AiHummer Knowledge / RAG
Knowledge is how AiHummer grounds answers in your documents rather than the
model’s training data. You ingest content, the agent retrieves the relevant
passages with the search_knowledge tool, and answers come back with
citations. For harder questions, the deep_research tool runs a multi-step
investigation and produces a cited report. Knowledge is administered under
/v1/admin/knowledge/* and /v1/admin/knowledge/connectors/*.
Ingesting content
You can ingest documents, PDFs and URLs into the knowledge base. Ingested content is indexed so it can be retrieved at answer time and attributed back to its source.
[!TIP] Ingest the canonical version of a document once and let agents cite it, rather than pasting long passages into prompts. Cited retrieval keeps answers verifiable and your context small.
Grounded answers and the tools
Two tools expose the knowledge base to an agent:
| Tool | What it does |
|---|---|
search_knowledge | Retrieves relevant passages and grounds the answer with citations. |
deep_research | Runs a multi-step research process across the knowledge base and produces a cited report. |
Both arrive in the turn as tool results, never as injected instructions — the same prompt-injection discipline applied everywhere in AiHummer. Answers carry citations so a reader can trace a claim back to its source.
[!NOTE]
deep_researchis for genuine multi-step questions — it costs more time and tokens than a singlesearch_knowledgecall. Reach for it when one retrieval is not enough.
Knowledge connectors
Beyond manual ingest, knowledge can be pulled from external sources via
connectors managed under /v1/admin/knowledge/connectors/*:
| Connector | Status |
|---|---|
| Google Drive (Service Account) | Live |
| Microsoft Graph | Implemented, not live-proven |
[!WARNING] The Microsoft Graph connector is implemented but has not been validated against a live tenant. Treat it as preview and verify it in your environment before relying on it in production.
[!NOTE] Notion and Slack knowledge connectors are not available — they were dropped. Do not plan around them.
Vector store and embeddings
By default, retrieval can run on an in-memory store with a hash embedder, which is enough to get started. For production-quality semantic retrieval, point AiHummer at a real vector store and embedder:
AIHUMMER_QDRANT_URL=http://localhost:6333
AIHUMMER_EMBEDDER_URL=http://localhost:8081When these are set, ingestion and search_knowledge use the external vector
store and embedder instead of the in-memory fallback.
Admin API
| Resource | Purpose |
|---|---|
/v1/admin/knowledge | Knowledge base management, including ingest |
/v1/admin/knowledge/connectors | Configure KB connectors (Drive, MS Graph) |
Where to next
- See
search_knowledgeanddeep_researchalongside every other tool in Tools and the tools catalog. - Add per-conversation long-term recall with Memory (Einstein).