title	rag-context-optimizer
emoji	📚
colorFrom	blue
colorTo	green
sdk	docker
app_port	7860
pinned	false
tags	openenv incident-ops benchmark enterprise

rag-context-optimizer

rag-context-optimizer is now a real-world OpenEnv environment for enterprise incident operations.

Instead of only picking context chunks, the agent must work through a realistic operational loop:

• inspect incident and support artifacts
• prioritize the evidence that belongs in the working set
• summarize heavy artifacts when token pressure rises
• draft a resolution plan
• submit a grounded final memo or escalation note

This models work that support leads, incident commanders, and release managers actually do during outages and security escalations.

Why This Is A Real Environment

The environment simulates operational decisions humans make during live enterprise incidents:

• refund triage after a confirmed outage
• cross-functional outage briefing across support, incident response, and release engineering
• executive escalation handling during a suspected admin compromise

That makes it useful for:

• evaluating operational agent behavior
• training evidence prioritization policies
• benchmarking grounded reporting under token pressure
• comparing safe workflow planning against premature free-form answering

OpenEnv API

Standard endpoints:

• POST /reset
• POST /step
• GET /state

Additional helper endpoints:

• GET /health
• GET /tasks
• POST /optimize-step
• POST /optimize-prompt

Metadata lives in openenv.yaml.

Observation Space

RagObservation includes:

Field	Type	Description
case_id	str	Unique simulated case identifier
case_summary	str	Real-world case context
objective	str	Deliverable the agent must produce
workflow_stage	triage | analysis | resolution | submitted	Current stage
customer_tier	standard | business | enterprise	Customer criticality
incident_severity	sev3 | sev2 | sev1	Incident severity
available_artifacts	List[ChunkSummary]	Artifacts available for inspection or prioritization
reviewed_artifacts	List[str]	Artifacts the agent has inspected
prioritized_artifacts	List[str]	Artifacts in the working set
plan_draft	Optional[str]	Current operational plan
report_requirements	List[str]	Final memo requirements
progress_signals	Dict[str, float]	Partial progress metrics
total_tokens_used	int	Current working-set token cost
token_budget	int	Allowed token budget

Compatibility mirrors are also present for legacy clients:

• query
• available_chunks
• selected_chunks

Action Space

Canonical actions:

Action Type	Parameters	Effect
inspect_artifact	artifact_id	Review an artifact without yet committing it to the working set
prioritize_artifact	artifact_id	Add a reviewed artifact to the working set
summarize_artifact	artifact_id, compression_ratio	Compress a prioritized artifact to reduce token cost
set_resolution_plan	plan	Draft the operational plan before submission
submit_report	answer	Submit the final grounded memo and end the episode

Legacy aliases are still accepted for compatibility:

• select_chunk
• deselect_chunk
• compress_chunk
• submit_answer

Tasks

Task	Difficulty	Max Steps	Token Budget	Description
refund_triage_easy	easy	7	850	Build a refund-review memo from support policy evidence after an outage
cross_function_brief_medium	medium	8	620	Prepare a cross-functional outage brief spanning support, incident command, and release controls
executive_escalation_hard	hard	10	360	Draft a terse executive escalation note for a suspected admin compromise

Task definitions live in env/tasks.py.

Reward Design

The environment provides shaped signal across the trajectory:

• positive reward for inspecting required evidence
• positive reward for prioritizing the right artifacts
• positive reward for multi-domain coverage on cross-functional tasks
• positive reward for high-quality operational plans
• positive reward for safe token compression
• penalty for over-compressing critical evidence
• penalty for deprioritizing required artifacts
• final deterministic score in [0, 1] based on:
  • artifact coverage
  • review coverage
  • domain coverage
  • plan quality
  • report quality
  • citation accuracy
  • token efficiency
  • workflow readiness
  • unsupported claim penalty

The grader is deterministic and task-specific.

LLM-backed Helpers

The environment includes optional LLM-backed helpers:

• /optimize-step proposes the next workflow action
• /optimize-prompt rewrites prompts under budget while preserving grounding

The authoritative grader remains deterministic for reproducibility.

Local Setup

Requirements

• Python 3.11+ recommended
• Docker
• openenv-core

Install

pip install -r requirements.txt
pip install openenv-core

Run

uvicorn app:app --host 0.0.0.0 --port 7860

Validate

docker build .
openenv validate
python validate.py

API Examples

Reset

curl -X POST http://localhost:7860/reset \
  -H "Content-Type: application/json" \
  -d '{"task_name":"refund_triage_easy"}'

Inspect

curl -X POST http://localhost:7860/step \
  -H "Content-Type: application/json" \
  -d '{"action_type":"inspect_artifact","artifact_id":"support_003"}'

Prioritize

curl -X POST http://localhost:7860/step \
  -H "Content-Type: application/json" \
  -d '{"action_type":"prioritize_artifact","artifact_id":"support_003"}'

Plan

curl -X POST http://localhost:7860/step \
  -H "Content-Type: application/json" \
  -d '{"action_type":"set_resolution_plan","plan":"Verify outage evidence, confirm the billing ledger, and route exceptions to finance review."}'

Submit

curl -X POST http://localhost:7860/step \
  -H "Content-Type: application/json" \
  -d '{"action_type":"submit_report","answer":"Proceed to refund review only after outage evidence and the billing ledger are confirmed. [support_001] [support_003]"}'

Baseline Inference

The baseline runner is inference.py.

Submission-critical requirements satisfied:

• file name is exactly inference.py
• located at the project root
• uses the OpenAI client
• reads:
  • API_BASE_URL with a default
  • MODEL_NAME with a default
  • HF_TOKEN as the published credential path
  • API_KEY when validator proxy credentials are injected
• emits strict [START], [STEP], [END] stdout logs

Environment variables

Variable	Required	Default	Purpose
API_BASE_URL	no	https://router.huggingface.co/v1	OpenAI-compatible endpoint
MODEL_NAME	no	Qwen/Qwen2.5-72B-Instruct	Model used for baseline inference
HF_TOKEN	yes	none	Primary token
API_KEY	no	none	Validator-injected proxy key; overrides HF_TOKEN
RAG_ENV_URL	no	http://localhost:7860	Environment base URL
RAG_ENV_TASK	no	refund_triage_easy	Preferred starting task

Baseline Scores

Current local validation run:

Policy	refund_triage_easy	cross_function_brief_medium	executive_escalation_hard
baseline script	reproducible via python validate.py	reproducible via python validate.py	reproducible via python validate.py

Deployment

Live deployment:

• Space URL: nitishrg15102007-rag-context-optimizer.hf.space
• Space repo: NITISHRG15102007/rag-context-optimizer

Recommended pre-submission flow:

docker build .
openenv validate
python validate.py