Build RAG Chatbot with Llamaindex, Zilliz Cloud, Mistral Codestral Mamba, 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.
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.
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 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 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 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.

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, what a journey you've just taken! After diving into this tutorial, you’ve unlocked the magic of building your own RAG (Retrieval-Augmented Generation) system by seamlessly integrating a framework, a vector database, a large language model (LLM), and an embedding model. You learned how to utilize LlamaIndex for structuring your data, and Zilliz Cloud to efficiently store and retrieve that data with lightning speed. By leveraging Mistral Codestral Mamba, you discovered how to harness the power of LLMs to enhance the quality of responses. And let’s not forget about the Cohere embed-multilingual-light-v3.0 model, which equips your RAG system with remarkable multilingual capabilities, allowing your application to speak the languages of your users.

As you explored the nuances of optimization tips to enhance your system's performance, you also got your hands on a free RAG cost calculator that empowers you to efficiently manage your resources. How cool is that? With all these tools and insights, you are now equipped to build, optimize, and innovate your very own RAG applications tailored to your needs. So why wait? Get creative, experiment, and let your imagination run wild—start crafting those incredible applications that can change the way we interact with information! Your journey is just beginning, and the possibilities are endless!

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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