397 lines
16 KiB
Python
397 lines
16 KiB
Python
"""Chat service module"""
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import json
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import uuid
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from typing import List, Dict, Any, AsyncGenerator, Optional
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from luxx.models import Conversation, Message
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from luxx.tools.executor import ToolExecutor
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from luxx.tools.core import registry
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from luxx.services.llm_client import LLMClient
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from luxx.config import config
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# Maximum iterations to prevent infinite loops
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MAX_ITERATIONS = 10
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def _sse_event(event: str, data: dict) -> str:
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"""Format a Server-Sent Event string."""
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return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
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def get_llm_client(conversation: Conversation = None):
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"""Get LLM client, optionally using conversation's provider. Returns (client, max_tokens)"""
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max_tokens = None
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if conversation and conversation.provider_id:
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from luxx.models import LLMProvider
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from luxx.database import SessionLocal
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db = SessionLocal()
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try:
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provider = db.query(LLMProvider).filter(LLMProvider.id == conversation.provider_id).first()
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if provider:
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max_tokens = provider.max_tokens
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client = LLMClient(
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api_key=provider.api_key,
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api_url=provider.base_url,
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model=provider.default_model
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)
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return client, max_tokens
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finally:
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db.close()
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# Fallback to global config
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client = LLMClient()
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return client, max_tokens
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class ChatService:
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"""Chat service with tool support"""
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def __init__(self):
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self.tool_executor = ToolExecutor()
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def build_messages(
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self,
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conversation: Conversation,
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include_system: bool = True
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) -> List[Dict[str, str]]:
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"""Build message list"""
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from luxx.database import SessionLocal
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from luxx.models import Message
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messages = []
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if include_system and conversation.system_prompt:
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messages.append({
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"role": "system",
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"content": conversation.system_prompt
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})
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db = SessionLocal()
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try:
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db_messages = db.query(Message).filter(
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Message.conversation_id == conversation.id
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).order_by(Message.created_at).all()
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for msg in db_messages:
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# Parse JSON content if possible
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try:
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content_obj = json.loads(msg.content) if msg.content else {}
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if isinstance(content_obj, dict):
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content = content_obj.get("text", msg.content)
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else:
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content = msg.content
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except (json.JSONDecodeError, TypeError):
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content = msg.content
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messages.append({
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"role": msg.role,
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"content": content
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})
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finally:
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db.close()
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return messages
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async def stream_response(
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self,
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conversation: Conversation,
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user_message: str,
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tools_enabled: bool = True,
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enabled_tools: Optional[List[str]] = None,
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thinking_enabled: bool = False
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) -> AsyncGenerator[Dict[str, str], None]:
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"""
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Streaming response generator
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Yields raw SSE event strings for direct forwarding.
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"""
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try:
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messages = self.build_messages(conversation)
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messages.append({
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"role": "user",
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"content": json.dumps({"text": user_message, "attachments": []})
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})
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# Filter tools by enabled list if provided
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if enabled_tools is not None and tools_enabled:
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all_tools = registry.list_all()
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tools = [t for t in all_tools if t.get("name") in enabled_tools]
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else:
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tools = registry.list_all() if tools_enabled else None
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# Only include enabled tools
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all_tools = registry.list_all() if tools_enabled else None
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tools = [t for t in all_tools] if all_tools else None
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llm, provider_max_tokens = get_llm_client(conversation)
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model = conversation.model or llm.default_model or "gpt-4"
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# 使用 provider 的 max_tokens，如果 conversation 有自己的 max_tokens 则覆盖
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max_tokens = conversation.max_tokens if hasattr(conversation, 'max_tokens') and conversation.max_tokens else provider_max_tokens
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# State tracking
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all_steps = []
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all_tool_calls = []
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all_tool_results = []
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step_index = 0
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# Global step IDs for thinking and text (persist across iterations)
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thinking_step_id = None
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thinking_step_idx = None
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text_step_id = None
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text_step_idx = None
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for iteration in range(MAX_ITERATIONS):
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print(f"[CHAT] Starting iteration {iteration + 1}, messages: {len(messages)}")
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# Stream from LLM
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full_content = ""
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full_thinking = ""
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tool_calls_list = []
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# Step tracking - use unified step-{index} format
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thinking_step_id = None
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thinking_step_idx = None
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text_step_id = None
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text_step_idx = None
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async for sse_line in llm.stream_call(
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model=model,
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messages=messages,
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tools=tools,
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temperature=conversation.temperature,
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max_tokens=max_tokens or 8192,
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thinking_enabled=thinking_enabled or conversation.thinking_enabled
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):
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# Parse SSE line
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# Format: "event: xxx\ndata: {...}\n\n"
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event_type = None
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data_str = None
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for line in sse_line.strip().split('\n'):
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if line.startswith('event: '):
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event_type = line[7:].strip()
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elif line.startswith('data: '):
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data_str = line[6:].strip()
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if data_str is None:
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continue
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# Handle error events from LLM
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if event_type == 'error':
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try:
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error_data = json.loads(data_str)
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yield _sse_event("error", {"content": error_data.get("content", "Unknown error")})
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except json.JSONDecodeError:
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yield _sse_event("error", {"content": data_str})
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return
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# Parse the data
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try:
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chunk = json.loads(data_str)
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except json.JSONDecodeError:
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continue
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# Get delta
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choices = chunk.get("choices", [])
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if not choices:
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continue
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delta = choices[0].get("delta", {})
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# Handle reasoning (thinking)
