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Documentation Index

Fetch the complete documentation index at: https://docs.memvid.com/llms.txt

Use this file to discover all available pages before exploring further.

memvid-core exposes structured request/response types for retrieval: 
use memvid_core::{AclContext, AclEnforcementMode, AskMode, AskRequest, Memvid, SearchRequest};

let mut mv = Memvid::open_read_only("notes.mv2")?;

let search = mv.search(SearchRequest {
    query: "Stardust".into(),
    top_k: 5,
    snippet_chars: 200,
    uri: None,
    scope: None,
    cursor: None,
    #[cfg(feature = "temporal_track")]
    temporal: None,
    as_of_frame: None,
    as_of_ts: None,
    no_sketch: false,
    acl_context: Some(AclContext {
        tenant_id: Some("tenant-123".into()),
        subject_id: Some("matt".into()),
        roles: vec!["finance".into()],
        group_ids: vec![],
    }),
    acl_enforcement_mode: AclEnforcementMode::Enforce,
})?;

let context_only = mv.ask(
    AskRequest {
        question: "What powers the deterministic index?".into(),
        top_k: 5,
        snippet_chars: 200,
        uri: None,
        scope: None,
        cursor: None,
        start: None,
        end: None,
        #[cfg(feature = "temporal_track")]
        temporal: None,
        context_only: true,
        mode: AskMode::Lex,
        as_of_frame: None,
        as_of_ts: None,
        adaptive: None,
        acl_context: Some(AclContext {
            tenant_id: Some("tenant-123".into()),
            subject_id: Some("matt".into()),
            roles: vec!["finance".into()],
            group_ids: vec![],
        }),
        acl_enforcement_mode: AclEnforcementMode::Enforce,
    },
    None,
)?;
  • Memvid::search(SearchRequest) performs lexical retrieval and returns a SearchResponse with the selected engine
  • Memvid::ask(AskRequest, Option<&impl VecEmbedder>) runs retrieval + optional synthesis (set context_only: true to only fetch context)
  • Memvid::vec_search_with_embedding(...) performs pure vector search when you already have a query embedding (and validates dimensions)
search() and ask() require the lex crate feature (enabled by default). For semantic-only or hybrid ranking, pass a VecEmbedder implementation (or use vec_search_with_embedding with a precomputed query vector).

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Permission-Aware Retrieval (ACL)

Use acl_context + acl_enforcement_mode to enforce tenant isolation and RBAC at retrieval time. See Permission-Aware Retrieval (ACL).
Tip: If you want to plug in an LLM, use AskResponse.retrieval.context as the prompt context and keep context_only: true in the core call.