Welcome to LlamaIndex 🦙 LlamaIndex (formerly GPT Index) is a data framework for ingesting, structuring, and accessing private or domain-specific data for LLM applications. 🚀 Why Choose LlamaIndex?[1] At their core, LLMs provide a natural language interface between humans and inferred data. The widely available models are pre-trained on a large amount of publicly available … Read more
The explosive popularity of ChatGPT proves the capabilities of large language models (LLMs) in generating knowledge and reasoning. However, ChatGPT is a model pre-trained on public datasets, which may not provide specific answers or results related to user businesses. So, how can we maximize the capabilities of LLMs using private data? LlamaIndex can solve this … Read more
1. Overview The retriever is a part of the query engine, responsible for obtaining the most relevant context based on user queries (or chat messages). It can be built on existing indexes but can also be defined independently. It serves as a key building block for query engines (and chat engines) to retrieve relevant context. … Read more
1. Introduction to LlamaIndex LlamaIndex is a Python library created by Jerry Liu that enables efficient text search and summarization of large document collections using language models.Developers can quickly add private/custom data to enhance existing LLMs with LlamaIndex. It provides personalized and data-driven responses without the need for retraining large models. Due to the limited … 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
LlamaIndex is a simple and flexible data framework that connects custom data sources with large language models. LlamaIndex provides comprehensive support for RAG. Advanced RAG (Retrieval-Augmented Generation) techniques can be modeled using a composable hierarchical abstraction. The retrieved text can be linked to the following elements: Retriever Text Pipeline Query Engine The retrieval of RAG … Read more
Overview The ContextChatEngine class is a contextual chat engine designed to provide a smooth chat experience by retrieving contextual information from the chat and using a language model (LLM) to generate responses based on system prompts. It is a simple chat mode built on top of a data retriever. For each chat interaction: First, retrieve … Read more
Overview ReAct is an agent-based chat mode built on top of a data query engine. For each chat interaction, the agent enters a ReAct loop: First, decide whether to use the query engine tool and propose appropriate input (Optional) Use the query engine tool and observe its output Decide whether to repeat or give a … Read more
Abstract Large language models (LLMs) have demonstrated impressive capabilities. However, this does not mean they are error-free; anyone who has experienced ChatGPT’s “hallucinations” can attest to that. Retrieval Augmented Generation (RAG) is a framework designed to make LLMs more reliable by extracting relevant, up-to-date data directly related to user queries. In this article, I analyze … Read more
Overview The Query Engine is a generic interface that allows you to query data. It accepts natural language queries and returns rich responses. It is typically (but not always) built on one or more indexes through a retriever. You can combine multiple query engines to achieve more advanced functionality. Note: If you want to have … Read more