README.md

metadata

library_name: efficient-context
language: code
tags:
  - context-optimization
  - llm
  - cpu-optimization
  - resource-constrained
  - memory-management
  - python
license: mit
datasets:
  - None

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