triplets

WORK IN PROGRESS. THIS API IS BEING PROTOTYPED AND MAY CHANGE WITHOUT NOTICE.

triplets is a reusable core of composable data sampling primitives for deterministic multi-source ML/AI training-data orchestration, with sampler primitives, split/state persistence, chunking and weighting mechanics, and source abstractions (DataSource, DataRecord) that avoid tying behavior to proprietary corpora.

Note: This crate is intended primarily for textual (or textualized) data — records that can be represented as text (for example: documents, QA pairs, logs, or metadata-prefixed chunks) suitable for language-model training, embedding/metric-learning workflows, and related text-model pipelines.

CI is configured to run tests/linting on macOS, Linux, and Windows.

What are triplets?

In metric learning, a triplet is a training example composed of:

• Anchor: a reference example.
• Positive: another example that should be close to the anchor.
• Negative: an example that should be farther from the anchor.

      Anchor
      /    \
 Positive Negative

 Triplet: (Anchor, Positive, Negative)

Training on many (anchor, positive, negative) groups helps a model learn useful embedding space structure (similar items closer together, dissimilar items farther apart).

In this crate, those triplets are built automatically from one or more data sources using metadata-driven, user-defined recipes/selectors for anchor/positive/negative section choice.

It is designed for multi-source training pipelines where each batch can mix records from several sources, while source contribution is controlled independently (for example, over/under-sampling frequency and trust/quality weighting per source) to rebalance representation and reduce source-driven bias. Because source weights can be set per batch call, they can be wired to training-time loss/metric signals and adjusted dynamically during training.

High-level features

• Automatic deterministic splits (train/validation/test) from record IDs + seed.
• Sampler-seed-driven source determinism for built-in deterministic source ordering (file + Hugging Face).
• Runtime batch sampling via next_triplet_batch, next_pair_batch, and next_text_batch.
• Recipe-driven sample construction for triplet/pair/text generation (anchor/positive/negative selectors).
• Weight-aware sampling controls across source weights, recipe weights, and chunk trust/quality weighting.
• Anti-shortcut metadata-prefix variation via KvpPrefixSampler (variant choice, per-field presence probabilities, field-order shuffle, and prefix dropout) to reduce rigid header-pattern dependence.
• Per-source batch mixing controls so multiple sources can contribute to the same batch, with independent source frequency controls (including over/under-sampling).
• Per-source trust controls to weight quality/trust independently by source/taxonomy and help mitigate bias from uneven source quality.
• Per-batch dynamic source reweighting so source weights can be changed across batches (for example from loss/metric feedback) while training.
• Resume support via persist_state() and split-store persistence.
• Source-agnostic backends (DataSource or IndexableSource + IndexableAdapter).
• Supply-chain style orchestration (core layer): multi-source intake (refresh) with per-call parallel ingest, optional per-source weighting, staged buffering, deterministic split routing, and batch assembly into train-ready outputs.
• Bounded ingestion windows instead of loading full corpora into memory.
• Per-call source threading: during refresh, each source is fetched on its own short-lived thread, then merged deterministically for batch assembly.
• Streaming-friendly: sources can be finite or unbounded.

This crate does not perform semantic mining/retrieval scoring by itself; instead, it gives you deterministic, metadata-driven sampling primitives you can feed into your downstream mining/retrieval stack.

Using a source for sampling

Create a sampler, register your source, then ask for a batch:

use std::sync::Arc;

use chrono::Utc;
use triplets::{
  DataRecord, DeterministicSplitStore, Sampler, SamplerConfig, SplitLabel, SplitRatios,
  TripletSampler,
};
use triplets::source::InMemorySource;

let record = DataRecord {
  id: "r1".into(),
  source: "demo".into(),
  created_at: Utc::now(),
  updated_at: Utc::now(),
  quality: Default::default(),
  taxonomy: Vec::new(),
  sections: Vec::new(),
  meta_prefix: None,
};

let source = InMemorySource::new("demo", vec![record]);

let split = SplitRatios {
  train: 1.0,
  validation: 0.0,
  test: 0.0,
};
// Deterministic split seed; keep stable to preserve split assignments across runs.
let store = Arc::new(DeterministicSplitStore::new(split, 42)?);
let sampler = TripletSampler::new(SamplerConfig::default(), Arc::clone(&store));

sampler.register_source(Box::new(source));

let _batch = sampler.next_triplet_batch(SplitLabel::Train)?;
# Ok::<(), triplets::SamplerError>(())

DataRecord is the core sampling primitive, but this in-memory example is only for illustration and not a scalable or memory-efficient pattern. For real datasets, prefer the built-in integrated sources or an IndexableSource implementation.

