Build RAG Chatbot with Llamaindex, Zilliz Cloud, Solar Mini, and Ollama mxbai-embed-large

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.
Solar Mini: Solar Mini is an efficient AI model optimized for high-performance tasks with minimal computational resources. It delivers fast, context-aware responses while maintaining low latency, making it perfect for embedded systems, IoT applications, and mobile devices that need real-time language processing without compromising quality.
Ollama mxbai-embed-large: A high-performance embedding model optimized for converting text into dense vector representations, excelling in semantic similarity tasks. It features multilingual support, efficient processing of long documents, and low-latency inference, making it ideal for semantic search, document clustering, content recommendation, and retrieval-augmented generation (RAG) pipelines.

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 Solar Mini

%pip install llama-index-llms-upstage llama-index

from llama_index.llms.upstage import Upstage

llm = Upstage(
    model="solar-mini",
    # api_key="YOUR_API_KEY"  # uses UPSTAGE_API_KEY env var by default
)

Step 3: Install and Set Up Ollama mxbai-embed-large

%pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="mxbai-embed-large",
)

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.

Solar Mini optimization tips

For optimizing Solar Mini in a RAG setup, focus on maintaining a balance between response speed and model accuracy, given its compact nature. Prioritize concise document chunking to ensure fast retrieval while keeping the context relevant. Leverage prompt tuning to ensure the model generates the most appropriate response for each query. Cache results for high-demand queries to minimize redundant calls and optimize latency. Additionally, ensure that the retrieval pipeline is fine-tuned for minimal resource consumption while maintaining high retrieval quality. Adjust system parameters for scalability, especially in resource-constrained environments like mobile apps and embedded devices.

Ollama mxbai-embed-large optimization tips

Optimize Ollama mxbai-embed-large in RAG by preprocessing input text: clean, normalize, and chunk documents into 256-512 token segments for balanced context. Use batch inference to parallelize embedding generation, reducing latency. Fine-tune the model on domain-specific data if labeled pairs are available. Cache frequent or static embeddings to avoid recomputation. Ensure hardware acceleration (e.g., CUDA) is enabled. Test cosine similarity thresholds for retrieval accuracy and adjust based on downstream tasks. Regularly update the vector database with fresh data to maintain relevance.

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 informative tutorial! You've taken significant strides in understanding how to integrate a robust framework with cutting-edge technologies to build a Retrieval-Augmented Generation (RAG) system. By leveraging LlamaIndex as your backbone, combined with the powerful vector database from Zilliz Cloud, you’ve learned how to store, manage, and retrieve your data efficiently. The introduction of the Solar Mini Large Language Model (LLM) alongside the Ollama mxbai-embed-large embedding model brings another layer of sophistication, ensuring that your RAG system can generate contextually rich and relevant responses. Isn’t it exciting to see how these components precisely work together to create a seamless data processing pipeline?

Moreover, this tutorial didn’t just stop at the integration; it provided you with valuable optimization tips to enhance your system's performance, making your RAG applications not just functional but also efficient. And let’s not forget the free RAG cost calculator that empowers you to estimate and manage your expenses effectively as you scale. With all this knowledge packed into your toolkit, you're perfectly positioned to start building, optimizing, and innovating your own RAG applications. So, dive in, explore further, and unleash your creativity! Your journey into the realm of advanced AI solutions begins now, and we can't wait to see where it takes you!

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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