MaxKB effectively controls large model illusions through the Retrieval Augmented Generation (RAG) technology framework, which consists of three key components:
- Knowledge structuring: Automatically performs text splitting and vectorized embedding when uploading documents to build a searchable knowledge graph.
- Accurate content retrieval: When a user asks a question, the system first retrieves the most relevant document fragment in the knowledge base as the context for generating the answer.
- Constrained Content Generation: Large model for constrained generation based on retrieved authoritative content, avoiding fabrication of non-existent information
In terms of specific implementation, the system adopts Chunk Embedding to process long documents, matches questions with knowledge fragments through similarity algorithms (e.g., cosine similarity), and finally utilizes the contextual understanding capability of LLM to generate answers that match the retrieval results. This mechanism improves the answer accuracy by about 40%, which is especially suitable for the scenarios that need to be answered strictly based on the content of the document.
This answer comes from the articleMaxKB: Out-of-the-box AI Knowledge Base Q&A System for Smart Customer Service and In-house Knowledge BaseThe
