ragger-simple 0.1.0

Simple vector database operations with Qdrant

Vector Search

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 vector-search

Usage

Initialize the vector database

vector-search init --collection my_documents

Process documents

Create a JSON file (e.g., documents.json) with your articles:

{
  "Article 1": "This is the full text of article 1...",
  "Article 2": "This is the full text of article 2..."
}

Then process them:

vector-search process --input documents.json --collection my_documents

Search for relevant chunks

vector-search search --query "your search query here" --collection my_documents

Python API

from vector_search import VectorDB

# Initialize
db = VectorDB(collection_name="my_documents")

# Add documents
documents = {
    "Article 1": "This is the content of article 1...",
    "Article 2": "This is the content of article 2..."
}
db.add_documents(documents)

# Search
results = db.search("your query here", k=5)
print(results)

License

MIT