427 lines
19 KiB
Python
427 lines
19 KiB
Python
"""Chat completion service"""
|
|
import json
|
|
import uuid
|
|
from flask import current_app, Response
|
|
from backend import db
|
|
from backend.models import Conversation, Message
|
|
from backend.tools import registry, ToolExecutor
|
|
from backend.utils.helpers import (
|
|
get_or_create_default_user,
|
|
record_token_usage,
|
|
build_messages,
|
|
ok,
|
|
err,
|
|
to_dict,
|
|
)
|
|
from backend.services.glm_client import GLMClient
|
|
|
|
|
|
class ChatService:
|
|
"""Chat completion service with tool support"""
|
|
|
|
MAX_ITERATIONS = 5
|
|
|
|
def __init__(self, glm_client: GLMClient):
|
|
self.glm_client = glm_client
|
|
self.executor = ToolExecutor(registry=registry)
|
|
|
|
|
|
def sync_response(self, conv: Conversation, tools_enabled: bool = True, project_id: str = None):
|
|
"""Sync response with tool call support
|
|
|
|
Args:
|
|
conv: Conversation object
|
|
tools_enabled: Whether to enable tools
|
|
project_id: Project ID for workspace isolation
|
|
"""
|
|
tools = registry.list_all() if tools_enabled else None
|
|
messages = build_messages(conv, project_id)
|
|
|
|
# Clear tool call history for new request
|
|
self.executor.clear_history()
|
|
|
|
# Build context for tool execution
|
|
context = {"project_id": project_id} if project_id else None
|
|
|
|
all_tool_calls = []
|
|
all_tool_results = []
|
|
|
|
for _ in range(self.MAX_ITERATIONS):
|
|
try:
|
|
resp = self.glm_client.call(
|
|
model=conv.model,
|
|
messages=messages,
|
|
max_tokens=conv.max_tokens,
|
|
temperature=conv.temperature,
|
|
thinking_enabled=conv.thinking_enabled,
|
|
tools=tools,
|
|
)
|
|
resp.raise_for_status()
|
|
result = resp.json()
|
|
except Exception as e:
|
|
return err(500, f"upstream error: {e}")
|
|
|
|
choice = result["choices"][0]
|
|
message = choice["message"]
|
|
|
|
# No tool calls - return final result
|
|
if not message.get("tool_calls"):
|
|
usage = result.get("usage", {})
|
|
prompt_tokens = usage.get("prompt_tokens", 0)
|
|
completion_tokens = usage.get("completion_tokens", 0)
|
|
|
|
# Build content JSON
|
|
content_json = {
|
|
"text": message.get("content", ""),
|
|
}
|
|
if message.get("reasoning_content"):
|
|
content_json["thinking"] = message["reasoning_content"]
|
|
if all_tool_calls:
|
|
content_json["tool_calls"] = self._build_tool_calls_json(all_tool_calls, all_tool_results)
|
|
|
|
# Create message
|
|
msg = Message(
|
|
id=str(uuid.uuid4()),
|
|
conversation_id=conv.id,
|
|
role="assistant",
|
|
content=json.dumps(content_json, ensure_ascii=False),
|
|
token_count=completion_tokens,
|
|
)
|
|
db.session.add(msg)
|
|
db.session.commit()
|
|
|
|
user = get_or_create_default_user()
|
|
record_token_usage(user.id, conv.model, prompt_tokens, completion_tokens)
|
|
|
|
# Set title if needed (first message)
|
|
suggested_title = None
|
|
if 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:
|
|
# Parse content JSON to get text
|
|
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"
|
|
conv.title = suggested_title
|
|
db.session.commit()
|
|
|
|
return ok({
|
|
"message": self._message_to_dict(msg),
|
|
"usage": {
|
|
"prompt_tokens": prompt_tokens,
|
|
"completion_tokens": completion_tokens,
|
|
"total_tokens": usage.get("total_tokens", 0)
|
|
},
|
|
"suggested_title": suggested_title,
|
|
})
|
|
|
|
# Process tool calls
|
|
tool_calls = message["tool_calls"]
|
|
all_tool_calls.extend(tool_calls)
|
|
messages.append(message)
|
|
|
|
tool_results = self.executor.process_tool_calls(tool_calls, context)
|
|
all_tool_results.extend(tool_results)
|
|
messages.extend(tool_results)
|
|
|
|
return err(500, "exceeded maximum tool call iterations")
|
|
|
|
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)
|
|
|
|
# Clear tool call history for new request
|
|
self.executor.clear_history()
|
|
|
|
# Build context for tool execution
|
|
context = {"project_id": project_id} if project_id else None
|
|
|
|
def generate():
|
|
messages = list(initial_messages)
|
|
all_tool_calls = []
|
|
all_tool_results = []
|
|
step_index = 0 # Track global step index for ordering
|
|
|
|
for iteration in range(self.MAX_ITERATIONS):
|
|
full_content = ""
|
|
full_thinking = ""
|
|
token_count = 0
|
|
prompt_tokens = 0
|
|
msg_id = str(uuid.uuid4())
|
|
tool_calls_list = []
|
|
|
|
# Send thinking_start event to clear previous thinking in frontend
|
|
yield f"event: thinking_start\ndata: {{}}\n\n"
|
|
|
|
try:
|
|
with app.app_context():
|
|
active_conv = db.session.get(Conversation, conv_id)
|
|
resp = self.glm_client.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()
|
|
|
|
for line in resp.iter_lines():
|
|
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
|
|
|
|
delta = chunk["choices"][0].get("delta", {})
|
|
|
|
# Process thinking - send as process_step
|
|
reasoning = delta.get("reasoning_content", "")
|
|
if reasoning:
|
|
full_thinking += reasoning
|
|
# Still send thinking event for backward compatibility
|
|
