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* fix(rag): per-index chunk floors — journals and learnings were never indexed The global 200-char garbage floor (sized for code/doc chunks) discarded every templated journal note and most distilled org-memory lessons, silently: ingest returned success with zero chunks, so agent journals and learnings were never retrievable via RAG. IndexConfig now carries a per-type min_chunk_length (journals 40, learnings 80, others unchanged). * fix(mcp): git-readonly tools default project_slug from the container env Agents 404ed /api/git/status with 'Project not found: roboco' — the tools made the LLM supply the slug and six doc examples taught a slug that matches no registered project. The tools now fall back to the ROBOCO_PROJECT_SLUG the orchestrator already injects, and the stale examples are corrected. * feat(rag): startup backfill re-ingests zero-chunk journals and learnings Before the per-index chunk-floor fix, ingest() returned success with chunk_count=0 for undersized content: every historical journal entry and distilled learning below the (then-global) 200-char floor was durably recorded in journal_entries but silently never got a chunks_journals / chunks_learnings row, and no exception meant the existing dead-letter (rag_index_failures) never saw it either. Extends the startup reconcile (roboco/api/app.py _reconcile_rag_indexes) with a new pass: backfill_unindexed_journals (roboco/services/ rag_index_failures.py) queries journal_entries for rows missing from each vector table and re-ingests them through the same live code paths (_reindex_journal_entry / record_learning). Journals and learnings are backfilled independently since a LEARNING entry can clear the (lower) JOURNALS floor while still failing the (higher) LEARNINGS floor — a learning's doc_source is a content hash, not the entry id, so presence there is checked by hashing each candidate the same way LearningsIndexPlugin.record_learning does and batch-querying chunks_learnings for those exact sources. Bounded to 200 rows per pass per boot (converges over restarts on a larger backlog) and best-effort per row (one failure never aborts the pass). Rows still under the current floor are excluded by a length filter in the SELECT so they are never retried forever, and private entries are excluded from the JOURNALS pass exactly like the live indexing path. * test(rag): scope backfill assertions to their own rows --------- Co-authored-by: Renn F <rennf93@users.noreply.github.com>
103 lines
2.4 KiB
Markdown
103 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-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(
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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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| `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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