Contextual Compression

Original Paper: https://arxiv.org/pdf/2405.13792v1

Code Sample: Embed GitHub

Contextual Compression in Document Retrieval

Overview

Contextual compression is a technique used in document retrieval systems to improve the relevance and conciseness of the retrieved information. It involves compressing and extracting the most pertinent parts of documents based on the context of a given query. This is particularly useful when the retrieved documents contain a significant amount of irrelevant information, which can lead to increased computational costs and poorer responses from language models.

How it Works

The process of contextual compression in document retrieval involves the following steps:

1. Retrieving relevant documents using a base retriever, such as a vector database or a search engine.
2. Passing the retrieved documents through a document compressor, which is responsible for extracting the most relevant parts of each document based on the given query.
3. The document compressor typically uses a language model, such as GPT-4, to analyze the context of the query and the content of each document. It then extracts the most relevant segments or filters out irrelevant documents altogether.
4. The compressed and filtered documents are then returned as the final result of the retrieval process, providing a more concise and relevant set of information to the user or the downstream application.

Advantages

1. Improved relevance: By focusing on the most relevant parts of the retrieved documents, contextual compression ensures that the returned information is highly relevant to the user's query, reducing the amount of irrelevant content.
2. Reduced computational costs: By compressing the retrieved documents and filtering out irrelevant information, contextual compression can significantly reduce the computational load on language models, leading to faster response times and lower costs.
3. Enhanced user experience: The improved relevance and conciseness of the retrieved information can lead to a better user experience, as users can quickly find the most pertinent information they are looking for.

Limitations

1. Additional computational overhead: Implementing contextual compression requires an additional step in the retrieval process, which involves passing the retrieved documents through a document compressor. This additional step can introduce some computational overhead and latency.
2. Potential loss of context: While contextual compression aims to extract the most relevant parts of documents, it may also lead to a loss of context or important information that is not directly related to the query but still relevant to the overall understanding of the topic.
3. Dependence on language model performance: The effectiveness of contextual compression relies heavily on the performance of the language model used for the compression task. If the language model is not accurate or reliable, it can lead to suboptimal compression and extraction of relevant information.
4. Potential for bias: Like any system that relies on language models, contextual compression can be susceptible to biases present in the training data or the language model itself. This can lead to skewed or biased results in certain contexts.

Implementation Example

The code provided in the GitHub repository demonstrates the implementation of contextual compression using LangChain and OpenAI's language models. It includes the following key steps:

1. Loading and splitting documents into chunks using a web loader and a text splitter.
2. Creating a vector store using OpenAI embeddings and the Chroma library.
3. Setting up a document compressor using LLMChainExtractor and an OpenAI language model.
4. Creating a ContextualCompressionRetriever by combining the base retriever (vector store) and the document compressor.
5. Retrieving relevant documents using the compression retriever and comparing the results with vanilla retrieval.

By following this implementation, you can integrate contextual compression into your own document retrieval system and leverage the advantages it offers in terms of improved relevance and reduced computational costs.

This is an AI generated summary by Athina AI

Relevant Links:

1. https://medium.aiplanet.com/implement-contextual-compression-and-filtering-in-rag-pipeline-4e9d4a92aa8f?gi=17bd4cb9fa7b 2. https://boramorka.github.io/LLM-Book/CHAPTER-2/2.5 Semantic Search. Advanced Retrieval Strategies/ 3. https://python.langchain.com/v0.2/docs/how_to/contextual_compression/ 4. https://community.fullstackretrieval.com/document-transform/contextual-compression 5. https://github.com/NirDiamant/RAG_Techniques/blob/main/all_rag_techniques/contextual_compression.ipynb 6. https://www.researchgate.net/publication/380821537_xRAG_Extreme_Context_Compression_for_Retrieval-augmented_Generation_with_One_Token 7. https://www.linkedin.com/posts/srgrace_contextual-compression-langchain-llamaindex-activity-7159878261496238080-ZL8V