Builder

Builder

Forgetless is a chainable builder. Inputs stay buffered until run() executes preview filtering, chunking, scoring, and final budget selection.

Builder shape#

rust

use forgetless::{Config, Forgetless, WithPriority};

let result = Forgetless::new()
    .config(
        Config::default()
            .context_limit(64_000)
            .chunk_size(256),
    )
    .add_pinned("You are writing release notes.")
    .add(WithPriority::high(conversation))
    .add_file("design-review.pdf")
    .query("What changed in the API surface?")
    .run()
    .await?;

Core methods#

Method	Purpose	Notes from the crate
new()	Create an empty optimizer	Uses `ForgetlessConfig::default()` internally.
config(Config)	Merge runtime options	Non-default `context_limit` and `chunk_size` override the current config.
query(...)	Attach a relevance query	When more than five lazy files exist, preview filtering is attempted before full reads.
add(...)	Push eager string content	Accepts plain strings or `WithPriority` wrappers.
add_pinned(...)	Shortcut for critical text	Wraps the content with `WithPriority::critical`.
add_file(...)	Register one lazy file	The file path is stored and read later inside `run()`.
add_files(...)	Register many lazy files	Every item is converted through the private `IntoLazyFile` trait.
add_bytes(...)	Attach in-memory bytes	Text MIME types are decoded as UTF-8 when possible; non-text MIME types become placeholder records.
run()	Execute the pipeline	Returns an `OptimizedContext`.

Execution flow#

Step	What happens
1. Model init	Vision and text models are only initialized when the current config enables them.
2. Preview filter	If `query` exists and there are many files, only the most relevant lazy files are read.
3. File load	Selected files are read in parallel with Rayon and converted into `ContentInput` items.
4. Chunking	Each input gets a `ChunkConfig` derived from content type and current runtime options.
5. Scoring	Priority, semantic similarity, position heuristics, and conversation-style recency contribute to each score.
6. Selection	Top chunks are kept until the configured token budget is satisfied.

Introduction

Learn how Forgetless, a Rust LLM context optimizer, ranks and compresses oversized prompts and files into budget-aware context for production workflows.

Getting Started

Install the Forgetless crate with Cargo, enable optional features, add content and files, and run your first LLM context optimization pass in Rust.

Inputs and Priorities

Control what the LLM context optimizer keeps using WithPriority, FileWithPriority, byte payloads, and lazy file inputs to rank and preserve critical content.