rag-implementation

To build a retrieval-augmented generation pipeline, ingest your document corpus and configure embedding models with a vector database to enable semantic search for grounded LLM responses. The pipeline supports configurable corpus ingestion, retrieval, reranking, and evaluation of sourced answers.

Retrieval-augmented generation (RAG) connects LLMs to external knowledge sources via vector databases, providing accurate, grounded responses instead of relying solely on internal model knowledge. You need RAG when building Q&A systems, knowledge-grounded chatbots, or research tools that require sourced, up-to-date information.

Yes, you can use semantic search and vector databases to create knowledge-grounded chatbots with source citations. By applying configurable corpus ingestion and embedding model selection, the chatbot retrieves relevant external documents to ground its responses with accurate citations.

The best way to evaluate grounded responses in a RAG system is to use the pipeline's dedicated evaluation features after configuring retrieval and reranking. This validates that your embedding models and vector database return accurate, sourced information for your document Q&A queries.

Yes, this RAG implementation supports configurable embedding models and vector store selection. You can select your preferred vector database and embedding setup during corpus ingestion to optimize semantic search and retrieval performance for your specific document Q&A or chatbot application.

Reranking refines the initial semantic search results from your vector database to improve the relevance of documents passed to the LLM. In a RAG pipeline, applying reranking after retrieval ensures the generation step uses the most accurate, grounded information available for sourced responses.