Struct PutArgs 

pub struct PutArgs {

    pub file: PathBuf,
    pub json: bool,
    pub input: Option<String>,
    pub uri: Option<String>,
    pub title: Option<String>,
    pub timestamp: Option<i64>,
    pub track: Option<String>,
    pub kind: Option<String>,
    pub video: bool,
    pub transcript: bool,
    pub tags: Vec<(String, String)>,
    pub labels: Vec<String>,
    pub metadata: Vec<String>,
    pub no_auto_tag: bool,
    pub no_extract_dates: bool,
    pub no_extract_triplets: bool,
    pub audio: bool,
    pub transcribe: bool,
    pub audio_segment_seconds: Option<u32>,
    pub embedding: bool,
    pub no_embedding: bool,
    pub embedding_vec: Option<PathBuf>,
    pub embedding_vec_model: Option<String>,
    pub vector_compression: bool,
    pub contextual: bool,
    pub contextual_model: Option<String>,
    pub tables: bool,
    pub no_tables: bool,
    pub extraction_budget_ms: Option<u64>,
    pub clip: bool,
    pub no_clip: bool,
    pub update_existing: bool,
    pub allow_duplicate: bool,
    pub temporal_enrich: bool,
    pub memory_id: Option<MemoryId>,
    pub logic_mesh: bool,
    pub parallel_segments: bool,
    pub no_parallel_segments: bool,
    pub parallel_segment_tokens: Option<usize>,
    pub parallel_segment_pages: Option<usize>,
    pub parallel_threads: Option<usize>,
    pub parallel_queue_depth: Option<usize>,
    pub raw: bool,
    pub no_raw: bool,
    pub dedup: bool,
    pub enrich: bool,
    pub no_enrich: bool,
    pub lock: LockCliArgs,
}

Arguments for the put subcommand

Fields

file: PathBuf

Path to the memory file to append to

json: bool

Emit JSON instead of human-readable output

input: Option<String>

Read payload bytes from a file instead of stdin

uri: Option<String>

Override the derived URI for the document

title: Option<String>

Override the derived title for the document

timestamp: Option<i64>

Optional timestamp to store on the frame. Accepts epoch seconds (1673740800) or human-readable dates: “Jan 15, 2023”, “2023-01-15”, “01/15/2023”

track: Option<String>

Track name for the frame

kind: Option<String>

Kind metadata for the frame

video: bool

Store the payload as a binary video without transcoding

transcript: bool

Attempt to attach a transcript (Dev/Enterprise tiers only)

tags: Vec<(String, String)>

Tags to attach in key=value form

labels: Vec<String>

Free-form labels to attach to the frame

metadata: Vec<String>

Additional metadata payloads expressed as JSON

no_auto_tag: bool

Disable automatic tag generation from extracted text

no_extract_dates: bool

Disable automatic date extraction from content

no_extract_triplets: bool

Disable automatic triplet extraction (SPO facts as MemoryCards)

audio: bool

Force audio analysis and tagging when ingesting binary data

transcribe: bool

Transcribe audio using Whisper and use the transcript for text embedding Requires the ‘whisper’ feature to be enabled

audio_segment_seconds: Option<u32>

Override the default segment duration (in seconds) when generating audio snippets

embedding: bool

Compute semantic embeddings using the installed model Increases storage overhead from ~5KB to ~270KB per document

no_embedding: bool

Explicitly disable embeddings (default, but kept for clarity)

embedding_vec: Option<PathBuf>

Path to a JSON file containing a pre-computed embedding vector (e.g., from OpenAI) The JSON file should contain a flat array of floats like [0.1, 0.2, …]

embedding_vec_model: Option<String>

Embedding model identity for the provided --embedding-vec vector.

This is written into per-frame extra_metadata (memvid.embedding.*) so that semantic queries can auto-select the correct runtime across CLI/SDKs.

