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feat(ai-chat-api): implement streaming tool calls with incremental parameter accumulation
- Update API documentation to reflect new streaming tool call mechanism - Add `message_id` optional field to content and tool_call events - Change tool_call structure from array to single object with incremental updates - Add `tool_call_id` to tool_start events for better event correlation - Implement ToolCallStreamAccumulator class to handle parameter accumulation - Provide frontend example code for handling streaming tool calls - Maintain backward compatibility with existing session_id tracking
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# SPDX-License-Identifier: GPL-3.0-or-later
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#
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# GNS3-Copilot - AI-powered Network Lab Assistant for GNS3
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#
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# This file is part of GNS3-Copilot project.
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#
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# GNS3-Copilot is free software: you can redistribute it and/or modify it
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# under the terms of the GNU General Public License as published by the
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# Free Software Foundation, either version 3 of the License, or (at your
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# option) any later version.
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#
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# GNS3-Copilot is distributed in the hope that it will be useful, but
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# WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
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# or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License
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# for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with GNS3-Copilot. If not, see <https://www.gnu.org/licenses/>.
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#
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# Copyright (C) 2025 Guobin Yue
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# Author: Guobin Yue
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#
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# Project Home: https://github.com/yueguobin/gns3-copilot
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#
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"""
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Tool call streaming accumulator for handling progressive tool call arguments
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Maintains state for streaming tool call chunks
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Based on FlowNet-Lab implementation
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"""
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from typing import Any, Dict, List, Optional
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class ToolCallStreamAccumulator:
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"""
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Accumulates tool call information from streaming events
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Handles the progressive build-up of tool call arguments
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This class processes LangGraph streaming events and accumulates
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tool call arguments that come in chunks, emitting progressive
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tool_call events to the frontend.
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"""
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def __init__(self) -> None:
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# Current active tool call being accumulated
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# Format: {"id": str, "name": str, "args_string": str}
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self._current_tool_call: Optional[Dict[str, str]] = None
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def process_event(self, event: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""
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Process a streaming event and return one or more response chunks
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Args:
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event: LangGraph streaming event (on_chat_model_stream)
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Returns:
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List of response chunks to send to frontend
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Processing phases:
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1. Initialize: Extract tool ID and name from tool_calls
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2. Accumulate: Concatenate argument strings from tool_call_chunks
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3. Complete: Mark as complete when finish_reason is "tool_calls" or "stop"
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"""
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event_type = event.get("event", "")
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chunks = []
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if event_type == "on_chat_model_stream":
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chunk = event.get("data", {}).get("chunk", {})
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# ========== Phase 1: Initialize tool call from tool_calls ==========
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# Get metadata (ID and name) from tool_calls
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if hasattr(chunk, "tool_calls") and chunk.tool_calls:
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for tool_call in chunk.tool_calls:
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if isinstance(tool_call, dict):
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tc_id = tool_call.get("id")
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tc_name = tool_call.get("name", "")
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else:
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tc_id = getattr(tool_call, "id", None)
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tc_name = getattr(tool_call, "name", "")
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# Only when ID is not empty, consider it as the start of a new tool call
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if tc_id:
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# Initialize current tool state (this is the only time to get ID)
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# Note: only one tool can be called at a time
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self._current_tool_call = {
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"id": tc_id,
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"name": tc_name if tc_name else "UNKNOWN_TOOL",
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"args_string": "",
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}
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# Send initial tool_call event with empty args
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chunks.append({
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"type": "tool_call",
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"tool_call": {
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"id": tc_id,
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"type": "function",
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"function": {
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"name": self._current_tool_call["name"],
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"arguments": ""
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}
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}
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})
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# ========== Phase 2: Concatenate parameter strings from tool_call_chunks ==========
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# Concatenate parameter strings from tool_call_chunk
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if hasattr(chunk, "tool_call_chunks") and chunk.tool_call_chunks:
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if self._current_tool_call:
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tool_data = self._current_tool_call
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for tc_chunk in chunk.tool_call_chunks:
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# Default to "" instead of None
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if isinstance(tc_chunk, dict):
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args_chunk = tc_chunk.get("args", "")
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else:
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args_chunk = getattr(tc_chunk, "args", "")
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# Core: string concatenation
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if isinstance(args_chunk, str):
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tool_data["args_string"] += args_chunk
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# Send updated tool_call event with accumulated args
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chunks.append({
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"type": "tool_call",
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"tool_call": {
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"id": tool_data["id"],
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"type": "function",
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"function": {
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"name": tool_data["name"],
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"arguments": tool_data["args_string"]
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}
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}
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})
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# ========== Phase 3: Determine if tool_calls_chunks output is complete ==========
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# Check finish_reason == "tool_calls" or "STOP"
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response_metadata = getattr(chunk, "response_metadata", {})
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finish_reason = response_metadata.get("finish_reason") if isinstance(response_metadata, dict) else None
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if (finish_reason == "tool_calls") or (finish_reason == "stop" and self._current_tool_call is not None):
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if self._current_tool_call:
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tool_data = self._current_tool_call
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# Send final complete tool_call event
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chunks.append({
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"type": "tool_call",
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"tool_call": {
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"id": tool_data["id"],
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"type": "function",
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"function": {
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"name": tool_data["name"],
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"arguments": tool_data["args_string"],
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"complete": True # Mark as complete
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}
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}
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})
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# Clear the current tool call state
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self._current_tool_call = None
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# Also handle regular content (when not in tool call mode)
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content = getattr(chunk, "content", "")
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if content and not self._current_tool_call:
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chunks.append({
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"type": "content",
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"content": content,
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"message_id": event.get("metadata", {}).get("msg_id")
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})
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return chunks
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def reset(self) -> None:
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"""Reset the accumulator state"""
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self._current_tool_call = None
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