f9f6bc01fd
CI / test (push) Canceled after 0s
## Summary epic r04 - begin refactor ## Change Type - [x] Cowork feature - [ ] Bug fix - [ ] Core AI contribution - [ ] Test / hardening - [ ] Performance - [ ] Documentation ## Related Work Cowork Task: Core Repo: http://34.143.229.138/gitea-admin/fsg-ai-core-assets Core AI Issue: Core Task: Related PR: ## Scope What is intentionally included? What is intentionally NOT included? ## Validation - [ ] Unit tests - [ ] Integration tests - [ ] Manual verification - [ ] Regression check Commands / evidence: ## Security Impact Permission / credential / network / customer data impact: ## Compatibility - [ ] No breaking change - [ ] Breaking change documented ## Reviewer Notes Anything Cowork reviewers should pay attention to. --------- Co-authored-by: Anh Tran Nguyen Minh <anhtnm1@fpt.com> Co-authored-by: Huong Le Thi Thien <huongltt35@fpt.com> Co-authored-by: Nam Pham Dinh Thanh <nampdt@fpt.com> Co-authored-by: Vu Dam Tuan <vudt15@fpt.com> Co-authored-by: Hiep Ha Van <hiephv3@fpt.com> Co-authored-by: Lam Hoang Van <lamhv7@fpt.com> Reviewed-on: #7 Co-authored-by: Duy Le Huu <duylh19@fpt.com>
95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
"""A thin, unified calling surface over the app's existing Provider layer.
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The task asks for a ``clients.py`` abstraction that talks to Anthropic / OpenAI
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behind one interface. This app **already has** that — ``providers/`` with
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``build_provider`` and a canonical ``chat()`` that streams text and returns the
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final assistant message. Rather than duplicate it (and re-solve TLS trust,
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429-retry, gateway config…), this module adapts it to the shape the prober
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wants: a single blocking ``complete()`` that returns text + token estimate.
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Tests inject a fake :class:`ProbeClient` so assessment never hits a real API.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any, Dict, List, Optional, Protocol
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@dataclass
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class CompletionResult:
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"""Outcome of one non-streaming completion used for probing."""
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text: str = ""
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tokens_out: int = 0
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error: Optional[str] = None
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@property
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def ok(self) -> bool:
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"""Lượt dò có thành công không (không có lỗi)."""
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return self.error is None
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class ProbeClient(Protocol):
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"""Minimal interface the prober/judge depend on (so they're mockable)."""
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def complete(
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self,
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provider: str,
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model_id: str,
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messages: List[Dict[str, Any]],
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) -> CompletionResult:
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"""Gọi một model và trả về kết quả kèm số token, độ trễ và lỗi (nếu có)."""
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...
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def _estimate_tokens(text: str) -> int:
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"""Rough output-token count. Uses the app's estimator when importable
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(keeps the number consistent with the usage tracker), else ~4 chars/token."""
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try:
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from ..usage_tracker import estimate_tokens
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return int(estimate_tokens(text or ""))
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except Exception: # noqa: BLE001
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return max(0, len(text or "") // 4)
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class AppProbeClient:
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"""Real :class:`ProbeClient` backed by :class:`AppContext`.
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Builds a fresh provider per call via ``ctx.build_provider_for`` — the same
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path interactive chat uses — so the internal gateway, per-host TLS trust and
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rate-limit retry all apply to assessment calls too.
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"""
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def __init__(self, ctx: Any) -> None:
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"""Giữ ``AppContext`` để dựng provider lúc cần thăm dò."""
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self.ctx = ctx
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def complete(
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self,
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provider: str,
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model_id: str,
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messages: List[Dict[str, Any]],
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) -> CompletionResult:
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"""Gọi model qua provider thật; lỗi được gói vào kết quả chứ không ném ra —
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một model hỏng không được làm dừng cả lượt chấm điểm danh mục.
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"""
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try:
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prov = self.ctx.build_provider_for(provider, model_id or None)
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# Non-streaming: no on_text/on_reasoning callbacks. cancel=None.
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result = prov.chat(messages, tools=None, on_text=None, cancel=None)
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except Exception as exc: # noqa: BLE001 — surfaced as a failed probe
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return CompletionResult(error=str(exc))
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content = ""
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if isinstance(result, dict):
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content = result.get("content") or ""
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# Strip any inline <think> block a reasoning model may have inlined.
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try:
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from ...providers.base import Provider
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content = Provider.strip_think(content)
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except Exception: # noqa: BLE001
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pass
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return CompletionResult(text=content, tokens_out=_estimate_tokens(content))
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__all__ = ["CompletionResult", "ProbeClient", "AppProbeClient"]
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