Options: bge-small, bge-base, nomic, gte-large, openai, openai-small, openai-ada, nvidia (also accepts canonical IDs like text-embedding-3-small and BAAI/bge-small-en-v1.5).

vector_compression: bool

Enable Product Quantization compression for vectors (16x compression) Reduces vector storage from ~270KB to ~20KB per document with ~95% accuracy Only applies when –embedding is enabled

contextual: bool

Enable contextual retrieval: prepend LLM-generated context to each chunk before embedding This improves retrieval accuracy for preference/personalization queries Requires OPENAI_API_KEY or a local model (phi-3.5-mini)

contextual_model: Option<String>

Model to use for contextual retrieval (default: gpt-4o-mini, or “local” for phi-3.5-mini)

tables: bool

Automatically extract tables from PDF documents Uses aggressive mode with fallbacks for best results

no_tables: bool

Disable automatic table extraction (when –tables is the default)

extraction_budget_ms: Option<u64>

Time budget for text extraction per document (milliseconds) When set, extraction stops early and queues the frame for background enrichment Use memvid process-queue to complete extraction later

clip: bool

Enable CLIP visual embeddings for images and PDF pages Allows text-to-image search using natural language queries Disabled by default; use –clip to enable

no_clip: bool

Disable CLIP visual embeddings (no-op, CLIP is disabled by default)

update_existing: bool

Replace any existing frame with the same URI instead of inserting a duplicate

allow_duplicate: bool

Allow inserting a new frame even if a frame with the same URI already exists

temporal_enrich: bool

Enable temporal enrichment: resolve relative time phrases (“last year”) to absolute dates Prepends resolved temporal context to chunks for improved temporal question accuracy Requires the temporal_enrich feature in memvid-core

memory_id: Option<MemoryId>

Bind to a dashboard memory ID (UUID or 24-char ObjectId) for capacity and tracking If the file is not already bound, this will sync the capacity ticket from the dashboard

logic_mesh: bool

Build Logic-Mesh entity graph during ingestion (opt-in) Extracts entities (people, organizations, locations, etc.) and their relationships Enables graph traversal with memvid follow Requires NER model: memvid models install --ner distilbert-ner

parallel_segments: bool

Use the experimental parallel segment builder (requires –features parallel_segments)

no_parallel_segments: bool

Force the legacy ingestion path even when the parallel feature is available

parallel_segment_tokens: Option<usize>

Target tokens per segment when using the parallel builder

parallel_segment_pages: Option<usize>

Target pages per segment when using the parallel builder

parallel_threads: Option<usize>

Worker threads used by the parallel builder

parallel_queue_depth: Option<usize>

Worker queue depth used by the parallel builder

raw: bool

Store raw binary content along with extracted text. By default, only extracted text + BLAKE3 hash is stored (–no-raw behavior). Use –raw when you need to retrieve the original file later.

no_raw: bool

Don’t store raw binary content (DEFAULT behavior). Only extracted text + BLAKE3 hash is stored. This flag is kept for backwards compatibility but is now the default.

dedup: bool

Skip ingestion if identical content (by BLAKE3 hash) already exists in the memory. Returns the existing frame’s ID instead of creating a duplicate.

enrich: bool

Run rules-based memory extraction after ingestion (default: enabled) Extracts facts, preferences, events, relationships from ingested content

no_enrich: bool

Disable automatic rules-based enrichment

lock: LockCliArgs

Implementations

impl PutArgs

pub fn wants_parallel(&self) -> bool

pub fn sanitized_parallel_opts(&self) -> BuildOpts

Trait Implementations

impl Args for PutArgs

fn group_id() -> Option<Id>

Report the ArgGroup::id for this set of arguments

fn augment_args<'b>(__clap_app: Command) -> Command

Append to Command so it can instantiate Self via FromArgMatches::from_arg_matches_mut

fn augment_args_for_update<'b>(__clap_app: Command) -> Command

Append to Command so it can instantiate self via FromArgMatches::update_from_arg_matches_mut

impl FromArgMatches for PutArgs

fn from_arg_matches(__clap_arg_matches: &ArgMatches) -> Result<Self, Error>

Instantiate Self from ArgMatches, parsing the arguments as needed.

fn from_arg_matches_mut( __clap_arg_matches: &mut ArgMatches, ) -> Result<Self, Error>

Instantiate Self from ArgMatches, parsing the arguments as needed.

fn update_from_arg_matches( &mut self, __clap_arg_matches: &ArgMatches, ) -> Result<(), Error>

Assign values from ArgMatches to self.

fn update_from_arg_matches_mut( &mut self, __clap_arg_matches: &mut ArgMatches, ) -> Result<(), Error>

Assign values from ArgMatches to self.