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"]}')
1 maintainer
Metadata
1 maintainer
Release files for ragpy 0.1.0
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| File | Size | Uploaded | Provenance | |
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| ragpy-0.1.0.tar.gz | 113.5 kB | Without provenance | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| ragpy-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 117.6 kB
Release files / ragpy-0.1.0.tar.gz
| Download URL | ragpy-0.1.0.tar.gz |
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| Size | 113.5 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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| Download URL | ragpy-0.1.0-py3-none-any.whl |
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| Size | 4.1 kB |
| Tags | Python 3 |
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uv/0.8.23
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