Build RAG Chatbot with Llamaindex, Milvus, Mistral Ministral 8B, and Ollama snowflake-arctic-embed

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 Ministral 8B: A compact, high-efficiency language model optimized for scalable natural language processing. It excels in rapid text generation, multilingual tasks, and context-aware reasoning while maintaining low computational demands. Ideal for real-time chatbots, content summarization, and edge-device deployment where speed and resource efficiency are critical.
Ollama Snowflake-Arctic-Embed: A high-performance embedding model optimized for semantic understanding and retrieval tasks. It excels in generating dense vector representations for text, offering robust accuracy and scalability. Ideal for enterprise applications like semantic search, recommendation systems, and data clustering, particularly in environments leveraging Snowflake’s data ecosystem for seamless integration and large-scale analytics.

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 Ministral 8B

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="ministral-8b-latest")

Step 3: Install and Set Up Ollama snowflake-arctic-embed

%pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="snowflake-arctic-embed",
)

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 Ministral 8B optimization tips

To optimize Mistral Ministral 8B in RAG setups, ensure input prompts are tightly formatted with clear context-question separation using [INST] tags. Use smaller retrieval chunks (256-512 tokens) to reduce noise and improve relevance. Adjust the temperature (0.1-0.3) for deterministic, fact-focused outputs. Employ a hybrid retrieval approach (dense + sparse methods) to balance precision and recall. Prune retrieved documents with cross-encoder re-rankers before feeding to the model. Limit input context length to avoid truncation, and fine-tune the model on domain-specific data for better alignment. Enable flash attention and quantization (e.g., 4-bit) to reduce latency and memory usage.

Ollama Snowflake-Arctic-Embed optimization tips

To optimize Ollama Snowflake-Arctic-Embed in a RAG setup, ensure input text is cleanly chunked (e.g., 256-512 tokens) to align with its context window. Use batch processing for embeddings to reduce latency, and leverage hardware acceleration (e.g., CUDA for GPUs). Fine-tune with domain-specific data to improve retrieval relevance. Quantize the model for faster inference with minimal accuracy loss. Cache frequently accessed embeddings, and experiment with dimensionality reduction techniques like PCA if storage or speed constraints exist. Regularly validate embedding quality using similarity benchmarks.

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 through the integration of LlamaIndex, Milvus, Mistral Ministral 8B, and the Ollama snowflake-arctic-embed into a robust Retrieval-Augmented Generation (RAG) system! You’ve explored how to leverage LlamaIndex as your go-to framework to seamlessly manage and structure your data, ensuring that you can access the right information at the right time. The use of Milvus as a vector database empowers you to perform lightning-fast searches through massive datasets, while the Mistral Ministral 8B LLM gives your applications the language understanding prowess they need to generate coherent and contextually relevant responses. Plus, embedding models like the Ollama snowflake-arctic-embed equip you with the ability to transform textual data into embeddings, allowing for even more nuanced retrieval and personalization in your applications.

Throughout the tutorial, you’ve also delved into crucial optimization strategies to enhance the performance of your RAG system, showcasing the importance of fine-tuning each component for maximum efficiency. And let's not forget the bonus free RAG cost calculator, which is a handy tool to optimize your resources and keep your project budget-friendly while scaling. Now that you have this foundational knowledge, the possibilities for innovation are endless! So, don’t just stop here—dive in and start building, optimizing, and dreaming up your own RAG applications. The future is yours to create, and we can’t wait to see the amazing things you’ll achieve!

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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