import ModelResponseStream
 
+    from dspy.clients.base_lm import BaseLM
     from dspy.signatures.signature import Signature
 
 CUSTOM_TYPE_START_IDENTIFIER = "<<CUSTOM-TYPE-START-IDENTIFIER>>"
@@ -81,7 +80,7 @@ def adapt_to_native_lm_feature(
         cls,
         signature: type["Signature"],
         field_name: str,
-        lm: BaseLM,
+        lm: "BaseLM",
         lm_kwargs: dict[str, Any],
     ) -> type["Signature"]:
         """Adapt the custom type to the native LM feature if possible.