aidp-knowledge-bases

# `aidp-knowledge-bases` — RAG corpus + vector index A Knowledge Base is AIDP's managed RAG store: it embeds source data into a vector index you can retrieve from (directly, or via a `RAG_TOOL` node in an agent flow). Runs over the **LA AgentFlows family** REST API. > **Engine:** `oci raw-request --profile DEFAULT` (no CLI group for KB in v1.0.0). **Lake-scoped**, live-verified > 2026-06-10: `GET …/dataLakes/<ocid>/knowledgeBases?catalogKey=<key>&schemaKey=<key>` → **400 InvalidParameter** > means the route exists but needs **real catalog/schema keys** (resolve via `aidp-catalog-explore`); the > workspace-scoped path 404s. Confirm with a live read before any write; record in `references/rest-endpoint-map.md`. > > **Live-verified 2026-06-10 on de-agent — correction:** a bare `GET …/knowledgeBases` (no query string) > returns **400 InvalidParameter** requiring **both** `schemaKey` and `catalogKey` query params — the route is > provisioned (lake-scoped), it is the missing params that 400, not a missing route. ## When to use - "Build a RAG / knowledge base / vector index", "embed these docs/tables", "semantic search over X", or any prerequisite for a `RAG_TOOL` agent-flow node. - NOT ad-hoc LLM-in-SQL (→ `aidp-ai-sql`); NOT the agent that *uses* the KB (→ `aidp-agent-flows` / `aidp-agent-highcode`). ## Create a KB (`CreateKnowledgeBaseDetails`, camelCase wire fields) `POST …/dataLakes/<ocid>/knowledgeBases` ```json { "displayName": "policy_kb", "description": "RAG over policy docs", "catalogKey": "<catalog-key>", "schemaKey": "<schema-key>", "workspaceKey": "<ws-key>", "clusterKey": "<cluster-key>", "type": "...", "modality": "...", "embeddingModelSourceType": "...", "embeddingModelName": "<embedding-model>", "chunkSize": 512, "chunkOverlap": 64, "sourceFilePattern": "*.pdf", "indexDetails": { "type": "HNSW", "distance": "COSINE", "neighbors": 32, "efConstruction": 200, "targetAccuracy": 95 } } ``` - `indexDetails.type` ∈ **`HNSW` | `IVF`**. **Both** index types accept the full 7-value `distance` enum: `COSINE` | `DOT` | `EUCLIDEAN` | `HAMMING` | `JACCARD` | `L2_SQUARED` | `MANHATTAN` (SDK `KbVHnswIndexDetails` / `KbVIvfIndexDetails` `distance` setter `allowed_values` — `kb_v_hnsw_index_details.py:110`, `kb_v_ivf_index_details.py:110`). Tuning values below are **illustrative**. - **HNSW tuning params** (SDK `KbVHnswIndexDetails`, camelCase wire fields, all `int` — `kb_v_hnsw_index_details.py:74-79`): | Field | Meaning | |---|---| | `neighbors` | max neighbors each vector can have on any layer (the HNSW *M* parameter) — `:146` | | `efConstruction` | max closest-vector candidates considered during index construction — `:170` | | `targetAccuracy` | target accuracy percentage 1–100 — `:122` | - **IVF tuning params** (SDK `KbVIvfIndexDetails`, camelCase wire fields, all `int` — `kb_v_ivf_index_details.py:74-79`): | Field | Meaning | |---|---| | `neighborPartitions` | number of partitions (clusters) to divide the vector data into — `:146` | | `neighborPartitionProbes` | max partitions to probe during a search (higher = more accurate, slower) — `:170` | | `targetAccuracy` | target accuracy percentage 1–100 — `:122` | - `embeddingModelName`/`SourceType` — list available embedding models via `aidp-models-catalog` (`modelType=EMBEDDING`); needs a RUNNING `clusterKey` for embedding compute. - **Verify-first:** the field *names* above are from the SDK `CreateKnowledgeBaseDetails`; the create was not round-tripped (it triggers embedding compute). Confirm the embedding-model + `type`/`modality` enums against a live read / `aidp help` before a production create — do not invent values. ## Ingest + maintain | Action | Endpoint / body | |---|---| | Add/remove a data source | `UpdateKnowledgeBaseAddSourceDetails` / `…DeleteSourceDetails` (source kind volume / table) | | Run an ingestion job | `POST …/knowledgeBases/<key>/jobs` — `CreateKnowledgeBaseJobDetails {displayName, type, goal, sources, sourceKey, schedule}`; trigger runs via `…/jobs/<key>/jobRuns` | | List job runs / status | `GET …/knowledgeBases/<key>/jobs/<key>/jobRuns` | | Permissions | `assign`/`manage`/`revoke` KnowledgeBasePermission (`aidp-roles-access` for principals) | | Update / delete KB | `PUT`/`DELETE …/knowledgeBases/<key>` | ## Wire it to an agent Once the index is built, reference the KB from a **`RAG_TOOL`** node (`aidp-agent-flows`, `references/agent-flow-nodes.md`) or from high-code (`aidp-agent-highcode`). The KB must exist + be ingested before the RAG tool can retrieve. ## Guardrails - Mutation gate: KB create/ingest consumes embedding compute — show the body, confirm first, persist to `.aidp/payloads/` ([references/payloads.md](../../references/payloads.md)). - Resolve `catalogKey`/`schemaKey`/`clusterKey` (real keys) first via `aidp-catalog-explore` / `aidp-cluster-ops`. ## References - [aidp-agent-flows](../aidp-agent-flows/SKILL.md) (RAG_TOOL consumer) · [aidp-models-catalog](../aidp-models-catalog/SKILL.md) (embedding models) · [aidp-catalog-explore](../aidp-catalog-explore/SKILL.md) (keys) - [references/oci-raw-request.md](../../references/oci-raw-request.md) · [references/rest-endpoint-map.md](../../references/rest-endpoint-map.md) · [references/payloads.md](../../references/payloads.md)