Blame for README.md

---

title: Invoice Guard Environment Server
emoji: 📋
colorFrom: purple
colorTo: yellow
sdk: docker
pinned: false
app_port: 8000
base_path: /web
tags:
  - openenv
---


# InvoiceGuard -- Three-Way Invoice Matching Environment

An OpenEnv environment that simulates accounts payable exception resolution. An AI agent investigates multi-document business cases -- invoices, purchase orders, goods receipt notes, vendor profiles, and company policies -- to detect discrepancies, classify exception types, and render correct decisions.

## Motivation

Three-way invoice matching is one of the most common and error-prone tasks in enterprise finance. Accounts payable teams manually compare invoices against purchase orders and goods receipt notes to detect overbilling, partial shipments, duplicate submissions, and price variances. This environment turns that real-world workflow into a structured evaluation benchmark where an AI agent must gather evidence through sequential investigation actions and reach a correct, policy-compliant decision.

## Tasks

| Task ID | Description | Difficulty | Expected Decision | Exception Type |
|---------|-------------|------------|-------------------|----------------|
| `task_1_clean_match` | All documents align within tolerance | Easy | `approve_for_payment` | `clean_match` |
| `task_2_partial_receipt` | Billed quantity exceeds received quantity | Moderate | `place_on_hold` | `partial_receipt` |
| `task_3_price_variance` | Unit price exceeds PO price beyond tolerance | Moderate | `escalate_for_supervisor_review` | `price_mismatch` |
| `task_4_duplicate_invoice` | Previously processed invoice resubmitted | Hard | `reject_invoice` | `duplicate_invoice` |
| `task_5_mixed_discrepancy` | Invoice with both price variance and partial receipt; conflicting signals | Hard | `escalate_for_supervisor_review` | `price_mismatch` |
| `task_6_false_positive_duplicate` | Invoice looks like a duplicate but is a legitimate recurring order for a different PO | Hard | `approve_for_payment` | `clean_match` |
| `task_7_retroactive_price` | Vendor applied a price increase retroactively; PO predates the effective date | Hard | `escalate_for_supervisor_review` | `price_mismatch` |
| `task_8_split_invoice_pattern` | Supplier splits large order into sub-threshold invoices to dodge auto-approval | Hard | `escalate_for_supervisor_review` | `policy_violation` |
| `task_9_clean_from_risky_vendor` | Clean invoice from high-risk vendor with 5 prior incidents -- false-positive trap | Hard | `approve_for_payment` | `clean_match` |
| `task_10_rounding_false_alarm` | Invoice total off by $0.01 due to line-item rounding -- all else matches perfectly | Hard | `approve_for_payment` | `clean_match` |
| `task_11_authorized_overship` | GRN shows 110 received vs 100 ordered, but PO amendment authorized 10% overship | Hard | `approve_for_payment` | `clean_match` |
| `task_12_corrected_resubmission` | Corrected invoice (INV-R1) looks like a duplicate of rejected original | Hard | `approve_for_payment` | `clean_match` |

Each task includes fully synthetic business documents with deterministic ground truth and a multi-criteria grader. Tasks 5-8 test ambiguity, temporal reasoning, and cross-case pattern detection. Tasks 9-12 are false-positive traps where surface signals mislead toward rejection but deeper investigation reveals the correct answer is approval.

## Action Space

The agent has 12 available actions divided into investigation, proposal, and terminal categories.

### Investigation Actions (provide `action_type` only)



| Action | Description |

|--------|-------------|

| `inspect_invoice_line_items` | Reveal detailed invoice line items (codes, quantities, prices, totals) |
| `inspect_purchase_order` | Reveal purchase order details (ordered quantities, agreed prices) |
| `inspect_goods_receipt_note` | Reveal goods receipt note (received/accepted/rejected quantities) |
| `inspect_vendor_profile` | Reveal vendor risk tier, duplicate history, escalation thresholds |
| `inspect_policy_rules` | Reveal company matching tolerances and escalation rules |
| `check_for_duplicate_invoice` | Search case history for similar/processed invoices |
| `compare_quantity` | Compare billed vs ordered vs received quantities per line item |
| `compare_price` | Compare billed unit prices vs PO-agreed prices per line item |
| `compare_totals` | Verify subtotal consistency, PO total match, tax, and grand total |
| `summarize_findings` | Get a numbered summary of all collected findings |

### Proposal Action

| Action | Description |
|--------|-------------|
| `propose_exception_type` | Declare the suspected exception type (with `exception_type` field) |

### Terminal Action

| Action | Required Fields | Description |
|--------|----------------|-------------|
| `submit_final_resolution` | `final_decision`, `exception_type`, `evidence_references`, `explanation` | End the episode with a decision |

### Action JSON Format

```json

{"action_type": "inspect_purchase_order"}

```

```json

{

  "action_type": "submit_final_resolution",

  "final_decision": "escalate_for_supervisor_review",

  "exception_type": "price_mismatch",

  "evidence_references": ["inspect_purchase_order", "compare_price", "inspect_policy_rules"],

  "explanation": "Price variance of 10% exceeds 5% tolerance, requiring supervisor escalation per company policy."

}

```

## Observation Space

Each step returns an `InvoiceGuardObservation` with these fields:

| Field | Type | Description |
|-------|------|-------------|
| `case_id` | `str` | Unique case identifier |
| `task_id` | `str` | Which task is being evaluated |
| `difficulty` | `str` | `easy`, `moderate`, or `hard` |
| `invoice_summary` | `str` | One-line invoice overview (supplier, amount, PO ref) |
| `goal` | `str` | Natural language description of the agent's objective |
| `available_actions` | `list[str]` | Actions the agent can take |
| `revealed_documents` | `list[str]` | Documents the agent has already inspected |
| `findings` | `list[str]` | Accumulated investigation findings |
| `remaining_steps` | `int` | Steps left before timeout |
| `last_action_result` | `str` | Detailed output from the most recent action |
| `last_action_error` | `bool` | Whether the last action had an error |
| `warnings` | `list[str]` | System warnings (e.g., low steps remaining) |
| `reward` | `float` | Reward signal for the last action |
| `done` | `bool` | Whether the episode has ended |
| `metadata` | `dict` | Grader results (on episode end) |

