Small refactoring

haystack/components/generators/chat/openai.py

@@ -344,46 +344,37 @@ def _convert_streaming_chunks_to_chat_message(self, chunk: Any, chunks: List[Str
         :param chunk: The last chunk returned by the OpenAI API.
         :param chunks: The list of all `StreamingChunk` objects.
         """
-
         text = "".join([chunk.content for chunk in chunks])
         tool_calls = []
 
-        # are there any tool calls in the chunks?
-        if any(chunk.meta.get("tool_calls") for chunk in chunks):
-            payloads = {}  # Use a dict to track tool calls by ID
-            for chunk_payload in chunks:
-                deltas = chunk_payload.meta.get("tool_calls") or []
-
-                # deltas is a list of ChoiceDeltaToolCall
-                for delta in deltas:
-                    if delta.id not in payloads:
-                        payloads[delta.id] = {"id": delta.id, "arguments": "", "name": "", "type": None}
-                    # ChoiceDeltaToolCall has a 'function' field of type ChoiceDeltaToolCallFunction
+        # Process tool calls if present in any chunk
+        tool_call_data = {}  # Track tool calls by ID
+        for chunk_payload in chunks:
+            tool_calls_meta = chunk_payload.meta.get("tool_calls")
+            if tool_calls_meta:
+                for delta in tool_calls_meta:
+                    if not delta.id in tool_call_data:
+                        tool_call_data[delta.id] = {"id": delta.id, "name": "", "arguments": ""}
+
                     if delta.function:
-                        # For tool calls with the same ID, use the latest values
-                        if delta.function.name is not None:
-                            payloads[delta.id]["name"] = delta.function.name
-                        if delta.function.arguments is not None:
-                            # Use the latest arguments value
-                            payloads[delta.id]["arguments"] = delta.function.arguments
-                    if delta.type is not None:
-                        payloads[delta.id]["type"] = delta.type
-
-            for payload in payloads.values():
-                arguments_str = payload["arguments"]
-                try:
-                    # Try to parse the concatenated arguments string as JSON
-                    arguments = json.loads(arguments_str)
-                    tool_calls.append(ToolCall(id=payload["id"], tool_name=payload["name"], arguments=arguments))
-                except json.JSONDecodeError:
-                    logger.warning(
-                        "OpenAI returned a malformed JSON string for tool call arguments. This tool call "
-                        "will be skipped. To always generate a valid JSON, set `tools_strict` to `True`. "
-                        "Tool call ID: {_id}, Tool name: {_name}, Arguments: {_arguments}",
-                        _id=payload["id"],
-                        _name=payload["name"],
-                        _arguments=arguments_str,
-                    )
+                        if delta.function.name:
+                            tool_call_data[delta.id]["name"] = delta.function.name
+                        if delta.function.arguments:
+                            tool_call_data[delta.id]["arguments"] = delta.function.arguments
+
+        # Convert accumulated tool call data into ToolCall objects
+        for call_data in tool_call_data.values():
+            try:
+                arguments = json.loads(call_data["arguments"])
+                tool_calls.append(ToolCall(id=call_data["id"], tool_name=call_data["name"], arguments=arguments))
+            except json.JSONDecodeError:
+                logger.warning(
+                    "Skipping malformed tool call due to invalid JSON. Set `tools_strict=True` for valid JSON. "
+                    "Tool call ID: {_id}, Tool name: {_name}, Arguments: {_arguments}",
+                    _id=call_data["id"],
+                    _name=call_data["name"],
+                    _arguments=call_data["arguments"],
+                )
 
         meta = {
             "model": chunk.model,