reasoning-gym/tools/cli/rgc/client.py
Andreas Köpf e2702092f4
reasoning-gym-server & cli tool (#154)
* feat: Add initial server structure with configuration, registry, and middleware

* feat: Add chain_sum dataset to experiment registry test

* fix: Update test_registry to use DatasetSpec for composite config validation

* refactor: Update Pydantic config to use json_schema_extra and ConfigDict

* feat: Add Pydantic models for API request/response data

* feat: Implement basic experiment management endpoints with tests

* feat: Implement composite configuration endpoints for experiments

* fix: Add missing DatasetConfigUpdate import in server.py

* refactor: Update dataset config update method to properly merge config updates

* fix: Correctly retrieve current dataset config in composite endpoint

* feat: Add basic CLI structure with experiments and config commands

* feat: Add initial CLI tool with basic experiment management commands

* refactor: Reorganize CLI package structure and fix import paths

* refactor: Implement initial CLI commands for experiment management

* feat: Implement HTTP client for Reasoning Gym server in RGC CLI tool

* fix: Move print statements inside try block to resolve SyntaxError

* fix: Resolve SyntaxError in edit_config function by adding missing except block

* feat: Add default app instance in server module for easier uvicorn startup

* docs: Add README.md with server and RGC tool documentation

* remove unused files

* refactor: Remove unsupported type annotation in registry.py

* refactor: Move ExperimentRegistry to coaching module and add Experiment class

* fix: Add missing CompositeDataset import in test_registry.py

* refactor: Implement lazy ASGI app creation for server initialization

* feat: Add health check command to RGC CLI for server connection

* feat: Add version tracking support to CompositeDataset

* feat: Add DatasetVersionManager for tracking dataset versions

* feat: Add entry_id metadata and score_answer_with_id method to CompositeDataset

* feat: Add entry_id metadata combining version and index

* fix: Resolve undefined variable by storing version_id before use

* test: Add comprehensive unit tests for score_answer_with_id() function

* test: Add comprehensive version tracking test for dataset config updates

* feat: Validate dataset weights are positive in CompositeDataset initialization

* feat: Add weight update and normalization methods to CompositeDataset

* refactor: Centralize weight normalization in CompositeDataset and allow zero-weight datasets

* feat: Add negative weight validation to CompositeDataset constructor

* feat: Add duplicate dataset name check in CompositeDataset and update test

* refactor: Move duplicate dataset name check inside dataset iteration loop

* refactor: Update CompositeDataset weight management to use config as source of truth

* refactor: Move duplicate dataset name check to CompositeConfig.validate()

* test: Update composite dataset weight test assertions and validation

* feat: Add methods to add and remove datasets in CompositeDataset

* refactor: Remove weight normalization and use unnormalized weights directly

* refactor: Remove redundant total weight check in update_dataset_weights

* feat: Add batch generation and scoring endpoints to server

* fix: Import BatchEntry in server.py to resolve undefined name error

* refactor: Update ReasoningGymDataset to use server for batch generation and scoring

* fix: Add missing List and Dict type imports

* feat: Add get_batch() and score_outputs() methods to RGClient

* test: Add unit tests for generate_batch and score_outputs endpoints

* refactor: Add DatasetVersionManager to Experiment class and CompositeDataset constructor

* feat: Add validation for base_index and batch_size in generate_batch endpoint

* refactor: Remove unused BatchRequest type from imports

* refactor: Convert models to use Pydantic exclusively

* test: Update scoring endpoint tests to use correct request model format

* refactor: Rename ScoreItem to AnswerItem and update related code

* feat: Update scoring endpoint to return ordered ScoringResponse with scores and entry_ids