yield f"event: thinking\ndata: {json.dumps({'content': reasoning}, ensure_ascii=False)}\n\n"
|
|
|
|
# Process text
|
|
text = delta.get("content", "")
|
|
if text:
|
|
full_content += text
|
|
yield f"event: message\ndata: {json.dumps({'content': text}, ensure_ascii=False)}\n\n"
|
|
|
|
# Process tool calls
|
|
tool_calls_list = self._process_tool_calls_delta(delta, tool_calls_list)
|
|
|
|
usage = chunk.get("usage", {})
|
|
if usage:
|
|
token_count = usage.get("completion_tokens", 0)
|
|
prompt_tokens = usage.get("prompt_tokens", 0)
|
|
|
|
except Exception as e:
|
|
yield f"event: error\ndata: {json.dumps({'content': str(e)}, ensure_ascii=False)}\n\n"
|
|
return
|
|
|
|
# Tool calls exist - execute and continue
|
|
if tool_calls_list:
|
|
all_tool_calls.extend(tool_calls_list)
|
|
|
|
# Send thinking as a complete step if exists
|
|
if full_thinking:
|
|
yield f"event: process_step\ndata: {json.dumps({'index': step_index, 'type': 'thinking', 'content': full_thinking}, ensure_ascii=False)}\n\n"
|
|
step_index += 1
|
|
|
|
# Also send legacy tool_calls event for backward compatibility
|
|
yield f"event: tool_calls\ndata: {json.dumps({'calls': tool_calls_list}, ensure_ascii=False)}\n\n"
|
|
|
|
# Process each tool call one by one, send result immediately
|
|
tool_results = []
|
|
for tc in tool_calls_list:
|
|
# Send tool call step
|
|
yield f"event: process_step\ndata: {json.dumps({'index': step_index, 'type': 'tool_call', 'id': tc['id'], 'name': tc['function']['name'], 'arguments': tc['function']['arguments']}, ensure_ascii=False)}\n\n"
|
|
step_index += 1
|
|
|
|
# Execute this single tool call
|
|
single_result = self.executor.process_tool_calls([tc], context)
|
|
tool_results.extend(single_result)
|
|
|
|
# Send tool result step immediately
|
|
tr = single_result[0]
|
|
try:
|
|
result_content = json.loads(tr["content"])
|
|
skipped = result_content.get("skipped", False)
|
|
except:
|
|
skipped = False
|
|
yield f"event: process_step\ndata: {json.dumps({'index': step_index, 'type': 'tool_result', 'id': tr['tool_call_id'], 'name': tr['name'], 'content': tr['content'], 'skipped': skipped}, ensure_ascii=False)}\n\n"
|
|
step_index += 1
|
|
|
|
# Also send legacy tool_result event
|
|
yield f"event: tool_result\ndata: {json.dumps({'id': tr['tool_call_id'], 'name': tr['name'], 'content': tr['content'], 'skipped': skipped}, ensure_ascii=False)}\n\n"
|
|
|
|
messages.append({
|
|
"role": "assistant",
|
|
"content": full_content or None,
|
|
"tool_calls": tool_calls_list
|
|
})
|
|
messages.extend(tool_results)
|
|
all_tool_results.extend(tool_results)
|
|
continue
|
|
|
|
# No tool calls - finish
|
|
# Send thinking as a step if exists
|
|
if full_thinking:
|
|
yield f"event: process_step\ndata: {json.dumps({'index': step_index, 'type': 'thinking', 'content': full_thinking}, ensure_ascii=False)}\n\n"
|
|
step_index += 1
|
|
|
|
suggested_title = None
|
|
with app.app_context():
|
|
# Build content JSON
|
|
content_json = {
|
|
"text": full_content,
|
|
}
|
|
if full_thinking:
|
|
content_json["thinking"] = full_thinking
|
|
if all_tool_calls:
|
|
content_json["tool_calls"] = self._build_tool_calls_json(all_tool_calls, all_tool_results)
|
|
|
|
msg = Message(
|
|
id=msg_id,
|
|
conversation_id=conv_id,
|
|
role="assistant",
|
|
content=json.dumps(content_json, ensure_ascii=False),
|
|
token_count=token_count,
|
|
)
|
|
db.session.add(msg)
|
|
db.session.commit()
|
|
|
|
user = get_or_create_default_user()
|
|
record_token_usage(user.id, conv_model, prompt_tokens, token_count)
|
|
|
|
# Check if we need to set title (first message in conversation)
|
|
conv = db.session.get(Conversation, conv_id)
|
|
if conv and (not conv.title or conv.title == "新对话"):
|
|
# Get user message content
|
|
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:
|
|
# Parse content JSON to get text
|
|
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"
|
|
# Refresh conv to avoid stale state
|
|
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': token_count, '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"
|
|
|
|
return Response(
|
|
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 _message_to_dict(self, msg: Message) -> dict:
|
|
"""Convert message to dict, parsing JSON content"""
|
|
result = to_dict(msg)
|
|
|
|
# Parse content JSON
|
|
if msg.content:
|
|
try:
|
|
content_data = json.loads(msg.content)
|
|
if isinstance(content_data, dict):
|
|
result["text"] = content_data.get("text", "")
|
|
if content_data.get("attachments"):
|
|
result["attachments"] = content_data["attachments"]
|
|
if content_data.get("thinking"):
|
|
result["thinking"] = content_data["thinking"]
|
|
if content_data.get("tool_calls"):
|
|
result["tool_calls"] = content_data["tool_calls"]
|
|
else:
|
|
result["text"] = msg.content
|
|
except (json.JSONDecodeError, TypeError):
|
|
result["text"] = msg.content
|
|
|
|
if "text" not in result:
|
|
result["text"] = ""
|
|
|
|
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
|