Detailed steps and code are provided for how to choose the best embedding model and reranker model. When building a Retrieval-Augmented Generation (RAG) pipeline, one of the key components is the retriever. We have various embedding models to choose from, including OpenAI, CohereAI, and open-source sentence transformers. Additionally, there are several rerankers available from CohereAI … Read more
1. Why Introduce LightRAG? Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, allowing LLMs to generate more accurate and contextually relevant responses, significantly improving utility in practical applications. • By adapting to domain-specific knowledge, RAG systems ensure that the information provided is not only relevant but also meets user needs. … Read more
Source: DeepHub IMBA This article is approximately 5400 words long and is recommended for a reading time of over 10 minutes. This article discusses 12 pain points encountered during the development of RAG pipelines (7 of which are sourced from papers, and 5 from our own summary), and proposes corresponding solutions to these pain points. … Read more
Author: Kuang Ji Reviewed by: Los Introduction: The author provides the most important design guidelines for RAG system design at various stages. This analysis covers five stages: indexing, storage, retrieval, synthesis, and evaluation, detailing the important design rules for each stage of the RAG system. The author combines practical experience in building RAG systems with … Read more
Click the “Blue WeChat Name” below the title to quickly follow In the era of large models, many new terms have emerged, and RAG is one of them. This article from the tech community, “Understanding RAG (Retrieval Augmented Generation) in One Article,” explains what RAG is, its functions, and the associated challenges. Related historical articles … Read more
Overview As most LLMs are only trained periodically on a large amount of public data, they cannot access the latest information and/or private data. Retrieval-Augmented Generation (RAG) is a core paradigm for developing applications with LLMs, addressing this issue by connecting to external data sources. A basic RAG pipeline includes embedding user queries, retrieving relevant … Read more
Introduction Hello everyone, I am Liu Cong from NLP. As the context length supported by large models continues to increase, a debate has emerged online (many groups are discussing this topic, so I would like to share my thoughts) regarding RAG and Long-Context, which is really unnecessary… The main point is that the two are … Read more
Explanation Querying is the most important part of LLM applications. In LlamaIndex, once you have completed: data loading, building the index, and storing the index, you can proceed to the most crucial part of LLM applications: querying. A simple query is just a prompt call to the large language model: it can be a question … Read more
Pinecone + LangChain: Building an Efficient AI Retrieval System Recently, while experimenting with AI retrieval systems, I found that Pinecone and LangChain are a match made in heaven. Pinecone is a powerful vector database, and LangChain is a flexible framework. Combining the two allows you to easily build an efficient AI retrieval system. Today, I … Read more
In this blog post, we introduce a brand new data structure in LlamaIndex: the Document Summary Index. We describe how it helps provide better retrieval performance compared to traditional semantic search, along with an example. https://github.com/jerryjliu/llama_index 1 Background One of the core scenarios for large language models (LLM) is question answering on user data. To … Read more