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
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
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
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
Overview This article introduces how to use LlamaIndex to write your own Agent handling functions. Note that this article uses a locally deployed LLM supported by Ollama for practical implementation, rather than remotely calling the OpenAI API. The goal of this article is to save the output content to a PDF file and then stop … Read more
In the previous article “The Future of Application Orchestration is Pipeline, LlamaIndex Releases Query Pipeline in Preview to Enhance Application Development Flexibility” we mentioned that LlamaIndex has released a new experimental feature that supports defining a Query Pipeline in a declarative manner to create personalized application workflows, along with a case study for RAG applications. … Read more
Overview This article implements a simple intelligent Agent that first queries data from a database and then processes the data using utility functions. This is a very common scenario that can be extended to multiple practical situations. Similarly, all experiments in this article are conducted on a local machine with 16C32G Linux (CPU). Data Preparation … Read more
Overview This article introduces how to use LlamaIndex’s Agent to call multiple custom Agent tool functions. As with the previous articles in this series, this article does not use the OpenAI API and relies entirely on a local large model to complete the entire functionality. The goal of this article is simple: to save the … Read more
Prompt is the fundamental input that grants LLM expressive capabilities. LlamaIndex uses prompts to build indexes, execute inserts, retrieve during queries, and synthesize final answers. LlamaIndex provides a set of out-of-the-box default prompt templates: https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/prompts/default_prompts.py Additionally, here are some prompts specifically written for chat models like gpt-3.5-turbo: https://github.com/run-llama/llama_index/blob/main/llama-index-core/llama_index/core/prompts/chat_prompts.py Custom Prompts Users can also provide their … Read more