efficient-context

A Python library for optimizing LLM context handling in CPU-constrained environments.

Overview

efficient-context addresses the challenge of working with large language models (LLMs) on CPU-only and memory-limited systems by providing efficient context management strategies. The library focuses on:

Context Compression: Reduce memory requirements while preserving information quality
Semantic Chunking: Go beyond token-based approaches for more effective context management
Retrieval Optimization: Minimize context size through intelligent retrieval strategies
Memory Management: Handle large contexts on limited hardware resources

Installation

pip install efficient-context

Quick Start

from efficient_context import ContextManager
from efficient_context.compression import SemanticDeduplicator
from efficient_context.chunking import SemanticChunker
from efficient_context.retrieval import CPUOptimizedRetriever

# Initialize a context manager with custom strategies
context_manager = ContextManager(
    compressor=SemanticDeduplicator(threshold=0.85),
    chunker=SemanticChunker(chunk_size=256),
    retriever=CPUOptimizedRetriever(embedding_model="lightweight")
)

# Add documents to your context
context_manager.add_documents(documents)

# Generate optimized context for a query
optimized_context = context_manager.generate_context(query="Tell me about the climate impact of renewable energy")

# Use the optimized context with your LLM
response = your_llm_model.generate(prompt=prompt, context=optimized_context)

Features

Context Compression

Semantic deduplication to remove redundant information
Importance-based pruning that keeps critical information
Automatic summarization of less relevant sections

Advanced Chunking

Semantic chunking that preserves logical units
Adaptive chunk sizing based on content complexity
Chunk relationships mapping for coherent retrieval

Retrieval Optimization

Lightweight embedding models optimized for CPU
Tiered retrieval strategies (local vs. remote)
Query-aware context assembly

Memory Management

Progressive loading/unloading of context
Streaming context processing
Memory-aware caching strategies

Maintainer

This project is maintained by Biswanath Roul

License

MIT