Build RAG Chatbot with Llamaindex, Milvus, Gemini 1.5 Pro, and OpenAI text-embedding-3-small

Introduction to RAG

Retrieval-Augmented Generation (RAG) is a game-changer for GenAI applications, especially in conversational AI. It combines the power of pre-trained large language models (LLMs) like OpenAI’s GPT with external knowledge sources stored in vector databases such as Milvus and Zilliz Cloud, allowing for more accurate, contextually relevant, and up-to-date response generation. A RAG pipeline usually consists of four basic components: a vector database, an embedding model, an LLM, and a framework.

Key Components We'll Use for This RAG Chatbot

This tutorial shows you how to build a simple RAG chatbot in Python using the following components:

Llamaindex: a data framework that connects large language models (LLMs) with various data sources, enabling efficient retrieval-augmented generation (RAG). It helps structure, index, and query private or external data, optimizing LLM applications for search, chatbots, and analytics.
Milvus: An open-source vector database optimized to store, index, and search large-scale vector embeddings efficiently, perfect for use cases like RAG, semantic search, and recommender systems. If you hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on Milvus and offers a free tier supporting up to 1 million vectors.
Gemini 1.5 Pro: A highly capable AI model designed for advanced reasoning, contextual understanding, and in-depth text generation. Well-suited for research, enterprise knowledge management, and sophisticated AI applications requiring accurate, multi-turn dialogue and deep contextual comprehension.
OpenAI text-embedding-3-small: Designed to generate dense vector representations of text, this model excels in efficiency and cost-effectiveness, optimized for speed and low resource usage. It delivers competitive performance in semantic similarity, retrieval, and clustering tasks, making it ideal for large-scale applications like search engines, recommendation systems, and text classification where balancing accuracy with computational cost is crucial.

By the end of this tutorial, you’ll have a functional chatbot capable of answering questions based on a custom knowledge base.

Note: Since we may use proprietary models in our tutorials, make sure you have the required API key beforehand.

Step 1: Install and Set Up Llamaindex

pip install llama-index

Step 2: Install and Set Up Gemini 1.5 Pro

%pip install llama-index-llms-google-genai llama-index

from llama_index.llms.google_genai import GoogleGenAI

llm = GoogleGenAI(
    model="gemini-1.5-pro",
    # api_key="some key",  # uses GOOGLE_API_KEY env var by default
)

Step 3: Install and Set Up OpenAI text-embedding-3-small

%pip install llama-index-embeddings-openai

from llama_index.embeddings.openai import OpenAIEmbedding

embed_model = OpenAIEmbedding(
    model="text-embedding-3-small",
)

Step 4: Install and Set Up Milvus

pip install llama-index-vector-stores-milvus

from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.milvus import MilvusVectorStore

vector_store = MilvusVectorStore(
    uri="./milvus_demo.db",
    dim=1536,  # You can replace it with your embedding model's dimension.
    overwrite=True,
)

Step 5: Build a RAG Chatbot

Now that you’ve set up all components, let’s start to build a simple chatbot. We’ll use the Milvus introduction doc as a private knowledge base. You can replace it with your own dataset to customize your RAG chatbot.

import requests

from llama_index.core import SimpleDirectoryReader

# load documents
url = 'https://raw.githubusercontent.com/milvus-io/milvus-docs/refs/heads/v2.5.x/site/en/about/overview.md'
example_file = 'example_file.md'  # You can replace it with your own file paths.
response = requests.get(url)
with open(example_file, 'wb') as f:
    f.write(response.content)
documents = SimpleDirectoryReader(
    input_files=[example_file]
).load_data()

print("Document ID:", documents[0].doc_id)

storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context, embed_model=embed_model
)

query_engine = index.as_query_engine(llm=llm)
res = query_engine.query("What is Milvus?")  # You can replace it with your own question.
print(res)

Example output

Milvus is a high-performance, highly scalable vector database designed to operate efficiently across various environments, from personal laptops to large-scale distributed systems. It is available as both open-source software and a cloud service. Milvus excels in managing unstructured data by converting it into numerical vectors through embeddings, which facilitates fast and scalable searches and analytics. The database supports a wide range of data types and offers robust data modeling capabilities, allowing users to organize their data effectively. Additionally, Milvus provides multiple deployment options, including a lightweight version for quick prototyping and a distributed version for handling massive data scales.

Optimization Tips

As you build your RAG system, optimization is key to ensuring peak performance and efficiency. While setting up the components is an essential first step, fine-tuning each one will help you create a solution that works even better and scales seamlessly. In this section, we’ll share some practical tips for optimizing all these components, giving you the edge to build smarter, faster, and more responsive RAG applications.

