dv-hyperrag 0.1.0

Python SDK for RAG serving optimization — RAGO Pareto scheduler + RAGCache KV cache manager

HyperRAG

KV cache + Pareto scheduling middleware for RAG pipelines. Plugs in between your vector search and your LLM. Built on two systems papers: RAGO (ISCA'25) and RAGCache (TOCS'25).

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The problem

Every RAG request re-processes the same documents from scratch. At 70B params, that's ~650ms of wasted prefill before you see a single output token — and you just paid for the same compute last request.

The fix: cache the transformer's KV state per document. When a document appears again, load the cache instead of recomputing. TTFT drops proportionally to hit rate.

This SDK manages that cache and finds the optimal GPU/batch/cache configuration for your workload.

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Install

pip install hyperrag           # core (schedule optimizer + cache planner)
pip install "hyperrag[gpu]"    # + vLLM for real inference
pip install "hyperrag[all]"    # + dev + eval tooling

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Quickstart

→ quickstart/README.md

Install, configure, optimize, serve. Five steps.

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Pipeline Integration

→ docs/pipeline-integration.md

Drop this into a pipeline you already have (LangChain, LlamaIndex, custom).

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How it works

1. optimize() — Pareto search over GPU counts, batch sizes, and cache hit rates. Returns the config that minimises TTFT (or maximises QPS) for your workload.
2. recommend_cache() — Sweeps GPU/host DRAM split. Tells you how to allocate cache budget.
3. build_controller() — Returns a live serving controller backed by vLLM. Every request goes through cache lookup → speculative pipelining → KV cache admission.

from hyperrag import RAGOptimize, RAGOptimizeConfig, LLMModel, Query

rago = RAGOptimize(RAGOptimizeConfig(
    paradigm="long_context",
    model=LLMModel.LLAMA_3_1_70B,
    gpu_budget_gb=8.0,
    host_budget_gb=32.0,
))

# Pre-flight: find the best schedule before you commit hardware
result = rago.optimize()
print(result.summary())
# TTFT=3.4ms  QPS=12.5  hit_rate=0.82  gpus=4  batch=8

# Production: real vLLM inference with KV caching active
ctrl = rago.build_controller()   # requires NVIDIA GPU + pip install "hyperrag[gpu]"
resp = ctrl.process(Query("q1", "What is transformer attention?", ["d1", "d2"], [512, 256]))
print(f"TTFT={resp.ttft_s*1000:.1f}ms  cache_hit={resp.cache_hit}")

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Model presets

from hyperrag import LLMModel

# LLMs
LLMModel.LLAMA_3_1_8B    LLMModel.LLAMA_3_1_70B    LLMModel.LLAMA_3_1_405B
LLMModel.MISTRAL_7B      LLMModel.MISTRAL_NEMO_12B
LLMModel.GEMMA_2_9B      LLMModel.GEMMA_2_27B
LLMModel.QWEN_2_5_72B    LLMModel.DEEPSEEK_R1_70B

# SLMs
LLMModel.LLAMA_3_2_1B    LLMModel.LLAMA_3_2_3B
LLMModel.PHI_3_5_MINI    LLMModel.GEMMA_2_2B
LLMModel.QWEN_2_5_7B     LLMModel.DEEPSEEK_R1_7B

# Custom
LLMModel.custom("MyModel-7B", "myorg/mymodel-7b", 7.0,
                num_layers=32, q_heads=32, kv_heads=8, head_dim=128)

All presets: from hyperrag import ALL_MODELS.

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RAG paradigms

paradigm=	Default model	Bottleneck	Use when
"hyperscale"	8B	FAISS scan	Standard single-hop RAG
"long_context"	70B	LLM prefill	1M+ token context, no retrieval
"iterative"	70B	FAISS × 4	Multi-hop / agentic retrieval
"rewriter_reranker"	70B	Encoder + rewriter	Query rewrite + cross-encoder rerank

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Config reference

RAGOptimizeConfig(
    paradigm="hyperscale",     # see table above
    model=LLMModel.LLAMA_3_1_8B,
    gpu_budget_gb=4.0,         # GPU HBM for KV cache (GB)
    host_budget_gb=16.0,       # host DRAM for KV cache (GB)
    hardware_profile=None,     # path to JSON from scripts/profile_hardware.py
    num_gpus=None,             # override GPU count (or RAGO_NUM_GPUS env)
    max_ttft_s=None,           # filter: reject schedules with TTFT > this
    min_qps=None,              # filter: reject schedules with QPS < this
)

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Key classes

Class	What it does
RAGOptimize	Facade: optimize(), recommend_cache(), build_controller()
RAGServeController	Serving: process(), process_batch(), warmup(), metrics(), reset()
LLMModel	Model spec with 17 built-in presets + custom()
Query	(query_id, text, doc_ids, doc_tokens)
QueryResult	(ttft_s, latency_s, cache_hit, cached_tokens, speculative)
OptimizeResult	(ttft_s, qps_per_chip, cache_hit_rate, pareto_size)
CacheRecommendation	(gpu_gb, host_gb, estimated_hit_rate, reasoning)
ServeMetrics	(hit_rate, avg_ttft_s, gpu_used_mib, host_used_mib, ...)

Exceptions: RAGOptimizeError → ConfigError, ScheduleError, HardwareError, ServeError.

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Hardware calibration

For tighter schedule predictions, profile once and pass the result:

python scripts/profile_hardware.py --output profiles/my_gpu.json

RAGOptimizeConfig(model=LLMModel.LLAMA_3_1_8B, hardware_profile="profiles/my_gpu.json")

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Benchmarks

4× A100-SXM4-40GB, 1000 queries, Zipfian workload, calibrated cost model.

Paradigm	Baseline	+RAGCache	+RAGO	Speedup
Hyperscale 8B	264.8 ms	251.6 ms	243.6 ms	1.09×
Long-context 70B	30.9 ms	13.7 ms	3.4 ms	9.02×
Iterative 70B	264.8 ms	251.6 ms	243.6 ms	1.09×
Rewriter-Reranker 70B	649.2 ms	635.9 ms	339.7 ms	1.91×

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Tests

pytest tests/ -m "not gpu" -v    # 100 tests, no GPU needed
pytest tests/ -m gpu -v          # serving tests (requires NVIDIA GPU + vllm)

MS-MARCO v2.1 fixture (50 queries) at tests/fixtures/ms_marco_sample.json.

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Source layout

src/
├── hyperrag/        SDK (public API)
│   ├── client.py            RAGOptimize facade
│   ├── config.py            RAGOptimizeConfig
│   ├── serve.py             RAGServeController
│   ├── models.py            LLMModel + result types
│   └── exceptions.py
└── engine/                  Engine (RAGO + RAGCache algorithms)
    ├── schema/              RAGSchema workload model
    ├── cost_model/          Roofline / calibrated / adaptive
    ├── knowledge_tree/      Prefix trie for KV cache
    ├── cache/               Multi-tier cache + PGDSF
    ├── request_scheduler/   Cache-aware reorder + speculative pipeline
    ├── scheduler/           Pareto scheduler
    ├── inference/           vLLM backend
    └── serving/             RAGController

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References

1. Nawras Alnaasan et al. "RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving." ISCA 2025. arXiv:2503.14649
2. Chao Jin et al. "RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation." ACM TOCS 2025. arXiv:2404.12457

MIT License