A RAG agent that answers questions against any document corpus with citations.
Organizations with large document libraries (quality manuals, standards, procedures) spend significant time manually searching for policy answers. This agent provides instant, cited answers to natural-language questions — reducing lookup time and ensuring answers are traceable to source documents.
Architecture diagram added Week 4 of Phase 1. See ARCHITECTURE.md for component breakdown.
Rather than relying on an LLM's training data (which may be outdated or hallucinated), RAG agents retrieve relevant document chunks first, then generate answers grounded in those retrieved sources. Every answer includes citations showing exactly which document sections were used.
| Metric | Target | Method |
|---|---|---|
| Retrieval Accuracy | ≥85% | 25 curated Q&A pairs |
| Citation Correctness | ≥90% | 50 spot-check citations |
| Response Time | <8s | Automated timing |
Active development begins Week 4 (Phase 1). Full demo available at Phase 1 completion.
git clone https://github.com/Jlowpez/knowledge-navigator
cd knowledge-navigator
pip install uv && uv pip install -e ".[dev]"
pytest tests/ -vReplace the document corpus in data/synthetic/ with your organization's documents. The ingestion pipeline and agent logic are unchanged — only the source files differ. The data access layer (src/dal/) abstracts all backend calls, enabling a data source swap via config change only.
→ ops-orchestrator — full platform view
| Agent | Pattern | Status |
|---|---|---|
| knowledge-navigator | RAG Agent | 🔄 Active |
| intake-agent | Structured Output | ⏳ Phase 2 |
| compliance-checker | Multi-Agent Pipeline | ⏳ Phase 3 |
| workload-scheduler | Planner-Executor | ⏳ Phase 4 |
| ops-orchestrator | Orchestrator | ⏳ Phase 5 |
- ARCHITECTURE.md — System design and data flow
- TOOLS.md — Tool selection rationale