LlamaIndex optimization tips

To optimize LlamaIndex for a Retrieval-Augmented Generation (RAG) setup, structure your data efficiently using hierarchical indices like tree-based or keyword-table indices for faster retrieval. Use embeddings that align with your use case to improve search relevance. Fine-tune chunk sizes to balance context length and retrieval precision. Enable caching for frequently accessed queries to enhance performance. Optimize metadata filtering to reduce unnecessary search space and improve speed. If using vector databases, ensure indexing strategies align with your query patterns. Implement async processing to handle large-scale document ingestion efficiently. Regularly monitor query performance and adjust indexing parameters as needed for optimal results.

Milvus optimization tips

Milvus serves as a highly efficient vector database, critical for retrieval tasks in a RAG system. To optimize its performance, ensure that indexes are properly built to balance speed and accuracy; consider utilizing HNSW (Hierarchical Navigable Small World) for efficient nearest neighbor search where response time is crucial. Partitioning data based on usage patterns can enhance query performance and reduce load times, enabling better scalability. Regularly monitor and adjust cache settings based on query frequency to avoid latency during data retrieval. Employ batch processing for vector insertions, which can minimize database lock contention and enhance overall throughput. Additionally, fine-tune the model parameters by experimenting with the dimensionality of the vectors; higher dimensions can improve retrieval accuracy but may increase search time, necessitating a balance tailored to your specific use case and hardware infrastructure.

Gemini 1.5 Pro optimization tips

Gemini 1.5 Pro benefits from structured and high-quality retrieval in a RAG pipeline. Optimize by ensuring vector search returns highly relevant documents using fine-tuned embeddings. Use adaptive chunking strategies based on query complexity to control token usage effectively. Implement document reranking techniques to improve result relevance. Leverage structured prompting to guide the model’s reasoning. Monitor latency and retrieval efficiency to refine your pipeline continuously.

OpenAI text-embedding-3-small optimization tips

Optimize input text by truncating or chunking to stay within the 8191-token limit while preserving semantic context. Normalize embeddings to unit vectors to improve cosine similarity accuracy. Batch embedding requests to reduce API calls and latency. Experiment with dimensionality reduction (e.g., 256-dim) to balance performance and storage costs. Preprocess text by removing redundant or noisy content and standardizing formats (lowercase, trimming whitespace). Cache frequent or static embeddings to avoid redundant computations. Monitor retrieval quality via metrics like recall@k and adjust chunking strategies or hybrid retrieval methods if needed. Fine-tune temperature and top-k parameters during generation to align with embedding outputs.

By implementing these tips across your components, you'll be able to enhance the performance and functionality of your RAG system, ensuring it’s optimized for both speed and accuracy. Keep testing, iterating, and refining your setup to stay ahead in the ever-evolving world of AI development.

RAG Cost Calculator: A Free Tool to Calculate Your Cost in Seconds

Estimating the cost of a Retrieval-Augmented Generation (RAG) pipeline involves analyzing expenses across vector storage, compute resources, and API usage. Key cost drivers include vector database queries, embedding generation, and LLM inference.

RAG Cost Calculator is a free tool that quickly estimates the cost of building a RAG pipeline, including chunking, embedding, vector storage/search, and LLM generation. It also helps you identify cost-saving opportunities and achieve up to 10x cost reduction on vector databases with the serverless option.

Calculate your RAG cost now.

Calculate your RAG cost

What Have You Learned?

Congratulations on reaching the end of this tutorial! You’ve taken a giant leap into the world of Retrieval-Augmented Generation (RAG) systems, and it’s been a fantastic journey. By integrating powerful components like LlamaIndex for creating a robust framework, Milvus as a high-performance vector database, the cutting-edge Gemini 1.5 Pro large language model, and the OpenAI text-embedding-3-small embedding model, you’ve equipped yourself with an incredible toolkit for enhancing your applications. Each element plays a crucial role—LlamaIndex structures your data efficiently, Milvus offers rapid retrieval, Gemini 1.5 Pro generates coherent text, and the embedding model accurately represents your information. It’s a harmonious blend that transforms how you harness data and generate insights!

But that’s not all! The tutorial has equipped you with optimization tips that will make your RAG system more efficient and effective. Plus, you’ve got a free RAG cost calculator at your disposal, helping you assess the expenses involved as you develop your applications. The potential to create innovative solutions is in your hands, and we can’t wait to see what you’ll build! So roll up your sleeves, dive into development, and let your creativity shine. The future of RAG applications is bright, and you’re on the cutting edge—go out there and start crafting your unique solutions that can change the game!

Further Resources

🌟 In addition to this RAG tutorial, unleash your full potential with these incredible resources to level up your RAG skills.

How to Build a Multimodal RAG | Documentation
How to Enhance the Performance of Your RAG Pipeline
Graph RAG with Milvus | Documentation
How to Evaluate RAG Applications - Zilliz Learn
Generative AI Resource Hub | Zilliz

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