LlamaIndex

LlamaIndex is a data framework that integrates custom, proprietary data sources with Large Language Models (LLMs). It enables advanced AI applications like Retrieval Augmented Generation (RAG) and autonomous AI agents. This enhances LLM accuracy and utility by providing domain-specific, up-to-date information.

intermediate30 min5 steps

The play

Set Up Your Environment
Install the LlamaIndex library and any necessary LLM integrations (e.g., OpenAI, Hugging Face). Ensure you have your API keys configured for LLM access.
Prepare Your Data Sources
Identify the private or specialized datasets you want your LLM to access. Organize them into a format LlamaIndex can read (e.g., text files, PDFs, databases, APIs).
Ingest and Index Data
Use LlamaIndex data loaders to ingest your prepared data. Create an index (e.g., VectorStoreIndex) from the loaded documents to enable efficient retrieval.
Configure a Query Engine
Build a query engine on top of your index. This engine will process user queries, retrieve relevant information from your indexed data, and pass it to the LLM.
Query and Refine
Test your LlamaIndex setup by posing queries to the engine. Analyze the LLM's responses and refine your indexing strategy or prompting techniques for improved accuracy and context.

Starter code

import os
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI

# Ensure your OpenAI API key is set as an environment variable
# os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"

# 1. Create a 'data' directory and place some text files inside
#    e.g., data/report.txt, data/faq.txt

# 2. Load documents from the 'data' directory
documents = SimpleDirectoryReader("data").load_data()

# 3. Create an index from the documents
#    (Uses OpenAI's default embeddings and LLM unless configured otherwise)
index = VectorStoreIndex.from_documents(documents)

# 4. Create a query engine
query_engine = index.as_query_engine()

# 5. Query the engine
response = query_engine.query("What is this data about?")
print(response.response)

Source

Articlellamaindex.ai