LangChain, an open-source library for LLM orchestration, recently added the "Self Query" retriever. This feature allows users to query vector databases like Milvus using LangChain. The implementation of this self-query retriever is covered in lines 189 to 233 of the base.py file in the self-query folder. The only class method for the self-query base class is from_llm, which has eight specified parameters and one allowing keyword arguments (kwargs). Four required parameters are llm, vectorstore, document_contents, and metadata_field_info. Other optional parameters include structured_query_translator, chain_kwargs, enable_limit, and use_original_query. The self-query retriever implementation involves parsing the self-query parameters, creating an LLM chain, and returning a self-query retriever. This feature enables users to build simple retrieval augmented generation (RAG) applications using an LLM, vector database, and prompts to interface with the LLM.