Introduction
RAG (Retrieval Augmented Generation) is a new concept in the field of Natural Language Processing (NLP) that combines the strengths of retrieval-based and generation-based models. It is designed to provide more accurate and contextually relevant responses to user queries. RAG is particularly useful in applications where the retrieval of information from a large knowledge base is essential, such as chatbots and question-answering systems.
In a typical RAG pipeline, a vector search engine is used to retrieve relevant documents from a knowledge base, and a language model is used to generate responses based on the retrieved documents. MeiliSearch, a powerful and fast open-source search engine, is an ideal choice for the vector search component of a RAG system.
MeiliSearch is a powerful, fast, and open-source search engine that provides instant search results. It is designed to deliver a seamless search experience, enabling users to retrieve information quickly and efficiently. MeiliSearch is an ideal choice for applications that require real-time search capabilities and high performance.
In our recent project, we explored the use of MeiliSearch as the vector search engine in a RAG system. This case study provides an overview of our approach, the challenges we faced, and the benefits of using MeiliSearch in a RAG pipeline.
Objectives
The primary objective of this project was to evaluate the performance of MeiliSearch as the vector search engine in a RAG system. We aimed to assess its ability to retrieve relevant documents from a large knowledge base and its impact on the overall response generation process.
Issues and Challenges
While the popular LLM framework, Langchain offers support for MeiliSearch, The meilisearch-python client library version at the time the was not compatible with the Latest version (v1.6 or newer) of the MeiliSearch server. This incompatibility caused us to use an older version of the MeiliSearch server, which has a bug that causes the server to use a large amount of memory when indexing large documents. This issue was a significant challenge for us, as it affected the performance and scalability of the RAG system. We had to implement workarounds to mitigate the memory usage issue and ensure the stability of the system. but later on the issue was quickly resolved by the MeiliSearch team as they released a new version of the meilisearch-python client library that is compatible with the latest version of the MeiliSearch server.
Conclusion
Despite the challenges we faced, we were able to successfully integrate MeiliSearch into the RAG pipeline and evaluate its performance. MeiliSearch proved to be a powerful and efficient vector search engine, capable of retrieving relevant documents from a large knowledge base with high accuracy and speed. The seamless integration of MeiliSearch with the RAG system enabled us to deliver more accurate and contextually relevant responses to user queries, enhancing the overall user experience.