Integrated sources

triplets ships with two built-in sources; if you use either, deterministic paging is always enabled (FileSource, HuggingFaceRowSource).

• File source (FileSource): local files and folders.
• Hugging Face source (HuggingFaceRowSource) (feature: huggingface): HF dataset rows.

Hugging Face source lists (recommended)

Define HF sources in a text file and pass it to the demo or your own loader. The hf:// prefix is a triplets-specific shorthand used only in these lists:

hf://org/dataset/config/split anchor=... positive=... context=a,b text=x,y

Rules:

• Lines are whitespace-delimited; comments start with #.
• anchor=, positive=, context=, and text= are the only accepted keys.
• At least one mapping key is required per line.
• context= and text= accept comma-delimited column lists.
• Rows with missing/blank required fields are skipped.

Example list (see examples/common/hf_sources.txt):

# role columns
hf://labofsahil/hackernews-vector-search-dataset/default/train anchor=title positive=text
hf://wikimedia/wikipedia/20231101.en/train anchor=title positive=text

# explicit text-column mode
hf://pfox/71k-English-uncleaned-wordlist/default/train text=text

Row formats supported by the HF backend:

• .parquet
• .jsonl / .ndjson (one JSON object per line)
• plain text lines (each non-empty line becomes { "text": "..." })

Adding new sources

Use one of these two paths:

• Implement DataSource when your backend has its own paging/cursor model.
• Implement IndexableSource when you can fetch rows by a stable integer index, then wrap with IndexableAdapter.

Minimal IndexableSource example:

use triplets::{DataRecord, SamplerError};
use triplets::source::{IndexableAdapter, IndexableSource};
use chrono::Utc;

struct MySource {
  id: String,
}

impl IndexableSource for MySource {
  fn id(&self) -> &str {
    &self.id
  }

  fn len_hint(&self) -> Option<usize> {
    Some(0)
  }

  fn record_at(&self, _idx: usize) -> Result<Option<DataRecord>, SamplerError> {
    Ok(Some(DataRecord {
      id: format!("{}::0", self.id),
      source: self.id.clone(),
      created_at: Utc::now(),
      updated_at: Utc::now(),
      quality: Default::default(),
      taxonomy: Vec::new(),
      sections: Vec::new(),
      meta_prefix: None,
    }))
  }
}

let source = IndexableAdapter::new(MySource { id: "my_source".into() });
# let _ = source;

Then register the source with your sampler and call next_triplet_batch, next_pair_batch, or next_text_batch.

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_triplet_batch(split), sampler.next_pair_batch(split), or sampler.next_text_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 write persisted sampler/split state (typically at the end of an epoch or at explicit checkpoint boundaries). Do not call this every step. Very frequent writes can create high I/O overhead and, at very large write counts (for example, tens of millions), can also adversely affect split-store initialization time.
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, TripletSampler, Sampler, SamplerConfig, SplitLabel, SplitRatios,
};

# let split = SplitRatios { train: 0.8, validation: 0.1, test: 0.1 };
# let store = Arc::new(DeterministicSplitStore::new(split, 123).unwrap());
# let config = SamplerConfig::default();
let sampler = Arc::new(TripletSampler::new(config, store));
// register sources...

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

Expected batch output (assertion-style)

The most useful checks are shape/invariants, not exact record order. next_triplet_batch, next_pair_batch, and next_text_batch return exactly batch_size samples.

