Workspace RAG

Workspace RAG

Index project documents into the Soapbox RAG system and run semantic searches across them. The RAG index persists across sessions and is shared with all agents in the org.

Required: Soapbox RAG (provides rag_ingest, rag_search, rag_list, rag_delete)
Optional: .audette-config.json (for workspace scoping)

When to Use RAG vs. Direct File Reading

Step 1: Check What's Already Indexed

Call rag_list with the workspace_id from .audette-config.json (if available):

rag_list(workspace_id: "ws_abc123")

If the document you need is already indexed and recent, skip to Step 3. If the project has never been indexed, proceed to Step 2.

Step 2: Ingest Documents

Call rag_ingest for each relevant file. Supported types: PDF, XLSX, DOCX, TXT, MD, CSV.

rag_ingest(
  file_path: "/absolute/path/to/document.pdf",
  workspace_id: "ws_abc123",   // from .audette-config.json, or omit for org-wide
  source: "agent"
)

Returns: { documentId, chunkCount, skipped } — skipped: true means the file was already indexed with the same content (idempotent).

Batch indexing: To index an entire project folder, list the files first and call rag_ingest for each supported file. Skip images and binary files.

workspace_id: Always pass the workspace_id from .audette-config.json to scope documents to the current project. Omit only for org-wide reference documents.

Step 3: Search

Call rag_search with a natural language query:

rag_search(
  query: "what type of heating system does the building have",
  workspace_id: "ws_abc123",
  limit: 5
)

Returns: { results: [{ content, filename, workspaceId, score }] }

Search finds semantically relevant chunks — it doesn't need exact keyword matches. Good queries:

• "HVAC system type and installation year"
• "boiler capacity MBH"
• "year built construction type"
• "utility account number electricity"

Workspace scope: Searches the specified workspace AND org-wide documents together. Workspace results are weighted higher.

Step 4: Use the Results

Results come as text chunks with source filenames and relevance scores. For each result:

• Note the filename to understand the source
• The content is a 512-token excerpt — enough context for most extractions
• Higher score = more relevant

If results are insufficient, try:

• A different query phrasing
• Ingesting additional documents (Step 2)
• Increasing limit (max 20)

Step 5: Keep the Index Fresh

After adding new documents to the workspace:

• Call rag_ingest for each new file — already-indexed files are skipped automatically
• No need to delete and re-index unless a file's content changed

To remove a document:

rag_delete(document_id: "uuid-of-document")

Integration with Other Skills

workspace-setup calls this skill automatically after initialization to index the project folder.

audette-energy-data can search RAG for utility bill data instead of reading files directly, when documents are already indexed.

audette-equipment-survey uses RAG to find equipment details in PCNAs and CNAs.

system-details calls rag_search to pull context from documents before synthesizing a building systems report.

Rules

• Always use the workspace_id from .audette-config.json — never mix org-wide and workspace-scoped indexes
• Ingest before searching if the workspace has never been indexed
• rag_ingest is idempotent — safe to call repeatedly on the same file
• Don't ingest image files, videos, or binaries — they produce no text
• For sensitive documents, confirm with the user before indexing