Build RAG Chatbot with Llamaindex, Milvus, Jamba Mini, and HuggingFace all-MiniLM-L12-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.
Milvus: An open-source vector database optimized to store, index, and search large-scale vector embeddings efficiently, perfect for use cases like RAG, semantic search, and recommender systems. If you hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on Milvus and offers a free tier supporting up to 1 million vectors.
Jamba Mini: Jamba Mini is a lightweight AI model designed for real-time text generation and natural language understanding in resource-constrained environments. With a balance of efficiency and accuracy, it is ideal for mobile apps, edge devices, and interactive assistants that require quick, low-latency responses.
HuggingFace all-MiniLM-L12-v1: A compact sentence embedding model designed to convert text into dense vector representations for semantic understanding. It balances speed and efficiency with strong performance in tasks like semantic search, text clustering, and retrieval-augmented generation. Ideal for applications requiring low-latency inference or resource-constrained environments while maintaining robust semantic analysis capabilities.

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 Mini

%pip install llama-index-llms-ai21

from llama_index.llms.ai21 import AI21

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

Step 3: Install and Set Up HuggingFace all-MiniLM-L12-v1

%pip install llama-index-embeddings-huggingface

from llama_index.embeddings.huggingface import HuggingFaceEmbedding

embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")

Step 4: Install and Set Up Milvus

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="./milvus_demo.db",
    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.

Milvus optimization tips

Milvus serves as a highly efficient vector database, critical for retrieval tasks in a RAG system. To optimize its performance, ensure that indexes are properly built to balance speed and accuracy; consider utilizing HNSW (Hierarchical Navigable Small World) for efficient nearest neighbor search where response time is crucial. Partitioning data based on usage patterns can enhance query performance and reduce load times, enabling better scalability. Regularly monitor and adjust cache settings based on query frequency to avoid latency during data retrieval. Employ batch processing for vector insertions, which can minimize database lock contention and enhance overall throughput. Additionally, fine-tune the model parameters by experimenting with the dimensionality of the vectors; higher dimensions can improve retrieval accuracy but may increase search time, necessitating a balance tailored to your specific use case and hardware infrastructure.

Jamba Mini optimization tips

To optimize Jamba Mini in a Retrieval-Augmented Generation (RAG) setup, focus on fine-tuning the model to handle domain-specific language and user intents. Preprocess inputs effectively by normalizing text and removing noise, ensuring relevant context is fed into the model. Utilize caching for frequent queries to reduce latency, and experiment with prompt engineering to guide the model’s responses toward greater accuracy. Monitor system performance and adjust batch sizes to maintain efficiency, while minimizing resource consumption. You can also optimize memory management by using lightweight data structures and ensuring fast retrieval from the knowledge base.

HuggingFace all-MiniLM-L12-v1 optimization tips

To optimize the all-MiniLM-L12-v1 model in a RAG setup: preprocess input data by cleaning and normalizing text (lowercasing, removing special characters) to improve embedding quality. Use batch inference for embedding generation to maximize GPU utilization. Fine-tune the model on domain-specific data via contrastive learning to enhance retrieval relevance. Reduce vector dimensionality via PCA if storage or latency is critical. Cache frequently accessed embeddings to minimize recomputation. Quantize the model with Hugging Face’s transformers library for faster inference with minimal accuracy loss. Regularly benchmark performance against your retrieval metrics (e.g., recall@k) to validate optimizations.

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 reaching the end of this tutorial! You’ve just unlocked the potential to create powerful Retrieval-Augmented Generation (RAG) systems using an incredible blend of tools. By integrating LlamaIndex as your robust framework with Milvus as your efficient vector database, you’ve learned to effectively manage and retrieve information at lightning speed. And with the help of the Jamba Mini LLM and the HuggingFace all-MiniLM-L12-v1 embedding model, you now have the capabilities to process and understand data in ways that were previously only possible in your imagination. This combination not only enhances your model's performance but also fine-tunes your ability to deliver accurate, context-rich responses—an absolute game-changer in the world of AI!

Throughout this tutorial, you’ve not only grasped the basics but also collected nifty optimization tips that will refine your RAG pipeline. Plus, the free RAG cost calculator will undoubtedly assist you in budgeting your projects smartly. Imagine the innovation waiting for you on the other side—whether you’re aiming to build a smart chatbot, an informative search engine, or something entirely unique. So go ahead—start building, optimizing, and pushing the boundaries of what you can achieve with RAG applications. The world is waiting for your creations, and I can’t wait to see what incredible things you’ll bring to life!

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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