mirror of
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83c0334ccd
Updates across multiple docs to reflect the new message-level compression with TOIN + CCR integration: - docs/ccr.md: Add CCR-enabled components table, message-level CCR section - docs/ARCHITECTURE.md: Expand Transform 6 with TOIN + CCR integration details - docs/configuration.md: Add CCR integration config and marker format - docs/proxy.md: Add CCR integration note for context management - docs/README.md: Update to reference IntelligentContextManager as default Also adds examples/test_intelligent_context_toin_ccr.py for scale testing the TOIN + CCR integration with real API calls.
76 lines
2.6 KiB
Markdown
76 lines
2.6 KiB
Markdown
# Headroom Documentation
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Welcome to the Headroom documentation.
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## Getting Started
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| Guide | Description |
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|-------|-------------|
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| [Quickstart](quickstart.md) | 5-minute setup |
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| [SDK Guide](sdk.md) | Python SDK usage |
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| [Proxy Guide](proxy.md) | Proxy server deployment |
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## Framework Integrations
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| Framework | Description |
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|-----------|-------------|
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| [LangChain](langchain.md) | Chat models, memory, retrievers, agents, streaming |
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| [Agno](agno.md) | Model wrapper, hooks, multi-provider support |
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| MCP | See [CCR Guide](ccr.md) for tool compression |
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## Core Concepts
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| Topic | Description |
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|-------|-------------|
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| [Universal Compression](compression.md) | ML-based content detection + structure preservation |
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| [Image Compression](image-compression.md) | 40-90% token reduction for images via trained ML router |
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| [Transforms](transforms.md) | How compression works |
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| [CCR](ccr.md) | Reversible compression architecture |
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| [Configuration](configuration.md) | All configuration options |
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## Advanced
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| Topic | Description |
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|-------|-------------|
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| [Text Compression](text-compression.md) | Opt-in utilities for search/logs |
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| [LLMLingua](llmlingua.md) | ML-based compression |
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| [Metrics](metrics.md) | Monitoring and observability |
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| [Errors](errors.md) | Error handling |
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## Deployment & Operations
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| Guide | Description |
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|-------|-------------|
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| [macOS Deployment](macos-deployment.md) | Run proxy as background service on macOS |
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## Reference
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| Topic | Description |
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|-------|-------------|
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| [API Reference](api.md) | Complete API docs |
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| [Architecture](ARCHITECTURE.md) | Internal design |
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| [Troubleshooting](troubleshooting.md) | Common issues |
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## Overview
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Headroom is the Context Optimization Layer for LLM applications. It reduces your LLM costs by 50-90% through intelligent context compression.
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### How It Works
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1. **Universal Compression** — ML-based content detection with structure-preserving compression
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2. **SmartCrusher** — Compresses JSON tool outputs, keeping errors, anomalies, and relevant items
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3. **CacheAligner** — Stabilizes message prefixes so provider caching works
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4. **IntelligentContextManager** — Score-based context dropping using TOIN-learned importance (default)
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5. **CCR** — Caches original data so compression is reversible
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### Safety Guarantees
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- Never removes human content
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- Never breaks tool call ordering
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- Parse failures pass through unchanged
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- LLM can always retrieve original data
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### Getting Help
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- [GitHub Issues](https://github.com/chopratejas/headroom/issues) — Bug reports
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- [GitHub Discussions](https://github.com/chopratejas/headroom/discussions) — Questions
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