Files
scud_context_api/llm/agent.py
T

127 lines
5.7 KiB
Python

import json
import urllib.request
import urllib.error
from typing import List, Dict, Any, Tuple
from datetime import datetime
from .db_tools import (
db_get_active_system_prompt,
db_add_system_prompt,
db_get_tasks,
db_update_task_status,
db_delete_task,
db_add_task,
db_get_rules
)
from .schemas import TOOLS_SCHEMA
OLLAMA_URL = "http://192.168.11.3:11434/api/chat"
MODEL_NAME = "qwen2.5:14b"
def format_rules_output(rules: List[Dict[str, Any]]) -> str:
if not rules:
return "База знаний пока пуста."
lines = [f"📚 База знаний и правила арбитража ({len(rules)}):\n"]
for idx, r in enumerate(rules, 1):
rule_text = r.get("rule_text", "").strip()
lines.append(f"{idx}. {rule_text}\n")
return "\n".join(lines).strip()
def process_chat_message(user_id: int, user_message: str, chat_history: List[Dict[str, Any]] = None) -> Tuple[str, List[Dict[str, Any]]]:
if chat_history is None:
chat_history = []
current_now = datetime.now().strftime("%Y-%m-%d %H:%M")
# Запрос к локальной модели Qwen (полный цикл Function Calling)
dynamic_prompt_text = db_get_active_system_prompt()
system_prompt = {
"role": "system",
"content": f"Текущая дата и время сервера: {current_now}.\n\n{dynamic_prompt_text}"
}
messages = [system_prompt] + chat_history + [{"role": "user", "content": user_message}]
payload = {
"model": MODEL_NAME,
"messages": messages,
"tools": TOOLS_SCHEMA,
"stream": False,
"options": {"num_predict": 2048, "num_ctx": 8192, "temperature": 0.1}
}
try:
req = urllib.request.Request(
OLLAMA_URL,
data=json.dumps(payload).encode("utf-8"),
headers={"Content-Type": "application/json"}
)
with urllib.request.urlopen(req) as response:
res_data = json.loads(response.read().decode("utf-8"))
msg = res_data.get("message", {})
tool_calls = msg.get("tool_calls", [])
if tool_calls:
messages.append(msg)
for tool in tool_calls:
fn_name = tool["function"]["name"]
fn_args = tool["function"].get("arguments", {})
tool_result_content = ""
if fn_name == "db_get_tasks":
tasks = db_get_tasks(user_id)
tool_result_content = json.dumps(tasks, ensure_ascii=False)
elif fn_name in ["db_get_system_prompt", "db_get_system_prompts"]:
tool_result_content = db_get_active_system_prompt()
elif fn_name == "db_add_system_prompt":
res = db_add_system_prompt(
name=fn_args.get("name", "main_agent"),
prompt_text=fn_args.get("prompt_text")
)
tool_result_content = json.dumps(res, ensure_ascii=False)
elif fn_name == "db_get_rules":
tool_result_content = json.dumps(db_get_rules(), ensure_ascii=False)
elif fn_name == "db_add_task":
res = db_add_task(user_id=user_id, module=fn_args.get("module", "general"), title=fn_args.get("title"), priority=fn_args.get("priority", "MEDIUM"), due_date=fn_args.get("due_date"))
tool_result_content = json.dumps(res, ensure_ascii=False)
elif fn_name == "db_update_task_status":
res = db_update_task_status(user_id=user_id, task_id=str(fn_args.get("task_id")), status=fn_args.get("status", "COMPLETED"), due_date=fn_args.get("due_date"))
tool_result_content = json.dumps(res, ensure_ascii=False)
elif fn_name == "db_delete_task":
res = db_delete_task(user_id=user_id, task_id=str(fn_args.get("task_id", "")).upper())
tool_result_content = json.dumps(res, ensure_ascii=False)
messages.append({
"role": "tool",
"content": tool_result_content
})
# Вторичный запрос модели для формирования итогового ответа оператору
second_payload = {
"model": MODEL_NAME,
"messages": messages,
"stream": False,
"options": {"num_predict": 2048, "num_ctx": 8192, "temperature": 0.1}
}
sec_req = urllib.request.Request(
OLLAMA_URL,
data=json.dumps(second_payload).encode("utf-8"),
headers={"Content-Type": "application/json"}
)
with urllib.request.urlopen(sec_req) as sec_response:
sec_res_data = json.loads(sec_response.read().decode("utf-8"))
final_content = sec_res_data.get("message", {}).get("content", "").strip().replace("**", "")
return final_content, chat_history + [{"role": "user", "content": user_message}, {"role": "assistant", "content": final_content}]
content_str = msg.get("content", "").strip().replace("**", "")
return content_str or "Запрос обработан.", chat_history + [{"role": "user", "content": user_message}, {"role": "assistant", "content": content_str}]
except urllib.error.URLError as e:
return f"Ошибка связи с Ollama ({OLLAMA_URL}): {e}", chat_history