"""Chat completion service""" import json import uuid from flask import current_app, g, Response, request as flask_request from werkzeug.exceptions import ClientDisconnected from backend import db from backend.models import Conversation, Message from backend.tools import registry, ToolExecutor from backend.utils.helpers import ( record_token_usage, build_messages, ) from backend.services.llm_client import LLMClient from backend.config import MAX_ITERATIONS def _client_disconnected(): """Check if the client has disconnected.""" try: stream = flask_request.input_stream # If input_stream is unavailable, assume still connected if stream is None: return False return stream.closed except Exception: return False class ChatService: """Chat completion service with tool support""" def __init__(self, llm: LLMClient): self.llm = llm def stream_response(self, conv: Conversation, tools_enabled: bool = True, project_id: str = None): """Stream response with tool call support Uses 'process_step' events to send thinking and tool calls in order, allowing them to be interleaved properly in the frontend. Args: conv: Conversation object tools_enabled: Whether to enable tools project_id: Project ID for workspace isolation """ conv_id = conv.id conv_model = conv.model app = current_app._get_current_object() tools = registry.list_all() if tools_enabled else None initial_messages = build_messages(conv, project_id) # Create per-request executor for thread-safe isolation. # Each request gets its own _call_history and _cache, eliminating # race conditions when multiple conversations stream concurrently. executor = ToolExecutor(registry=registry) # Build context for tool execution context = {"model": conv_model} if project_id: context["project_id"] = project_id elif conv.project_id: context["project_id"] = conv.project_id def generate(): messages = list(initial_messages) all_tool_calls = [] all_tool_results = [] all_steps = [] # Collect all ordered steps for DB storage (thinking/text/tool_call/tool_result) step_index = 0 # Track global step index for ordering total_completion_tokens = 0 # Accumulated across all iterations prompt_tokens = 0 # Not accumulated — last iteration's value is sufficient # (each iteration re-sends the full context, so earlier # prompts are strict subsets of the final one) for iteration in range(MAX_ITERATIONS): full_content = "" full_thinking = "" token_count = 0 msg_id = str(uuid.uuid4()) tool_calls_list = [] # Streaming step tracking — step ID is assigned on first chunk arrival. # thinking always precedes text in GLM's streaming order, so text gets step_index+1. thinking_step_id = None thinking_step_idx = None text_step_id = None text_step_idx = None try: with app.app_context(): active_conv = db.session.get(Conversation, conv_id) resp = self.llm.call( model=active_conv.model, messages=messages, max_tokens=active_conv.max_tokens, temperature=active_conv.temperature, thinking_enabled=active_conv.thinking_enabled, tools=tools, stream=True, ) resp.raise_for_status() # Stream LLM response chunk by chunk for line in resp.iter_lines(): # Early exit if client has disconnected if _client_disconnected(): resp.close() return if not line: continue line = line.decode("utf-8") if not line.startswith("data: "): continue data_str = line[6:] if data_str == "[DONE]": break try: chunk = json.loads(data_str) except json.JSONDecodeError: continue # Extract usage first (present in last chunk when stream_options is set) usage = chunk.get("usage", {}) if usage: token_count = usage.get("completion_tokens", 0) prompt_tokens = usage.get("prompt_tokens", 0) choices = chunk.get("choices", []) if not choices: continue delta = choices[0].get("delta", {}) # Accumulate thinking content for this iteration reasoning = delta.get("reasoning_content", "") if reasoning: full_thinking += reasoning if thinking_step_id is None: thinking_step_id = f'step-{step_index}' thinking_step_idx = step_index yield f"event: process_step\ndata: {json.dumps({'id': thinking_step_id, 'index': thinking_step_idx, 'type': 'thinking', 'content': full_thinking}, ensure_ascii=False)}\n\n" # Accumulate text content for this iteration text = delta.get("content", "") if text: full_content += text if text_step_id is None: text_step_idx = step_index + (1 if thinking_step_id is not None else 0) text_step_id = f'step-{text_step_idx}' yield f"event: process_step\ndata: {json.dumps({'id': text_step_id, 'index': text_step_idx, 'type': 'text', 'content': full_content}, ensure_ascii=False)}\n\n" # Accumulate tool calls from streaming deltas tool_calls_list = self._process_tool_calls_delta(delta, tool_calls_list) except Exception as e: yield f"event: error\ndata: {json.dumps({'content': str(e)}, ensure_ascii=False)}\n\n" return # --- Finalize: save thinking/text steps to all_steps for DB storage --- # No need to yield to frontend — incremental process_step events already sent. if thinking_step_id is not None: all_steps.append({ 'id': thinking_step_id, 'index': thinking_step_idx, 'type': 'thinking', 'content': full_thinking, }) step_index += 1 if text_step_id is not None: all_steps.append({ 'id': text_step_id, 'index': text_step_idx, 'type': 'text', 'content': full_content, }) step_index += 1 # --- Branch: tool calls vs final --- if tool_calls_list: all_tool_calls.extend(tool_calls_list) # Phase 1: emit all tool_call steps (before execution) for tc in tool_calls_list: call_step = { 'id': f'step-{step_index}', 