Build RAG Chatbot with Llamaindex, Zilliz Cloud, Cohere Command R+, and AmazonBedrock titan-embed-text-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.
Cohere Command R+: A state-of-the-art enterprise-focused LLM optimized for high-efficiency Retrieval-Augmented Generation (RAG) and tool use, designed to automate complex workflows. Strengths include multilingual support, scalability, and robust accuracy for enterprise-grade applications. Ideal for automating customer support, data analysis, and knowledge-intensive tasks while ensuring secure, reliable collaboration between AI and human teams.
AmazonBedrock Titan-Embed-Text-v1: A high-performance embedding model designed to convert text into dense vector representations, enabling semantic search, clustering, and retrieval tasks. Strengths include scalability, multilingual support, and robust accuracy. Ideal for enterprise applications like recommendation systems, document similarity analysis, and AI-driven search engines within AWS environments.

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 Cohere Command R+

%pip install llama-index-llms-cohere

from llama_index.llms.cohere import Cohere

llm = Cohere(model="command-r-plus", api_key=api_key)

Step 3: Install and Set Up AmazonBedrock titan-embed-text-v1

%pip install llama-index-embeddings-bedrock

from llama_index.embeddings.bedrock import BedrockEmbedding

ebed_model = BedrockEmbedding(model_name="amazon.titan-embed-text-v1")

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.

Cohere Command R+ optimization tips

To optimize Cohere Command R+ in a RAG setup, preprocess input queries by chunking large texts and filtering irrelevant context to reduce noise. Use retrieval-friendly parameters like temperature=0.3 for focused responses and max_tokens=512 to balance detail and conciseness. Fine-tune document retrieval with semantic reranking and metadata filtering to prioritize high-relevance sources. Enable confidence_score to validate output reliability, and cache frequent queries to reduce latency. Monitor token usage and response quality to iteratively adjust retrieval thresholds and generation settings for cost-performance balance.

AmazonBedrock titan-embed-text-v1 optimization tips

To optimize titan-embed-text-v1 in a RAG setup, preprocess inputs by removing redundant whitespace and truncating excessively long texts to fit its 8K-token limit. Use batch embedding requests to reduce latency and costs. Fine-tune chunking strategies to balance context retention (e.g., 512-token segments) and avoid fragmentation. Normalize embeddings to improve retrieval accuracy. Leverage metadata filtering to refine retrieved results. Test newer model versions for performance gains. Cache frequent or repeated queries to minimize redundant computations. Monitor embedding quality via cosine similarity thresholds and adjust retrieval thresholds dynamically.

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 tutorial! You've taken significant steps into the exciting world of Retrieval-Augmented Generation (RAG) systems. You've learned how to integrate essential components like the LlamaIndex framework, Zilliz Cloud as your vector database, Cohere Command R+ as your powerful language model, and the Amazon Bedrock titan-embed-text-v1 embedding model. You've seen firsthand how each piece complements the others, allowing you to build an efficient and dynamic RAG pipeline. With LlamaIndex, you can seamlessly manage your data flow, while the vector database from Zilliz Cloud optimally handles data retrieval to ensure you get relevant results fast. The Cohere Command R+ elevates your text generation capabilities, and with the embedding model, you gain the power to transform your content into a rich, searchable format.

But that’s not all! The tutorial also provided you with valuable optimization tips, helping you refine your RAG system for peak performance. Plus, the inclusion of a free RAG cost calculator empowers you to keep track of your resources and manage your project effectively. Now it's your turn! Dive in and start building your own RAG applications, experimenting and innovating along the way. The possibilities are endless, so harness what you've learned here and create something remarkable. Your journey in the world of intelligent applications has just begun, and we can’t wait to see where your creativity takes you next!

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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