A minimal assertion pattern:

use std::borrow::Cow;
use std::sync::Arc;

use chrono::Utc;
use triplets::data::RecordSection;
use triplets::source::InMemorySource;
use triplets::{
  DataRecord, DeterministicSplitStore, NegativeStrategy, PairLabel, Sampler, SamplerConfig,
  SectionRole, Selector, SplitLabel, SplitRatios, TripletRecipe, TripletSampler,
};

fn record(id: &str) -> DataRecord {
  DataRecord {
    id: id.into(),
    source: "demo".into(),
    created_at: Utc::now(),
    updated_at: Utc::now(),
    quality: Default::default(),
    taxonomy: Vec::new(),
    sections: vec![
      RecordSection {
        role: SectionRole::Anchor,
        heading: Some("title".into()),
        text: format!("anchor {id}"),
        sentences: vec![format!("anchor {id}")],
      },
      RecordSection {
        role: SectionRole::Context,
        heading: Some("body".into()),
        text: format!("context {id}"),
        sentences: vec![format!("context {id}")],
      },
    ],
    meta_prefix: None,
  }
}

let source = InMemorySource::new("demo", vec![record("r1"), record("r2"), record("r3")]);

let split = SplitRatios {
  train: 1.0,
  validation: 0.0,
  test: 0.0,
};
let store = Arc::new(DeterministicSplitStore::new(split, 42)?);

let mut config = SamplerConfig::default();
config.batch_size = 2;
config.recipes = vec![TripletRecipe {
  name: Cow::Borrowed("title_ctx"),
  anchor: Selector::Role(SectionRole::Anchor),
  positive_selector: Selector::Role(SectionRole::Context),
  negative_selector: Selector::Role(SectionRole::Context),
  negative_strategy: NegativeStrategy::WrongArticle,
  weight: 1.0,
  instruction: None,
}];

let sampler = TripletSampler::new(config, Arc::clone(&store));
sampler.register_source(Box::new(source));

let triplets = sampler.next_triplet_batch(SplitLabel::Train)?;
assert_eq!(triplets.triplets.len(), 2);
assert!(triplets.triplets.iter().all(|t| t.recipe == "title_ctx"));

let pairs = sampler.next_pair_batch(SplitLabel::Train)?;
assert_eq!(pairs.pairs.len(), 2);
assert!(pairs
  .pairs
  .iter()
  .all(|p| matches!(p.label, PairLabel::Positive | PairLabel::Negative)));

let text = sampler.next_text_batch(SplitLabel::Train)?;
assert_eq!(text.samples.len(), 2);
assert!(text.samples.iter().all(|s| s.recipe.starts_with("title_ctx_")));

# Ok::<(), triplets::SamplerError>(())

If a next_*_batch call fails to produce batch_size samples, the call returns an error.

• 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.

Example (different source mix across consecutive batches):

use std::collections::HashMap;
use std::sync::Arc;
use triplets::{
  DeterministicSplitStore, TripletSampler, Sampler, SamplerConfig, SplitLabel, SplitRatios,
};

# let split = SplitRatios { train: 0.8, validation: 0.1, test: 0.1 };
# let store = Arc::new(DeterministicSplitStore::new(split, 123).unwrap());
# let config = SamplerConfig::default();
# let sampler = Arc::new(TripletSampler::new(config, store));

let mut weights_a = HashMap::new();
weights_a.insert("source_a".to_string(), 1.0);
weights_a.insert("source_b".to_string(), 0.2);

let mut weights_b = HashMap::new();
weights_b.insert("source_a".to_string(), 0.2);
weights_b.insert("source_b".to_string(), 1.0);

let batch_a = sampler
  .next_triplet_batch_with_weights(SplitLabel::Train, &weights_a)
  .unwrap();
let batch_b = sampler
  .next_triplet_batch_with_weights(SplitLabel::Train, &weights_b)
  .unwrap();

let _ = (batch_a, batch_b);

• 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: if SamplerConfig.recipes is non-empty, those recipes are used for all sources; otherwise each source's default_triplet_recipes() is used (if any).
• Pair batches: derived from triplets and follow the same source/recipe selection behavior.
• Text recipe behavior:
  • If SamplerConfig.text_recipes is non-empty, those are used directly.
  • Else if triplet recipes are configured/available, text recipes are derived as {triplet_name}_anchor, {triplet_name}_positive, {triplet_name}_negative.
  • Else per-source text recipes are used when available.
• Oversampling: when sources run dry, cached records may be reused (no global no-repeat guarantee).

Reducing shortcut learning

When you use DataRecord.meta_prefix / KvpPrefixSampler, prefer varied prefix rendering instead of a single rigid header format.

• Use multiple renderings per key (KvpField variants) and per-field presence/dropout.
• Vary field order and enable prefix dropout so headers are informative but not mandatory.
• This helps avoid narrow sampling regimes and model shortcuts tied to one repeated prefix pattern.
• Prefixes decorate sampled text only; they do not change deterministic split assignment.

