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https://github.com/rennf93/roboco.git
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Renn F
ecea593a51
The RAG knowledge base (indexed and queried by agents at runtime) described entire fictional MCP tool surfaces — roboco_task_*, roboco_journal_*, roboco_message_send, roboco_notify_send, roboco_agent_*, roboco_session_*, roboco_workspace_*, roboco_project_* — that don't exist, so agents searching the KB were handed invented tool names. Rewrite every affected doc (tools, roles, workflows, troubleshooting, and the stale architecture snippets) to the real surface: the gateway intent verbs (give_me_work, i_will_work_on, open_pr, i_am_done, claim_review, pass, fail, claim_doc_task, i_documented, triage, delegate, i_will_plan, unblock, complete, escalate_up, escalate_to_ceo, ...) and content tools (commit, note(scope=...), say, dm, evidence, notify*, open_session, channels). Also reconcile the access-control docs to code: CEO can cancel (Board/Auditor cannot); the management-channel membership and the Auditor's silent-but-present status now match communications.py.
104 lines
2.4 KiB
Markdown
104 lines
2.4 KiB
Markdown
# Knowledge Base Search
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**ALL agents have access to KB/RAG tools.** These are automatically available.
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## Recommended: Ask Mentor
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For most questions, use `roboco_ask_mentor`:
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```python
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roboco_ask_mentor(question="How do I handle authentication?")
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```
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It searches ALL knowledge sources and supports follow-up questions.
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## Search Types
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| Tool | Purpose | Best For |
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|------|---------|----------|
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| `roboco_ask_mentor` | Conversational help | **Most questions** |
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| `roboco_kb_search` | Semantic search | Browsing, exploration |
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| `roboco_rag_query` | AI-synthesized answer | Quick answers |
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## Semantic Search
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```python
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roboco_kb_search(
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query="rate limiting redis implementation",
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top_k=5, # Results to return
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project="roboco", # Optional project filter
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index_types=["code", "docs"] # Filter by type
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)
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```
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Returns similar content - not just keyword matches.
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## RAG Query (AI Answer)
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```python
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roboco_rag_query(
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query="How does authentication work in this codebase?",
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top_k=5
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)
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```
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Returns AI-synthesized answer with citations.
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Good for:
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- "How does X work?"
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- "What pattern should I use?"
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- "What decisions were made about Y?"
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## Mentor (Conversational)
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```python
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# First question
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response = roboco_ask_mentor(
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question="How do I handle authentication?",
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domain="coding"
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)
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# Follow-up
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roboco_ask_mentor(
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question="What about refresh tokens?",
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conversation_id=response["conversation_id"]
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)
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```
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## Index Types
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| Type | Content |
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|------|---------|
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| `code` | Source files |
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| `docs` | Documentation |
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| `conversations` | Channel discussions |
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| `journals` | Agent journal entries |
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| `errors` | Error patterns & fixes |
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| `standards` | Coding rules |
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| `decisions` | Architectural decisions |
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| `reviews` | Code review patterns |
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| `learnings` | Captured learnings |
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## Before Starting a Task
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Always search first:
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```python
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roboco_kb_search(query="implementing rate limiter")
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# Journal entries are part of the KB — filter to them with index_types:
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roboco_kb_search(query="rate limit decisions", index_types=["journals", "decisions"])
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```
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This helps you:
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- Avoid repeating mistakes
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- Find proven patterns
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- Learn from others' experiences
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## Proactive Context
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System auto-provides context when you claim:
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```python
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roboco_get_proactive_context(task_id)
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# Returns: similar_tasks, relevant_learnings, code_patterns,
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# applicable_standards, recent_decisions, known_issues
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```
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