Build RAG Chatbot with Llamaindex, Milvus, Mixtral 8x7B, and Ollama nomic-embed-text

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.
Mixtral 8x7B: A sparse mixture-of-experts (MoE) model with eight 7B parameter networks, designed for efficient, high-performance NLP tasks. Excels in text generation, reasoning, and multilingual support while minimizing computational costs. Ideal for scalable enterprise applications, real-time chatbots, and multi-task environments requiring optimized resource utilization and versatile AI capabilities.
Ollama nomic-embed-text: A versatile text embedding model optimized for efficient generation of high-dimensional vector representations, excelling in semantic search, clustering, and similarity analysis. Strengths include robust performance on long-context inputs and scalability for local deployment. Ideal for applications requiring accurate document retrieval, recommendation systems, or contextual understanding in resource-constrained 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 Mixtral 8x7B

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="open-mixtral-8x7b")

Step 3: Install and Set Up Ollama nomic-embed-text

%pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="nomic-embed-text",
)

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.

Mixtral 8x7B optimization tips

To optimize Mixtral 8x7B in RAG, prioritize efficient context retrieval by fine-tuning chunk size and overlap for balanced relevance and latency. Use sparse attention configurations to reduce computational overhead, and enable tensor parallelism to leverage its mixture-of-experts architecture. Quantize the model to 4-bit precision (e.g., via GPTQ) for faster inference with minimal accuracy loss. Pre-filter retrieved documents to remove noise, and cache frequent query embeddings. Adjust temperature (0.2-0.5) and max tokens to balance creativity and focus. Profile expert routing to ensure balanced workload distribution across GPU resources.

Ollama nomic-embed-text optimization tips

To optimize Ollama nomic-embed-text in RAG, ensure input text is preprocessed (normalize casing, remove redundant whitespace) and chunked into coherent segments matching the model’s optimal context window. Use batch inference for bulk embeddings to reduce latency, and leverage GPU acceleration if available. Fine-tune embedding dimensions via dimensionality reduction if retrieval speed is critical. Regularly validate embedding quality with domain-specific benchmarks, and cache frequently queried embeddings to minimize redundant computations. Adjust temperature and similarity thresholds during retrieval to balance precision and recall.

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 making it through this tutorial! You've unlocked the secrets of building a powerful Retrieval-Augmented Generation (RAG) system by seamlessly integrating a framework, vector database, large language model (LLM), and embedding model. By using LlamaIndex with Milvus, you learned how to efficiently manage and retrieve relevant data to enhance the generative capabilities of the Mixtral 8x7B model. We explored the importance of the Ollama nomic-embed-text model, showcasing how embeddings can capture the nuances of your text data, which is fundamental for ensuring that your retrieval and generation processes work hand in hand. As you embarked on the optimization tips shared throughout the tutorial, you discovered ways to fine-tune your pipeline for performance and efficacy, setting the stage for a totally customized experience.

What’s even better is that you now have access to a free RAG cost calculator, empowering you to strategize and budget your projects effectively! Think about the possibilities that await you: from building intelligent chatbots to crafting dynamic content generation applications. The tools and techniques you've learned are just the beginning. So don’t hesitate—dive in, experiment, and innovate! Your journey into creating exceptional RAG applications starts now, and who knows what incredible solutions you might come up with? Go ahead and unleash your creativity; the world is waiting for your unique contributions!

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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