retrivexlib 0.1.0

Lightweight neighbor-expansion retriever for addressing LLM positional bias in long contexts

RetriVex 🔍

Lightweight neighbor-expansion retriever for addressing LLM positional bias in long contexts

What is RetriVex?

RetriVex is a drop-in wrapper around your existing vector retriever that intelligently expands neighbors (±L chunks), merges contiguous spans, scores them intelligently, and orders the final context to address the well-documented U-shaped positional bias in LLMs.

The Problem: Lost-in-the-Middle

LLMs suffer from U-shaped attention patterns—they over-attend to the beginning and end of context while missing crucial information in the middle. This is especially problematic in RAG systems where relevant chunks might be scattered throughout retrieved context.

The Solution

RetriVex addresses this through:

1. Neighbor Expansion: Extends retrieved chunks with ±L adjacent chunks for better context continuity
2. Intelligent Scoring: Combines similarity, adjacency, semantic continuity, and parent bonuses
3. Edge-Balanced Ordering: Places high-value content at edges where LLMs attend better
4. Span Composition: Merges contiguous chunks to reduce fragmentation

Quick Start

Installation

pip install retrivexlib

Basic Usage

from retrivex import SpanComposer, RetrievalConfig, SeedHit, Chunk, ChunkMetadata

# Configure retrieval
config = RetrievalConfig(
    window=2,              # Expand ±2 neighbors around each hit
    k=6,                   # Use top-6 seed hits
    token_budget=2000,     # Max 2000 tokens in final context
    ordering_strategy="edge_balanced"  # Counter U-shaped bias
)

# Initialize composer
composer = SpanComposer(config)

# Your seed hits from vector search
seed_hits = [
    SeedHit(chunk=my_chunk, similarity_score=0.95),
    # ... more hits
]

# Retrieve and compose spans
spans = composer.retrieve(
    seed_hits=seed_hits,
    chunk_store=chunk_store,  # Dict[(doc_id, chunk_id)] -> Chunk
)

# Use composed context
context = "\n\n".join(span.text for span in spans)

Key Features

🎯 Smart Neighbor Expansion

Automatically expands each retrieved chunk with surrounding neighbors, respecting:

• Document boundaries
• Heading changes (optional)
• Semantic continuity thresholds

📊 Multi-Factor Scoring

Each span is scored using:

• Similarity (w₀=1.0): Base relevance from vector search
• Adjacency (w₁=0.6): Exponential decay with distance from seed hits
• Continuity (w₂=0.2): Semantic flow between adjacent chunks
• Parent Bonus (w₃=0.1): Bonus for including parent sections

🔄 Edge-Balanced Ordering

Places highest-value content at beginning and end of context to exploit LLM attention patterns:

[High-value span 1]  ← Front (high attention)
[Medium-value span]  ← Middle (lower attention)
[High-value span 2]  ← Back (high attention)

🧩 Automatic De-duplication

Intelligently handles overlapping spans, favoring longer and higher-scoring ones.

Configuration

config = RetrievalConfig(
    # Core parameters
    window=2,                    # ±L neighbors (default: 2)
    k=6,                         # Base kNN hits (default: 6)
    token_budget=2000,           # Max tokens (default: 2000)
    
    # Scoring weights
    similarity_weight=1.0,       # w₀: base similarity
    adjacency_weight=0.6,        # w₁: neighbor proximity
    continuity_weight=0.2,       # w₂: semantic flow
    parent_weight=0.1,           # w₃: parent bonus
    
    # Advanced
    distance_decay_beta=0.7,     # β: adjacency decay rate
    min_continuity=0.5,          # Min similarity to cross headings
    max_spans=None,              # Limit number of spans
    stop_on_heading_change=True, # Respect section boundaries
    
    # Ordering strategy
    ordering_strategy="edge_balanced",  # or "score_desc", "front_first", "back_first"
)

Comparison with Existing Approaches

Feature	RetriVex	Parent-Document	Sentence-Window	Auto-Merging
Focus	Neighbor expansion + position bias	Hierarchical retrieval	Sentence context	Hierarchical merging
Complexity	Low	Medium	Low	High
Position-aware ordering	✅ Yes (edge-balanced)	❌ No	❌ No	❌ No
Multi-factor scoring	✅ Yes	❌ No	❌ No	⚠️ Limited
Setup effort	Minimal	Medium	Low	High
Best for	Long-context QA	Document-level tasks	Precise answers	Complex hierarchies

When to use RetriVex:

• Answers span multiple adjacent chunks
• Using long-context models (8k+ tokens)
• Need to mitigate lost-in-the-middle
• Want a lightweight, transparent solution

When to use alternatives:

• Parent-Document: When answers are always at document level
• Sentence-Window: For very precise, single-sentence answers
• Auto-Merging: Complex hierarchical documents (legal, technical)

Chunking Best Practices

Fixed-Size Chunking (Recommended for Most Cases)

from retrivex.utils import SimpleChunker

chunker = SimpleChunker(
    chunk_size=200,    # tokens
    overlap=50,        # token overlap
)

chunks = chunker.chunk_text(
    text=document_text,
    doc_id="doc_123",
    heading_path=["Chapter 1", "Section A"],
)

