Update README.md

README.md

@@ -1,14 +1,4 @@
-# CustomerSupportEnv
-
-> An OpenEnv-compatible reinforcement learning environment for training and evaluating AI customer support agents.
-
-[![OpenEnv](https://img.shields.io/badge/OpenEnv-1.0.0-blue)](openenv.yaml)
-[![HF Spaces](https://img.shields.io/badge/HuggingFace-Spaces-yellow)](https://huggingface.co/spaces)
-[![Docker](https://img.shields.io/badge/Docker-ready-brightgreen)](Dockerfile)
-
----
 ---
-
 title: CustomerSupportEnv
 emoji: 🎧
 colorFrom: blue
@@ -16,265 +6,6 @@ colorTo: indigo
 sdk: docker
 app_file: server.py
 pinned: false
-tags:
-
-* openenv
-* reinforcement-learning
-* customer-support
-* nlp
-
 ---
 
-## Overview
-
-**CustomerSupportEnv** simulates a real-world Tier-1 customer support workflow. An agent handles inbound support tickets by searching a knowledge base, empathising with customers, asking clarifying questions, and delivering concrete solutions — all within a multi-turn conversation.
-
-This environment is designed for:
-- Training RL agents on real-world NLP tasks
-- Benchmarking LLM-based tool-use and retrieval-augmented reasoning
-- Evaluating customer satisfaction optimisation policies
-
----
-
-## Quick Start
-
-### Docker (recommended)
-```bash
-git clone https://huggingface.co/spaces/<your-username>/customer-support-env
-cd customer-support-env
-docker build -t customer-support-env .
-docker run -p 7860:7860 customer-support-env
-```
-
-### Local
-```bash
-pip install -r requirements.txt
-uvicorn server:app --host 0.0.0.0 --port 7860
-```
-
-### Run baseline inference
-```bash
-export API_BASE_URL=https://api.openai.com/v1
-export MODEL_NAME=gpt-4o-mini
-export HF_TOKEN=sk-...
-python inference.py
-```
-
----
-
-## Environment Description
-
-Each **episode** = one customer support ticket. The agent takes a sequence of actions (turns) until it calls `resolve()` or exceeds `max_turns`.
-
-### Real-world fidelity
-- Tickets span 5 categories: **auth**, **billing**, **fulfillment**, **bug**, **sales**
-- Customers have dynamic sentiment: **positive / neutral / frustrated / angry**
-- Knowledge base retrieval is gated — agent must explicitly call `search_kb`
-- Conversation history accumulates across turns, mirroring real support tooling
-- CSAT (customer satisfaction) is a synthetic secondary objective
-
----
-
-## OpenEnv API
-
-### `POST /reset`
-```json
-{ "task_id": "task_1" }
-```
-Returns an `Observation`. Initialises a fresh episode.
-
-### `POST /step`
-```json
-{ "task_id": "task_1", "action_type": "search_kb", "payload": null }
-```
-Returns a `StepResult` containing `observation`, `reward`, `done`, `info`.
-
-### `GET /state?task_id=task_1`
-Returns the current `Observation` without advancing the environment.
-
-### `POST /grade`
-```json
-{ "task_id": "task_1" }
-```
-Returns a `GraderResult` with score (0.0–1.0), breakdown, and pass/fail.
-
-### `GET /tasks`
-Lists all task specs.
-
-### `GET /health`
-Returns `{"status": "ok"}`.
-
----
-
-## Observation Space
-
-| Field | Type | Description |
-|-------|------|-------------|
-| `ticket_id` | string | Ticket identifier (e.g. `TKT-001`) |
-| `task_id` | string | Active task (`task_1` / `task_2` / `task_3`) |
-| `status` | enum | `idle` \| `open` \| `resolved` \| `escalated` \| `timeout` |
-| `sentiment` | enum | `positive` \| `neutral` \| `frustrated` \| `angry` |
-| `priority` | enum | `low` \| `medium` \| `high` \| `urgent` |
-| `category` | enum | `auth` \| `billing` \| `fulfillment` \| `bug` \| `sales` |
-| `turn` | int | Current turn number |
-| `max_turns` | int | Maximum turns before timeout |
-| `history` | Message[] | Full conversation: `{role, text, turn}` |
-| `kb_results` | string[] | KB articles retrieved (empty until `search_kb` called) |
-| `kb_searched` | bool | Whether KB has been consulted |
-| `empathized` | bool | Whether agent expressed empathy |
-| `clarified` | bool | Whether agent asked a clarifying question |
-| `solution_offered` | bool | Whether a solution has been offered |
-| `escalated` | bool | Whether ticket was escalated |
-| `cumulative_reward` | float | Running total reward |
-| `done` | bool | Episode termination flag |
-
----
-
-## Action Space
-
-| Action | Payload | Reward | Notes |
-|--------|---------|--------|-------|
-| `search_kb` | — | **+2.0** | Retrieves KB articles for this ticket's category. Penalty −1.0 on duplicate. |
-| `empathize` | — | **+1.0** | Acknowledges customer frustration. Zero reward on repeat. |
-| `ask_clarify` | question text | **+1.0** | Requests more detail. Zero reward on repeat. |
-| `offer_solution` | solution text | **+3.0 × quality** | Solution is scored against expected keywords. Penalty −1.0 if KB not searched first. |
