mirror of
https://github.com/whit3rabbit/anyllm-proxy.git
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whit3rabbitandClaude Opus 4.8
d155bfd148
RTK (anyllm_rtk crate): command-aware filtering of tool-result text (test/build/git/log output) via a catalog of 55 declarative filters ported from OmniRoute (MIT). IO-free, deterministic, prompt-cache safe (cache_control blocks preserved byte-for-byte). Wired into the Anthropic passthrough (stream + non-stream) and OpenAI-translate paths, gated per-model via RTK_MODELS. RTK_COMPRESS env / admin toggle, rtk_compress/rtk_models runtime config. Optimizer (anyllm_optimize_* crates): opt-in Frozen-Frontier Extractive Compression of long client-sent conversation history for OpenAI Chat Completions, the Anthropic translate path, and the Anthropic passthrough path (client history only, never proxy tool-loop turns). OPTIMIZER_MODE=off|shadow| live env / admin toggle; live places a cache_control breakpoint at the frontier over raw bytes. Optional LLMLingua-2 ONNX scorer behind the optimizer-onnx feature (model fetched on demand, never bundled). New optimizer_* metrics counters and GET/POST /admin/api/optimizer/model endpoints. Both features expose runtime config + Settings UI controls and fail open on any error. Adds workspace members, CI lint/test for the optimizer-onnx feature, and gitignore rules for downloaded ONNX artifacts. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
236 lines
3.2 KiB
JSON
236 lines
3.2 KiB
JSON
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"input": "def compute_running_average(values):\n \"\"\"Return the running average of a list of numbers.\"\"\"\n total = 0\n averages = []\n for i, v in enumerate(values):\n total += v\n averages.append(total / (i + 1))\n return averages\n\n# Example usage: prints the running average after each new sample.\nif __name__ == \"__main__\":\n print(compute_running_average([1, 2, 3, 4, 5]))\n",
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"compressed": "compute running average values Return average list numbers total = 0 averages = i, v total + = v averages append ( total i + 1 ) return averages prints running average after each sample main print ( compute running average ( 1, 2, 3, 4, 5 ]",
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"reference": {
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"model": "microsoft/llmlingua-2-bert-base-multilingual-cased-meetingbank",
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"llmlingua_version": "0.2.2"
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}
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}
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