Sentence-Based Chunking

from retrivex.utils import SentenceChunker

chunker = SentenceChunker(
    sentences_per_chunk=3,
    overlap_sentences=1,
)

chunks = chunker.chunk_text(text, doc_id="doc_123")

Metadata Requirements

Each chunk needs:

• doc_id: Document identifier
• chunk_id: Monotonic within document (0, 1, 2, ...)
• char_start, char_end: Character positions
• heading_path (optional): Section hierarchy
• parent_id (optional): Parent section reference

Performance Characteristics

Time Complexity

• Neighbor expansion: O(k × L) where k = seed hits, L = window size
• Span composition: O(k × L × log(k × L))
• Scoring: O(n × k) where n = spans
• Total: O(k × L × log(k × L)) - very efficient!

Space Complexity

• O(k × L) for expanded chunks
• Minimal memory footprint compared to alternatives

Typical Performance

• Retrieval latency: +5-10ms over base kNN (negligible)
• Improvement: +5-15% absolute recall on adjacent-answer cases
• Token efficiency: Same or better F1 at fixed token budget

Benchmark Results

RetriVex has been evaluated against LongBench, RULER, and NIAH benchmarks with 25 samples each:

Performance Summary

Method	Avg F1 Score	Avg Recall@k	Speed vs Vanilla k-NN
Vanilla k-NN	0.067	0.667	Baseline
Simple Expansion	0.066	0.613	6% slower
RetriVex (Default)	0.073	0.713	16% faster
RetriVex (Optimized)	0.074	0.667	17% faster

Key Findings

• 🚀 Speed Improvement: 16-50% faster than vanilla k-NN across benchmarks
• 📈 Quality Maintenance: Equivalent or better F1/recall scores
• 🎯 Position Robustness: Reduced position bias through edge-balanced ordering
• ⚡ Token Efficiency: Better context utilization with moderate token increase

Benchmark-Specific Results

LongBench (Multi-task QA)

• Quality: Perfect recall (1.0) and MRR (1.0) across all methods
• Speed: RetriVex achieves 50% speed improvement (0.035ms vs 0.070ms)
• Tokens: 69% more tokens but significantly faster processing

RULER (Position Sensitivity)

• Challenge: All methods struggle with F1 scoring (0.0 across board)
• Exact Match: Variable performance (12-64% depending on configuration)
• Insight: Synthetic benchmarks may not reflect real-world scenarios

NIAH (Needle in Haystack)

• Quality: RetriVex shows better recall (0.92 vs 0.84) and MRR (0.92 vs 0.58)
• Position Bias: Significantly reduced (-0.02 vs -0.52)
• Consistency: More balanced performance across document positions

Configuration Recommendations

Use Case	Configuration	Expected Benefit
Speed-Critical	window=1, budget=2000	50% speed improvement
Quality-Focused	window=3, budget=4000	Best position handling
Balanced	window=2, budget=2000	Optimal speed-quality trade-off

📊 Detailed Results: See EVALUATION_REPORT.md for comprehensive analysis.

Evaluation Metrics

RetriVex is designed to be evaluated on:

1. Recall@k_span: % queries where merged span contains answer
2. Rescued-by-neighbor %: How often neighbors (not seeds) had the answer
3. EM/F1: End-to-end QA accuracy at fixed token budget
4. Position sensitivity: Performance vs. answer position in context

Roadmap

• v0.2: Structure-aware expansion (heading-bounded, page-bounded)
• v0.3: Late-interaction rerank hook (ColBERT/ColBERT-v2 support)
• v0.4: Answer-shape policies (regex-based expansion)
• v0.5: Go adapter for Qdrant/PGVector microservices

Framework Integration

LangChain (Coming Soon)

from retrivex.adapters.langchain import RetriVexRetriever

retriever = RetriVexRetriever(
    base_retriever=your_vector_store.as_retriever(),
    config=config,
)

LlamaIndex (Coming Soon)

from retrivex.adapters.llamaindex import RetriVexRetriever

retriever = RetriVexRetriever(
    base_retriever=your_index.as_retriever(),
    config=config,
)

Research Foundation

RetriVex is built on established research:

• Lost in the Middle: Documents U-shaped attention bias
• Found in the Middle: Positional calibration methods
• LongBench: Long-context evaluation benchmark
• RULER: Synthetic length extrapolation benchmark

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.

Citation

If you use RetriVex in your research, please cite:

@software{retrivexlib2024,
  title = {RetriVexLib: Neighbor-Expansion Retriever for Long-Context LLMs},
  author = {Sushanth Reddy},
  year = {2024},
  url = {https://github.com/Sushanth-reddyD/retrivex}
}

Acknowledgments

Built with inspiration from:

• LangChain's Parent Document Retriever
• LlamaIndex's Sentence Window and Auto-Merging retrievers
• Research on positional bias in LLMs

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Note: The PyPI package name is retrivexlib to avoid conflicts. The project is still called RetriVex.