llmsearchindex 1.1.0

A high-performance static internet index for LLM RAG applications

llmsearchindex

LLMSearchIndex is a Python library for internet-scale retrieval in LLM RAG applications using a fully local search index.

We trained a search index on 203,169,792 web pages sourced from:

• Wikipedia dataset
• FineWeb dataset

This index can be used as external context to significantly improve LLM responses without requiring external API calls at query time.

Installation

pip install llmsearchindex

PyPI: https://pypi.org/project/llmsearchindex/

Model: https://huggingface.co/zakerytclarke/llmindex

Github: https://github.com/zakerytclarke/llmsearchindex

Example Usage:

from llmsearchindex import LLMIndex

# Initializes and downloads index
index = LLMIndex()

# Standard search (Fastest)
results = index.search("who invented sliced bread", top_k=5)

# High-precision search (Reranked)
results = index.search("who invented sliced bread", top_k=5, rerank=True)

for result in results:
  print(result.get('text'))
  print(result.get('url'))
  print("==="*100)

System requirements

• ~6 GB RAM
• ~10 GB disk space
• CPU inference supported (GPU optional)

Architecture

flowchart LR
    A[User Query] --> B(Embed Sentence Transformers all-MiniLM-L6)
    B --> C(PCA: 384d → 64d)
    C --> D(Binary Quantize)
    X[HuggingFace FineWeb] --> G
    Y[HuggingFace Wikipedia] --> G
    D --> E{FAISS Index}
    E --> G(Fetch Indexed Rows from HuggingFace Server)
    G --> H{Rerank?}
    H -->|Yes| I(Cosine Similarity)
    H -->|No| J[Final Results]
    I --> J
    B -->I

Resources

Embeddings: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2

FAISS Vector search: https://github.com/facebookresearch/faiss

Wikipedia: https://huggingface.co/datasets/wikimedia/wikipedia

FineWeb: https://huggingface.co/datasets/HuggingFaceFW/fineweb

License- MIT License