📚 HieRAG – Hierarchical Retrieval-Augmented Generation

hie_rag is a modular, extensible Python package designed for Hierarchical Retrieval-Augmented Generation (Hie-RAG). It enables you to extract, split, embed, summarize, and query documents using both chunk- and tree-level semantics, all backed by a vector database.

✅ Features

• PDF/DOCX/XLSX/CSV/PPT ingestion and intelligent semantic splitting
• Hierarchical summarization tree building
• Embedding-based similarity search
• Vector DB indexing and querying (e.g., Qdrant)
• Full streaming interface for frontend integration

📦 Components Used

🛠 Installation

pip install hie-rag

⏯︎ How to Use

Initialize HieRAG

from hie_rag import HieRag

hierag = HieRag(base_url="http://localhost:11434")

[!NOTE] Ensure you have set u an AI server. You should have a chatting model and a embedding model running.

Process and Index a File

with open("sample.pdf", "rb") as f:
    file_bytes = f.read()

for status in hierag.process_and_save_index_stream(
    file_name="sample.pdf",
    uploaded_file=file_bytes,
    min_chunk_size=300,
    max_chunk_size=500
):
    print(status)

{
  "status": "✅ Done",
  "file_id": "abc123",
  "summary_count": 5,
  "chunk_count": 22
}

Query the Summaries or Chunks

Query Summaries by text:

results = hierag.query_summaries_by_text("What is the contract duration?")

Query Chunks by text:

results = hierag.query_chunks_by_text("Explain clause 3.4", file_id="abc123")

List & Manage Indexed Files

List All Indexed Files

hierag.list_summaries()

View Chunks of a File

hierag.list_chunks(file_id="abc123")

Delete a File Index

hierag.delete_index(file_id="abc123")

Get the Summary of a File

hierag.get_summary(file_id="abc123")