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5850885 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 | """Run one SQLDrift episode with an OpenAI-compatible chat endpoint.
Usage:
uv run python utilities/verbose_api_rollout.py --scenario 07_drift_column_rename
Configuration is read from the repo-local ``.env`` file:
* ``SQL_DRIFT_API_KEY``
* ``SQL_DRIFT_API_BASE_URL``
* ``SQL_DRIFT_API_MODEL``
The environment itself runs in-process, so you do NOT need to start the
OpenEnv server just to watch one episode.
"""
from __future__ import annotations
import argparse
import sys
import textwrap
from pathlib import Path
from typing import cast
# Make the repo root importable when the script is run as
# ``python utilities/verbose_api_rollout.py`` from any cwd.
_REPO_ROOT = Path(__file__).resolve().parent.parent
if str(_REPO_ROOT) not in sys.path:
sys.path.insert(0, str(_REPO_ROOT))
from openai import OpenAI
from openai.types.chat import ChatCompletionMessageParam
from models import SqlDriftAction, SqlDriftObservation
from server import SqlDriftEnvironment
from training.llm_agent import (
_TOOL_CONTRACT,
_canonicalise_action,
_parse_completion_as_action,
_summarise_tool_result,
)
from training.prompt import render_system_prompt
from utilities.env_loader import env_str
from utilities.logger import log_interaction
class ApiEpisodeAgent:
"""Minimal chat agent backed by an OpenAI-compatible endpoint."""
def __init__(
self,
*,
model: str,
api_key: str,
base_url: str,
temperature: float,
max_tokens: int,
history_turns: int,
) -> None:
self._client = OpenAI(api_key=api_key, base_url=base_url)
self._model = model
self._temperature = temperature
self._max_tokens = max_tokens
self._history_turns = max(history_turns, 1)
self._history: list[dict[str, str]] = []
self._scenario_id = "unknown"
self._system_prompt = ""
self.last_completion = ""
self.last_parsed_ok = False
def reset(self, *, scenario_id: str) -> None:
self._history = []
self._scenario_id = scenario_id
self._system_prompt = ""
self.last_completion = ""
self.last_parsed_ok = False
def act(self, obs: SqlDriftObservation) -> SqlDriftAction:
if not self._system_prompt:
self._system_prompt = self._initial_system_prompt(obs)
user_message = self._render_user_message(obs)
completion = self._generate(user_message)
action, parsed_ok = _parse_completion_as_action(completion)
self.last_completion = completion
self.last_parsed_ok = parsed_ok
self._history.append({"role": "user", "content": user_message})
self._history.append(
{
"role": "assistant",
"content": completion if parsed_ok else _canonicalise_action(action),
}
)
self._trim_history()
return action
def _initial_system_prompt(self, obs: SqlDriftObservation) -> str:
base = render_system_prompt(
scenario_id=self._scenario_id,
learned_hints=obs.learned_hints,
phase=obs.phase,
budget_steps_remaining=obs.budget_steps_remaining,
drift_fired=obs.drift_fired,
)
task_block = ""
if obs.schema_synopsis:
task_block += f"\n\nSchema synopsis:\n{obs.schema_synopsis}"
if obs.baseline_sql:
task_block += f"\n\nBaseline query:\n{obs.baseline_sql}"
return f"{base}{task_block}\n\n{_TOOL_CONTRACT}"
def _render_user_message(self, obs: SqlDriftObservation) -> str:
parts: list[str] = []
if obs.drift_fired and not self._history_mentions_drift():
parts.append("Drift has fired.")
parts.append(f"Remaining steps: {obs.budget_steps_remaining}")
tool_summary = _summarise_tool_result(obs)
if tool_summary:
parts.append(tool_summary)
if obs.learned_hints:
parts.append("Learned hints:\n" + obs.learned_hints)
else:
parts.append("Pick the next tool call.")
return "\n".join(parts)
def _history_mentions_drift(self) -> bool:
for msg in reversed(self._history):
if msg["role"] == "user":
return "Drift has fired." in msg["content"]
return False
def _generate(self, user_message: str) -> str:
messages = cast(
list[ChatCompletionMessageParam],
[{"role": "system", "content": self._system_prompt}]
+ self._history
+ [{"role": "user", "content": user_message}],
)
try:
response = self._client.chat.completions.create(
model=self._model,
messages=messages,
temperature=self._temperature,
max_tokens=self._max_tokens,
)
except Exception as exc:
log_interaction(
event_type="llm_call",
agent_id=self._agent_id(),
llm_prompt=messages,
error=repr(exc),
)
raise
content = response.choices[0].message.content
if content is None:
text = ""
elif isinstance(content, str):
text = content.strip()
else:
text = str(content).strip()
log_interaction(
event_type="llm_call",
agent_id=self._agent_id(),
llm_prompt=messages,
llm_response=text,
)
return text
def _trim_history(self) -> None:
max_messages = self._history_turns * 2
if len(self._history) > max_messages:
self._history = self._history[-max_messages:]
def _agent_id(self) -> str:
return f"api_agent:{self._scenario_id}:{self._model}"
def _parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Verbose one-episode API rollout for SQLDrift.")
