Knowledge bases

Knowledge bases store documents for retrieval-augmented generation (RAG). Internal bases use PostgreSQL pgvector plus object storage. Azure AI Search remains available as a KnowledgeBase provider backend.

Admin UI: Personal → Knowledge → Documents (requires Internal KB enabled and a PostgreSQL + pgvector deployment).

Create a base

1. Pick the owning project.
2. Choose an embedding provider (Azure or Gemini with embeddings capability) and model.
3. Optionally tune chunk size / overlap (defaults work for most corpora).
4. Save and keep the base active.

You can share a base with additional projects so teams reuse the same corpus.

Upload documents

Supported uploads typically include PDF, DOCX, XLSX/XLS, CSV, and TXT.

1. Open the base → Documents.
2. Upload one or more files.
3. Watch status: Pending → Extracting → Embedding → Indexed (or a failed state with an error).
4. Use Reindex after fixing a failure or changing embedding settings.

Use knowledge in chat

In Playground or chat integrations:

1. Select one or more knowledge bases visible to the project.
2. Ask a question; retrieved chunks are injected as context (often with citation markers).
3. Open cited blobs through the platform’s download/view helpers when links are shown.

Alternatively, admins can create a KnowledgeBase provider (KbBackend=Internal) and route queries to it like any other provider.

Tips

• Prefer sharing bases over uploading the same files twice.
• Reindex after changing the embedding model.
• Empty allow-lists on sessions/tasks usually mean “no extra restriction” — set explicit IDs when you need a hard boundary.