337 lines
15 KiB
Python
337 lines
15 KiB
Python
"""Chat completion service"""
|
|
import json
|
|
import uuid
|
|
from flask import current_app, g, Response
|
|
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
|
|
|
|
|
|
class ChatService:
|
|
"""Chat completion service with tool support"""
|
|
|
|
def __init__(self, llm: LLMClient):
|
|
self.llm = llm
|
|
self.executor = ToolExecutor(registry=registry)
|
|
|
|
|
|
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 = None
|
|
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():
|
|
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)
|
|
|
|
# Execute each tool call, emit tool_call + tool_result as paired steps
|
|
tool_results = []
|
|
for tc in tool_calls_list:
|
|
# Emit tool_call step (before execution)
|
|
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
|
|
|
|
# Execute the tool
|
|
with app.app_context():
|
|
single_result = self.executor.process_tool_calls([tc], context)
|
|
tool_results.extend(single_result)
|
|
|
|
# Emit tool_result step (after execution)
|
|
tr = single_result[0]
|
|
try:
|
|
result_content = json.loads(tr["content"])
|
|
skipped = result_content.get("skipped", False)
|
|
except:
|
|
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"
|
|
|
|
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 _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
|