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Today:《LlamaIndex Architecture Design and Application Case Practice》
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Analysis of the LlamaIndex Development Framework
LlamaIndex is a data development framework that provides applications based on LLM to acquire, build, and access private or domain-specific data. It establishes a bridge between natural language and data sources, which can include structured data such as SQL and Excel from enterprises, semi-structured data like Elasticsearch and business APIs, and more non-structured data sources like text, emails, PDFs, PPTs, videos, audios, and images.
Compared to the business development framework Spring Cloud for microservices and the data proxy framework MyBatis, LangChain is the new Spring Cloud of the AGI era, while LlamaIndex is the MyBatis of the AGI era.
We know that LLMs are usually trained on a large amount of public data and do not include private company data, so LLMs typically cannot accurately answer private questions. LlamaIndex implements a Retrieval-Augmented Generation (RAG) development framework that vectorizes private data, retrieves relevant contextual data based on the question content, and then hands it over to the LLM for inference and answering.
LlamaIndex implements the entire process of RAG, as shown below:
Loading step: Loads private data stored in different locations (local text files, PDFs, other websites, databases, APIs) into memory using different methods, providing hundreds of data connectors through Llama Hub.
Indexing step: Converts data into a structured index that is easy to query, usually by vectorizing the data, as vectorized data contains more dimensions of semantic features. By calculating similarity, contextual information for the query question can be obtained.
Storing step: Stores vectorized data in a database for reuse. LlamaIndex supports various vector databases, such as Redis, Elasticsearch, Tair, etc.
Querying step: Supports various LLMs, such as OpenAI, Llama2, PaLM, etc. It also supports loading various language models through Hugging Face, answering questions based on index data through different query strategies: routing queries, subqueries, step-by-step queries.
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Routing query: Automatically selects the required data source based on the question content.
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Subquery: Generates multiple sub-questions based on the main question, then automatically selects data sources to answer, and summarizes the answers from multiple sub-questions.
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Step-by-step query: Breaks the question into multiple steps, with each step responding based on the answer from the previous step.
Evaluation step: Provides objective metrics to assess the accuracy, fidelity, and speed of query responses.
So, how does LlamaIndex implement RAG applications? How is the business architecture designed? How are key issues handled? Today at noon, the live broadcast will tell you, highlights of the live broadcast:
1. In-depth analysis of the LlamaIndex development framework
2. Practical case of building enterprise-level applications based on LlamaIndex
3. Practical case of building enterprise-level agents in collaboration with LlamaIndex and LangChain
Please click the button below to make an appointment for the live broadcast, see you at 11:30 noon today!
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