Build RAG Chatbot with Llamaindex, Faiss, Gemini 2.0 Flash-Lite, and OpenAI text-embedding-3-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.
- Faiss: also known as Facebook AI Similarity Search, is an open-source vector search library that allows developers to quickly search for semantically similar multimedia data within a massive dataset of unstructured data. (If you want a much more scalable solution or hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvus and offers a free tier supporting up to 1 million vectors.)
Gemini 2.0 Flash-Lite: An ultra-lightweight AI model optimized for cost-effective, real-time language tasks. Designed for efficiency, it is ideal for embedded AI applications, mobile devices, and environments where fast response times and minimal resource usage are critical.
OpenAI text-embedding-3-large: A state-of-the-art embedding model designed to convert text into high-dimensional vectors, capturing deep semantic relationships. Renowned for its accuracy, scalability, and ability to handle long contexts (up to 8192 tokens), it excels in semantic search, retrieval-augmented generation (RAG), recommendation systems, and multilingual NLP tasks requiring nuanced language understanding.

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 Gemini 2.0 Flash-Lite

%pip install llama-index-llms-google-genai llama-index

from llama_index.llms.google_genai import GoogleGenAI

llm = GoogleGenAI(
    model="gemini-2.0-flash-lite",
    # api_key="some key",  # uses GOOGLE_API_KEY env var by default
)

Step 3: Install and Set Up OpenAI text-embedding-3-large

%pip install llama-index-embeddings-openai

from llama_index.embeddings.openai import OpenAIEmbedding

embed_model = OpenAIEmbedding(
    model="text-embedding-3-large",
)

Step 4: Install and Set Up Faiss

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

from llama_index.core import (
    SimpleDirectoryReader,
    load_index_from_storage,
    VectorStoreIndex,
    StorageContext,
)
from llama_index.vector_stores.faiss import FaissVectorStore

vector_store = FaissVectorStore(faiss_index=faiss_index)

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.

Faiss Optimization Tips

To enhance the performance of the Faiss library in a Retrieval-Augmented Generation (RAG) system, begin by selecting the appropriate index type based on your data volume and query speed requirements; for example, using an IVF (Inverted File) index can significantly speed up queries on large datasets by reducing the search space. Optimize your indexing process by using the nlist parameter to partition data into smaller clusters and set an appropriate number of probes (nprobe) during retrieval to balance between speed and accuracy. Ensure the vectors are properly normalized and consider using 16-bit or 8-bit quantization during indexing to reduce memory footprints for large datasets while maintaining reasonable retrieval accuracy. Additionally, consider leveraging GPU acceleration if available, as Faiss highly benefits from parallel processing, leading to faster nearest neighbor searches. Continuous fine-tuning and benchmarking with varying parameters and configurations can guide you in finding the most efficient setup specific to your data characteristics and retrieval requirements.

Gemini 2.0 Flash-Lite optimization tips

Gemini 2.0 Flash-Lite is designed for lightweight and efficient processing, so optimize retrieval by focusing on precision rather than volume. Use minimal yet highly relevant document chunks to keep token usage low. Implement approximate nearest neighbor (ANN) search for faster retrieval without sacrificing quality. Cache frequently used knowledge snippets for instant responses. Fine-tune prompts to be as concise as possible while maintaining clarity.

OpenAI text-embedding-3-large optimization tips

Optimize OpenAI text-embedding-3-large in RAG by adjusting the dimensions parameter to balance accuracy and efficiency—lower values reduce latency and cost while retaining semantic relevance. Batch embedding requests to maximize throughput, preprocess text to remove noise (e.g., truncate to 8191 tokens, normalize whitespace), and cache frequent queries. Use cosine similarity for retrieval alignment, validate embeddings with domain-specific benchmarks, and fine-tune hybrid search strategies (e.g., combining sparse/dense vectors) to improve recall. Monitor API rate limits and leverage asynchronous calls for scalability.

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 completing this tutorial! You've just unlocked the door to an exciting realm of artificial intelligence by learning how to seamlessly integrate a framework, a vector database, a large language model (LLM), and an embedding model to build your very own Retrieval-Augmented Generation (RAG) system. You dived into the specifics of using LlamaIndex, Faiss, Gemini 2.0 Flash-Lite, and OpenAI’s text-embedding-3-large, each playing a pivotal role in enhancing the efficiency and intelligence of your application. With LlamaIndex as your framework for data management and retrieval, Faiss as your powerful vector database to facilitate rapid searches, and Gemini 2.0 Flash-Lite coupled with OpenAI’s embedding model to bring deep learning capabilities into the mix, you've equipped yourself with a robust toolkit for tackling complex information retrieval tasks.

But that’s not all! The tutorial didn't just stop at the basics; you also explored valuable optimization tips to enhance your RAG pipeline's performance and even had access to a free RAG cost calculator to keep your projects budget-friendly. With these insights and practical tools, you're now well-prepared to take the leap into building your own innovative applications. So, roll up your sleeves, and let your creativity flow! Dive into your projects, optimize your services, and pioneer new possibilities with RAG systems. The sky's the limit—your journey toward transforming ideas into impactful applications begins now!

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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