Comprehensive guide covering Optimizing Token Usage: Context Compression Techniques with practical implementation details.

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A practical guide to reducing LLM token costs in RAG and agentic pipelines using two context compression strategies: extraction-based (LLMChainExtractor) and selection-based (LLMChainFilter) via LangChain. Covers precise token counting with TikToken, implementing both approaches against the same FAISS-backed retrieval pipeline, comparing token reduction percentages, and calculating real-dollar savings at scale. Also addresses best practices like avoiding over-compression, caching compressed results, layering selection before extraction for maximum reduction, and handling production edge cases.

Token Optimization: Compressing Context for Cheaper Agents

How to Optimize Token Usage with Context Compression

Extraction vs. Selection: When to Use Which

Implementing Context Compression with LangChain