Build RAG Chatbot with Llamaindex, Zilliz Cloud, Databricks DBRX, and AmazonBedrock cohere embed-english-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.
Databricks DBRX: DBRX is a transformer-based decoder-only large language model (LLM) that was trained using next-token prediction. It uses a fine-grained mixture-of-experts (MoE) architecture with 132B total parameters of which 36B parameters are active on any input. It was pre-trained on 12T tokens of text and code data.
AmazonBedrock Cohere Embed-English-v3: A state-of-the-art text embedding model designed to convert English text into high-dimensional vector representations, excelling in semantic understanding and scalability. Its strengths include robust performance in semantic search, clustering, and retrieval-augmented generation (RAG), making it ideal for applications like recommendation systems, document similarity analysis, and AI-driven content organization within enterprise workflows.

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 Databricks DBRX

% pip install llama-index-llms-databricks

from llama_index.llms.databricks import Databricks

llm = Databricks(
    model="databricks-dbrx-instruct",
    api_key="your_api_key",
    api_base="https://[your-work-space].cloud.databricks.com/serving-endpoints/",
)

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

%pip install llama-index-embeddings-bedrock

from llama_index.embeddings.bedrock import BedrockEmbedding

ebed_model = BedrockEmbedding(model_name="cohere.embed-english-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.

Databricks DBRX optimization tips

To optimize Databricks DBRX in a Retrieval-Augmented Generation (RAG) setup, focus on efficiently integrating DBRX with your vector store for seamless retrieval and real-time document processing. Leverage the platform’s scalability by partitioning large datasets and using optimized cluster configurations to handle intensive tasks. Ensure your embeddings are stored in the most efficient format, and use indexing techniques like HNSW or IVF for faster retrieval. Optimize Spark jobs by fine-tuning resource allocation, minimizing shuffle operations, and utilizing caching to reduce query latency. Finally, monitor and adjust the job execution plan to avoid unnecessary overhead, ensuring optimal performance while maintaining cost efficiency.

AmazonBedrock Cohere Embed-English-v3 optimization tips

To optimize Cohere Embed-English-v3 in RAG, preprocess input text by removing redundant whitespace, normalizing casing, and filtering low-relevance content to reduce noise. Use batch embedding generation for bulk documents to minimize API calls and latency. Adjust the input_type parameter (e.g., "document" or "query") to align with use cases for context-aware embeddings. Experiment with chunk sizes (e.g., 256-512 tokens) to balance semantic capture and computational efficiency. Cache frequent or static embeddings to avoid reprocessing. Monitor embedding quality via cosine similarity checks and fine-tune retrieval thresholds for your dataset.

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?

What an incredible journey you've been on in this tutorial! You've successfully uncovered the powerful synergy of a robust framework, cutting-edge vector database, and the magic of a large language model (LLM) combined with an advanced embedding model. By integrating these components, you've learned how to build a Retrieval-Augmented Generation (RAG) system that can not only enhance your data retrieval capabilities but also create contextually rich and responsive applications. The hands-on exploration of LlamaIndex, Zilliz Cloud, Databricks DBRX, and Amazon Bedrock's embedding model shows how these technologies work together seamlessly, allowing you to access and generate information more effectively than ever.

And it doesn't stop there! As you dive deeper, you’ve also picked up some optimization tips that will help you fine-tune your system for efficiency and performance. Plus, the RAG cost calculator you discovered is an amazing resource that empowers you to strategize your project budget effectively. So why not take the next thrilling step? Put your newfound knowledge to the test and start building, optimizing, and innovating your own RAG applications today! The possibilities are endless, and your creativity is the only limit. Let's see what you can create!

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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