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879afc14a4
* fix(board): LEARN decisions name the item, not its per-cycle index A cycle's reject reasons are rendered into the NEXT cycle's exploration prompt, but the ref recorded alongside each reason was the item's stored id (item-0/item-1) — a per-cycle index that means something different every cycle and appears nowhere the explorer can resolve. The reason survived the loop; what it was about did not. Record the item's title instead, via a shared learn_ref() helper (falls back to the id when title-less, and reads target_task_title for Scales, whose items name the live task they mutate). * chore(lint): satisfy ruff 0.16 — keyword-only signatures and markdown formatting The dev toolchain resolved ruff 0.16.0, which stabilises PLR0917 (too many positional arguments) and formats python code blocks inside markdown. Both fired repo-wide and neither had anything to do with the code they flagged. - 36 signatures gain a `*` so their tail arguments are keyword-only, and the 104 call sites that passed them positionally are converted. mypy was the safety net for the static ones; the full suite caught nine more that only bind at runtime (the MCP tool functions, whose real callers already pass named JSON arguments). - 28 markdown files reformatted by 0.16's code-block formatter. - One RUF036 (`None` mid-union) autofixed in the GitLab provider. * fix(gateway): log the reason when a verb rejects A rejected envelope rides an HTTP 200, its body is never logged, and there is no trace table — so in the access log a verb an agent could not satisfy looks identical to one that worked. On 2026-07-25 four Board Programs (Periscope, Sentinel, Scales, Barfly) each POSTed their propose verb three or four times, persisted nothing, and left their exploration tasks PENDING; the reason was unrecoverable afterwards, from the logs or from the agents' own transcripts. Log error/message/remediate/missing plus the calling agent at envelope_to_response — the one chokepoint every v1 flow and do route returns through. Success envelopes stay silent. --------- Co-authored-by: Renn F <rennf93@users.noreply.github.com>
98 lines
2.3 KiB
Markdown
98 lines
2.3 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-api", # 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(query="How does authentication work in this codebase?", top_k=5)
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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?", 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?", 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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| `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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