trueno-rag 0.2.3

Table of Contents

• Installation
• Usage
• Architecture
• API Reference
• Examples
• Testing
• Contributing
• License

Pure-Rust Retrieval-Augmented Generation Pipeline

SIMD-accelerated RAG pipeline built on Trueno compute primitives. Part of the Sovereign AI Stack.

Features

• Pure Rust - Zero Python/C++ dependencies
• Chunking - Recursive, Fixed, Sentence, Paragraph, Semantic, Structural
• Hybrid Retrieval - Dense (vector) + Sparse (BM25) search
• Fusion - RRF, Linear, DBSF, Convex, Union, Intersection
• Reranking - Lexical, cross-encoder, and composite rerankers
• Metrics - Recall, Precision, MRR, NDCG, MAP
• Semantic Embeddings - Production ONNX models via FastEmbed (optional)
• Nemotron Embeddings - NVIDIA Embed Nemotron 8B via GGUF (optional)
• Index Compression - LZ4/ZSTD compressed persistence (optional)

Installation

[dependencies]
trueno-rag = "0.1.8"

Usage

use trueno_rag::{
    pipeline::RagPipelineBuilder,
    chunk::RecursiveChunker,
    embed::MockEmbedder,
    rerank::LexicalReranker,
    fusion::FusionStrategy,
    Document,
};

let mut pipeline = RagPipelineBuilder::new()
    .chunker(RecursiveChunker::new(512, 50))
    .embedder(MockEmbedder::new(384))
    .reranker(LexicalReranker::new())
    .fusion(FusionStrategy::RRF { k: 60.0 })
    .build()?;

let doc = Document::new("Your content here...").with_title("Doc Title");
pipeline.index_document(&doc)?;

let (results, context) = pipeline.query_with_context("your query", 5)?;

Examples

# Basic examples
cargo run --example basic_rag
cargo run --example chunking_strategies
cargo run --example hybrid_search
cargo run --example metrics_evaluation

# With semantic embeddings (downloads ~90MB ONNX model on first run)
cargo run --example semantic_embeddings --features embeddings

# With compressed index persistence
cargo run --example compressed_index --features compression

# With NVIDIA Nemotron embeddings (requires GGUF model file)
NEMOTRON_MODEL_PATH=/path/to/model.gguf cargo run --example nemotron_embeddings --features nemotron

API Reference

Semantic Embeddings (FastEmbed)

Production-quality vector embeddings via FastEmbed (ONNX Runtime):

trueno-rag = { version = "0.1.8", features = ["embeddings"] }

use trueno_rag::embed::{FastEmbedder, EmbeddingModelType, Embedder};

let embedder = FastEmbedder::new(EmbeddingModelType::AllMiniLmL6V2)?;
let embedding = embedder.embed("Hello, world!")?;
// 384-dimensional embeddings

Available models:

• AllMiniLmL6V2 - Fast, 384 dims (default)
• AllMiniLmL12V2 - Better quality, 384 dims
• BgeSmallEnV15 - Balanced, 384 dims
• BgeBaseEnV15 - Higher quality, 768 dims
• NomicEmbedTextV1 - Retrieval optimized, 768 dims

NVIDIA Embed Nemotron 8B

High-quality 4096-dimensional embeddings via GGUF model inference:

trueno-rag = { version = "0.1.8", features = ["nemotron"] }

use trueno_rag::embed::{NemotronEmbedder, NemotronConfig, Embedder};

let config = NemotronConfig::new("models/NV-Embed-v2-Q4_K.gguf")
    .with_gpu(true)
    .with_normalize(true);
let embedder = NemotronEmbedder::new(config)?;

// Asymmetric retrieval - different prefixes for queries vs documents
let query_emb = embedder.embed_query("What is machine learning?")?;
let doc_emb = embedder.embed_document("Machine learning is a branch of AI...")?;

Index Compression

LZ4/ZSTD compressed index persistence:

trueno-rag = { version = "0.1.8", features = ["compression"] }

use trueno_rag::{compressed::Compression, BM25Index};

let bytes = index.to_compressed_bytes(Compression::Zstd)?;
// 4-6x compression ratio

Architecture

┌─────────────────────────────────────────────┐
│            RAG Pipeline API                  │
│      (RagPipelineBuilder, query)             │
├──────────┬──────────┬───────────────────────┤
│ Chunking │ Embedding│  Retrieval            │
│ (6 modes)│ (ONNX/   │  (Dense + BM25)       │
│          │  GGUF)   │                       │
├──────────┴──────────┴───────────────────────┤
│          Fusion & Reranking                  │
│  (RRF, Linear, DBSF, Lexical, Cross-Enc)    │
├─────────────────────────────────────────────┤
│        Storage & Indexing                    │
│  (BM25 inverted index, vector store, SQLite) │
├─────────────────────────────────────────────┤
│     Trueno SIMD Compute Primitives           │
└─────────────────────────────────────────────┘

• Chunking Layer: Recursive, Fixed, Sentence, Paragraph, Semantic, and Structural chunkers
• Embedding Layer: Mock (testing), FastEmbed (ONNX), Nemotron (GGUF) embedders
• Retrieval Layer: Dense vector similarity + BM25 sparse retrieval with hybrid fusion
• Fusion/Reranking: RRF, Linear, DBSF, Convex combination; lexical and cross-encoder rerankers
• Storage: In-memory BM25 index with optional LZ4/ZSTD persistence and SQLite backend

Testing

cargo test --lib          # Unit tests
cargo test                # All tests including integration
make coverage             # Coverage report (target: >=95%)
make lint                 # Clippy lints

Property-based tests cover chunking boundary conditions, BM25 scoring invariants, and fusion correctness.

Stack Dependencies

trueno-rag is part of the Sovereign AI Stack:

Development

make test       # Run tests
make lint       # Clippy lints
make coverage   # Coverage report (95%+ target)
make book       # Build documentation book

Documentation

• API Documentation
• User Guide

Contributing

Contributions are welcome! Please see the CONTRIBUTING.md guide for details.

MSRV

Minimum Supported Rust Version: 1.75

License

MIT