Build RAG Chatbot with Llamaindex, HNSWlib, Mistral Pixtral Large, and Ollama granite-embedding

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.
HNSWlib: a high-performance C++ and Python library for approximate nearest neighbor (ANN) search using the Hierarchical Navigable Small World (HNSW) algorithm. It provides fast, scalable, and efficient similarity search in high-dimensional spaces, making it ideal for vector databases and AI applications.
Mistral Pixtral Large: A high-performance language model optimized for advanced natural language processing tasks, excelling in multilingual understanding, contextual accuracy, and scalable deployment. Its efficiency in processing complex queries and real-time data makes it ideal for enterprise applications like AI-driven analytics, dynamic content generation, and multilingual customer support automation.
Ollama Granite-Embedding: A high-performance embedding model designed for semantic understanding and retrieval tasks. It excels at generating dense vector representations for text, enabling robust similarity search, clustering, and retrieval-augmented generation (RAG). Ideal for enterprise applications requiring scalable, privacy-preserving semantic analysis in on-premises or edge 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 Mistral Pixtral Large

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="pixtral-large-latest")

Step 3: Install and Set Up Ollama granite-embedding

%pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="granite-embedding",
)

Step 4: Install and Set Up HNSWlib

%pip install llama-index-vector-stores-hnswlib

from llama_index.vector_stores.hnswlib import HnswlibVectorStore
from llama_index.core import (
    VectorStoreIndex,
    StorageContext,
    SimpleDirectoryReader,
)

vector_store = HnswlibVectorStore.from_params(
    space="ip",
    dimension=embed_model._model.get_sentence_embedding_dimension(),
    max_elements=1000,
)

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.

HNSWlib optimization tips

To optimize HNSWlib for a Retrieval-Augmented Generation (RAG) setup, fine-tune the M parameter (number of connections per node) to balance accuracy and memory usage—higher values improve recall but increase indexing time. Adjust ef_construction (search depth during indexing) to enhance retrieval quality. During queries, set ef_search dynamically based on latency vs. accuracy trade-offs. Use multi-threading for faster indexing and querying. Ensure vectors are properly normalized for consistent similarity comparisons. If working with large datasets, periodically rebuild the index to maintain efficiency. Store the index on disk and load it efficiently for persistence in production environments. Monitor query performance and tweak parameters to achieve optimal speed-recall balance.

Mistral Pixtral Large optimization tips

To optimize Mistral Pixtral Large in a RAG setup, prioritize chunk sizing for retrieval—experiment with 256-512 token chunks to balance context and relevance. Use dense embeddings (e.g., SBERT) paired with hybrid search (ANN + keyword) for efficient document retrieval. Fine-tune the model on domain-specific data to enhance response accuracy. Adjust generation parameters: lower temperature (0.2-0.4) for factual consistency and limit top-k to 50-100. Implement query caching for repetitive inputs and use metadata filtering to prune irrelevant documents. Regularly evaluate retrieval hit rate and latency to refine thresholds and indexing strategies.

Ollama Granite-Embedding optimization tips

To optimize Ollama Granite-Embedding in RAG, ensure input text is cleanly chunked (avoid truncation by splitting documents into 512-token segments). Fine-tune embedding parameters like temperature and batch size for speed-quality balance. Use hardware acceleration (e.g., CUDA) and quantize the model for faster inference. Normalize embeddings to improve similarity calculations. Regularly evaluate retrieval accuracy with benchmarks like NDCG or recall@k. Cache frequent queries to reduce redundant computations, and pre-filter low-relevance documents using metadata to lighten embedding workloads.

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?

Wow, what an exciting journey we’ve just taken together! By exploring the integration of a robust framework like LlamaIndex alongside the powerful HNSWlib vector database, we’ve unraveled the magic behind building a Retrieval-Augmented Generation (RAG) system. You’ve learned how to orchestrate these components with the Mistral Pixtral Large LLM and the Ollama granite-embedding model, allowing you to harness their unique capabilities for retrieving relevant information while generating meaningful content. The step-by-step approach showcased not only how to set up these components but also how they can work in unison to optimize your applications for dynamic data retrieval and processing.

Isn’t it thrilling to think about the possibilities you can create? We even shared optimization tips to fine-tune your RAG pipeline, ensuring your system runs smoothly and efficiently. If that wasn't enough, the free RAG cost calculator we introduced is a fantastic tool to help you manage your resources wisely as you expand your projects. Now it’s your turn! Dive into building, optimizing, and innovating your own RAG applications! Every step you take brings you closer to mastering this transformative technology. Let your creativity flow, and remember, the only limit is your imagination! You've got this!

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

We'd Love to Hear What You Think!

We’d love to hear your thoughts! 🌟 Leave your questions or comments below or join our vibrant Milvus Discord community to share your experiences, ask questions, or connect with thousands of AI enthusiasts. Your journey matters to us!

If you like this tutorial, show your support by giving our Milvus GitHub repo a star ⭐—it means the world to us and inspires us to keep creating! 💖