Optimize tool call conversions to eliminate redundant API lookups

libs/oci/langchain_oci/chat_models/oci_generative_ai.py

@@ -100,22 +100,37 @@ def remove_signature_from_tool_description(name: str, description: str) -> str:
     @staticmethod
     def convert_oci_tool_call_to_langchain(tool_call: Any) -> ToolCall:
         """Convert an OCI tool call to a LangChain ToolCall."""
-        parsed = json.loads(tool_call.arguments)
-
-        # If the parsed result is a string, it means the JSON was escaped, so parse again  # noqa: E501
-        if isinstance(parsed, str):
-            try:
-                parsed = json.loads(parsed)
-            except json.JSONDecodeError:
-                # If it's not valid JSON, keep it as a string
-                pass
+        # Check if this is a Generic/Meta format (has arguments as JSON string)
+        # or Cohere format (has parameters as dict)
+        attribute_map = getattr(tool_call, "attribute_map", None) or {}
+
+        if "arguments" in attribute_map and tool_call.arguments is not None:
+            # Generic/Meta format: parse JSON arguments
+            parsed = json.loads(tool_call.arguments)
+
+            # If the parsed result is a string, it means JSON was escaped
+            if isinstance(parsed, str):
+                try:
+                    parsed = json.loads(parsed)
+                except json.JSONDecodeError:
+                    # If it's not valid JSON, keep it as a string
+                    pass
+            args = parsed
+        else:
+            # Cohere format: parameters is already a dict
+            args = tool_call.parameters
+
+        # Get tool call ID (generate one if not present)
+        tool_id = (
+            tool_call.id
+            if "id" in attribute_map
+            else uuid.uuid4().hex[:]
+        )
 
         return ToolCall(
             name=tool_call.name,
-            args=parsed
-            if "arguments" in tool_call.attribute_map
-            else tool_call.parameters,
-            id=tool_call.id if "id" in tool_call.attribute_map else uuid.uuid4().hex[:],
+            args=args,
+            id=tool_id,
         )
 
 
@@ -263,19 +278,19 @@ def chat_generation_info(self, response: Any) -> Dict[str, Any]:
         }
 
         # Include token usage if available
-        if (
-            hasattr(response.data.chat_response, "usage")
-            and response.data.chat_response.usage
-        ):
-            generation_info["total_tokens"] = (
-                response.data.chat_response.usage.total_tokens
-            )
+        try:
+            if (
+                hasattr(response.data.chat_response, "usage")
+                and response.data.chat_response.usage
+            ):
+                generation_info["total_tokens"] = (
+                    response.data.chat_response.usage.total_tokens
+                )
+        except (KeyError, AttributeError):
+            pass
 
-        # Include tool calls if available
-        if self.chat_tool_calls(response):
-            generation_info["tool_calls"] = self.format_response_tool_calls(
-                self.chat_tool_calls(response)
-            )
+        # Note: tool_calls are now handled in _generate() to avoid redundant conversions
+        # The formatted tool calls will be added there if present
         return generation_info
 
     def chat_stream_generation_info(self, event_data: Dict) -> Dict[str, Any]:
@@ -643,18 +658,19 @@ def chat_generation_info(self, response: Any) -> Dict[str, Any]:
         }
 
         # Include token usage if available
-        if (
-            hasattr(response.data.chat_response, "usage")
-            and response.data.chat_response.usage
-        ):
-            generation_info["total_tokens"] = (
-                response.data.chat_response.usage.total_tokens
-            )
+        try:
+            if (
+                hasattr(response.data.chat_response, "usage")
+                and response.data.chat_response.usage
+            ):
+                generation_info["total_tokens"] = (
+                    response.data.chat_response.usage.total_tokens
+                )
+        except (KeyError, AttributeError):
+            pass
 
-        if self.chat_tool_calls(response):
-            generation_info["tool_calls"] = self.format_response_tool_calls(
-                self.chat_tool_calls(response)
-            )
+        # Note: tool_calls are now handled in _generate() to avoid redundant conversions
+        # The formatted tool calls will be added there if present
         return generation_info
 
     def chat_stream_generation_info(self, event_data: Dict) -> Dict[str, Any]:
@@ -1400,6 +1416,9 @@ def _generate(
         if stop is not None:
             content = enforce_stop_tokens(content, stop)
 
+        # Fetch raw tool calls once to avoid redundant calls
+        raw_tool_calls = self._provider.chat_tool_calls(response)
+
         generation_info = self._provider.chat_generation_info(response)
 
         llm_output = {
@@ -1408,12 +1427,19 @@ def _generate(
             "request_id": response.request_id,
             "content-length": response.headers["content-length"],
         }
+
+        # Convert tool calls once for LangChain format
         tool_calls = []
-        if "tool_calls" in generation_info:
+        if raw_tool_calls:
             tool_calls = [
                 OCIUtils.convert_oci_tool_call_to_langchain(tool_call)
-                for tool_call in self._provider.chat_tool_calls(response)
+                for tool_call in raw_tool_calls
             ]
+            # Add formatted version to generation_info if not already present
+            # This avoids redundant formatting in chat_generation_info()
+            if "tool_calls" not in generation_info:
+                formatted = self._provider.format_response_tool_calls(raw_tool_calls)
+                generation_info["tool_calls"] = formatted
         message = AIMessage(
             content=content or "",
             additional_kwargs=generation_info,