Build RAG Chatbot with Llamaindex, Milvus, Gemini 1.5 Flash, and voyage-3-lite

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 Flash: A fast, streamlined AI model optimized for real-time applications and low-latency environments. While lightweight, it maintains high accuracy in text-based tasks, making it ideal for rapid document summarization, chatbot interactions, and AI-driven customer support with minimal computational overhead.
Voyage-3-Lite: This model is designed for resource-constrained environments, offering a lightweight and efficient solution for various NLP tasks. It excels in applications such as chatbots, content generation, and real-time text analysis, providing quick responses while maintaining a balance between performance and computational efficiency. Ideal for developers needing high throughput with limited resources.

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 Flash

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

from llama_index.llms.google_genai import GoogleGenAI

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

Step 3: Install and Set Up voyage-3-lite

%pip install llama-index-embeddings-voyageai

from llama_index.embeddings.voyageai import VoyageEmbedding

embed_model = VoyageEmbedding(
    voyage_api_key="",
    model_name="voyage-3-lite",
)

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 Flash optimization tips

For Gemini 1.5 Flash, prioritize lightweight and high-speed retrieval to match its fast inference capabilities. Optimize document embeddings for quick similarity search and use pre-filtering techniques to reduce retrieval overhead. Maintain a small but high-quality context window by fine-tuning chunking strategies. Leverage caching for commonly accessed queries to minimize API calls. Monitor token usage and adjust retrieval scope dynamically to balance speed and response accuracy.

voyage-3-lite optimization tips

voyage-3-lite is optimized for speed and efficiency, making it a strong choice for low-latency RAG applications. Improve retrieval by minimizing the number of retrieved documents while maintaining relevance using adaptive filtering techniques. Keep prompts concise and to the point, avoiding redundant context to reduce processing overhead. Set temperature between 0.1 and 0.2 to prioritize factual accuracy and prevent unnecessary response variation. Use caching to reduce repeated API calls for common queries. Implement response streaming to improve user experience in real-time applications. Optimize resource usage by running voyage-3-lite in high-throughput scenarios where speed is prioritized over deep reasoning, reserving larger models for more complex analysis.

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?

Wow, what an incredible journey we've taken together in this tutorial! You've now gained a comprehensive understanding of how to integrate a robust framework like LlamaIndex with a powerful vector database, Milvus, to create a seamless RAG (Retrieval-Augmented Generation) system. With the LLM, Gemini 1.5 Flash, at your side, you're equipped to generate rich, dynamic responses to user queries by efficiently retrieving relevant data. Pairing this with the voyage-3-lite embedding model ensures that your system is not just fast but also incredibly intelligent in understanding context and nuances in the input data. You’ve seen firsthand how each of these components plays a vital role, enabling you to create a truly interactive and responsive application. Remember those optimization tips we discussed? They’ll be vital as you scale your applications and enhance their performance, allowing you to serve even more queries without breaking a sweat.

But wait—there's more! You now have access to a free RAG cost calculator that makes it easy to analyze and optimize your RAG deployment costs. Take a moment to reflect on how much you've learned and how these tools can empower you to push the boundaries of what's possible in your projects. So, what are you waiting for? It's time to roll up your sleeves, start building, optimizing, and innovating your very own RAG applications! The landscape of AI and information retrieval is at your fingertips—go explore, create, and revolutionize the way we interact with technology!

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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