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reasoning = delta.get("reasoning_content", "")
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if reasoning:
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prev_thinking_len = len(full_thinking)
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full_thinking += reasoning
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if prev_thinking_len == 0: # New thinking stream started
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thinking_step_idx = step_index
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thinking_step_id = f"step-{step_index}"
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step_index += 1
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yield _sse_event("process_step", {
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"step": {
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"id": thinking_step_id,
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"index": thinking_step_idx,
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"type": "thinking",
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"content": full_thinking
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}
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})
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# Handle content
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content = delta.get("content", "")
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if content:
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prev_content_len = len(full_content)
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full_content += content
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if prev_content_len == 0: # New text stream started
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text_step_idx = step_index
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text_step_id = f"step-{step_index}"
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step_index += 1
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yield _sse_event("process_step", {
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"step": {
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"id": text_step_id,
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"index": text_step_idx,
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"type": "text",
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"content": full_content
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}
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})
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# Accumulate tool calls
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tool_calls_delta = delta.get("tool_calls", [])
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for tc in tool_calls_delta:
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idx = tc.get("index", 0)
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if idx >= len(tool_calls_list):
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tool_calls_list.append({
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"id": tc.get("id", ""),
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"type": "function",
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"function": {"name": "", "arguments": ""}
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})
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func = tc.get("function", {})
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if func.get("name"):
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tool_calls_list[idx]["function"]["name"] += func["name"]
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if func.get("arguments"):
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tool_calls_list[idx]["function"]["arguments"] += func["arguments"]
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# Save thinking step
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if thinking_step_id is not None:
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all_steps.append({
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"id": thinking_step_id,
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"index": thinking_step_idx,
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"type": "thinking",
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"content": full_thinking
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})
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# Save text step
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if text_step_id is not None:
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all_steps.append({
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"id": text_step_id,
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"index": text_step_idx,
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"type": "text",
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"content": full_content
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})
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# Handle tool calls
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if tool_calls_list:
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all_tool_calls.extend(tool_calls_list)
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# Yield tool_call steps - use unified step-{index} format
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tool_call_step_ids = [] # Track step IDs for tool calls
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for tc in tool_calls_list:
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call_step_idx = step_index
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call_step_id = f"step-{step_index}"
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tool_call_step_ids.append(call_step_id)
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step_index += 1
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call_step = {
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"id": call_step_id,
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"index": call_step_idx,
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"type": "tool_call",
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"id_ref": tc.get("id", ""),
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"name": tc["function"]["name"],
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"arguments": tc["function"]["arguments"]
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}
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all_steps.append(call_step)
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yield _sse_event("process_step", {"step": call_step})
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# Execute tools
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tool_results = self.tool_executor.process_tool_calls_parallel(
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tool_calls_list, {}
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)
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# Yield tool_result steps - use unified step-{index} format
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for i, tr in enumerate(tool_results):
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tool_call_step_id = tool_call_step_ids[i] if i < len(tool_call_step_ids) else f"step-{i}"
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result_step_idx = step_index
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result_step_id = f"step-{step_index}"
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step_index += 1
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result_step = {
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"id": result_step_id,
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"index": result_step_idx,
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"type": "tool_result",
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"id_ref": tool_call_step_id, # Reference to the tool_call step
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"name": tr.get("name", ""),
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"content": tr.get("content", "")
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}
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all_steps.append(result_step)
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yield _sse_event("process_step", {"step": result_step})
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all_tool_results.append({
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"role": "tool",
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"tool_call_id": tr.get("tool_call_id", ""),
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"content": tr.get("content", "")
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})
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# Add assistant message with tool calls for next iteration
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messages.append({
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"role": "assistant",
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"content": full_content or "",
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"tool_calls": tool_calls_list
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})
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messages.extend(all_tool_results[-len(tool_results):])
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all_tool_results = []
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continue
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# No tool calls - final iteration, save message
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msg_id = str(uuid.uuid4())
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self._save_message(
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conversation.id,
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msg_id,
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full_content,
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all_tool_calls,
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all_tool_results,
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all_steps
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)
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yield _sse_event("done", {
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"message_id": msg_id,
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"token_count": len(full_content) // 4
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})
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return
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# Max iterations exceeded
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yield _sse_event("error", {"content": "Exceeded maximum tool call iterations"})
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except Exception as e:
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print(f"[CHAT] Exception: {type(e).__name__}: {str(e)}")
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yield _sse_event("error", {"content": str(e)})
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def _save_message(
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self,
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conversation_id: str,
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msg_id: str,
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full_content: str,
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all_tool_calls: list,
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all_tool_results: list,
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all_steps: list
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):
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"""Save the assistant message to database."""
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from luxx.database import SessionLocal
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from luxx.models import Message
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content_json = {
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"text": full_content,
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"steps": all_steps
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}
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if all_tool_calls:
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content_json["tool_calls"] = all_tool_calls
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db = SessionLocal()
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try:
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msg = Message(
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id=msg_id,
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conversation_id=conversation_id,
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role="assistant",
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content=json.dumps(content_json, ensure_ascii=False),
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token_count=len(full_content) // 4
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)
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db.add(msg)
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db.commit()
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except Exception as e:
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print(f"[CHAT] Failed to save message: {e}")
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db.rollback()
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finally:
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db.close()
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# Global chat service
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chat_service = ChatService()
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