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"""JSON LLM adapters. Offline tests use ReplayClient or ScriptedClient."""
from __future__ import annotations
import json
from collections import defaultdict, deque
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any, Protocol
@dataclass(frozen=True)
class ModelResponse:
data: dict[str, Any]
raw_text: str
model: str
response_id: str | None = None
usage: dict[str, Any] = field(default_factory=dict)
class JsonLLM(Protocol):
model: str
async def generate_json(
self,
*,
system_prompt: str,
user_prompt: str,
request_id: str,
) -> ModelResponse: ...
class OpenAIJsonClient:
"""Optional OpenAI chat-completions adapter.
No API request is made until ``generate_json`` is awaited.
"""
def __init__(
self,
model: str,
*,
api_key: str | None = None,
max_completion_tokens: int = 16_000,
seed: int | None = None,
) -> None:
try:
from openai import AsyncOpenAI
except ImportError as exc:
raise RuntimeError(
"Install the optional API dependency: pip install -e '.[api]'"
) from exc
self.model = model
self._client = AsyncOpenAI(api_key=api_key)
self.max_completion_tokens = max_completion_tokens
self.seed = seed
async def generate_json(
self,
*,
system_prompt: str,
user_prompt: str,
request_id: str,
) -> ModelResponse:
kwargs: dict[str, Any] = {
"model": self.model,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
],
"response_format": {"type": "json_object"},
"max_completion_tokens": self.max_completion_tokens,
}
if self.seed is not None:
kwargs["seed"] = self.seed
response = await self._client.chat.completions.create(**kwargs)
raw = response.choices[0].message.content or "{}"
data = json.loads(raw)
usage = (
response.usage.model_dump()
if response.usage is not None and hasattr(response.usage, "model_dump")
else {}
)
return ModelResponse(
data=data,
raw_text=raw,
model=self.model,
response_id=response.id,
usage=usage,
)
class ReplayClient:
"""Replay archived call responses keyed by request ID."""
def __init__(self, path: Path, model: str = "replay") -> None:
self.model = model
payload = json.loads(path.read_text(encoding="utf-8"))
if isinstance(payload, list):
self._responses = {
str(row["request_id"]): row["response_data"] for row in payload
}
elif isinstance(payload, dict):
self._responses = payload
else:
raise ValueError("replay file must be a JSON object or list")
async def generate_json(
self,
*,
system_prompt: str,
user_prompt: str,
request_id: str,
) -> ModelResponse:
if request_id not in self._responses:
raise KeyError(f"no replay response for {request_id}")
data = self._responses[request_id]
return ModelResponse(
data=data,
raw_text=json.dumps(data, ensure_ascii=False),
model=self.model,
response_id=f"replay:{request_id}",
)
class ScriptedClient:
"""Small deterministic client for tests and offline smoke runs."""
def __init__(
self,
responses: dict[str, list[dict[str, Any]] | dict[str, Any]],
model: str = "scripted",
) -> None:
self.model = model
self.calls: list[str] = []
self._responses: dict[str, deque[dict[str, Any]]] = {}
for prefix, values in responses.items():
sequence = values if isinstance(values, list) else [values]
self._responses[prefix] = deque(sequence)
self._prefix_counts: defaultdict[str, int] = defaultdict(int)
async def generate_json(
self,
*,
system_prompt: str,
user_prompt: str,
request_id: str,
) -> ModelResponse:
self.calls.append(request_id)
prefixes = sorted(
(prefix for prefix in self._responses if request_id.startswith(prefix)),
key=len,
reverse=True,
)
if not prefixes:
raise KeyError(f"no scripted response matches {request_id}")
prefix = prefixes[0]
queue = self._responses[prefix]
if not queue:
raise IndexError(f"scripted responses exhausted for {prefix}")
data = queue.popleft()
self._prefix_counts[prefix] += 1
return ModelResponse(
data=data,
raw_text=json.dumps(data, ensure_ascii=False),
model=self.model,
response_id=f"scripted:{prefix}:{self._prefix_counts[prefix]}",
)
|
