QilbeeDB documentation
Build agent applications with durable memory, scoped retrieval and traceable learning records. QilbeeDB stores evidence and retrieves it under explicit identity and revision contracts. Your application chooses the language model, generates embeddings externally and decides how retrieved evidence is used.
Start building#
- Quickstart: create a memory and retrieve it with a scoped credential.
- Authentication and scopes: understand tenants, resource grants and private subjects.
- Memory API: create, update and delete records with durable receipts and revision checks.
- Local Docker deployment: run the platform and locate its API reference.
Choose a retrieval mode#
| Mode | Input | Ranking signal | Use when |
|---|---|---|---|
| Lexical | Text | BM25 | Exact words and identifiers matter, or no embedding is available |
| Semantic | External vector and model identity | Cosine | Meaning should be matched through your selected embedding model |
| Hybrid, experimental | Text, external vector and ranking version | Weighted reciprocal rank fusion | You want to evaluate complementary lexical and semantic candidates |
Each mode isolates the authorized tenant, project, agent, mission and private subject before candidate selection. Responses distinguish ranking scores from probabilities and disclose bounded scan coverage. The hybrid endpoint uses an immutable server-owned profile; it does not change the cosine score returned by the semantic endpoint.
Evaluate retrieval on frozen, judged queries before adopting a ranking version. Report retrieval quality separately from end-to-end agent task outcomes.
Build learning workflows#
Experience receipts, introduced in the 0.7.0 contract, preserve execution intent, authenticated observations and unknown resource consumption. They do not automatically qualify a procedure.
Procedural learning records proposals, evaluation evidence and publication decisions. Learned tools add immutable artifacts, executor profiles and a durable development ledger. Workers execute tool development outside the database process. These contracts make outcomes observable; they do not guarantee that every learned procedure improves an agent or that a generated program is safe merely because it was recorded.
Use the exact deployed contract#
The server publishes its OpenAPI document at /openapi.json and an interactive
reference at /docs. On the local Docker host, open
http://localhost:7474/docs. Check /health for the
running version. Release-stage metadata in these Markdown sources distinguishes
preview documentation from a validated deployment.
The platform rejects unknown request fields. Vector attachment, cosine search and hybrid search accept JSON bodies up to 2 MiB; other routes retain the 65536-byte limit. External vector spaces support 1–32768 dimensions, including 3072, subject to the operator capacity configuration. Start with the HTTP contract and errors when integrating a new client. The current default router covers the documented platform endpoints; legacy graph and memory routes are a separate compatibility surface.
Evaluate experience evidence explains how to freeze observations, preserve incomplete outcomes and compare agent tasks.
Use the memory change feed to reconcile scoped caches and resume from a durable cursor after disconnection.
Use memory review to record decisions and exclude rejected revisions while preserving historical evidence.
Use derived memories to keep conclusions dependent on exact source revisions and transitive source eligibility, with bounded diagnostics.
Use consumer checkpoints to persist each subject’s feed progress and reconnect safely after a process restart.