Build RAG Chatbot with Llamaindex, Milvus, Gemini 2.0 Flash-Lite, and Cohere embed-multilingual-v3.0

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 2.0 Flash-Lite: An ultra-lightweight AI model optimized for cost-effective, real-time language tasks. Designed for efficiency, it is ideal for embedded AI applications, mobile devices, and environments where fast response times and minimal resource usage are critical.
Cohere embed-multilingual-v3.0: A multilingual text embedding model designed to convert text in over 100 languages into high-dimensional vectors (1024 dimensions), excelling in semantic understanding and cross-lingual tasks. Its strengths include robust multilingual alignment and nuanced context capture, ideal for cross-language semantic search, multilingual document clustering, and enhancing NLP applications like recommendation systems in diverse linguistic environments.

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 2.0 Flash-Lite

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

from llama_index.llms.google_genai import GoogleGenAI

llm = GoogleGenAI(
    model="gemini-2.0-flash-lite",
    # api_key="some key",  # uses GOOGLE_API_KEY env var by default
)

Step 3: Install and Set Up Cohere embed-multilingual-v3.0

%pip install llama-index-embeddings-cohere

from llama_index.embeddings.cohere import CohereEmbedding

embed_model = CohereEmbedding(
    api_key=cohere_api_key,
    model_name="embed-multilingual-v3.0",
)

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 2.0 Flash-Lite optimization tips

Gemini 2.0 Flash-Lite is designed for lightweight and efficient processing, so optimize retrieval by focusing on precision rather than volume. Use minimal yet highly relevant document chunks to keep token usage low. Implement approximate nearest neighbor (ANN) search for faster retrieval without sacrificing quality. Cache frequently used knowledge snippets for instant responses. Fine-tune prompts to be as concise as possible while maintaining clarity.

Cohere embed-multilingual-v3.0 optimization tips

To optimize Cohere embed-multilingual-v3.0 in RAG, preprocess text by normalizing casing, removing redundant whitespace, and filtering low-relevance content. Use appropriate chunk sizes (200–500 tokens) to balance context retention and embedding quality. Batch embedding requests to reduce latency. Leverage its multilingual strength by aligning input language with supported locales and applying language-specific stopword filtering. Fine-tune retrieval with hybrid search (semantic + keyword) and metadata filters. Regularly update embeddings to reflect new data and test retrieval accuracy using diverse multilingual queries to ensure robust cross-lingual performance.

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 just taken a significant step forward in your journey to mastering the integration of powerful tools to build an effective RAG (Retrieval-Augmented Generation) system. Throughout the tutorial, you’ve learned how to seamlessly connect LlamaIndex, Milvus, Gemini 2.0 Flash-Lite, and the Cohere embed-multilingual-v3.0 embedding model. Each of these components plays a crucial role: the LlamaIndex framework serves as a sturdy foundation for managing your data and queries, while Milvus delivers high-performance vector storage and retrieval to ensure rapid access to relevant information. Meanwhile, Gemini 2.0 Flash-Lite injects cutting-edge generative capabilities, and the Cohere embedding model allows for nuanced understanding across multiple languages, ensuring you're always a step ahead in production-ready applications.

You didn’t just learn how to piece these components together to form a robust RAG pipeline; you also picked up invaluable tips to optimize performance and efficiency. The extra features, like the handy free RAG cost calculator, empower you to forecast your operational expenditures, making it easier than ever to keep your projects on track. Now that you’ve grasped these fundamental concepts and practical applications, it’s time to dive in! Let your newfound knowledge fuel your creativity and innovation as you start building, optimizing, and experimenting with your own RAG applications. The possibilities are vast, and the journey ahead is exciting—so roll up your sleeves and get to work!

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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