Fixing local LLM knowledge bases requires better retrieval, not new models

Setting up a local LLM knowledge base often yields poor results due to issues in the retrieval pipeline, not the model itself. Common problems include inadequate chunking that splits sentences or groups unrelated content, using an embedding model that doesn't capture semantic nuances for specific domains, and retrieving too few chunks to reconstruct the necessary context. Solutions involve using recursive splitters with overlap and semantic boundaries for better chunking, testing various embedding models like BAAI/bge-base-en-v1.5 or intfloat/e5-base-v2 to find one suited to the data, and increasing the number of retrieved chunks or employing reranking to ensure comprehensive context. AI

IMPACT Improves the usability and accuracy of local LLM applications for personal knowledge management.

RANK_REASON The article provides practical advice and code snippets for improving the performance of existing local LLM setups, rather than announcing a new model or significant research breakthrough.

Read on dev.to — LLM tag →

• BAAI/bge-base-en-v1.5
• chromadb
• intfloat/e5-base-v2
• langchain_text_splitters
• LLM
• RecursiveCharacterTextSplitter
• SentenceTransformer

• product
• infra

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