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Initialize LlamaIndex & Vast.ai Workflow

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Linked challenge: LlamaIndex & Vast.ai Decentralized KV-Cache Quantization Profiler

Format
Code-aware
Lines
10
Sections
3
Linked challenge
LlamaIndex & Vast.ai Decentralized KV-Cache Quantization Profiler

Prompt source

Original prompt text with formatting preserved for inspection.

10 lines
3 sections
No variables
0 checklist items
from llama_index.core import VectorStoreIndex, Document
import requests

# Tool function to query Vast.ai marketplace GPUs
def search_vast_ai_gpus(min_vram_gb: int = 24, max_dph: float = 0.50):
    """Queries Vast.ai API for instances matching required hardware specs."""
    # Vast.ai query logic here
    pass

# Initialize LlamaIndex document store for benchmarking results
benchmark_docs = [Document(text="FP8 KV cache reduces VRAM consumption by 48% on RTX 4090.")]
index = VectorStoreIndex.from_documents(benchmark_docs)

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.