How to Improve RAG Performance: 5 Key Techniques with Examples

Retrieval Augmented Generation (RAG) significantly enhances Large Language Model (LLM) accuracy and reliability by incorporating external data. This contextualizes answers and allows for updates without retraining. However, RAG isn't universally effective. This article details building robust RAG pipelines, explores limitations, and offers solutions.

OpenAI Fundamentals (brief mention, no need to expand)

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How RAG Works:

(Image from LlamaIndex Documentation)

A RAG pipeline comprises three stages: Indexing, Retrieval, and Generation.

• Indexing: Data (various formats) is cleaned, converted to plain text, chunked into manageable pieces, and transformed into numerical vectors (embeddings) using an embedding model. These embeddings and chunks are indexed for efficient search.

• Retrieval: User queries are vectorized using the same embedding model. The system calculates similarity scores between the query vector and indexed chunk vectors, retrieving the top K most similar chunks.

• Generation: The query and retrieved chunks are formatted into a prompt and fed to the LLM for answer generation.

RAG Limitations:

Challenges exist in each stage:

• Indexing: Noisy data leads to unhelpful LLM responses.
• Retrieval: The system may not always retrieve relevant chunks.
• Generation: Even with good retrieval, the LLM might hallucinate incorrect answers.

Improving RAG Performance:

Three key strategies address these limitations: Chunking, Re-Ranking, and Query Transformations. The following demonstrates these using LlamaIndex and the OpenAI API to answer questions about Wikipedia entries (Emma Stone, Ryan Gosling, La La Land).

Baseline RAG System:

Install necessary packages:

!pip install llama-index openai wikipedia

Set your OpenAI API key:

import os
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

Load Wikipedia data and build the index:

from llama_index.core import (VectorStoreIndex, ServiceContext, download_loader)
WikipediaReader = download_loader("WikipediaReader")
loader = WikipediaReader()
pages = ['Emma_Stone', 'La_La_Land', 'Ryan_Gosling']
documents = loader.load_data(pages=pages, auto_suggest=False, redirect=False)

from llama_index.llms import OpenAI
from llama_index.embeddings import OpenAIEmbedding, OpenAIEmbeddingModelType
gpt3 = OpenAI(temperature=0, model="gpt-3.5-turbo-instruct", api_key=os.environ["OPENAI_API_KEY"])
embed_model = OpenAIEmbedding(model=OpenAIEmbeddingModelType.TEXT_EMBED_ADA_002, api_key=os.environ["OPENAI_API_KEY"])
service_context_gpt3 = ServiceContext.from_defaults(llm=gpt3, chunk_size=256, chunk_overlap=0, embed_model=embed_model)
index = VectorStoreIndex.from_documents(documents, service_context=service_context_gpt3)
retriever = index.as_retriever(similarity_top_k=3)

Create a prompt template to ensure context-based answers:

from llama_index.core.prompts import PromptTemplate
template = (
    "We have provided context information below. \n"
    "---------------------\n"
    "{context_str}"
    "\n---------------------\n"
    "Given this information, please answer the question: {query_str}\n"
    "Don't give an answer unless it is supported by the context above.\n"
)
qa_template = PromptTemplate(template)

(The initial queries and subpar responses are omitted for brevity. The focus is on improvement strategies.)

Improving with Chunking:

Adjust chunk_size and chunk_overlap in service_context_gpt3 to optimize retrieval.

Improving with Re-Ranking:

(Code examples for FlagEmbeddingReranker and RankGPTRerank are omitted for brevity, but the descriptions remain)

• FlagEmbeddingReranker: Uses a Hugging Face re-ranking model (e.g., BAAI/bge-reranker-base) to improve chunk relevance. Requires a Hugging Face access token.

• RankGPTRerank: Employs an LLM (e.g., gpt-3.5-turbo-0125) to re-rank retrieved chunks based on semantic understanding.

Improving with Query Transformations:

(Code examples for HyDE and Multi-Step Query Transformations are omitted for brevity, but the descriptions remain)

• HyDE (Hypothetical Document Embeddings): Generates a hypothetical answer, creates embeddings for both the query and hypothetical answer, and retrieves documents closest to these embeddings.

• Multi-Step Query Transformations: Breaks down complex queries into simpler sub-queries for more effective processing.

Conclusion:

This article showcased several techniques to enhance RAG performance. The optimal approach depends on the specific application and desired results. Further exploration of RAG can be found in [link to code-along videos here].

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