ge-eval 0.2.1

CLI toolkit for Gemini Enterprise Connector evaluation — init, check, run, and evaluate search quality against golden datasets.

Gemini Enterprise Connector — Evaluation Toolkit

CLI toolkit for evaluating Gemini Enterprise Connector search quality. Compares actual responses against a golden dataset, producing pass/fail grades, root cause analysis, and an interactive HTML dashboard.

Installation

Using uv (recommended)

uv pip install ge-eval

Using pip

pip install ge-eval

From source

git clone https://github.com/cloud-ai-fde/weiyih-gemini-enterprise-connector-eval.git
cd weiyih-gemini-enterprise-connector-eval
uv sync

Quick Start

# Step 1: Scaffold a working directory with sample inputs
ge-eval init

# Step 2: Edit .env with your Google Cloud project settings

# Step 3: Validate configuration
ge-eval check

# Step 4: Query the API (sends questions, gets responses)
ge-eval run

# Step 5: Run LLM-judge evaluation
ge-eval eval

# Step 6: View results in the browser
ge-eval serve
# Then open http://localhost:8080

Note: Run ge-eval init to generate an INSTRUCTION.md file with a comprehensive setup guide covering authentication, input structure, and command details.

What the Pipeline Does

ge-eval eval orchestrates 6 steps in sequence:

Step	Action	Description
1	Validate Inputs	Checks that golden dataset, CSV, and HTML folder exist; validates question alignment
2	Generate Summaries	Extracts agent trajectories from dolphin debug HTML files → outputs/summaries/
3	Run LLM Judge	Calls Gemini to evaluate all questions
4	Enrich Golden Source	Adds expected citations from golden dataset to the CSV
5	Normalize Columns	Reorders CSV to final 15-column schema
6	Generate Report	Creates outputs/G_REPORT.md with stats and detailed RCA

CLI Commands

Command	Description
ge-eval init	Scaffold a working directory with sample inputs, .env, and INSTRUCTION.md
ge-eval check	Validate config, env vars, and input file alignment
ge-eval run	Batch-query the GE streamAssist API
ge-eval eval	Run the full LLM-judge evaluation pipeline
ge-eval serve	Start a local HTTP server for the evaluation viewer
ge-eval clean	Remove all files from inputs/, outputs/, and INSTRUCTION.md

Documentation

Full documentation is available at: https://cloud-ai-fde.github.io/weiyih-gemini-enterprise-connector-eval/

After running ge-eval init, see the generated INSTRUCTION.md for a detailed setup guide including authentication, input structure, and output column schema.

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines on how to get started.

Development Setup

# Clone and install dev dependencies
git clone https://github.com/cloud-ai-fde/weiyih-gemini-enterprise-connector-eval.git
cd weiyih-gemini-enterprise-connector-eval
uv sync

# Run tests
uv run pytest tests/ -v

# Build documentation locally
uv run mkdocs serve

Running Tests

# Run all tests
uv run pytest tests/ -v

# Run specific test module
uv run pytest tests/test_ge_eval_cli.py -v

# Run with coverage
uv run pytest tests/ --cov=ge_eval -v

License

This project is licensed under the Apache License 2.0 — see the LICENSE file for details.