Project Proposal Canadian Government Data Intelligence System
Project Proposal: Canadian Government Data Intelligence System (CGDIS)
1. Executive Summary
The Canadian Government Data Intelligence System (CGDIS) is a proposed high-scale LLM-driven platform designed to index, sectorize, and automate the retrieval of information across all levels of Canadian government (Federal, Provincial, Municipal). By leveraging a HybridRAG (Retrieval-Augmented Generation) architecture, CGDIS aims to provide a unified, explainable, and high-performance intelligence layer over an estimated 100–300 Terabytes of government data.
2. Problem Statement
Canadian government information is currently siloed across thousands of disconnected portals and trapped in unstructured legacy formats (PDFs, scanned records, complex HTML).
- Scalability: Brute-force scraping is unsustainable and often violates terms of service.
- Precision: Standard vector-based RAG lacks the hierarchical awareness to accurately “sectorize” data across complex jurisdictions.
- Performance: High-volume document sets (100TB+) create “noise” that degrades LLM retrieval quality.
3. Proposed Solution: HybridRAG Architecture
The CGDIS will implement a “Semantic-First” pipeline that combines two distinct but complementary indexing strategies:
A. The Vector Layer (Semantic Search)
- Purpose: High-speed retrieval of unstructured text chunks based on semantic meaning.
- Tooling: Qdrant or Milvus (Vector Databases).
- Processing: Documents are converted to Semantic Markdown using Unstructured.io to preserve tables and hierarchies.
B. The Knowledge Graph Layer (Structural Reasoning)
- Purpose: Enforcing strict bureaucratic taxonomies and sectoral relationships (e.g., Fisheries and Oceans Canada -> Aquaculture Policy -> Regional Regulation).
- Tooling: Neo4j (Graph Database).
- Logic: Every document chunk in the Vector DB is linked to a corresponding node in the Knowledge Graph, allowing the LLM to perform multi-hop reasoning across jurisdictions.
4. Implementation Strategy
We will execute in four distinct phases to ensure sustainability and performance:
| Phase | Milestone | Objective |
|---|---|---|
| Phase 1 | Metadata MVP | Harvest metadata from CKAN APIs (Federal, ON, BC, etc.) to build the initial Graph taxonomy. |
| Phase 2 | Unstructured ETL | Deploy high-scale PDF-to-Markdown pipelines (Marker/Unstructured.io) for active policy documents. |
| Phase 3 | Hybrid RAG Core | Integrate LlamaIndex to fuse Vector and Graph layers for unified querying. |
| Phase 4 | Incremental Scale | Shift to a 24/7 sustainable update model monitoring sitemaps and API activity feeds. |
5. Technical Requirements & Tooling
- Ingestion:
ckanapi(Python),Spider(Distributed Crawling). - Transformation: Unstructured.io (VLM-based PDF parsing).
- Orchestration: LlamaIndex or LangChain.
- Storage: 100TB+ Object Storage (S3-compatible) + Managed Vector/Graph DBs.
6. Sustainability & Risk Mitigation
- Legal Compliance: Strictly API-first ingestion to respect GoC Terms and Conditions and
robots.txt. - Data Privacy: Automated PII (Personally Identifiable Information) scrubbing using LLM-based entity recognition before indexing.
- Cost Management: Use of Content Hashing to ensure no document is ever embedded or processed more than once.
7. Conclusion
The CGDIS represents a leap forward in public sector data accessibility. By moving beyond simple search and into Autonomous Sectorization, this system will allow researchers, policy analysts, and citizens to query the entirety of the Canadian government’s collective knowledge with unprecedented speed and accuracy.
Related Documents:
- [[Canadian Government Data Indexing]] (Technical Research)
- [[Canadian Government Data RAG Roadmap]] (Tools & Links)
- [[Retrieval-Augmented Generation (RAG)]]
- [[Vector Databases]]