| name | querying-knowledge-and-memory |
| description | Use when the agent needs to answer questions from stored documents, recall past observations or context, store new knowledge for later, or discover available knowledge bases. Triggers on any of: "search the knowledge base", "what do you know about X", "remember this", "recall what happened", "find in documents", "store this observation", "look it up", "do you remember". Covers HetaDB (human document KBs), MemoryKB (agent knowledge graph), and MemoryVG (personal episodic memory). |
Querying Knowledge and Memory
Three Systems — Know Which to Use
| System | Built by | Role |
|---|
| HetaDB | Humans (file upload) | Multimodal document knowledge bases |
| MemoryKB | Agent (text insert) | Agent's own accumulating knowledge graph |
| MemoryVG | Agent (conversation) | Fast personal memory & cross-session cache |
Base URL: http://<host>:8000
Orchestration — Always Follow This Order
When answering a knowledge question:
Step 1 — Search MemoryVG (fast personal recall)
→ Answer is clear and specific → return it, done
→ Vague or absent → continue to Step 2
Step 2 — Query HetaDB (human knowledge base)
→ Synthesised answer + citations
Step 3 — Agent decides what to store:
→ Quick recall next time? → MemoryVG add
→ Worth accumulating? → MemoryKB insert
→ Ephemeral / trivial? → skip
API Summary
HetaDB — Document Knowledge Bases
GET /api/v1/hetadb/files/knowledge-bases
→ Returns data[].name — use as kb_id. Skip KBs with status: "deleting"; all others are queryable.
POST /api/v1/hetadb/chat
{ "query": "...", "kb_id": "...", "user_id": "agent", "query_mode": "naive" }
→ Required: query, kb_id. user_id is always "agent". Read response field from result.
→ Full query mode guide: see hetadb-api.md
MemoryVG — Personal Memory Cache
POST /api/v1/hetamem/vg/search
{ "query": "...", "agent_id": "agent" }
→ agent_id is always "agent". Read results[].memory.
POST /api/v1/hetamem/vg/add
{ "messages": [{"role": "assistant", "content": "..."}], "agent_id": "agent" }
→ agent_id is always "agent". LLM extracts facts from messages. Available immediately.
MemoryKB — Agent Knowledge Graph
POST /api/v1/hetamem/kb/insert (multipart/form-data)
query=<text to add to knowledge graph>
→ Async — LightRAG graph construction takes ~200s. Do not query immediately.
POST /api/v1/hetamem/kb/query
{ "query": "...", "mode": "hybrid" }
→ Read final_answer from result.
When NOT to Use Each
| Don't use | For |
|---|
| HetaDB | Storing agent observations (no insert API) |
| MemoryKB | Quick recall — async delay makes it unsuitable as cache |
| MemoryVG | Deep multi-document reasoning — use HetaDB instead |
Full parameter reference: hetadb-api.md, hetamem-api.md
