Search the indexed documentation knowledge graph and return a grounded answer. Use this for questions about the libraries and tools in the corpus (for example an engine's class API or a text editor's scripting API) instead of relying on recall. The answer is generated from retrieved graph context and comes with the source files that grounded it, so prefer it over an ungrounded answer and cite the returned file paths. Expect this call to take up to several minutes on a local model; it is not a fast lookup.

Insert text documents into the LightRAG index. Indexing is a graph-extraction pass, not a cheap vector write: budget roughly a minute per chunk on a local model, and expect the corpus to stay queryable-but-partial while it runs. Identical content is de-duplicated by hash, so re-submitting an unchanged document is a no-op rather than a duplicate.

Report the state of the LightRAG instance: document counts by pipeline stage, whether an indexing job is currently running, and which model and embedding endpoints it is configured to use. Check this before a large ingest, and to distinguish 'the corpus is still indexing' from 'the corpus is empty' when a query returns nothing.