triplets

WORK IN PROGRESS

Composable Rust crate for deterministic multi-source sampling and split persistence for ML/AI training data.

triplets is a reusable core for ML/AI training-data orchestration. It provides sampler primitives, split/state persistence, chunking and weighting mechanics, and source abstractions (DataSource, DataRecord) without tying behavior to proprietary corpora.

At a glance

triplets is for building reproducible ML/AI training batches from multiple data sources.

Compared with a static prebuilt dataset, it lets you sample at runtime while preserving deterministic behavior.

Threading model: source refresh work is parallelized per sampling call, while batch assembly remains serialized and deterministic.

Core capabilities

• Source-agnostic sampling: implement DataSource for filesystem, APIs, DBs, streams, etc.
• Runtime example generation: produce triplet/pair/text batches from recipe selectors.
• Deterministic split assignment: stable train/validation/test assignment from record IDs + seed.
• Resume support: persist sampler/split state and continue after restart.
• Bounded ingestion: refresh in controlled windows instead of loading full corpora into memory.
• Per-source progression: each source has its own cursor; sources do not need to advance in lockstep.
• Per-call concurrency: source refreshes run in parallel within a sampling call, then merge before batch assembly.

Not included

• This crate does not do semantic mining/retrieval scoring by itself.
• This crate does not guarantee semantic hardness beyond your recipes and source metadata design.
• Sources can be finite or unbounded; infinite streaming is supported but not required.

Getting started

Add triplets to a downstream crate:

cargo add triplets

To run the included examples in this repository (for exploration/contributor workflow):

cargo run --example multi_source_demo -- --help

For contributors (development check):

cargo test

Minimal shape:

1. Implement one or more DataSource backends.
2. Create SamplerConfig (chunking, recipes, split policy).
3. Open a split store (DeterministicSplitStore or FileSplitStore).
4. Construct PairSampler and register sources.
5. Call one of the batch APIs: next_triplet_batch(split), next_pair_batch(split), or next_text_batch(split).
6. Call persist_state() when you want restart-resume behavior.

Examples

From the triplets crate:

# sample triplet batches
cargo run --example multi_source_demo

# inspect CLI flags
cargo run --example multi_source_demo -- --help

# metadata-only capacity estimation
cargo run --example estimate_capacity -- --help
cargo run --example estimate_capacity

Source roots can be overridden with repeatable flags:

cargo run --example multi_source_demo -- \
  --source-root /path/to/source_1 \
  --source-root /path/to/source_2

Split-store path configuration

The multi_source_demo example persists sampler/split state by default to:

• .sampler_store/split_store.bin

You can override persistence location with either:

• --split-store-path <FILE> for an explicit file path
• --split-store-dir <DIR> to keep filename split_store.bin in a custom directory

Usage flow

Short version:

• Call sampler.next_*_batch(split) to sample batches (ingestion happens automatically).
• Call sampler.persist_state() when you want restart-resume behavior.
• Optionally call sampler.set_epoch(n) for explicit epoch control.

Step-by-step:

1. Build config + open the split store.
2. Register sources.
3. Call one of sampler.next_triplet_batch(split), sampler.next_pair_batch(split), or sampler.next_text_batch(split).
4. Call sampler.persist_state() when you want to save progress (typically at the end of an epoch, or at explicit checkpoint boundaries).
5. Optionally call sampler.set_epoch(n) for explicit epoch replay/order.

Operational notes:

• File-backed indexing is rebuilt per process/run and stored in an OS temp-backed index store.
• Persisting sampler/split state is explicit and manual.
• One split-store file shares sampler/source cursor + RNG state unless you use separate store files.
• Batch calls are thread-safe but serialized; refresh work within a call can be parallelized per source.
• Source cursors advance independently per source, so one source can continue making progress even if another source is sparse or slower.
• Refresh concurrency is per call: source refreshes run in parallel for that call, then the sampler joins all refresh threads before merging buffers (not an always-on per-source background ingest loop).
• Prefetchers smooth latency by filling bounded queues from the existing batch APIs (next_triplet_batch, next_pair_batch, next_text_batch).
• New data from streaming sources is pulled in on the next batch call.
• sampler.persist_state() is manual; skipping it means no resume state after restart.
• sampler.set_epoch(n) is an advanced override and is not required for normal resume behavior.
• IngestionManager::source_refresh_stats() exposes per-source refresh duration/records/throughput/errors.
• metrics::source_skew summarizes per-source sample imbalance for a batch.

Example:

use std::sync::Arc;
use triplets::{
  DeterministicSplitStore, PairSampler, Sampler, SamplerConfig, SplitLabel, SplitRatios,
};

# let split = SplitRatios { train: 1.0, validation: 0.0, test: 0.0 };
# let store = Arc::new(DeterministicSplitStore::new(split, 123).unwrap());
# let config = SamplerConfig::default();
let sampler = Arc::new(PairSampler::new(config, store));
// register sources...

let prefetcher = Arc::clone(&sampler).prefetch_triplet_batches(SplitLabel::Train, 4);
let batch = prefetcher.next().unwrap();
let _ = batch;

• For per-call source weighting, use next_triplet_batch_with_weights(...), next_pair_batch_with_weights(...), or next_text_batch_with_weights(...).
• Missing source ids default to 1.0; 0.0 disables a source for that call.
• Production readiness note: if len_hint drifts in streaming/append-only sources, epoch order/coverage can repeat/skip records within an epoch, even though split assignment remains deterministic.

