Compress engine reference

Skims the first chunk of lines and guesses whether you mostly pasted JSON, code, logs, or plain text.

Samples up to 50 lines, tallies classifyLine types. Prefers code when import/definition/fence/comment dominate; log/trace when those counts are high; JSON when config/table dominate or the sample looks like a keyed object/array. Avoids treating C#/Java null literals as JSON.

Finds JSON blobs in the text and shrinks them: empty junk out, huge blobs truncated, big same-shaped arrays sampled.

Brace-walks for top-level {…}/[…], JSON.parses, then crushValue. Replaces each successful span with compacted JSON. Non-JSON text outside spans is preserved.

Recursively cleans one JSON value so it takes less space without inventing new meaning.

Drops null/empty arrays; truncates long non-whitespace strings; samples homogeneous arrays; strips some high-cardinality id-like keys when values are long; depth-caps nesting.

Tidies code whitespace and tags the language. It does not delete comments or “boring” lines by keyword — that is the ML engine’s job.

Calls detectLanguage, collapses runs of blank lines to at most one, returns { lines, preprocessor: "code", language }. Comment/docstring/license stripping and fixture phrase blacklists were removed.

Looks at the top of the file and names the programming language (Python, Rust, SQL, …) or unknown.

Ordered regex heuristics on the first ~80 lines, then markdown fence aliases (```ts, ```py, …). Specific patterns run before greedy ones so e.g. Python import os is not mislabeled as JavaScript.

Collapses giant stack traces and repeated identical log lines. It no longer throws away DEBUG lines just because the question didn’t say “debug”.

Accumulates stack frames → keep first/last + count; fingerprints timestamp/number-normalized messages and caps duplicates; collapses blank runs. Question arg is unused (kept for API stability). Level-based drops removed.

Picks the right tidy-up for the paste (JSON, code, log, or none).

Dispatches on routeContentType to crushJSONLines / compressCodeLines / compressLogLines, else pass-through.

Breaks each line into word-like pieces the model can score.

Regex /[A-Za-z_][A-Za-z0-9_]*|[^\s]/g per line; returns flat token list (and parallel line indices elsewhere in the pipeline).

Pulls out the “proper nouns” of the question: names, paths, IDs, code-ish tokens.

Heuristic extractors for CamelCase, paths, dotted ids, quoted spans, ALL_CAPS, numerics — used as soft overlap features, not hard drop rules.

Guesses if the question is about code (so we can focus the query).

Keyword/syntax cues for coding tasks.

Shortens a code-ish question to the useful part.

Strips boilerplate instruction wrappers when looksLikeCodePrefix is true.

Cleans the question text so matching is consistent.

Whitespace/Unicode normalize; optional code-query focusing.

Chooses the best question form for this particular paste.

May adapt normalization based on content shape (code vs prose).

Standardizes newlines and odd characters before compression.

Line-ending unify, soft wrapping of runaway lines, light cleanup without semantic rewrite.

Makes two spellings of the same thing compare equal.

Lowercases / folds punctuation for soft matching keys.

Checks if an entity appears in text even if spacing/case differ a bit.

Exact + folded containment; used for entity/term hit counting.

Keeps entities that actually appear and aren’t everywhere (so they’re useful signals).

Filters to corpus-present, down-weights ultra-common tokens via document frequency style checks.

Finds question entities that are rare across blocks — the good “needles.”

Entity list filtered by block DF for multi-hop protection.

Normalizes a line before comparing “did we keep the important evidence?”

Whitespace/fingerprint style normalize for retention metrics.

Scores whether the compressed text still has the lines that look critical for answering.

Ranks original lines by entity overlap; reports kept vs dropped critical previews. Measurement only — not an eviction policy.

Splits the question into searchable words.

Tokenizes / filters stop-ish short terms for overlap features.

Tags a token as code-ish, comment-ish, chat-ish, or boilerplate-ish.

Maps into SEM enum used as a categorical feature.

A hand-crafted “attention” score when no neural boost is present — still a feature, not a keyword blacklist.

Combines semantic class, entity match, and recency into a bounded prior for the MLP / fallback.

Builds one feature row per token for the model.

Tokenizes, attaches line context, entity flags, entropy/fingerprint/ngram features, position encoding.

Packs those rows into a dense matrix the network expects.

Pads/truncates to seqLen; returns Float32-style flat or 2D feature array.

Smooth activation used inside the tiny network.

Gaussian Error Linear Unit approximation.

Rescales a feature vector so training stays stable.

Per-row mean/var normalize + affine.

One dense layer: multiply, add bias.

Row-vector × weight matrix + bias.

Runs the keep/drop neural net on every token and returns scores.

Stacked linear + GELU + LayerNorm from model.json weights; outputs per-token keep probabilities/logits.

Turns “which tokens survived” back into whole lines, always keeping a bit of the start and end.

Union of token→line indices plus sink/recency line sets.

Labels tokens that look like they must survive for the question (for training/eval style checks).

Entity/term overlap oracle flags on tokens.

How relevant is this line to the question? Starts from the model score and bumps when question words appear.

modelLineScore * 2 + term/entity soft hits. Fixture penalties (e.g. Maycomb, Region CPU eviction, Appendix notes) removed — negatives are not applied from keyword lists.

