Build RAG Chatbot with Llamaindex, Milvus, Mistral Ministral 3B, and Cohere embed-multilingual-light-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.
Mistral Ministral 3B: A compact, high-efficiency language model optimized for fast inference and low-resource environments. With 3 billion parameters, it balances performance and scalability, excelling in text generation, summarization, and question-answering tasks. Ideal for edge computing, real-time applications, and cost-sensitive deployments requiring reliable NLP capabilities without heavy computational demands.
Cohere embed-multilingual-light-v3.0: A compact multilingual embedding model designed to generate high-quality text representations across 100+ languages. It excels in efficient semantic understanding and retrieval, optimized for low-resource environments. Ideal for multilingual search, content moderation, and customer support applications requiring fast, accurate cross-lingual text analysis.

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 3B

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="ministral-3b-latest")

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

Mistral Ministral 3B optimization tips

To optimize Mistral Ministral 3B in RAG, fine-tune it on domain-specific data to improve retrieval relevance and response accuracy. Use 4-bit or 8-bit quantization to reduce memory usage while maintaining performance. Implement dynamic batching during inference to handle multiple queries efficiently. Prune redundant layers or apply LoRA for lightweight adaptation. Cache frequent retrieval outputs to minimize recomputation. Optimize prompt engineering to reduce input token length, and leverage FlashAttention for faster processing. Monitor latency and adjust context window sizes based on use-case requirements to balance speed and coherence.

Cohere embed-multilingual-light-v3.0 optimization tips

To optimize Cohere’s embed-multilingual-light-v3.0 in RAG, preprocess text by truncating or chunking inputs to 512 tokens for efficiency. Use batch inference to parallelize embedding generation, balancing batch size with latency and memory constraints. Normalize embeddings post-generation to improve cosine similarity accuracy. Leverage multilingual capabilities by ensuring consistent language tagging and avoiding mixed-language batches. Cache frequently accessed embeddings to reduce redundant computations. Fine-tune retrieval thresholds to balance precision and recall. Monitor model performance using metrics like retrieval hit rate and latency, and update document embeddings periodically to reflect data changes.

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, you’ve made it through this tutorial and it’s time to celebrate what you've learned! You’ve successfully uncovered how to integrate a powerful framework like LlamaIndex with a robust vector database such as Milvus, and how these come together with an advanced LLM like Mistral Ministral 3B and the embedding model Cohere embed-multilingual-light-v3.0 to create a dynamic Retrieval-Augmented Generation (RAG) system. By harmonizing these components, you can now develop an efficient pipeline that enhances information retrieval and content generation in previously unimaginable ways! The tutorial also introduced optimization tips that can help elevate your RAG setup, ensuring that your system operates smoothly and efficiently. Plus, we highlighted a fantastic free RAG cost calculator to help you manage and project your expenses effectively.

Now that you've grasped the essentials of building and optimizing your own RAG applications, the possibilities are limitless! Imagine the innovative solutions you could create by harnessing this knowledge. Whether you're aiming to improve customer support, streamline document retrieval, or craft personalized content, the tools and techniques you've just learned are your keys to success. So, what’s stopping you? Dive in, start building, and let your creativity shine as you explore the exciting potential of your own RAG applications. Happy innovating!

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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