Langroid's Retrieval Augmentation Generation (RAG) byDocChatAgentRealization of complete workflows:
- file processingSupport for PDF/Word/web page formats, automatically perform text extraction and semantic chunking
- vector storage: 5 optional databases such as Qdrant/Chroma to store embedding vectors
- retrieval logic: Search for relevant document fragments when asking a question, and then submit the question to LLM to generate an answer.
The developer simply specifies thedoc_paths(local file or URL), the framework will automatically complete the whole process from document loading to answer generation. For example, when dealing with arXiv papers, the system will first retrieve the paragraphs of the paper related to the question, and then generate an accurate abstract.
This answer comes from the articleLangroid: Easily Navigating Large Language Models with Multi-Intelligent Body ProgrammingThe
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