How can I incorporate a custom knowledge base with RAG models for better context retrieval?

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How can I incorporate a custom knowledge base with RAG models for better context retrieval?

Asked on Nov 19, 2025

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To incorporate a custom knowledge base with Retrieval-Augmented Generation (RAG) models for better context retrieval, you can use a combination of document indexing and retrieval techniques. This involves creating embeddings for your knowledge base and using them to retrieve relevant information during inference.

Example Concept: In a RAG model, the process begins by converting your custom knowledge base documents into embeddings using a pre-trained model. These embeddings are stored in a vector database. During inference, the input query is also converted into an embedding, which is then used to search the vector database for the most relevant documents. The retrieved documents are combined with the query to generate a more informed response.

Additional Comment:

Start by selecting a pre-trained model for generating embeddings, such as BERT or Sentence Transformers.
Index your knowledge base by converting each document into an embedding and storing these in a vector database like FAISS or Pinecone.
During query time, convert the input question into an embedding and retrieve the top-k similar documents from the database.
Combine the retrieved documents with the original query to provide context to the RAG model for generating a response.
Ensure your vector database is optimized for fast retrieval to maintain system performance.

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How can I incorporate a custom knowledge base with RAG models for better context retrieval?

Asked on Nov 19, 2025

Answer

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