Build a Powerful RAG Pipeline with LLama Index to Augment LLMs with Custom Knowledge

ByJordan Brown

Introduction

Large language models like GPT-4 and PaLM are incredibly powerful tools, capable of engaging in human-like conversations, answering questions, and even coding. However, they have a key limitation—their knowledge is restricted to the data they were trained on, which is often dated and lacks information on niche topics or private data.

This is where Retrieval Augmented Generation, or RAG, comes in. RAG allows us to augment LLMs with up-to-date, domain-specific knowledge by retrieving relevant information from external data sources. With RAG, we can build AI assistants that draw upon both the broad knowledge of the base LLM and granular information from custom knowledge bases.

In this article, we‘ll explore what RAG is, how it works, and how to build a RAG pipeline using the open-source LLama Index framework. By the end, you‘ll be ready to create your own knowledge-augmented AI applications.

What is Retrieval Augmented Generation (RAG)?

Retrieval Augmented Generation refers to the process of augmenting a language model with retrieved information to improve the relevance and factual accuracy of its outputs. Rather than relying solely on the knowledge baked into the model during pre-training, a RAG system pulls in pertinent information from an external knowledge base.

Here‘s how it works at a high level:

  1. The user provides a query or prompt
  2. The RAG system searches an indexed knowledge base to find the most semantically relevant information to the query
  3. The retrieved information is injected into the model‘s context window alongside the user‘s query
  4. The model processes the augmented context to generate an informed final output

By giving the model access to up-to-date, query-specific information, RAG can help large language models dynamically expand their knowledge, stay current, and engage with niche topics outside their base training data. This makes RAG a powerful tool for question-answering over private data like research papers, legal contracts, and internal documentation.

Key Components of a RAG Pipeline

A typical RAG pipeline consists of the following components:

  • Text Splitter: Breaks source documents into smaller chunks that fit within the LLM‘s context window. This allows the model to "attend" to relevant snippets without overloading its input buffer.

  • Embedding Model: Converts text into a numerical representation called an embedding that captures its semantic meaning. By embedding queries and knowledge chunks in the same vector space, we can assess their similarity.

  • Vector Database: Special database optimized for storing embeddings and performing efficient similarity search. Indexes embeddings so relevant chunks can be quickly retrieved given a query embedding.

  • LLM: The core language model that ingests retrieved knowledge chunks alongside the user‘s query to generate a relevant, knowledge-augmented output.

  • Utility Functions: Other supporting functions for tasks like retrieving web pages, parsing different file formats, merging overlapping chunks, and cleaning text.

To build a RAG pipeline from scratch, you‘d need to find and integrate all of these components—embedding text with a model like OpenAI Ada, indexing embeddings in a vector database like Pinecone, and so on. Fortunately, the open-source LLama Index framework provides a flexible, unified toolkit for building RAG applications without needing to wire everything together yourself.

What is LLama Index?

LLama Index (GPT Index) is an open-source Python framework for connecting custom data sources to large language models. It aims to make it dead simple to construct index-augmented LLM applications by providing:

  • Streamlined data ingestion from many sources (PDFs, websites, Google Docs, Notion, etc.)
  • Customizable document chunking and embedding
  • Inbuilt and third-party vector databases for indexing embeddings
  • Query interface for retrieving top knowledge chunks given a query
  • Data connectors for popular LLMs like ChatGPT

With LLama Index, you can quickly set up a RAG pipeline to augment open-source LLMs like GPT and LLaMA with private knowledge for question-answering, chatbots, analysis, and other AI tasks. It vastly simplifies the plumbing required to go from documents to answers.

Now let‘s walk through how to build a basic RAG pipeline with LLama Index to answer questions over a set of PDF documents. We‘ll use LLama Index‘s built-in document reader, text splitter, OpenAI embeddings, and in-memory vector index.

Step 1: Set Up Environment and Install Dependencies

First, create a new Python virtual environment and install the required packages:

python -m venv venv
source venv/bin/activate
pip install llama-index openai tiktoken

This will install the core LLama Index package, the OpenAI package for embeddings and LLM access (you‘ll need an API key), and tiktoken for tokenizing text.

Step 2: Load and Parse Documents

With your environment ready, let‘s load some PDF documents for our knowledge base. Place the PDFs you want to index in a directory, say data/.

Then use LLama Index‘s SimpleDirectoryReader to load the documents: 

from llama_index import SimpleDirectoryReader

documents = SimpleDirectoryReader(‘data‘).load_data()

This will scan the data directory, automatically detect the file format of each document, and load them into a list of Document objects.

