Files
roboco/docs/rag/tools/kb-tools.md
T
4fb0059556 fix: journals/learnings never reached the RAG corpus + git-readonly slug 404s (#339)
* 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>
2026-07-08 16:03:00 +02:00

3.7 KiB

Knowledge Base Tools

Search and Query

Tool Purpose
roboco_kb_search Semantic search
roboco_rag_query AI-synthesized answer
roboco_ask_mentor Conversational help
roboco_kb_stats Index statistics
roboco_kb_search(
    query="rate limiting redis",
    top_k=5,
    project="roboco-api",
    index_types=["code", "docs"]
)

AI-Generated Answers

roboco_rag_query(
    query="How does authentication work?",
    top_k=5
)

Mentor (Conversational)

response = roboco_ask_mentor(
    question="How do I handle auth?",
    domain="coding"
)

# Follow-up
roboco_ask_mentor(
    question="What about refresh tokens?",
    conversation_id=response["conversation_id"]
)

Documentation Writing (Documenter, Cell PM)

# Write/update documentation (auto-dedup via RAG)
roboco_docs_write({
    "task_id": "task-uuid",
    "filename": "api-endpoints.md",
    "doc_type": "api",  # api, qa, guide, readme, changelog, architecture, design
    "title": "API Endpoints",
    "content": "# API Endpoints\n\n..."
})

# List docs for a task
roboco_docs_list(task_id="task-uuid")

# Read a doc
roboco_docs_read(path="backend/api/endpoints.md")

SMART DEDUPLICATION: roboco_docs_write searches RAG for similar existing docs. If high-similarity match found, updates instead of creating duplicate.

Bulk Indexing

# Index code (PM, Developer)
roboco_kb_index_code(
    sources=["src/**/*.py"],
    project="roboco-api"
)

# Index docs (PM, Documenter) - for bulk/explicit indexing
# Note: roboco_docs_write() auto-indexes when writing
roboco_kb_index_docs(
    sources=["docs/**/*.md"],
    project="roboco-api"
)

Error Tracking

# Search for similar errors
roboco_search_error(
    error_message="Redis connection timed out",
    context="startup"
)

# Record solution
roboco_record_error_solution(
    error_message="Redis connection timed out",
    solution="Added retry with backoff",
    worked=True
)

Decision Tracking

# Check for similar decisions
roboco_check_decision(topic="session storage")

# Record decision
roboco_record_decision(params={
    topic: "Session storage",
    decision: "Use Redis",
    rationale: "Sub-ms reads"
})

Standards & Validation

Get Standards

roboco_get_standards(domain="coding", language="python")

Domains: coding, security, workflow, architecture

Validate Action (LLM-Based)

Uses LLM to check code/context against organizational standards.

result = roboco_validate_action(
    action_type="create_endpoint",
    context="""
def create_user(email, password):
    user = User(email=email, password=password)
    db.add(user)
    return user
"""
)

Returns:

{
  "allowed": false,
  "violations": [
    {
      "rule_id": "SEC-001",
      "rule_title": "Password Hashing",
      "message": "Password stored in plaintext",
      "severity": "error",
      "suggestion": "Hash password with bcrypt before storage"
    }
  ],
  "warnings": [...],
  "relevant_standards": [...]
}

How it works:

  1. Searches KB for relevant standards based on action_type
  2. Sends standards + context to LLM for analysis
  3. Returns structured violations with fix suggestions
  4. Falls back to heuristic matching if LLM unavailable

Action types: create_endpoint, add_dependency, database_migration, auth_change, file_upload, external_api

Code Review

roboco_review_code(
    code="def handle(...):",
    file_path="src/api/auth.py",
    change_type="modify"  # add, modify, delete
)

Returns: Score (0-100), comments by severity, approval status