Build RAG Chatbot with Llamaindex, Milvus, Mistral Nemo, and jina-clip-v1

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.
Mistral Nemo: A high-efficiency multilingual AI model optimized for natural language understanding and generation. It excels in low-latency conversational applications, offering robust performance across languages with minimal computational resources. Ideal for real-time chatbots, customer service automation, and scalable multilingual NLP tasks requiring accuracy and speed.
Jina-CLIP-V1: A multimodal AI model that bridges text and images via shared embeddings, enabling cross-modal retrieval and understanding. Strengths include robust generalization, efficient scalability, and seamless integration for multilingual and visual-text tasks. Ideal for image-text search, content recommendation, and enhancing AI-driven applications in e-commerce, media, and digital asset management.

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 Mistral Nemo

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="open-mistral-nemo")

Step 3: Install and Set Up jina-clip-v1

%pip install llama-index-embeddings-jinaai

You may also need other packages that do not come direcly with llama-index.

!pip install Pillow

from llama_index.embeddings.jinaai import JinaEmbedding

embed_model = JinaEmbedding(
    api_key=jinaai_api_key,
    model="jina-clip-v1",
    # choose `retrieval.passage` to get passage embeddings
    task="retrieval.passage",
)

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.

Mistral Nemo optimization tips

To optimize Mistral Nemo in a RAG setup, focus on improving retrieval quality by fine-tuning embeddings for domain-specific data, chunking documents into 256-512 token segments for balanced context, and using metadata filtering to reduce noise. Adjust the top-k retrieval count dynamically based on query complexity. For generation, enable model quantization (e.g., 4-bit) to speed up inference and trim response length via max_token limits. Use caching for frequent queries and profile latency to identify bottlenecks. Regularly validate outputs against ground-truth datasets to refine accuracy.

Jina-CLIP-v1 optimization tips

To optimize Jina-CLIP-v1 in a RAG setup, preprocess inputs by normalizing text and resizing images to match the model’s expected dimensions (e.g., 224x224). Use batch inference to maximize GPU utilization and enable mixed-precision (FP16) for faster processing. Fine-tune the model on domain-specific data to improve retrieval relevance. Cache frequently accessed embeddings to reduce redundant computations. Optimize vector indexing with approximate nearest neighbor (ANN) libraries like FAISS or HNSW for efficient similarity search. Regularly validate embedding quality using downstream task metrics to ensure alignment with retrieval goals.

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 completing this exciting journey into the world of RAG systems! You've learned how to seamlessly integrate essential components like LlamaIndex, Milvus, Mistral Nemo, and Jina's clip-v1 into a powerful RAG pipeline. Each piece plays a crucial role: LlamaIndex serves as your robust framework to manage data and queries efficiently, Milvus provides a stellar vector database ensuring lightning-fast retrieval, Mistral Nemo unleashes the power of large language models, and Jina's clip-v1 helps in generating insightful embeddings. By combining these technologies, you've paved the way for creating a system that not only retrieves relevant information but also enhances it with meaningful context—making your applications smarter and more intuitive!

You also discovered some handy optimization tips along the way, elevating the performance of your RAG system. And let's not forget the useful free RAG cost calculator we introduced! This tool will guide you in estimating potential costs as you scale your projects, ensuring your innovations remain budget-friendly. Now that you have all this knowledge at your fingertips, it’s time to put it into action! Start building, optimizing, and customizing your own RAG applications that could revolutionize the way we access and interact with information. The possibilities are truly endless, so go ahead and let your creativity soar!

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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