A new abstraction and document retriever have been introduced to enhance the post-processing of retrieved documents in LLM-powered applications, particularly those using LangChain. This innovation addresses the challenge of irrelevant information being included in the retrieval process by implementing a DocumentCompressor, which compresses and filters documents based on the query context to ensure only relevant information is passed to the language model. This process allows for more precise and informative responses by enabling increased recall during the initial retrieval step, while the compressor refines the results for relevance. Key features include a set of ready-to-use DocumentCompressors in the LangChain Python package, such as the LLMChainExtractor and EmbeddingsFilter, which help extract relevant information and filter documents by similarity to the query. These tools can be integrated into existing retrieval systems, forming a pipeline of transformations to improve the efficiency and accuracy of information retrieval.