rag-plusplus 0.1.0

RAG++: High-performance retrieval for memory-conditioned selection (Rust core with Python bindings)

rag-plusplus

High-performance retrieval for memory-conditioned selection with Python bindings to a Rust core.

Installation

pip install rag-plusplus

Features

• SIMD-accelerated vector search - AVX2-optimized distance calculations
• HNSW indexing - Approximate nearest neighbor with sub-linear scaling
• Trajectory coordinates - 5D positioning for conversation context
• IRCP propagator - Information-Relevance Causal Propagation
• Salience scoring - Turn importance with recency decay
• Chain management - Cross-conversation knowledge links

Quick Start

import numpy as np
from rag_plusplus import HNSWIndex, FlatIndex, InMemoryStore, MemoryRecord

# Create an HNSW index for fast approximate search
index = HNSWIndex(dimension=128, m=16, ef_construction=200)

# Add vectors
for i in range(1000):
    vec = np.random.randn(128).astype(np.float32)
    index.add(f"vec_{i}", vec)

# Search
query = np.random.randn(128).astype(np.float32)
results = index.search(query, k=10)

for result in results:
    print(f"{result.id}: score={result.score:.4f}")

Trajectory Coordinates

from rag_plusplus import TrajectoryCoordinate5D, trajectory_weighted_cosine_5d

# Create 5D trajectory coordinates
# (depth, sibling_order, homogeneity, temporal, complexity)
coord1 = TrajectoryCoordinate5D(3, 0, 0.9, 0.7, 1)
coord2 = TrajectoryCoordinate5D(4, 1, 0.8, 0.6, 2)

# Trajectory-weighted similarity
score = trajectory_weighted_cosine_5d(vec1, vec2, coord1, coord2, coord_weight=0.3)

IRCP Propagation

from rag_plusplus import IRCPPropagator, IRCPConfig

config = IRCPConfig()  # temperature=1.0, spatial_weight=0.3
propagator = IRCPPropagator(config)

# Propagate attention through conversation turns
# attention_weights = propagator.propagate(...)

Available Classes

Core

• HNSWIndex - Hierarchical Navigable Small World graph for ANN
• FlatIndex - Exact brute-force search
• InMemoryStore - Fast in-memory record storage
• MemoryRecord - Record with embedding and outcome
• QueryCache - LRU cache with TTL

Trajectory

• TrajectoryCoordinate - 4D coordinate (depth, sibling, homogeneity, temporal)
• TrajectoryCoordinate5D - 5D coordinate (+complexity)
• TrajectoryPhase - Conversation phase enum
• PhaseInferencer - Infer phase from turn features

IRCP & Salience

• IRCPPropagator - Attention propagation
• SalienceScorer - Turn importance scoring
• TurnSalience - Salience result with factors

Chain Systems

• ChainManager - Cross-conversation chain management
• ChainLinkEstimator - Link strength estimation
• TrajectoryGraph - Episode graph structure

Performance

The Rust core provides:

• ~10x faster vector distance than pure Python
• Sub-linear search with HNSW indexing
• Zero-copy numpy array handling
• Parallel search across multiple indexes

License

MIT