Build RAG Chatbot with Llamaindex, Zilliz Cloud, Jamba Large, and Ollama bge-m3

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.
Jamba Large: Jamba Large is a robust language model built for high-performance tasks requiring deep contextual understanding. It offers strong capabilities in generating complex responses and handling intricate queries, making it ideal for advanced applications like virtual assistants, content creation, and conversational AI in enterprise solutions.
Ollama BGE-M3: A multilingual embedding model optimized for semantic understanding, retrieval, and clustering. Strengths include high accuracy across 100+ languages, robust performance in dense retrieval tasks, and scalability. Ideal for enterprise search systems, cross-lingual applications, and AI-driven knowledge management requiring precise semantic 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 Jamba Large

%pip install llama-index-llms-ai21

from llama_index.llms.ai21 import AI21

llm = AI21(
    model="jamba-large", api_key=api_key, max_tokens=100, temperature=0.5
)

Step 3: Install and Set Up Ollama bge-m3

%pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="bge-m3",
)

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.

Jamba Large optimization tips

Optimizing Jamba Large in a RAG setup involves utilizing its advanced capabilities for complex queries and multi-turn conversations. Leverage fine-tuning to improve domain-specific understanding, ensuring that the model can generate more relevant and accurate results. Optimize your retrieval pipeline by using well-structured, concise document chunks that maintain context without overwhelming the model’s input size. Reduce inference time by batching queries when possible and using efficient hardware acceleration, such as GPUs. Experiment with dynamic prompt adjustments to improve response quality, and ensure that document reranking is utilized for highly relevant results during retrieval.

Ollama bge-m3 optimization tips

To optimize Ollama bge-m3 in a RAG setup, ensure input text is preprocessed (lowercasing, removing noise) and split into semantically coherent chunks (300-500 tokens) to balance retrieval accuracy and computational load. Use dynamic pooling to prioritize key phrases in embeddings. Fine-tune the model on domain-specific data with contrastive learning to enhance relevance. Adjust temperature and top-k sampling for controlled generation. Leverage batch inference for parallel processing and enable hardware acceleration (e.g., CUDA) for faster embeddings. Regularly validate retrieval performance with benchmark datasets to refine thresholds and ranking strategies.

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 journeyed through some exciting and innovative terrain, learning how to integrate a robust framework with a vector database, a powerful large language model (LLM), and an embedding model to build a Retrieval-Augmented Generation (RAG) system. You've discovered how LlamaIndex helps structure your data, while Zilliz Cloud’s vector database effectively stores and retrieves your embeddings, ensuring your RAG pipeline operates smoothly and efficiently. You also explored Ollama's bge-m3 LLM and Jamba Large together, unlocking the potential for more nuanced and contextually aware responses from your data. Each of these components plays a vital role in ensuring that your RAG system not only functions well but excels in delivering quality information right when you need it.

But wait, there's more! Throughout this tutorial, you’ve also encountered valuable optimization tips to enhance your pipeline's performance and even a free RAG cost calculator to help you plan your resources effectively. Now armed with this knowledge, the possibilities for your own applications are endless! So why not roll up your sleeves and start building right away? Dive into creating, optimizing, and innovating your own RAG applications. The world of AI solutions is full of potential, and with your newfound skills, you're well on your way to making a meaningful impact. Get started and let your creativity take flight!

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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