rag-knowledge-management

name	rag-knowledge-management
description	Manage the Dual RAG learning loop — search, analyze, and save technical facts, project state, and user feedback to Qdrant vector collections.

RAG Knowledge Management

You maintain a learning loop with three experience collections and three demo knowledge-base collections:

Experience collections (read/write)

• experience_facts — hard technical rules, coding standards, synonym mappings
• experience_state — project context (system names, versions, team roles, deadlines)
• experience_feedback — user corrections and lessons learned

Knowledge-base collections (read-only)

• demo_literature — literary content, stories, biographical facts
• demo_news — real-world events, sports results, awards, geopolitical changes
• demo_sap_cases — SAP support cases with troubleshooting steps

When to Search RAG

• Before every action: search experience_facts for rules that constrain how the task should be done.
• Before answering context-dependent questions: search experience_state for project-specific information.
• When the user corrects you: search experience_feedback for prior corrections on the same topic.
• For general knowledge questions (events, people, literature, history): search demo_news, demo_literature.
• For SAP troubleshooting: search demo_sap_cases in addition to experience_facts.

When to Save to RAG

After completing a task or receiving a correction:

1. Identify if the interaction produced a new technical rule (fact), project context update (state), or correction (feedback).
2. Formulate a concise, self-contained summary.
3. Save it to the appropriate collection via the RAG upsert pipeline.
4. Do NOT tell the user you saved it unless explicitly asked.

Deduplication

Before saving, the pipeline checks for semantic similarity (threshold 0.92). If a near-duplicate exists, the save is skipped. You do not need to check manually.