Build RAG Chatbot with Llamaindex, Zilliz Cloud, Mistral Large, and AmazonBedrock cohere embed-multilingual-v3

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 Large: A state-of-the-art language model optimized for advanced reasoning, multilingual tasks, and high-stakes decision-making. It excels in code generation, complex analysis, and cross-lingual understanding, offering scalability, efficiency, and high accuracy for enterprise solutions, AI-driven research, and global customer interaction platforms.
AmazonBedrock Cohere Embed-Multilingual-v3: A multilingual text embedding model hosted on Amazon Bedrock designed to generate high-dimensional vector representations (1024 dimensions) for text in over 100 languages. It excels at semantic understanding, cross-lingual retrieval, and scalability, making it ideal for multilingual search, content recommendation, clustering, and retrieval-augmented generation (RAG) systems requiring broad language support and semantic accuracy.

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 Large

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

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

Step 3: Install and Set Up AmazonBedrock cohere embed-multilingual-v3

%pip install llama-index-embeddings-bedrock

from llama_index.embeddings.bedrock import BedrockEmbedding

ebed_model = BedrockEmbedding(model_name="cohere.embed-multilingual-v3")

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 Large optimization tips

To enhance Mistral Large’s performance in RAG systems, prioritize efficient context handling by truncating or summarizing retrieved documents to fit its token limit while retaining key information. Fine-tune prompts to explicitly guide the model to reference retrieved content, using phrases like “based on the provided context.” Adjust temperature settings (lower for factuality, higher for creativity) and max token limits to balance output quality and length. Implement caching for frequent queries, and use parallel processing to speed up document retrieval. Regularly evaluate retrieval relevance scores to ensure high-quality inputs, and experiment with chunk sizes/overlaps during indexing to optimize context granularity.

AmazonBedrock cohere embed-multilingual-v3 optimization tips

Optimize input preprocessing by normalizing text (lowercasing, removing special characters) and splitting documents into chunks aligned with the model’s 512-token limit. Use batch processing for bulk embeddings to reduce latency and costs. Filter irrelevant content before embedding to improve retrieval quality. For multilingual queries, ensure language-specific stopword removal and consider hybrid retrieval combining semantic and keyword search. Regularly validate embedding quality via cosine similarity checks and align vector dimensions with your database (e.g., PCA for dimensionality reduction). Cache frequent queries and update 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?

Congratulations on making it through this comprehensive tutorial! You've now acquired the skills to integrate a framework, vector database, large language model (LLM), and embedding model to create a Retrieval-Augmented Generation (RAG) system. By diving into LlamaIndex for data handling, you learned how to seamlessly connect to Zilliz Cloud, harnessing its powerful vector database capabilities to store and retrieve relevant data efficiently. The magic of Mistral Large as your LLM not only enhances the understanding of context but also elevates the generation of insightful responses. Coupling this with the cohere embed-multilingual-v3 model gives your system a rich, multilingual dimension, ensuring that you can reach and engage diverse user bases. Plus, the tutorial shared handy optimization tips and even a free RAG cost calculator to help you strategize your project budget – how great is that?

Now that you have mastered these components, the possibilities are endless! Imagine the RAG applications you can create, tailored specifically to meet user needs and provide valuable insights at lightning speed. Don't be afraid to experiment and innovate; each small tweak can lead to significant improvements. So, roll up your sleeves, start building, optimizing, and letting your creativity shine! The world of RAG awaits you, and we can't wait to see what incredible solutions you come up with. Go ahead and turn your ideas into reality!

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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