'index': step_index, 'type': 'tool_call', 'id_ref': tc['id'], 'name': tc['function']['name'], 'arguments': tc['function']['arguments'], } all_steps.append(call_step) yield f"event: process_step\ndata: {json.dumps(call_step, ensure_ascii=False)}\n\n" step_index += 1 # Phase 2: execute tools — parallel when multiple, sequential when single if len(tool_calls_list) > 1: with app.app_context(): tool_results = executor.process_tool_calls_parallel( tool_calls_list, context, max_workers=4 ) else: with app.app_context(): tool_results = executor.process_tool_calls( tool_calls_list, context ) # Phase 3: emit all tool_result steps (after execution, same order) for tr in tool_results: try: result_content = json.loads(tr["content"]) skipped = result_content.get("skipped", False) except Exception: skipped = False result_step = { 'id': f'step-{step_index}', 'index': step_index, 'type': 'tool_result', 'id_ref': tr['tool_call_id'], 'name': tr['name'], 'content': tr['content'], 'skipped': skipped, } all_steps.append(result_step) yield f"event: process_step\ndata: {json.dumps(result_step, ensure_ascii=False)}\n\n" step_index += 1 # Append assistant message + tool results for the next iteration messages.append({ "role": "assistant", "content": full_content or None, "tool_calls": tool_calls_list }) messages.extend(tool_results) all_tool_results.extend(tool_results) total_completion_tokens += token_count continue # --- No tool calls: final iteration — save message to DB --- suggested_title = None # prompt_tokens already holds the last iteration's value (set during streaming) total_completion_tokens += token_count with app.app_context(): # Build content JSON with ordered steps array for DB storage. # 'steps' is the single source of truth for rendering order. content_json = { "text": full_content, } if all_tool_calls: content_json["tool_calls"] = self._build_tool_calls_json(all_tool_calls, all_tool_results) # Store ordered steps — the single source of truth for rendering order content_json["steps"] = all_steps msg = Message( id=msg_id, conversation_id=conv_id, role="assistant", content=json.dumps(content_json, ensure_ascii=False), token_count=total_completion_tokens, ) db.session.add(msg) db.session.commit() # Auto-generate title from first user message if needed conv = db.session.get(Conversation, conv_id) # Record token usage (get user_id from conv, not g — # app.app_context() creates a new context where g.current_user is lost) if conv: record_token_usage(conv.user_id, conv_model, prompt_tokens, total_completion_tokens) if conv and (not conv.title or conv.title == "新对话"): user_msg = Message.query.filter_by( conversation_id=conv_id, role="user" ).order_by(Message.created_at.asc()).first() if user_msg and user_msg.content: try: content_data = json.loads(user_msg.content) title_text = content_data.get("text", "")[:30] except (json.JSONDecodeError, TypeError): title_text = user_msg.content.strip()[:30] if title_text: suggested_title = title_text else: suggested_title = "新对话" db.session.refresh(conv) conv.title = suggested_title db.session.commit() else: suggested_title = None yield f"event: done\ndata: {json.dumps({'message_id': msg_id, 'token_count': total_completion_tokens, 'suggested_title': suggested_title}, ensure_ascii=False)}\n\n" return yield f"event: error\ndata: {json.dumps({'content': 'exceeded maximum tool call iterations'}, ensure_ascii=False)}\n\n" def safe_generate(): """Wrapper that catches client disconnection during yield.""" try: yield from generate() except (ClientDisconnected, BrokenPipeError, ConnectionResetError): pass # Client aborted, silently stop return Response( safe_generate(), mimetype="text/event-stream", headers={ "Cache-Control": "no-cache, no-store, must-revalidate", "X-Accel-Buffering": "no", "Connection": "keep-alive", "Transfer-Encoding": "chunked", } ) def _build_tool_calls_json(self, tool_calls: list, tool_results: list) -> list: """Build tool calls JSON structure - matches streaming format""" result = [] for i, tc in enumerate(tool_calls): result_content = tool_results[i]["content"] if i < len(tool_results) else None # Parse result to extract success/skipped status success = True skipped = False execution_time = 0 if result_content: try: result_data = json.loads(result_content) success = result_data.get("success", True) skipped = result_data.get("skipped", False) execution_time = result_data.get("execution_time", 0) except: pass # Keep same structure as streaming format result.append({ "id": tc.get("id", ""), "type": tc.get("type", "function"), "function": { "name": tc["function"]["name"], "arguments": tc["function"]["arguments"], }, "result": result_content, "success": success, "skipped": skipped, "execution_time": execution_time, }) return result def _process_tool_calls_delta(self, delta: dict, tool_calls_list: list) -> list: """Process tool calls from streaming delta""" tool_calls_delta = delta.get("tool_calls", []) for tc in tool_calls_delta: idx = tc.get("index", 0) if idx >= len(tool_calls_list): tool_calls_list.append({ "id": tc.get("id", ""), "type": tc.get("type", "function"), "function": {"name": "", "arguments": ""} }) if tc.get("id"): tool_calls_list[idx]["id"] = tc["id"] if tc.get("function"): if tc["function"].get("name"): tool_calls_list[idx]["function"]["name"] = tc["function"]["name"] if tc["function"].get("arguments"): tool_calls_list[idx]["function"]["arguments"] += tc["function"]["arguments"] return tool_calls_list