Deploying RAGLight to enable local knowledge base Q&A requires 4 key steps:
- environmental preparation: Install Python 3.8+, run the
pip install raglightInstall the core libraries, if you use HuggingFace you need to install the additionalsentence-transformers - Model Configuration: Pull the desired model through Ollama (e.g.
ollama pull llama3) to ensure that local services are functioning properly - Data loading: Use
FolderSourceSpecify the local folder path (supports PDF/TXT and other formats), or configure it in the codeGitHubSourceImporting public repositories - Pipeline construction: Initialization
RAGPipelinepost-callbuild()Generate the vector index and finally pass thegenerate()Enter a question to get an answer
Special attention should be paid to the typical code examples: the path of the knowledge base should be replaced with the actual folder address, the model name should be the same as the one loaded in Ollama, and the default number of retrieved documents, k=5, can be adjusted as needed.
This answer comes from the articleRAGLight: Lightweight Retrieval Augmentation Generation Python LibraryThe
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