Bench data shows residual JSON-mode token runaway after the
pointer-ID switch: ~4 cases out of 66 land UNGROUNDED 0/0 at
12-15s instead of ~2-4s normal. Pattern: Hermes-3-8B emits a
valid claim object, then keeps generating whitespace / blank
lines until max_tokens (512) exhausts. The truncated payload
won't parse and the lenient pre-parser returns no claims.
Concrete instances in the latest bench (2026-04-30T19-55-11Z):
- tell me about the apollo program (3/3 samples runaway)
- tell me about the python programming language (1/3)
Fix: pass `stop=["\n\n"]` to the chat completion in JSON mode so
vLLM cuts generation at the first blank line. Well-formed JSON-
mode output never legitimately contains a blank line — Hermes
emits one object on a single line (or with simple internal
newlines), never `\n\n`. The stop sequence is the runaway
signature itself.
Plumbing:
- OpenAICompatibleClient.chat_completion: new `stop` kwarg,
injects into request payload when non-empty
- StubClient already absorbs **kwargs; no change needed
- DEFAULT_POLICY (runner) + DEFAULT_QUERY_POLICY (query) gain
`claim_lattice_json_stop_sequences = ["\n\n"]`. Folds into
governance_policy_hash so changing the stop list invalidates
prior cached records.
- Both call sites in runner.py / query.py read the policy
field and pass it only on JSON mode (pointer + quote modes
don't need it).
Defensive measure: worst case the stop sequence never fires;
best case the apollo/python residuals recover and JSON's
strict-rate climbs further.
161 lines
6 KiB
Python
161 lines
6 KiB
Python
"""Chat-completion clients.
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ChatClient is a Protocol — any object with a `chat_completion` method
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plugs in. We ship two concrete clients:
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- OpenAICompatibleClient — talks to any OpenAI-compatible /v1/chat/completions
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endpoint (vllm, llama.cpp server, ollama, TGI, hosted services).
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- StubClient — offline canned responses for tests and dry-runs. No
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network. Operation Voyeur safe.
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"""
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from __future__ import annotations
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from typing import Protocol
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class ChatClient(Protocol):
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def chat_completion(
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self,
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messages: list[dict],
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*,
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model: str,
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temperature: float = 0.1,
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max_tokens: int = 512,
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top_p: float = 1.0,
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extra_body: dict | None = None,
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) -> str:
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"""Return the assistant's text response.
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``extra_body`` is forwarded as additional fields in the JSON
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request payload — used for vLLM-specific knobs like
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``guided_json`` (constrain output to a JSON Schema at sampling
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time, eliminating SCHEMA_INVALID failures from prompt drift).
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Endpoints that don't recognize the field ignore it; the client
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passes it through opaque-ly.
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"""
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...
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class StubClient:
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"""Offline client for tests / --dry-run.
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Pass `answer=callable(messages, **kw) -> str` for dynamic stubbing.
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"""
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def __init__(self, answer="[STUB] dry-run answer; no LLM was called."):
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self._answer = answer
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self.calls: list[dict] = []
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def chat_completion(self, messages, **kwargs) -> str:
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self.calls.append({"messages": messages, "kwargs": kwargs})
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if callable(self._answer):
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return self._answer(messages, **kwargs)
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return self._answer
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# StubClient ignores extra_body — the offline path doesn't go through
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# any inference engine that would honor grammar guidance. Tests that
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# want to assert extra_body was passed should inspect `self.calls`.
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class OpenAICompatibleClient:
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"""OpenAI-compatible chat completion over HTTP.
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Default endpoint is configurable via env. Pass api_key only if the
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target requires it; uncloseai's free endpoint does not.
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Retries on transient upstream failures (HTTP 502/503/504) with
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exponential backoff. The 2026-04-30 QA-modes bench saw 19 of 66
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JSON-mode runs error out with 502 from vLLM — clustered, plausibly
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correlated with `guided_json` stressing the grammar engine. Retry
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smooths over the cluster without changing semantics: a 502 still
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fails the bench cell if all attempts exhaust, but transient bursts
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no longer dominate the error column.
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"""
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# HTTP status codes worth retrying — transient gateway/server
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# failures from a flaky upstream. 4xx codes are client errors and
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# never retried.
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_RETRY_STATUS = (502, 503, 504)
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def __init__(
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self,
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base_url: str,
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api_key: str | None = None,
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timeout: float = 60.0,
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max_retries: int = 3,
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retry_backoff_base_s: float = 0.5,
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):
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self.base_url = base_url.rstrip("/")
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self.api_key = api_key
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self.timeout = timeout
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self.max_retries = max(1, max_retries)
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self.retry_backoff_base_s = retry_backoff_base_s
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def chat_completion(
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self,
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messages: list[dict],
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*,
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model: str,
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temperature: float = 0.1,
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max_tokens: int = 512,
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top_p: float = 1.0,
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extra_body: dict | None = None,
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stop: list[str] | None = None,
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) -> str:
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import httpx
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import time as _time
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headers = {"Content-Type": "application/json"}
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if self.api_key:
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headers["Authorization"] = f"Bearer {self.api_key}"
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payload = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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"top_p": top_p,
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}
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if stop:
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payload["stop"] = list(stop)
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# extra_body merges into the payload root — vLLM accepts knobs
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# like {"guided_json": {...schema...}} or {"guided_grammar": "..."}.
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# Endpoints that don't recognize a key silently drop it.
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if extra_body:
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for k, v in extra_body.items():
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payload[k] = v
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url = f"{self.base_url}/chat/completions"
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last_exc: Exception | None = None
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for attempt in range(self.max_retries):
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try:
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with httpx.Client(timeout=self.timeout) as client:
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resp = client.post(url, headers=headers, json=payload)
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if resp.status_code in self._RETRY_STATUS and attempt < self.max_retries - 1:
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# Exponential backoff: 0.5s, 1.0s, 2.0s with the
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# default base. Last attempt raises through.
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sleep_s = self.retry_backoff_base_s * (2 ** attempt)
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_time.sleep(sleep_s)
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continue
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resp.raise_for_status()
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data = resp.json()
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return data["choices"][0]["message"]["content"]
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except httpx.HTTPStatusError as e:
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# Retry only the configured transient codes; raise others.
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if e.response.status_code in self._RETRY_STATUS and attempt < self.max_retries - 1:
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sleep_s = self.retry_backoff_base_s * (2 ** attempt)
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_time.sleep(sleep_s)
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last_exc = e
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continue
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raise
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except (httpx.ConnectError, httpx.ReadTimeout, httpx.RemoteProtocolError) as e:
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# Network-layer transient errors get the same retry.
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if attempt < self.max_retries - 1:
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sleep_s = self.retry_backoff_base_s * (2 ** attempt)
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_time.sleep(sleep_s)
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last_exc = e
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continue
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raise
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# Defensive — loop should have returned or raised.
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if last_exc is not None:
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raise last_exc
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raise RuntimeError("OpenAICompatibleClient.chat_completion: retry loop exhausted without raising")
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