llmsearchindex 1.2.0

A high-performance static internet index for LLM RAG applications

llmsearchindex

LLMSearchIndex LLMSearchIndex is a library and search index that provides fully local, internet-scale web search for LLM RAG applications, trained 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.

Demo

You can try out the search at: https://huggingface.co/spaces/zakerytclarke/llmsearchindex

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('url'))
  print(result.get('text'))
  print(result.get('snippet'))
  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