## Reward Design

The environment provides dense, per-step rewards:

| Event | Reward |
|-------|--------|
| Reveal a new document | +0.05 |
| Useful comparison finding discrepancy | +0.10 |
| Confirm no issue (clean comparison) | +0.02 |
| Propose correct exception type | +0.15 |
| Propose wrong exception type | -0.05 |
| Summarize findings | +0.03 |
| Repeat an already-seen action | -0.02 |
| Submit correct final decision | +0.30 |
| Submit wrong final decision | -0.20 |
| Correct exception type on resolution | +0.15 |

## Grading

Episodes are scored by a deterministic grader on six weighted criteria (total = 1.0):

| Criterion | Weight | Description |
|-----------|--------|-------------|
| Decision correctness | 0.35 | Exact match = 1.0, partial credit for related decisions |
| Exception type | 0.20 | Correct classification of the exception |
| Evidence sufficiency | 0.15 | Did the agent inspect the right documents? |
| Investigation quality | 0.10 | Breadth of document review and findings |
| Explanation quality | 0.10 | Cites specific numbers, references policy, uses correct terminology |
| Efficiency | 0.10 | Completing within step budget without waste |

## Decisions

| Decision | When to use |
|----------|-------------|
| `approve_for_payment` | All matches are clean and within tolerance |
| `place_on_hold` | Billed quantity exceeds received quantity |
| `reject_invoice` | Duplicate invoice or fraudulent submission |
| `escalate_for_supervisor_review` | Price/total variance exceeds tolerance, high-value invoice |

## Setup & Usage

### Prerequisites

- Python 3.10+
- [uv](https://docs.astral.sh/uv/) (recommended) or pip
- Docker (for containerized deployment)

### Local Development

```bash

cd invoice_guard



# Install dependencies with uv

uv sync



# Start the server

uv run uvicorn server.app:app --host 0.0.0.0 --port 8000



# Validate

openenv validate

```

### Running the Baseline Agent

```bash

cd invoice_guard



# Create .env from the example

cp .env.example .env

# Edit .env with your API key and model



# Run inference

uv run python inference.py

```

### Docker

```bash

cd invoice_guard



# Build

docker build -t invoiceguard .



# Run

docker run -p 8000:8000 invoiceguard



# Run with hackathon resource constraints

docker run --cpus=2 --memory=8g -p 8000:8000 invoiceguard



# Validate against running container

openenv validate --url http://localhost:8000

```

### Deploy to Hugging Face Spaces

```bash

cd invoice_guard

openenv push

# or: openenv push --namespace my-org --private

```

## Baseline Scores (12 tasks)

| Model | Avg Score | task_1 | task_2 | task_3 | task_4 | task_5 | task_6 | task_7 | task_8 | task_9 | task_10 | task_11 | task_12 |
|-------|-----------|--------|--------|--------|--------|--------|--------|--------|--------|--------|---------|---------|---------|
| **gpt-4.1-mini** | **0.87** | 0.95 | 0.78 | 0.75 | 0.95 | 0.78 | 0.95 | 0.75 | 0.75 | 0.95 | 0.95 | 0.98 | 0.95 |
| **gpt-5.4-mini** | **0.87** | 0.98 | 0.95 | 0.73 | 0.98 | 0.75 | 0.98 | 0.75 | 0.50 | 0.95 | 0.98 | 0.98 | 0.95 |
| **gpt-4.1** | **0.79** | 0.95 | 0.75 | 0.75 | 0.47 | 0.78 | 0.95 | 0.78 | 0.75 | 0.40 | 0.95 | 0.95 | 0.95 |
| **gpt-5.4** | **0.78** | 0.95 | 0.75 | 0.70 | 0.47 | 0.75 | 0.95 | 0.78 | 0.75 | 0.40 | 0.95 | 0.95 | 0.95 |

Key observations:
- **Task 9** (clean invoice from risky vendor) is a strong false-positive trap: both gpt-4.1 and gpt-5.4 escalated instead of approving, scoring only 0.40.
- **Task 8** (split invoice pattern) tripped gpt-5.4-mini, which rejected instead of escalating (0.50).
- **Task 4** (duplicate invoice) tripped both full-size models, which escalated instead of rejecting (0.47).
- Mini models consistently outperform their larger counterparts on this benchmark, suggesting the tasks reward focused analysis over verbose reasoning.

## Project Structure

```

invoice_guard/

|---- openenv.yaml              # OpenEnv manifest

|---- pyproject.toml             # Dependencies (managed by uv)

|---- uv.lock                   # Locked dependencies

|---- Dockerfile                # Container image definition

|---- models.py                 # All data models (Action, Observation, State, entities)

|---- client.py                 # InvoiceGuardEnv client (EnvClient subclass)

|---- inference.py              # Baseline LLM agent script

|---- .env.example              # Environment variable template

|---- tasks/

|   |---- __init__.py

|   |---- definitions.py        # Synthetic case templates and ground truth

|---- graders/

|   |---- __init__.py

|   |---- scoring.py            # Deterministic multi-criteria grader

|---- server/

    |---- __init__.py

    |---- app.py                 # FastAPI application (HTTP + WebSocket)

    |---- invoice_guard_environment.py  # Core Environment implementation

```