A fuzzy ID for a line so duplicates can be spotted.

Lowercase, digits→#, whitespace collapse.

Slightly downranks lines that appear over and over.

Fingerprint histogram → per-line penalty vector (structural, not lexical blacklist).

Joins the kept line numbers back into a string.

Sorted indices → join("\n").

Counts how many tokens sit on kept lines.

Iterates token→line map; increments when line is kept.

Did we keep the rare names from the question?

Distinctive entity hit ratio gate (strict for ≤3 entities, ≥0.9 otherwise).

Turns token scores into one score per line (best token on that line).

Max-pool over tokens belonging to each line index.

Always protect the very start and a bit of the end of the document.

Set containing index 0 and the last few lines (attention-sink style).

Broader check: entities plus question words still show up enough.

Requires entityRecallOk then term-hit ratio ≥ minRatio (default 0.66).

How many tokens does each line cost?

Histogram of token→line assignments.

Labels a line: heading, code, log, blank, etc.

Regex typology used by routing and block segmentation.

Decides when a new “paragraph/block” should begin.

Type transitions + structural cues (headings, fences).

Cuts the document into scored chunks.

Emits blocks with start/end, type, text, token mass.

Fuzzy ID for a whole block to detect copies.

Normalized text fingerprint for DF / duplicate penalties.

Ranks each chunk by how useful it looks for the question. When the hosted neural scores exist, they mostly decide.

IDF-weighted entity/term soft hits + light type priors + duplicate soft penalty. No copyright/Passage/“see also” keyword dumps. With neuralBoost: score = 0.1 * heuristic + 0.9 * neural.

Expands kept blocks into the set of line numbers.

Union of inclusive [start, end] ranges.

When keeping a chunk, also keep its nearby heading so context stays readable.

Adds block id; walks back a few blocks for a heading dependency.

Counts markdown code fences so we don’t leave one hanging open.

Counts ``` markers (parity check).

Fixes odd open fences after selection.

May add closing fence blocks when parity is odd.

Package of quality numbers for dashboards/benchmarks.

Entity/keyword recall, important-line retention, token ratios.

The compiler: given scores and a token budget, picks which lines survive, protecting answer-critical bridges and sinks.

Segments → scores → greedy/protected keep with neural seed protection, multi-hop bridge harvest, fence closing, and verifier loops. This is where eviction happens — driven by relative scores / neural ranks, not phrase blacklists.

Prepares block text for the hosted neural reranker.

Normalize/preprocess/segment; returns block payloads for cross-encoder scoring.

Main “smart compress” entry: runs the full pipeline and returns compressed text + metrics.

Preprocess → features → forward → compiler selection → optional neural blend → stats object.

Simpler wrapper: compress toward a fraction of the original size under a named policy.

Delegates to adaptive path with budgetRatio (default 0.35).

Runs a few strategies side-by-side for demos/benchmarks.

Returns per-policy compressed outputs and quality metrics.

Rough 0–1 score of how answerable the compressed text still is.

Combines entity/term recall and critical retention signals.

Turns “tokens not sent” into a CO₂ / energy estimate for the site.

Linear model over energy-per-token assumptions.

Demo fixture showing why dumb middle truncation fails.

Returns a canned {context, question, answer} style object.

Downloads the tiny browser weights file.

fetch → JSON parse of assets/data/model.json by default.

Second tiny net that checks if a compression looks safe.

MLP over hand-built verifier features → accept/reject style score.

Builds the checklist numbers the verifier reads.

Compression ratio, entity recall, kept-token stats, etc.

Loads the higher-precision weight file.

Fetch model_precision.json.

Loads verifier weights.

Fetch verifier.json.

Remembers the full original under a short hash so you can fetch it later.

In-memory map keyed by simpleHash; reversible compression companion.

Looks up the original by hash.

Map get; returns null if missing.

How many originals are cached right now.

Size / byte estimates of the CCR store.

Quick fingerprint string for CCR keys (not crypto security).

Non-cryptographic rolling hash → hex-ish string.

Compress and stash the original so decompression/retrieval is possible.

Store original → compressAdaptive → return compressed + retrieval hash.

More cautious compress: try a cut, ask the verifier if it’s safe, back off if not.

Precision weights + verifier loop until accept or budget floor.

Optional wrapper that puts a fixed header around compressed text so providers can reuse prompt/prefix cache. We never touch the model’s KV cache — we only change the string we send.

CacheAligner: deterministic XML preamble/postamble for provider prompt caching (OpenAI/Anthropic/vLLM). Not in-model KV eviction.

Rare tokens score higher than tokens that spam the file.

1 - freq*3 clamped to [0,1].

How similar do the line and question “feel” at character-pair level?

Character bigram Jaccard.

Does this token look like any question entity?

Max character-set overlap vs entity set.

Another rarity signal for tokens.

Same family as entropy; frequency-based divergence.

Tells the model where in the document a token sits.

Sinusoids of position ratio (Transformer-style scalar feature).

Overlapping 3-character chunks between line and question.

Trigram Jaccard.

How nested is this line (spaces/tabs)?

Leading whitespace / 40, capped at 1.0.