How bigsnake47/aiwithrag works
AIwithRAG is an in-development RAG project, but its README documents no capabilities and the sampled API, generation, retrieval, ingestion-pipeline, and storage modules are empty or lack implementation. The supported behavior is limited to a document-loader entry point whose file dispatcher is a stub, and a standalone sentence-transformer embedding service. No end-to-end ingestion, retrieval, or answer-generation flow can be established from the available evidence.
Main entry points: loaders.py Document loader · service.py Embedding service
Components 6
Document loading
- Document loader
app/ingestion/loaders.py
Embeddings
- Embedding service
app/embeddings/service.py
Other components
- Local files
- Loaded documents
- Text input
- Text vectors
Connections 4
- Local files → Document loader: loads paths
- Document loader → Loaded documents: returns documents
- Text input → Embedding service: encodes text
- Embedding service → Text vectors: returns vectors