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

tools/README.md

@@ -0,0 +1,83 @@
+# Reasoning Gym Tools
+
+This directory contains additional tools for working with Reasoning Gym:
+
+## Server
+
+A FastAPI server that manages reasoning gym experiments, allowing runtime configuration and monitoring.
+
+### Starting the Server
+
+1. Install server dependencies:
+```bash
+pip install -e ".[server]"
+```
+
+2. Set the API key environment variable:
+```bash
+export REASONING_GYM_API_KEY=your-secret-key
+```
+
+3. Start the server:
+```bash
+uvicorn tools.server.server:app
+```
+
+The server will be available at http://localhost:8000. You can access the API documentation at http://localhost:8000/docs.
+
+## RGC (Reasoning Gym Client)
+
+A command-line interface for interacting with the Reasoning Gym server.
+
+### Installation
+
+```bash
+pip install -e ".[cli]"
+```
+
+### Usage
+
+First, set the API key to match your server:
+```bash
+export REASONING_GYM_API_KEY=your-secret-key
+```
+
+Then you can use the CLI:
+
+```bash
+# List all commands
+rgc --help
+
+# List experiments
+rgc experiments list
+
+# Create a new experiment interactively
+rgc experiments create my-experiment
+
+# Create from config file
+rgc experiments create my-experiment -f config.yaml
+
+# Show experiment details
+rgc experiments show my-experiment
+
+# Edit dataset configuration
+rgc config edit my-experiment chain_sum
+```
+
+### Example Configuration File
+
+Here's an example `config.yaml` for creating an experiment:
+
+```yaml
+size: 500
+seed: 42
+datasets:
+  chain_sum:
+    weight: 1.0
+    config:
+      min_terms: 2
+      max_terms: 4
+      min_digits: 1
+      max_digits: 2
+      allow_negation: false
+```