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LlamaIndex is a simple and flexible data framework for connecting custom data sources with large language models.
RAG is the process of optimizing LLM outputs by referencing knowledge bases outside of their training data sources before generating responses. RAG extends the already powerful capabilities of LLMs, making them suitable for specific domain or organizational knowledge bases without the need to retrain the model. This is a cost-effective way to improve LLM outputs, keeping them relevant, accurate, and useful in various contexts.
LlamaIndex provides a command-line tool (CLI) that enhances the daily workflow efficiency of developers. This command-line tool supports RAG functionality.
In this session, we will quickly get started with the RAG features of LlamaIndex CLI:
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Build a basic RAG workflow
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Build a RAG chatbot based on FastAPI + Next.js
Install LlamaIndex CLI
Here, in addition to installing llama-index, we also need chromadb. This is because the RAG functionality in the CLI defaults to using chromadb as the vector data store.
Our sample data is a PDF file, so we will install pypdf as the file parsing component.
$ pip install -U llama-index chromadb pypdfPrepare Knowledge Base Files
This example uses the Uniswap V3 whitepaper as the knowledge base. File download:
https://uniswap.org/whitepaper-v3.pdf
Import Files via Command Line and Q&A
First, set the environment variable OPENAI_API_KEY to specify the OpenAI API Key. The command-line tool defaults to using OpenAI’s large model.
After completing the installation of llama-index, we will have a command-line tool llamaindex-cli. Now we can import the knowledge base file using the following command:
$ llamaindex-cli rag --files "<absolute path to the file>"Note that based on testing, if the data will be used in subsequent full-stack applications, the full path to the knowledge base file must be provided; otherwise, an error will occur during the creation of the full-stack application project.
Example:
$ llamaindex-cli rag --files "/tmp/uniswap-v3.pdf"Once the operation is completed, you can start the Q&A.
One-time Q&A:
Command llamaindex-cli rag -q
Q&A Chat in Command Line:
Command llamaindex-cli rag -c
Create Full-Stack Web Application Project via Command Line
After completing the above operations, the knowledge base is stored in the CLI’s data cache. This data can further be used by full-stack applications.
The following command can help developers quickly set up a chatbot web application, with the front end using Next.js and the back end using FastAPI:
$ llamaindex-cli rag --create-llamaDevelopers will input the necessary information step by step as prompted by the command line to complete the project creation.
After creation, start the project, and the application will run on the local port 3000. Visiting the following link will yield a page similar to this:
http://localhost:3000
Developers can complete a fully functional Q&A chatbot web application with almost zero overhead. The entire configuration process should take no more than 10 minutes. This can also serve as a foundation for subsequent feature development.
Interested students should try it out quickly.
Wishing everyone a Happy New Year!
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