Advanced source implementation examples

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, config: &SamplerConfig) -> Result<FileCorpusIndex, SamplerError> {
  let sampler_seed = config.seed;
  Ok(FileCorpusIndex::new(&self.root, &self.id)
    .with_sampler_seed(sampler_seed)
    .with_follow_links(true)
    .with_text_files_only(true)
    .with_directory_grouping(true))
}

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

fn reported_record_count(&self, config: &SamplerConfig) -> Result<u128, SamplerError> {
  self.source_index(config)?.indexed_record_count().map(|n| n as u128)
}

For time-ordered corpora, prefer the IndexableSource + IndexableAdapter path (and use IndexablePager directly only when you need a custom refresh(...)) for deterministic shuffled paging with cursor resume.

Helper-based example:

use triplets::source::{IndexableAdapter, IndexableSource};
use triplets::{data::DataRecord, SamplerError};

struct MyIndexableSource {
  // Could be DB/API client, manifest reader, etc.
  // No in-memory ID list required.
  total_records: usize,
}

impl MyIndexableSource {
  fn load_record(&self, _idx: usize) -> Result<Option<DataRecord>, SamplerError> {
    // Fetch by numeric position from your backend.
    // `None` means "no record at this index".
    todo!("load one record by index")
  }
}

impl IndexableSource for MyIndexableSource {
  fn id(&self) -> &str { "my_source" }
  fn len_hint(&self) -> Option<usize> { Some(self.total_records) }
  fn record_at(&self, idx: usize) -> Result<Option<DataRecord>, SamplerError> {
    self.load_record(idx)
  }
}

// register as a normal DataSource:
// sampler.register_source(Box::new(IndexableAdapter::new(MyIndexableSource { total_records }))); 

Manual path (does NOT use IndexableSource/IndexableAdapter directly):

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 {
  // Canonical record IDs for this source.
  // We keep IDs separate from record payloads so refresh can page deterministically.
  ids: Vec<String>,
}

impl MySource {
  fn load_record(&self, _id: &str) -> Result<DataRecord, SamplerError> {
    // Put your real fetch logic here (database call, API request, file read, etc.).
    // The sampler expects each loaded item to be returned as a DataRecord.
    todo!("load record from storage")
  }

  fn stable_hash(id: &str) -> u64 {
    // Convert each ID to a repeatable number so ordering is the same every run.
    // This avoids "newest-first" bias when IDs are naturally time-ordered.
    let mut hasher = DefaultHasher::new();
    id.hash(&mut hasher);
    hasher.finish()
  }

  fn refresh(
    &self,
    cursor: Option<&SourceCursor>,
    limit: Option<usize>,
  ) -> Result<SourceSnapshot, SamplerError> {
    // Make a sorted copy of IDs so this call runs in a repeatable order.
    // Note: this copy holds all IDs in memory for this refresh call.
    let mut ids = self.ids.clone();
    ids.sort_by_key(|id| Self::stable_hash(id));

    // How many records exist right now.
    let total = ids.len();

    // `revision` means "where to resume next time".
    // No cursor yet means this is the first run, so start at index 0.
    let mut start = cursor.map(|c| c.revision as usize).unwrap_or(0);

    // If data size changed and start is now invalid, safely reset to the beginning.
    if total > 0 && start >= total {
      start = 0;
    }

    // Hard cap for this call.
    // - If `limit` is Some(n), we load at most `n` records this call.
    // - If `limit` is None, we allow one full pass (`total` records).
    let max = limit.unwrap_or(total);
    let mut records = Vec::new();

    // Load records one-by-one, starting at `start`, and wrap at the end.
    // We stop as soon as `records.len() == max`.
    // So this does NOT always load everything; it only loads up to `max`.
    for idx in 0..total {
      if records.len() >= max {
        break;
      }
      let pos = (start + idx) % total;
      records.push(self.load_record(&ids[pos])?);
    }

    // Save where the next call should continue.
    let next_start = (start + records.len()) % total.max(1);
    Ok(SourceSnapshot {
      records,
      cursor: SourceCursor {
        // Record when this refresh happened.
        last_seen: Utc::now(),
        // Store resume position for the next refresh call.
        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.