ragger-simple 0.1.3

Simple vector database operations with Qdrant

Ragger Simple

A simple Python package for vector database operations using Qdrant.

Features

• Initialize connection with Qdrant vector database
• Parse, chunk, and process text into vector embeddings
• Search for relevant text chunks based on semantic similarity

Installation

pip install ragger-simple

Usage

Python API

Import and initialize VectorDB:

from ragger_simple import VectorDB

db = VectorDB(
    collection_name="my_documents",
    model_name="all-MiniLM-L6-v2",
    model_path=None,
    qdrant_url=None,
    qdrant_api_key=None,
    qdrant_path=None,
    qdrant_timeout=500.0,
)

Constructor parameters:

• collection_name (str, default: "documents") — Qdrant collection name
• model_name (str, default: "all-MiniLM-L6-v2") — sentence-transformers model
• model_path (str, optional) — local folder with your model
• qdrant_url (str, optional) — cloud URL
• qdrant_api_key (str, optional) — cloud API key
• qdrant_path (str, optional) — local path
• qdrant_timeout (float, default: 500) — request timeout in seconds

Methods:

db.add_documents(
    documents: Dict[str, str],
    chunk_size: int = 200,
    overlap: int = 50,
)

• documents — dict mapping doc names to text
• chunk_size — words per chunk
• overlap — overlapping words

results = db.search(
    query: str,
    k: int = 5,
) -> List[Dict]

• query — query text
• k — number of results

Example:

documents = {
    "Article 1": "This is the content of article 1...",
    "Article 2": "This is the content of article 2..."
}
db.add_documents(documents, chunk_size=200, overlap=50)
results = db.search("your query here", k=5)
print(results)

License

MIT