Small LLM wrapper used by the Action module. The default provider is ``mock`` so the repository can be smoke-tested without network access or API keys. Set ``AGENT4EDU_LLM_PROVIDER=openai`` for real simulation.
| 6 | |
| 7 | |
| 8 | class LLMClient: |
| 9 | """Small LLM wrapper used by the Action module. |
| 10 | |
| 11 | The default provider is ``mock`` so the repository can be smoke-tested without |
| 12 | network access or API keys. Set ``AGENT4EDU_LLM_PROVIDER=openai`` for real |
| 13 | simulation. |
| 14 | """ |
| 15 | |
| 16 | def __init__(self, provider: str | None = None, model: str | None = None): |
| 17 | self.provider = provider or SIM_PARAMS.get("llm_provider", "mock") |
| 18 | self.model = model or self._default_model() |
| 19 | self.client: Any | None = None |
| 20 | if self.provider == "openai": |
| 21 | from openai import OpenAI |
| 22 | |
| 23 | kwargs: dict[str, Any] = {"api_key": OPENAI_API_KEY} |
| 24 | if OPENAI_BASE_URL: |
| 25 | kwargs["base_url"] = OPENAI_BASE_URL |
| 26 | self.client = OpenAI(**kwargs) |
| 27 | |
| 28 | def _default_model(self) -> str: |
| 29 | mtype = SIM_PARAMS.get("gpt_type", 0) |
| 30 | if mtype == 0: |
| 31 | return os.getenv("AGENT4EDU_OPENAI_MODEL", "gpt-3.5-turbo-1106") |
| 32 | if mtype == 1: |
| 33 | return os.getenv("AGENT4EDU_OPENAI_MODEL", "gpt-4-1106-preview") |
| 34 | return os.getenv("AGENT4EDU_OPENAI_MODEL", "gpt-4o-mini") |
| 35 | |
| 36 | def call(self, messages: list[dict[str, str]]) -> str: |
| 37 | if self.provider == "mock": |
| 38 | return self._mock_call(messages) |
| 39 | if self.provider != "openai": |
| 40 | raise ValueError(f"Unsupported LLM provider: {self.provider}") |
| 41 | if not OPENAI_API_KEY: |
| 42 | raise RuntimeError("OPENAI_API_KEY is empty. Set it or use AGENT4EDU_LLM_PROVIDER=mock.") |
| 43 | assert self.client is not None |
| 44 | resp = self.client.chat.completions.create( |
| 45 | model=self.model, |
| 46 | messages=messages, |
| 47 | temperature=0, |
| 48 | timeout=120, |
| 49 | max_tokens=2048, |
| 50 | ) |
| 51 | return resp.choices[0].message.content or "" |
| 52 | |
| 53 | def _mock_call(self, messages: list[dict[str, str]]) -> str: |
| 54 | user = "\n".join(m.get("content", "") for m in messages if m.get("role") == "user") |
| 55 | if "reflection" in user.lower() and "learning status" in user.lower(): |
| 56 | return ( |
| 57 | "The recent practice suggests a stable learning state. The learner should keep reinforcing " |
| 58 | "concepts that appeared in recent mistakes and connect them with previously practiced concepts." |
| 59 | ) |
| 60 | concept = self._extract_first_option(user) or "unknown concept" |
| 61 | return ( |
| 62 | "Task1: Yes\n" |
| 63 | f"Task2: {concept}\n" |
| 64 | "Task3: I will use the relevant concept and previous practice experience to identify the key condition, " |
| 65 | "then derive the answer step by step.\n" |