A demonstration of Vectorless's ability to perform deep reasoning on complex technical documents.

This project compiles a realistic quantum computing research report and asks questions that require:

• Cross-referencing information across distant sections
• Tracing dependency chains between different entities
• Extracting details buried in nested structures
• Multi-step reasoning beyond simple keyword matching

Requires the Vectorless Python SDK:

pip install vectorless

Set your LLM API credentials and run:

LLM_API_KEY=sk-xxx LLM_MODEL=gpt-4o LLM_ENDPOINT=https://api.openai.com/v1 python main.py

1. Refrigerator cost & location — Connects Lab B's characterization requirements with Lab A's equipment specs and capital expenditure data

2. Materials dependency — Traces how Lab C's error correction milestone depends on Lab A's materials science improvement

3. Firmware bug impact — Calculates affected qubits by connecting Lab D's incident report with Lab A's hardware configuration

4. Gap to target — Computes the difference between current achievement and future goals using derived values

5. Revenue coverage — Evaluates whether a single revenue source can cover projected capital needs

Apache-2.0