parser.add_argument("--scenario", default="07_drift_column_rename")
parser.add_argument("--seed", type=int, default=7)
parser.add_argument("--difficulty", choices=("easy", "normal", "hard"), default="normal")
parser.add_argument("--budget-steps", type=int, default=25)
parser.add_argument("--max-steps", type=int, default=25)
parser.add_argument("--temperature", type=float, default=0.1)
parser.add_argument("--max-tokens", type=int, default=256)
parser.add_argument("--history-turns", type=int, default=6)
parser.add_argument("--enable-dba-oracle", action="store_true")
return parser.parse_args()
def _required_env(name: str) -> str:
value = env_str(name, "")
if not value:
raise RuntimeError(
f"Missing {name}. Set it in `.env` (see `.env.example`) or export it before running."
)
return value
def _print_header(args: argparse.Namespace, model: str, base_url: str) -> None:
print("=" * 88)
print("SQLDrift verbose API rollout")
print("=" * 88)
print(
f"scenario={args.scenario} seed={args.seed} difficulty={args.difficulty} "
f"budget_steps={args.budget_steps}"
)
print(f"model={model}")
print(f"base_url={base_url}")
def _print_reset(obs: SqlDriftObservation) -> None:
print("\n[reset]")
print(f"phase={obs.phase} budget={obs.budget_steps_remaining} drift_fired={obs.drift_fired}")
if obs.schema_synopsis:
print("\nSchema synopsis:")
print(obs.schema_synopsis)
if obs.baseline_sql:
print("\nBaseline SQL:")
print(obs.baseline_sql)
if obs.learned_hints:
print("\nLearned hints:")
print(obs.learned_hints)
def _print_step(
step: int, agent: ApiEpisodeAgent, action: SqlDriftAction, obs: SqlDriftObservation
) -> None:
print("\n" + "-" * 88)
print(f"[step {step:02d}] model completion")
print(textwrap.indent(agent.last_completion or "(empty)", " "))
print(f"[step {step:02d}] parsed_ok={agent.last_parsed_ok}")
print(f"[step {step:02d}] action={action.model_dump(mode='json')}")
print(
f"[step {step:02d}] phase={obs.phase} drift_fired={obs.drift_fired} "
f"budget={obs.budget_steps_remaining} reward={obs.reward}"
)
summary = _summarise_tool_result(obs) or "(no tool result)"
print(f"[step {step:02d}] result:")
print(textwrap.indent(summary, " "))
if obs.learned_hints:
print(f"[step {step:02d}] learned_hints:")
print(textwrap.indent(obs.learned_hints, " "))
if obs.reward_components:
print(f"[step {step:02d}] reward_components={obs.reward_components}")
def _print_final(env: SqlDriftEnvironment, obs: SqlDriftObservation) -> None:
print("\n" + "=" * 88)
print("Episode finished")
print("=" * 88)
print(f"done={obs.done} reward={obs.reward}")
print(f"reward_components={obs.reward_components}")
print(f"effective_speedup={env.effective_speedup()}")
print(f"final_state={env.state.model_dump()}")
def main() -> None:
args = _parse_args()
model = _required_env("SQL_DRIFT_API_MODEL")
base_url = _required_env("SQL_DRIFT_API_BASE_URL")
api_key = _required_env("SQL_DRIFT_API_KEY")
agent = ApiEpisodeAgent(
model=model,
api_key=api_key,
base_url=base_url,
temperature=args.temperature,
max_tokens=args.max_tokens,
history_turns=args.history_turns,
)
env = SqlDriftEnvironment()
try:
obs = env.reset(
seed=args.seed,
scenario_id=args.scenario,
difficulty=args.difficulty,
budget_steps=args.budget_steps,
enable_dba_oracle=args.enable_dba_oracle,
)
agent.reset(scenario_id=args.scenario)
_print_header(args, model, base_url)
_print_reset(obs)
for step in range(1, args.max_steps + 1):
if obs.done:
break
action = agent.act(obs)
obs = env.step(action)
_print_step(step, agent, action, obs)
_print_final(env, obs)
finally:
env.close()
if __name__ == "__main__":
main()
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