* fix: Add missing ScoringResponse import in server.py

* move verl ppo sample with server into own file

* refactor: Use Pydantic models for get_batch() and score_outputs() in RGClient

* refactor: Update client methods to use Pydantic models for type safety

* refactor: Use Pydantic models for experiment and dataset config operations

* refactor: Clean up duplicate methods and improve error handling in main.py

* first bits of rg server use for verl

* refactor: Optimize scoring with single HTTP request in _score_output

* fix: Correct experiment creation with ExperimentCreate object

* grpo tests with server
2025-02-19 22:41:33 +01:00

125 lines
4.2 KiB
Python

"""HTTP client for interacting with the Reasoning Gym server."""
import os
from typing import List, Optional
import httpx
from rich.console import Console
from tools.server.models import (
AnswerItem,
BatchResponse,
DatasetConfigUpdate,
ExperimentCreate,
ExperimentList,
ExperimentResponse,
ScoringRequest,
ScoringResponse,
)
console = Console()
DEFAULT_SERVER = "http://localhost:8000"
API_KEY = os.getenv("REASONING_GYM_API_KEY", "default-key")
class RGClient:
"""Client for interacting with Reasoning Gym server."""
def __init__(self, base_url: str = DEFAULT_SERVER, api_key: str = API_KEY):
"""Initialize client with server URL and API key."""
self.base_url = base_url.rstrip("/")
self.headers = {"X-API-Key": api_key}
def _url(self, path: str) -> str:
"""Construct full URL for given path."""
return f"{self.base_url}/{path.lstrip('/')}"
def check_health(self) -> bool:
"""Check server health status."""
try:
response = httpx.get(self._url("/health"), headers=self.headers)
response.raise_for_status()
return response.json()["status"] == "healthy"
except Exception:
return False
def list_experiments(self) -> ExperimentList:
"""List all registered experiments."""
response = httpx.get(self._url("/experiments"), headers=self.headers)
response.raise_for_status()
return ExperimentList.model_validate(response.json())
def create_experiment(self, name: str, config: ExperimentCreate) -> ExperimentResponse:
"""Create a new experiment."""
response = httpx.post(
self._url("/experiments"),
headers=self.headers,
json=config.model_dump(),
)
response.raise_for_status()
return ExperimentResponse.model_validate(response.json())
def delete_experiment(self, name: str) -> None:
"""Delete an experiment."""
response = httpx.delete(
self._url(f"/experiments/{name}"),
headers=self.headers,
)
response.raise_for_status()
def get_experiment_config(self, name: str) -> ExperimentResponse:
"""Get experiment configuration."""
response = httpx.get(
self._url(f"/experiments/{name}/composite"),
headers=self.headers,
)
response.raise_for_status()
return ExperimentResponse.model_validate(response.json())
def update_dataset_config(self, experiment: str, dataset: str, config: DatasetConfigUpdate) -> None:
"""Update dataset configuration."""
response = httpx.post(
self._url(f"/experiments/{experiment}/composite/{dataset}"),
headers=self.headers,
json=config.model_dump(),
)
response.raise_for_status()
def get_batch(self, experiment: str, base_index: int, batch_size: int) -> BatchResponse:
"""Get a batch of entries from an experiment.
Args:
experiment: Name of the experiment
base_index: Starting index for the batch
batch_size: Number of entries to retrieve
Returns:
BatchResponse containing entries with questions and metadata
"""
response = httpx.get(
self._url(f"/experiments/{experiment}/batch"),
headers=self.headers,
params={"base_index": base_index, "batch_size": batch_size},
)
response.raise_for_status()
return BatchResponse.model_validate(response.json())
def score_outputs(self, experiment: str, entry_answers: List[AnswerItem]) -> ScoringResponse:
"""Score a batch of answers.
Args:
experiment: Name of the experiment
entry_answers: List of AnswerItems with entry_ids and answers to score
Returns:
ScoringResponse containing scores and entry_ids
"""
request = ScoringRequest(answers=entry_answers)
response = httpx.post(
self._url(f"/experiments/{experiment}/score"),
headers=self.headers,
json=request.model_dump(),
)
response.raise_for_status()
return ScoringResponse.model_validate(response.json())