56 lines
2.0 KiB
Org Mode
56 lines
2.0 KiB
Org Mode
#+TITLE: PROJECT: Token Optimization (Universal Literate Note)
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#+ID: project-token-optimization
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#+STARTUP: content
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#+FILETAGS: :strategy:token:optimization:cost:psf:
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* Overview
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The *Token Optimization* project defines the strategy and implementation for cost-effective LLM usage. It implements a multi-tier, multi-provider approach to minimize inference costs while maximizing reasoning capability through smart routing and context compression.
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* Phase A: Demand (PRD)
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:PROPERTIES:
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:STATUS: FROZEN
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:END:
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** 1. Purpose
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Minimize LLM operational expenses while maintaining high-fidelity agentic performance.
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** 2. User Needs
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- *Multi-Tier Strategy:* Resolve tasks using the cheapest model that meets the required intelligence threshold.
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- *Failover Resilience:* Automated fallback chain (Gemini -> OpenRouter -> GPT-4o).
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- *Context Efficiency:* Implement pruning and RAG to avoid token bloat.
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- *Usage Transparency:* Real-time tracking and budget alerts.
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** 3. Success Criteria
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*** TODO 80% of queries handled by Tier 1 (Free/Fast) models
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*** TODO Automated fallback triggered on rate limits
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*** TODO Context compression reducing average prompt size by 30%
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*** TODO Budget alerts active at 80% threshold
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* Phase B: Blueprint (PROTOCOL)
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:PROPERTIES:
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:STATUS: SIGNED
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:END:
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** 1. Architectural Intent
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Interfaces for dynamic model selection and cost-aware request routing. Source of truth is the `openclaw.json` configuration and real-time provider telemetry.
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** 2. Semantic Interfaces
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#+begin_src lisp
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(defun token-resolve-model (task-complexity)
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"Selects the optimal model tier based on task metadata.")
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(defun token-compress-context (raw-context)
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"Applies pruning heuristics to reduce token count.")
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#+end_src
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* Phase D: Build (Implementation)
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Implementation consists of configuration and routing logic located in `projects/token-optimization/`.
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** Routing Logic
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#+begin_src lisp
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;; Logic for complexity-based routing stubs
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#+end_src
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* Phase E: Chaos (Verification)
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Verification involves A/B testing model choices and simulating rate limits to verify fallback integrity.
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