Step 3: Split Text into Chunks

Next, we need to split our loaded documents into smaller chunks that will fit into the LLM‘s context window. We can use LLama Index‘s TokenTextSplitter to chunk the text based on token count:

from llama_index.text_splitter import TokenTextSplitter
from llama_index.node_parser import SimpleNodeParser
import tiktoken

text_splitter = TokenTextSplitter(
    separator = " ",
    chunk_size = 1024, 
    chunk_overlap = 20,
    tokenizer = tiktoken.encoding_for_model(‘gpt-3.5-turbo‘).encode
)

node_parser = SimpleNodeParser(text_splitter=text_splitter)

Here we‘re creating a token-based text splitter that will break the text on word boundaries (`), with a target chunk size of 1024 tokens and a 20 token overlap between adjacent chunks. We‘re using the tokenizer for thegpt-3.5-turbo` model; be sure to match this to the model you plan to use.

The SimpleNodeParser then executes the text splitter on the loaded documents to produce a list of text chunks.

Step 4: Generate Embeddings for Text Chunks

In order to semantically index our text chunks and query them, we need to generate an embedding representation for each chunk. LLama Index provides convenience wrappers for common embedding providers.

Here we use OpenAI‘s text-embedding-ada-002 model:

from llama_index import LLMPredictor, OpenAIEmbedding, ServiceContext
import openai

openai.api_key = "YOUR_API_KEY"

llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"))
embed_model = OpenAIEmbedding()
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, embed_model=embed_model)

We initialize the OpenAI embedding model and bind it to a ServiceContext along with an LLM predictor (the model that will be used to generate the final response).

Step 5: Build the Index

Now we‘re ready to build our vector index from the document chunks and embeddings. We‘ll use LLama Index‘s inbuilt in-memory vector store for simplicity:

from llama_index import VectorStoreIndex

index = VectorStoreIndex.from_documents(documents, service_context=service_context)

This takes our loaded documents, splits them into chunks, computes embeddings for each chunk using the OpenAI model, and indexes both the embeddings and text in the vector store. Now we‘re ready to query our knowledge base!

Step 6: Query the Index

To retrieve relevant chunks from the vector index given a natural language query, we can use LLama Index‘s query engine interface:

query_engine = index.as_query_engine()
query = "What is the main idea of document X?"
response = query_engine.query(query)
print(response)

Here‘s what happens under the hood:

  1. The query is embedded into the same vector space as the document chunks
  2. The query embedding is used to perform a similarity search on the vector index to retrieve the top-k most relevant chunks (by default k=4)
  3. The retrieved chunks are injected into the LLM‘s prompt alongside the original query
  4. The LLM processes the context and query to generate a final response

And that‘s it! With just a few lines of code, we‘ve built a RAG pipeline to augment an LLM with information from our own PDF documents. You can easily swap in other embedding models, vector databases, and LLMs to customize your pipeline.

Real-World Use Cases for RAG

RAG pipelines have numerous practical applications across industries and domains:

  • Enterprise search: Augment chatbots and search systems with knowledge from internal documentation, research reports, and other proprietary data
  • Personal knowledge management: Create AI assistants to answer questions and summarize insights from your personal notes, web clippings, and more
  • Education and research: Augment LLMs with topic-specific knowledge for academic literature reviews, fact-checking, and question-answering
  • Healthcare and life sciences: Enable knowledge retrieval from biomedical literature, clinical trial reports, doctor notes, and other unstructured documents
  • Finance: Build AI applications to extract insights from SEC filings, earnings reports, news, and market research
  • Legal and compliance: Enhance legal research, contract analysis, and eDiscovery with RAG-powered search and retrieval

The ability to dynamically pull in relevant information during inference allows LLMs to engage with a much broader and more recent body of knowledge. This makes RAG a powerful tool for building AI applications that are simultaneously knowledgeable and up-to-date.

Conclusion

Retrieval Augmented Generation offers an efficient way to expand the knowledge of large language models and ground them in up-to-date, domain-specific information. By retrieving relevant context from an external knowledge base, RAG can enhance the accuracy and relevance of LLM-generated outputs.

The LLama Index framework provides a flexible toolkit for building RAG applications in Python. With just a few lines of code, you can set up a RAG pipeline to answer questions over your PDF documents, webpages, and more.

To learn more about LLama Index and RAG, check out the following resources:

Happy indexing!

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