Build RAG Chatbot with Llamaindex, HNSWlib, OpenAI GPT-o1, and Ollama nomic-embed-text

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.
OpenAI GPT-1: A foundational transformer-based language model designed for natural language understanding and generation. Strengths include coherent text generation, contextual comprehension, and adaptability to diverse NLP tasks. Ideal for text completion, basic conversational agents, and early-stage language research, serving as a precursor to more advanced models like GPT-3 and GPT-4.
Ollama nomic-embed-text: A versatile text embedding model optimized for efficient generation of high-dimensional vector representations, excelling in semantic search, clustering, and similarity analysis. Strengths include robust performance on long-context inputs and scalability for local deployment. Ideal for applications requiring accurate document retrieval, recommendation systems, or contextual understanding in resource-constrained 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 OpenAI GPT-o1

%pip install llama-index llama-index-llms-openai

from llama_index.llms.openai import OpenAI

llm = OpenAI(
    model="o1",
    # api_key="some key",  # uses OPENAI_API_KEY env var by default
)

Step 3: Install and Set Up Ollama nomic-embed-text

%pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="nomic-embed-text",
)

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.

OpenAI GPT-01 optimization tips

To optimize OpenAI GPT-01 in a RAG setup, fine-tune prompts to include explicit instructions and structured context (e.g., “Answer using: [retrieved text]”). Limit response length with max_tokens to reduce verbosity and cost. Use a lower temperature (0.2–0.5) for factual accuracy. Preprocess retrieved documents to remove irrelevant content, ensuring inputs fit token limits. Cache frequent queries to minimize API calls. Experiment with chunking strategies for context injection and prioritize critical information at the prompt’s start or end. Monitor latency and adjust batch sizes for throughput efficiency.

Ollama nomic-embed-text optimization tips

To optimize Ollama nomic-embed-text in RAG, ensure input text is preprocessed (normalize casing, remove redundant whitespace) and chunked into coherent segments matching the model’s optimal context window. Use batch inference for bulk embeddings to reduce latency, and leverage GPU acceleration if available. Fine-tune embedding dimensions via dimensionality reduction if retrieval speed is critical. Regularly validate embedding quality with domain-specific benchmarks, and cache frequently queried embeddings to minimize redundant computations. Adjust temperature and similarity thresholds during retrieval to balance precision and recall.

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 just unlocked a treasure trove of knowledge on building a Retrieval-Augmented Generation (RAG) system by seamlessly integrating a framework, vector database, LLM, and embedding model. By diving into LlamaIndex, HNSWlib, OpenAI's GPT-3.5 Turbo, and Ollama's nomic-embed-text, you've learned how each component plays a pivotal role in empowering your RAG pipeline. LlamaIndex serves as the backbone, managing your data and queries efficiently. Meanwhile, HNSWlib enhances your indexing for super-fast similarity searches, ensuring you retrieve the best information at lightning speed. The OpenAI LLM brings your interactions to life, generating insightful and contextually aware responses. Don’t forget about the embedding model, which breaks down your data into meaningful vectors, offering an excellent way to represent your content!

Throughout this journey, we also shared optimization tips that enhance performance and efficiency, alongside a super handy free RAG cost calculator to help you keep track of costs while building your applications. With all this knowledge under your belt, the possibilities ahead are endless! So take a deep breath, roll up your sleeves, and start building, optimizing, and innovating your own RAG applications today. The world is waiting for your creativity and ingenuity—let’s get started!

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! 💖