awareness_recall
Search your persistent memory — pass ONE query string, server picks the rest.
Call this BEFORE starting work to avoid re-solving solved problems.
PRIMARY USAGE (single-parameter, F-053):
awareness_recall(query="why did we pick pgvector over Pinecone?")
The server auto-routes across raw memories + knowledge cards + workspace graph
and picks the right detail level based on token budget. You do NOT need to
choose scope, recall_mode, detail, or the vector/bm25 weight manually.
LEGACY (still works, logs deprecation):
awareness_recall(semantic_query="...", keyword_query="...", detail="summary")
awareness_recall(detail="full", ids=["id1", "id2"])
Args:
query: Natural-language query (single-parameter API — preferred).
limit: Max results (1-30, default 6).
hyde_hint: F-060 optional HyDE boost. A short (100-200 char) hypothetical
answer to the query. When provided and >= 20 chars, the server embeds
it instead of the raw query to bridge the query-vs-document gap.
Client-generated (no LLM call in the backend); empty/absent disables.
[DEPRECATED] legacy args (accepted for backwards compat):
semantic_query / keyword_query: Use `query` instead.
scope / recall_mode / detail / ids / multi_level / cluster_expand /
include_installed / source_exclude / token_budget: Server now picks these.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| ids | No | ||
| limit | No | ||
| query | No | ||
| scope | No | all | |
| detail | No | ||
| api_key | No | ||
| user_id | No | ||
| hyde_hint | No | ||
| memory_id | No | ||
| agent_role | No | ||
| session_id | No | ||
| bm25_weight | No | ||
| multi_level | No | ||
| recall_mode | No | hybrid | |
| token_budget | No | ||
| keyword_query | No | ||
| vector_weight | No | ||
| cluster_expand | No | ||
| semantic_query | No | ||
| metadata_filter | No | ||
| include_installed | No | ||
| include_raw_chunks | No | ||
| reconstruct_chunks | No | ||
| confidence_threshold | No | ||
| document_graph_expand | No |