ragpy 0.1.0

Unified RAG service abstraction for vector stores like Qdrant.

RAGpy

Usage

Create a vectorstore client (currently only Qdrant is supported). Set QDRANT_API_KEY environment variable first.

from ragpy.vectorstore import QdrantService
from ragpy.embeddings import OpenAIEmbeddingService

embedder = OpenAIEmbeddingService(api_key="your-openai-key")
qdrant = QdrantService("https://your-qdrant-server-url", embedder=embedder)

Create collection:

qdrant.create_collection('collection-name')

Upload chunks of data to a collection:

chunks = ['list of', 'text chunks']
qdrant.upsert('collection-name', chunks)

Query collection:

query_points = qdrant.search('collection-name', 'your query')
for point in query_points:
    print(f'score: {point["score"]}')
    print(f'text: {point["text"]}')