Build RAG Chatbot with Llamaindex, Zilliz Cloud, Mistral Codestral Mamba, 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.
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 Codestral Mamba: A code-focused AI model designed to streamline software development by generating, analyzing, and optimizing code. Leveraging the Mamba architecture, it excels at processing long sequences efficiently, offering fast inference and precise code suggestions. Ideal for developers seeking productivity gains in code completion, debugging, and automation tasks.
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 Codestral Mamba

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="open-codestral-mamba")

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 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 Codestral Mamba optimization tips

Optimize Mistral Codestral Mamba in RAG by fine-tuning on domain-specific data to align outputs with context, adjusting temperature (lower for precision, higher for creativity), and trimming retrieved documents to relevant chunks to reduce noise. Use quantization for faster inference, enable dynamic batching, and limit max tokens to prevent overlong responses. Cache frequent queries, precompute embeddings for common contexts, and leverage hardware acceleration (e.g., CUDA cores). Monitor latency and accuracy to balance speed and quality, and apply prompt engineering with clear instructions to guide context integration.

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?

Wow, what an incredible journey you've just completed! You’ve learned how to harness the power of LlamaIndex, Zilliz Cloud, and Mistral Codestral Mamba to build a robust Retrieval-Augmented Generation (RAG) system. We explored how LlamaIndex serves as a flexible framework to structure your data and seamlessly integrate with the Zilliz Cloud vector database. The magic of Mistral Codestral Mamba shines through with its advanced capabilities, allowing for nuanced interactions with your data. And let’s not forget the embedding model, jina-clip-v1, which enriches your inputs, making your RAG system not just functional but truly intuitive!

But that's not all! This tutorial didn’t just stop at the basics; you also picked up some fantastic optimization tips along the way. The cost calculator we provided is a game-changer, empowering you to gauge your application's efficiency and tweak it for better performance. Now that you’ve got all these tools and insights at your fingertips, the possibilities are endless! So go ahead—dive in, start building, and innovate your own RAG applications. Remember, each step you take brings you closer to creating something amazing. Happy building!

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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