-| `escalate` | — | **−1.0** | Transfers to tier-2. Penalised to incentivise in-tier resolution. |
-| `resolve` | — | **+5.0 + CSAT×2** | Ends episode. Penalty −3.0 if no solution offered. |
-| `send_message` | message text | **+0.5** | Generic message. Useful for multi-turn clarification. |
-
-### Reward decomposition
-Every `Reward` object includes:
-- `total` — net step reward
-- `process_score` — correct action sequencing (0–1)
-- `quality_score` — solution quality (0–1)
-- `efficiency_score` — steps taken vs. optimal (0–1)
-- `csat_score` — synthetic customer satisfaction (0–1)
-- `penalties` — total penalties this step
-
----
-
-## Tasks
-
-### Task 1 — Easy: Resolve a Standard Auth Ticket
-- **Ticket**: TKT-001 (account lockout, frustrated customer)
-- **Max turns**: 8
-- **Optimal policy**: `search_kb → empathize → offer_solution → resolve`
-- **Max reward**: ~11.0
-- **Grader weights**: KB searched (0.30), empathy (0.25), solution quality (0.25), resolved (0.20)
-
-### Task 2 — Medium: Handle a Billing Dispute
-- **Ticket**: TKT-003 (wrong invoice amount after plan downgrade)
-- **Max turns**: 10
-- **Optimal policy**: `search_kb → ask_clarify → empathize → offer_solution → resolve`
-- **Challenge**: Generic solutions penalised; agent must cite a specific dollar credit.
-- **Grader weights**: clarify (0.20), KB (0.20), solution quality (0.30), empathy (0.15), resolved (0.15)
-
-### Task 3 — Hard: Triage a Critical Time-Sensitive Bug
-- **Ticket**: TKT-006 (data export stuck, compliance deadline tomorrow)
-- **Max turns**: 8
-- **Optimal policy**: `search_kb → empathize → ask_clarify → offer_solution → resolve`
-- **Challenge**: Two-part solution required (priority queue + partial export). Escalation is capped. Score requires urgency awareness.
-- **Grader weights**: KB (0.20), empathy (0.15), two-part solution (0.35), no escalation (0.15), resolved (0.15)
-
----
-
-## Reward Function Design
-
-The reward function encodes three business objectives simultaneously:
-
-1. **Resolution quality** — `offer_solution` reward scales with solution quality score (keyword matching against canonical solution). Forces the agent to consult the KB before improvising.
-
-2. **Process compliance** — Action sequencing is rewarded and penalised: searching KB first, empathising with high-sentiment customers, clarifying ambiguities before offering solutions.
-
-3. **Customer experience** — The CSAT bonus on `resolve` (up to +2.0) creates a secondary objective that rewards empathetic, knowledge-grounded interactions even when the base resolution is correct.
-
-### Shaped vs. sparse
-Reward is **dense** — every action produces a signal. The agent never needs to reach `resolve` to receive useful gradient. This allows value-function methods to learn efficient policies from incomplete trajectories.
-
----
-
-## Grader Specification
-
-All graders are **deterministic**: identical observations produce identical scores.
-
-- Scores are in `[0.0, 1.0]`
-- Each grader inspects the final `Observation`: flags (`kb_searched`, `empathized`, `clarified`, `solution_offered`, `escalated`, `status`) and conversation `history`
-- Solution quality is measured by keyword presence in agent turn text
-- **Pass threshold**: ≥ 0.70 on all tasks
-
----
-
-## Baseline Scores
-
-| Task | Difficulty | Model | Grader Score | Passed |
-|------|-----------|-------|-------------|--------|
-| task_1 | easy | gpt-4o-mini | 0.85 | ✓ |
-| task_2 | medium | gpt-4o-mini | 0.78 | ✓ |
-| task_3 | hard | gpt-4o-mini | 0.65 | — |
-| **avg** | | | **0.76** | |
-
----
-
-## Project Structure
-
-```
-customer_support_env/
-├── server.py              # FastAPI app — /reset, /step, /state, /grade
-├── inference.py           # Baseline inference script (OpenAI client)
-├── openenv.yaml           # OpenEnv spec file
-├── requirements.txt
-├── Dockerfile
-├── README.md
-├── env/
-│   ├── __init__.py
-│   ├── models.py          # Typed Pydantic models: Observation, Action, Reward
-│   ├── environment.py     # Core CustomerSupportEnv class
-│   └── tickets.py         # Ticket scenario database (6 tickets, KB articles)
-├── graders/
-│   ├── __init__.py
-│   └── graders.py         # Programmatic graders for all 3 tasks
-└── tests/
-    ├── __init__.py
-    └── test_env.py        # 25 unit tests
-```
-
----
-
-## Running Tests
-
-```bash
-pytest tests/ -v
-```
-
-Or without pytest:
-```bash
-python -m tests.test_env
-```
-
----
-
-## Hugging Face Space Configuration
-
-Add the following to the top of `README.md` for HF Spaces auto-detection:
-
-```yaml
----
-title: CustomerSupportEnv
-emoji: 🎧
-colorFrom: blue
-colorTo: indigo
-sdk: docker
-pinned: false
-tags:
-  - openenv
-  - reinforcement-learning
-  - customer-support
-  - nlp
----
-```
-
----
-
-## License
-
-MIT
+# CustomerSupportEnv