| |
| """Minimal API smoke test for fic-agent. |
| |
| Checks: |
| 1) LLM chat completion |
| 2) Embedding API call |
| |
| Exit code: |
| 0 = all requested checks passed |
| 1 = at least one requested check failed |
| """ |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import sys |
|
|
| from fic_agent.config import RuntimeConfig |
|
|
|
|
| def _mask_len(value: str | None) -> str: |
| if not value: |
| return "0" |
| return str(len(value)) |
|
|
|
|
| def _test_llm(cfg: RuntimeConfig) -> bool: |
| if not cfg.llm_api_key: |
| print("[LLM] FAIL: missing llm_api_key") |
| return False |
| try: |
| from openai import OpenAI |
| except Exception as e: |
| print(f"[LLM] FAIL: openai import error: {e}") |
| return False |
|
|
| try: |
| client = OpenAI(base_url=cfg.llm_base_url, api_key=cfg.llm_api_key) |
| resp = client.chat.completions.create( |
| model=cfg.llm_model, |
| messages=[ |
| {"role": "system", "content": "You are a concise assistant."}, |
| {"role": "user", "content": "Reply with exactly: API_OK"}, |
| ], |
| temperature=0.0, |
| max_tokens=20, |
| ) |
| text = (resp.choices[0].message.content or "").strip() |
| usage = getattr(resp, "usage", None) |
| total = getattr(usage, "total_tokens", None) if usage is not None else None |
| print(f"[LLM] PASS: model={cfg.llm_model} total_tokens={total} reply={text!r}") |
| return True |
| except Exception as e: |
| print(f"[LLM] FAIL: {type(e).__name__}: {e}") |
| return False |
|
|
|
|
| def _test_embedding(cfg: RuntimeConfig) -> bool: |
| if not cfg.embedding_api_key: |
| print("[EMBED] FAIL: missing embedding_api_key") |
| return False |
| try: |
| from openai import OpenAI |
| except Exception as e: |
| print(f"[EMBED] FAIL: openai import error: {e}") |
| return False |
|
|
| try: |
| client = OpenAI(base_url=cfg.embedding_base_url, api_key=cfg.embedding_api_key) |
| resp = client.embeddings.create( |
| model=cfg.embedding_model, |
| input=["api smoke test"], |
| ) |
| data = getattr(resp, "data", None) or [] |
| if not data: |
| print("[EMBED] FAIL: empty data") |
| return False |
| vec = getattr(data[0], "embedding", None) |
| dim = len(vec) if isinstance(vec, list) else 0 |
| usage = getattr(resp, "usage", None) |
| total = getattr(usage, "total_tokens", None) if usage is not None else None |
| print(f"[EMBED] PASS: model={cfg.embedding_model} dim={dim} total_tokens={total}") |
| return True |
| except Exception as e: |
| print(f"[EMBED] FAIL: {type(e).__name__}: {e}") |
| return False |
|
|
|
|
| def main() -> int: |
| parser = argparse.ArgumentParser(description="Minimal API smoke test for fic-agent") |
| parser.add_argument("--skip-llm", action="store_true", help="Skip LLM chat test") |
| parser.add_argument("--skip-embedding", action="store_true", help="Skip embedding test") |
| args = parser.parse_args() |
|
|
| cfg = RuntimeConfig() |
| print( |
| "[CFG] " |
| f"llm_base_url={cfg.llm_base_url} llm_model={cfg.llm_model} llm_key_len={_mask_len(cfg.llm_api_key)}" |
| ) |
| print( |
| "[CFG] " |
| f"embedding_base_url={cfg.embedding_base_url} embedding_model={cfg.embedding_model} " |
| f"embedding_key_len={_mask_len(cfg.embedding_api_key)}" |
| ) |
|
|
| ok = True |
| if not args.skip_llm: |
| ok = _test_llm(cfg) and ok |
| if not args.skip_embedding: |
| ok = _test_embedding(cfg) and ok |
|
|
| if ok: |
| print("API smoke test: PASS") |
| return 0 |
| print("API smoke test: FAIL") |
| return 1 |
|
|
|
|
| if __name__ == "__main__": |
| sys.exit(main()) |
|
|
|
|