Sampling behavior (current)

This reflects the built-in file-corpus helpers (FileCorpusIndex) used by filesystem-backed sources.

• Ingestion: next_triplet_batch(split), next_pair_batch(split), and next_text_batch(split) trigger refresh; per-source buffers refill when empty (or on force refresh).
• Memory bound: refresh/cache limits are bounded by ingestion_max_records with a floor at batch_size.
• File indexing: deterministic path ordering + deterministic index permutation for paging.
• Source ordering: round-robin by source, deterministic within-source ordering by seed/epoch.
• Splits: labels are deterministic from record_id + seed + ratios; split APIs enforce allowed_splits.
• Coverage caveat: if len_hint drifts mid-epoch in streaming backends, strict single-pass coverage is not guaranteed.
• Weights: recipe/source/chunk weights affect scaling, not deterministic ordering.
• Scale note: full scan/sort/index rebuild cost grows roughly linearly with file count and path bytes.
• Order note: index batching preserves permutation order; chunked index reads do not remove deterministic shuffling.
• Manual epoch control: sampler.set_epoch(n) resets per-source cursors and reshuffles deterministically for that epoch.
• Persisted state scope: epoch tracking is split-aware, but sampler/source cursors + RNG/round-robin state are persisted per store file.
• Triplet recipe behavior: per-source recipes are scanned from per-source round-robin hints until a match is found.
• Pair batches: derived from triplets and follow the same source/recipe selection behavior.
• Text recipes: follow per-source behavior when provided; otherwise config recipes are used.
• Oversampling: when sources run dry, cached records may be reused (no global no-repeat guarantee).

New-source implementation pattern

For any new backend (file/API/DB/stream), centralize backend configuration/state access in one helper reused by both refresh(...) and reported_record_count().

Why this matters: capacity estimates and runtime sampling stay aligned only when both methods represent the same logical corpus slice.

File-backed pattern:

fn source_index(&self) -> FileCorpusIndex {
  FileCorpusIndex::new(&self.root, &self.id)
    .with_follow_links(true)
    .with_text_files_only(true)
    .with_directory_grouping(true)
}

fn refresh(
  &self,
  cursor: Option<&SourceCursor>,
  limit: Option<usize>,
) -> Result<SourceSnapshot, SamplerError> {
  self.source_index()
    .refresh_indexable(cursor, limit, |path| self.build_record(path))
}

fn reported_record_count(&self) -> Option<u128> {
  self.source_index().indexed_record_count().ok().map(|n| n as u128)
}

If a source emits sequential IDs, implement indexable paging (IndexableSource + IndexablePager or IndexableAdapter) to avoid time-ordered ingestion bias.

Example hash-sorted refresh skeleton:

use chrono::Utc;
use std::collections::hash_map::DefaultHasher;
use std::hash::{Hash, Hasher};
use triplets::data::DataRecord;
use triplets::source::{SourceCursor, SourceSnapshot};
use triplets::SamplerError;

struct MySource {
  ids: Vec<String>,
}

impl MySource {
  fn load_record(&self, _id: &str) -> Result<DataRecord, SamplerError> {
    todo!("load record from storage")
  }

  fn stable_hash(id: &str) -> u64 {
    let mut hasher = DefaultHasher::new();
    id.hash(&mut hasher);
    hasher.finish()
  }

  fn refresh(
    &self,
    cursor: Option<&SourceCursor>,
    limit: Option<usize>,
  ) -> Result<SourceSnapshot, SamplerError> {
    let mut ids = self.ids.clone();
    ids.sort_by_key(|id| Self::stable_hash(id));
    let total = ids.len();
    let mut start = cursor.map(|c| c.revision as usize).unwrap_or(0);
    if total > 0 && start >= total {
      start = 0;
    }
    let max = limit.unwrap_or(total);
    let mut records = Vec::new();
    for idx in 0..total {
      if records.len() >= max {
        break;
      }
      let pos = (start + idx) % total;
      records.push(self.load_record(&ids[pos])?);
    }
    let next_start = (start + records.len()) % total.max(1);
    Ok(SourceSnapshot {
      records,
      cursor: SourceCursor {
        last_seen: Utc::now(),
        revision: next_start as u64,
      },
    })
  }
}

Capacity estimates

The estimate helpers compute metadata-only approximations from source-reported counts and recipe structure.

• They do not call source refresh.
• They are floor-like approximations for real chunked training.
• Effective triplet estimates use bounded assumptions (positives/negatives per anchor).

Potential future directions (optional)

These are ideas, not commitments.

• Add more backend adapters in downstream crates (APIs, DBs, manifests, streams)
• Improve strict-coverage options for drifting/streaming corpora
• Add optional split-keyed sampler cursor state in a single store file
• Extend observability hooks for ingestion latency/skew/error diagnostics

License

triplets is primarily distributed under the terms of both the MIT license and the Apache License (Version 2.0).

See LICENSE-APACHE and LICENSE-MIT for details.