Local LLM RAG Tutorial: Building a Retrieval-Augmented Generation System with Llama 3 and LlamaIndex

Introduction to Retrieval-Augmented Generation (RAG) Systems

In the world of Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG) is revolutionizing how AI models interact with external data. This technique merges the power of large language models (LLMs) with the ability to fetch and integrate relevant external information, improving the quality of generated responses.

In this tutorial, we will walk you through the process of setting up a local RAG system using Llama 3, Meta's cutting-edge LLM, and LlamaIndex, a Python library designed to simplify RAG system development. By the end of this guide, you will have a fully functional RAG system capable of providing more accurate, context-rich responses.

What You Will Need for This Tutorial

To build this system, you'll need:

• Llama 3 model by Meta AI
• LlamaIndex library for creating and querying the information index
• Python environment with necessary dependencies

Let’s get started by setting up the environment and moving through the steps!

Step 1: Install the Necessary Libraries

Start by installing the essential libraries for this project. Run the following commands in your terminal or command prompt to get everything ready:

bashCopy codepip install llama-index
pip install llama-index-llms-huggingface
pip install llama-index-embeddings-huggingface

These commands will install LlamaIndex and its dependencies required for working with Hugging Face models and embeddings.

Step 2: Set Up Tokenizer and Stopping IDs

The next step involves setting up the tokenizer and the stopping tokens. These are crucial for ensuring that the model correctly handles and ends its text generation process. In your Python script, use the following code:

pythonCopy codefrom transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct", token=hf_token)

stopping_ids = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

In this code:

• Replace hf_token with your Hugging Face token (if you have one).
• The stopping_ids will signal when the Llama 3 model should stop generating tokens.

Step 3: Set Up Llama 3 Using HuggingFaceLLM

Next, let's integrate Llama 3 into the LlamaIndex system using the HuggingFaceLLM class. This class connects your Llama 3 model with the LlamaIndex system seamlessly. Here’s how you set it up:

pythonCopy codeimport torch
from llama_index.llms.huggingface import HuggingFaceLLM

llm = HuggingFaceLLM(
model_name="meta-llama/Meta-Llama-3-8B-Instruct",
model_kwargs={"token": hf_token, "torch_dtype": torch.bfloat16},
generate_kwargs={"do_sample": True, "temperature": 0.6, "top_p": 0.9},
tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
tokenizer_kwargs={"token": hf_token},
stopping_ids=stopping_ids,
)

This code sets up the Llama 3 model for inference, with specified parameters like sampling and temperature, which control the randomness of the model's responses.

Step 4: Load and Preprocess Your Data

Before building the index, we need to load and preprocess the data you’ll use for the RAG system. The SimpleDirectoryReader class in LlamaIndex makes this easy. It reads documents from a specified directory:

pythonCopy codefrom llama_index.core import SimpleDirectoryReader

documents = SimpleDirectoryReader(input_files=["path/to/your/data/files"]).load_data()

Replace "path/to/your/data/files" with the actual path to your data files. This step loads the documents into memory for indexing.

Step 5: Set Up the Embedding Model

In order to index the documents efficiently, we need to convert them into vector embeddings. LlamaIndex supports various embedding models, including those from Hugging Face. Here’s how to set up an embedding model:

pythonCopy codefrom llama_index.embeddings.huggingface import HuggingFaceEmbedding

embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

You can replace this model with one that best suits your data’s needs.

Step 6: Set Default LLM and Embedding Model

Now, let’s configure LlamaIndex to use the default Llama 3 LLM and the embedding model:

pythonCopy codefrom llama_index.core import Settings

Settings.embed_model = embed_model
Settings.llm = llm

This ensures that the system uses the Llama 3 model and your embedding model whenever needed.

Step 7: Create the Index

Next, create an index from your loaded documents. This index will allow for fast retrieval of relevant information based on similarity:

pythonCopy codefrom llama_index.core import VectorStoreIndex

index = VectorStoreIndex.from_documents(documents)

The index will now contain vector representations of the documents, ready to be queried.

Step 8: Build the QueryEngine

The QueryEngine allows you to interact with the index and retrieve relevant documents. Set it up like this:

pythonCopy codequery_engine = index.as_query_engine(similarity_top_k=3)

This code sets up the query engine to return the top 3 most relevant documents when queried.

Step 9: Query the Index

With the query engine ready, you can now retrieve relevant information from the index using simple queries:

pythonCopy coderesponse = query_engine.query("What did Paul Graham do growing up?")
print(response)

This will return relevant documents from your dataset based on the question asked.

Step 10: Build Agents and Tools (Optional)

For more advanced use cases, LlamaIndex offers agent-based interfaces. This step is optional, but you can extend the system with custom tools and agents:

pythonCopy codefrom llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
from llama_index.core.agent import ReActAgent

query_engine_tool = QueryEngineTool(
query_engine=query_engine,
metadata=ToolMetadata(name="my_query_engine", description="Provides information from the indexed documents."),
)

agent = ReActAgent.from_tools([query_engine_tool], llm=llm, verbose=True)
response = agent.chat("What did Paul Graham do growing up?")
print(str(response))

This example demonstrates how to use an agent with a query engine tool to make more sophisticated queries.

Conclusion

Congratulations! You’ve successfully built a local LLM Retrieval-Augmented Generation (RAG) system using Llama 3 and LlamaIndex. This tutorial covered the essential steps, from setting up your environment to preprocessing data, creating an index, and querying it using the Llama 3 model.

RAG systems can significantly enhance the accuracy of AI-generated responses by combining the strengths of language models with real-time data retrieval. You now have the tools to explore and expand your system with different datasets, models, and configurations to suit your needs.

Frequently Asked Questions (FAQ)

Q1: What is Retrieval-Augmented Generation (RAG)?
RAG is a technique that enhances language models by integrating external data retrieval capabilities. It allows AI to retrieve relevant documents to improve the context of its responses.

Q2: Why use Llama 3 for building a RAG system?
Llama 3, developed by Meta AI, provides outstanding performance for NLP tasks, especially in dialogue systems, making it ideal for use in RAG-based applications.

Q3: Can I use other embedding models with LlamaIndex?
Yes, LlamaIndex supports various embedding models, including those from Hugging Face, allowing you to choose the one that best fits your use case.

Q4: Is it possible to deploy the RAG system without coding?
Yes, platforms like Merlio AI provide no-code solutions for building AI applications, including RAG systems, which is perfect for non-technical users.

Q5: How can I scale my RAG system?
You can scale your RAG system by incorporating larger datasets, utilizing more powerful models, and optimizing indexing and query performance.