Дополнительные возможности

Для модели Qwen3 доступны дополнительные возможности, расширяющие функционал LLM и дающие больше возможностей для использования. Примеры использования могут несколько отличаться от официальной документации OpenAI и приведены ниже.

Tools

Получение ответа от LLM в виде заранее определенной схемы данных.

Structured Output

Пример для ChatOpenAI

from langchain_openai import ChatOpenAI
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

message = "Hello, I'm John and I'm 25 years old"
url = "http://localhost:5000/v1/"

client = ChatOpenAI(
    model="Compressa-LLM",
    api_key="TOKEN_1",
    base_url=url,
    )
struct_model = client.with_structured_output(User, method="function_calling")

response = struct_model.invoke([
      {"role": "system", "content": "/think" },
      {"role": "user", "content": message },
      ],  tool_choice="auto", extra_body={"chat_template_kwargs": {"thinking": False}})
print(response.name, response.age)

Пример для ChatQwen

client = ChatQwen(
         model="Compressa-LLM",
         api_key="TOKEN_1",
         api_base=url,
         enable_thinking=False,
         )

Function Calling

Использование LLM для получения структурированного ответа в виде функции и последующего выполнения функции с retry запросами к LLM в случае неуспеха.

from openai import OpenAI
import os
import sys
import io


def exec_user_code(code, test_cases):
  local_vars = {}
  old_stdout = sys.stdout
  old_stderr = sys.stderr
  sys.stdout = io.StringIO()
  sys.stderr = io.StringIO()
  try:
      exec(code, local_vars)
      func_name = None
      for name in local_vars:
          if callable(local_vars[name]) and "palindrome" in name.lower():
              func_name = name
              break
      assert func_name is not None, "No palindrome function found in generated code!"
      results = []
      for case in test_cases:
          try:
              result = local_vars[func_name](case)
              results.append((case, result))
          except Exception as e:
              results.append((case, f"Exception: {e}"))
      out = sys.stdout.getvalue()
      err = sys.stderr.getvalue()
      return {"stdout": out, "stderr": err, "results": results}
  except Exception as e:
      return {"stdout": sys.stdout.getvalue(), "stderr": sys.stderr.getvalue() + str(e), "results": []}
  finally:
      sys.stdout = old_stdout
      sys.stderr = old_stderr

client = ChatOpenAI(
  model="Compressa-LLM",
  api_key="TOKEN_1",
  base_url=url,
  )

tools = [
{"type": "function",
      "function": {
          "name": "python_code",
          "description": "Prepares python code for execution.",
          "parameters": {
              "type": "object",
              "properties": {
                  "code": {"type": "string", "description": "Python code for execution"}
              },
              "required": ["code"]
          }
      }
      }
      ]
user_request = "Write a function that checks if a string is a palindrome, and test it (not more than 3 examples)."
cases = ["madam", "", "a man a plan a canal panama", "banana"]
expected = [
              ("madam", True),
              ("", True),
              ("a man a plan a canal panama", True),
              ("banana", False),
          ]
messages = [{"role": "user", "content": user_request}]
MAX_ATTEMPTS = 3
attempt = 0
success = False

while attempt < MAX_ATTEMPTS and not success:
  attempt += 1
  print(f"\n========== TRY {attempt} ==========\n")
  response = client.chat.completions.create(
              model=model,
              messages=messages,
              tools=tools,
              tool_choice="auto",
              extra_body={"chat_template_kwargs": {"thinking": False}},
              )

  tool_call = response.choices[0].message.tool_calls[0]
  user_code = args["code"]
  result = exec_user_code(user_code, cases)
  print("Execution result:\n")
  print(result)
  passed = True
  for (input_str, expected_output), (tested_str, got_output) in zip(expected, result["results"]):
      if got_output != expected_output:
          print(f"Failed: {tested_str!r} -> {got_output!r} (expected {expected_output!r})")
          passed = False

   if passed:
       print("All tests passed!")
       success = True
       break
   else:
       fail_report = "Your code did not pass the following tests:\n"
       for (input_str, expected_output), (tested_str, got_output) in zip(expected, result["results"]):
           if got_output != expected_output:
               fail_report += f"Test case: {repr(tested_str)}. Got: {got_output!r}, expected: {expected_output!r}\n"
       fail_report += "\nFix the code so that all tests pass."
       messages.append(
                  {"role": "assistant", "content": None, "tool_calls": [tool_call.model_dump()]}
              )
       messages.append(
                  {"role": "tool", "tool_call_id": tool_call.id, "name": tool_call.function.name, "content": json.dumps(result)}
              )
       messages.append({"role": "user", "content": fail_report})

if not success:
    print(f"\nModel {model} did not generate correct code after 3 attempts.")

Guidance

Официальная библиотека Guidance на момент осени 2025 года не поддерживает работу с OpenAI подобными клиентами. Вы можете использовать доработанную библиотеку из нашего репозитория.

pip uninstall guidance
pip install git+https://github.com/compressa-ai/compressa-guidance.git
pip show guidance

Должна отображаться версия Version: 0.3.0rc0

Примеры использования

Regex

Регулярные выражения

email_generator = model(
model=compressa,
base_url=url,
api_key=key
)

with system():
  email_generator += "You are a useful assistant. Don't use any other text than the answer."
with user():
  email_generator += "Generate a valid email address."
with assistant():
  email_generator += gen("lm_email", regex=r"^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$", temperature=0.0)

Select

Выбор из списка

quiz = model(
model=compressa,
base_url=url,
api_key=key
)

with system():
  quiz += "You are a geography expert"

with user():
  quiz += """What is the capital of Sweden? Answer with the correct letter only.
  A) Helsinki
  B) Reykjavík 
  C) Stockholm
  D) Oslo
  """

with assistant():
  quiz += select(["A", "B", "C", "D"], name="model_selection")

print(f"The model selected {quiz['model_selection']}")