ragkit
Modular RAG toolkit – A flexible and pluggable framework for Retrieval-Augmented Generation (RAG) workflows.
Features
- Plug-and-play architecture for embedding models and vector stores
- Support for hybrid search (vector + keyword)
- Extensible design with Pydantic models
- Qdrant vector store support
- OpenAI and SentenceTransformer embedding
Installation
pip install ragkit
## Usage
```python
from ragkit.search import DocumentSearchService
from ragkit.embedding import OpenAIEmbedder
from ragkit.vectorstore import QdrantVectorStore
# Initialize components
embedder = OpenAIEmbedder(api_key="your-openai-api-key")
store = QdrantVectorStore(vector_size=1536)
search_service = DocumentSearchService(embedder, store)
# Perform search
results = search_service.search("Tell me about vector databases")
1 maintainer
Metadata
1 maintainer
Release files for ragkitx 0.1.5
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