ai langchain reading-list

Most complex and knowledge-intensive LLM applications require runtime data retrieval for Retrieval Augmented Generation (RAG). A core component of the typical RAG stack is a vector store, which is used to power document retrieval.

Using a vector store requires setting up an indexing pipeline to load data from sources (a website, a file, etc.), transform the data into documents, embed those documents, and insert the embeddings and documents into the vector store.

If your data sources or processing steps change, the data needs to be re-indexed. If this happens regularly, and the changes are incremental, it becomes valuable to de-duplicate the content being indexed with the content already in the vector store. This avoids spending time and money on redundant work. It also becomes important to set up vector store cleanup processes to remove stale data from your vector store.

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