Build RAG Chatbot with Llamaindex, Zilliz Cloud, Mistral Pixtral Large, and Cohere embed-english-light-v2.0

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.
Zilliz Cloud: a fully managed vector database-as-a-service platform built on top of the open-source Milvus, designed to handle high-performance vector data processing at scale. It enables organizations to efficiently store, search, and analyze large volumes of unstructured data, such as text, images, or audio, by leveraging advanced vector search technology. It offers a free tier supporting up to 1 million vectors.
Mistral Pixtral Large: A high-performance language model optimized for advanced natural language processing tasks, excelling in multilingual understanding, contextual accuracy, and scalable deployment. Its efficiency in processing complex queries and real-time data makes it ideal for enterprise applications like AI-driven analytics, dynamic content generation, and multilingual customer support automation.
Cohere embed-english-light-v2.0: A lightweight embedding model optimized to convert English text into dense vector representations efficiently. It excels in semantic search, clustering, and similarity tasks, balancing speed and accuracy. Ideal for real-time applications, cost-sensitive deployments, and resource-constrained environments requiring scalable, rapid text analysis without compromising performance.

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 Pixtral Large

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="pixtral-large-latest")

Step 3: Install and Set Up Cohere embed-english-light-v2.0

%pip install llama-index-embeddings-cohere

from llama_index.embeddings.cohere import CohereEmbedding

embed_model = CohereEmbedding(
    api_key=cohere_api_key,
    model_name="embed-english-light-v2.0",
)

Step 4: Install and Set Up Zilliz Cloud

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=ZILLIZ_CLOUD_URI,
    token=ZILLIZ_CLOUD_TOKEN,
    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.

Zilliz Cloud optimization tips

Optimizing Zilliz Cloud for a RAG system involves efficient index selection, query tuning, and resource management. Use Hierarchical Navigable Small World (HNSW) indexing for high-speed, approximate nearest neighbor search while balancing recall and efficiency. Fine-tune ef_construction and M parameters based on your dataset size and query workload to optimize search accuracy and latency. Enable dynamic scaling to handle fluctuating workloads efficiently, ensuring smooth performance under varying query loads. Implement data partitioning to improve retrieval speed by grouping related data, reducing unnecessary comparisons. Regularly update and optimize embeddings to keep results relevant, particularly when dealing with evolving datasets. Use hybrid search techniques, such as combining vector and keyword search, to improve response quality. Monitor system metrics in Zilliz Cloud’s dashboard and adjust configurations accordingly to maintain low-latency, high-throughput performance.

Mistral Pixtral Large optimization tips

To optimize Mistral Pixtral Large in a RAG setup, prioritize chunk sizing for retrieval—experiment with 256-512 token chunks to balance context and relevance. Use dense embeddings (e.g., SBERT) paired with hybrid search (ANN + keyword) for efficient document retrieval. Fine-tune the model on domain-specific data to enhance response accuracy. Adjust generation parameters: lower temperature (0.2-0.4) for factual consistency and limit top-k to 50-100. Implement query caching for repetitive inputs and use metadata filtering to prune irrelevant documents. Regularly evaluate retrieval hit rate and latency to refine thresholds and indexing strategies.

Cohere embed-english-light-v2.0 optimization tips

To optimize Cohere embed-english-light-v2.0 in RAG, preprocess input text by truncating or chunking documents to the model’s 512-token limit for efficiency. Use batch processing to encode multiple texts simultaneously, reducing API overhead. Normalize embeddings to improve cosine similarity accuracy. Pair with a fast vector database (e.g., FAISS) for low-latency retrieval. Cache frequent queries to minimize redundant computations. Monitor embedding quality via retrieval hit rates and adjust text chunking strategies for domain-specific contexts. Fine-tune batch sizes to balance speed and memory usage.

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, what an incredible journey you’ve embarked on in this tutorial! You've just learned how to integrate some powerful components to create a Robust Augmented Generation (RAG) system. We explored how LlamaIndex serves as a flexible framework that efficiently organizes and retrieves your data, while Zilliz Cloud provides a top-notch vector database to seamlessly manage the embeddings you'll create. And let’s not forget the incredible capabilities of Mistral Pixtral Large and Cohere's embed-english-light-v2.0, which bring state-of-the-art language model magic into your RAG pipeline! Together, these components empower you to build applications that can process and generate information intelligently and contextually.

This tutorial didn’t just stop at the basics; we even sprinkled in optimization tips and a handy RAG cost calculator to help you fine-tune your projects for efficiency and cost-effectiveness. Aren’t you excited about the possibilities that lie ahead? With this knowledge, you are well-equipped to innovate and create applications that can tackle unique challenges in your field. So why wait? Get out there, start building, and unleash your creativity with RAG applications. The tech world is your oyster—let your imagination run wild and make something amazing! You've got this!

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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