Build RAG Chatbot with Llamaindex, Faiss, Gemini 1.5 Flash, and Cohere embed-english-light-v3.0

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 1.5 Flash: A fast, streamlined AI model optimized for real-time applications and low-latency environments. While lightweight, it maintains high accuracy in text-based tasks, making it ideal for rapid document summarization, chatbot interactions, and AI-driven customer support with minimal computational overhead.
Cohere embed-english-light-v3.0: A lightweight, efficient embedding model designed to convert English text into high-dimensional vector representations. Excelling in speed and scalability, it balances accuracy with low computational demands, making it ideal for semantic search, text clustering, and retrieval-augmented applications 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 Gemini 1.5 Flash

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

from llama_index.llms.google_genai import GoogleGenAI

llm = GoogleGenAI(
    model="gemini-1.5-flash",
    # api_key="some key",  # uses GOOGLE_API_KEY env var by default
)

Step 3: Install and Set Up Cohere embed-english-light-v3.0

%pip install llama-index-embeddings-cohere

from llama_index.embeddings.cohere import CohereEmbedding

embed_model = CohereEmbedding(
    api_key=cohere_api_key,
    model_name="embed-english-v3.0",
)

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 1.5 Flash optimization tips

For Gemini 1.5 Flash, prioritize lightweight and high-speed retrieval to match its fast inference capabilities. Optimize document embeddings for quick similarity search and use pre-filtering techniques to reduce retrieval overhead. Maintain a small but high-quality context window by fine-tuning chunking strategies. Leverage caching for commonly accessed queries to minimize API calls. Monitor token usage and adjust retrieval scope dynamically to balance speed and response accuracy.

Cohere embed-english-light-v3.0 optimization tips

To optimize Cohere embed-english-light-v3.0 in RAG, ensure input text is clean and concise by removing redundant whitespace, special characters, or irrelevant metadata. Use batch processing for embeddings to reduce API calls and latency. Align chunk sizes with the model’s 512-token limit, splitting longer texts into coherent segments. Cache frequent or static embeddings to save costs. Fine-tune retrieval scoring (e.g., cosine similarity) to match your data distribution, and pre-filter low-relevance documents using metadata to reduce computational overhead. Regularly validate embedding quality against domain-specific benchmarks.

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 unlocked the secrets of building a Retrieval-Augmented Generation (RAG) system by integrating some incredible components: the LlamaIndex framework, the Faiss vector database, the Gemini 1.5 Flash LLM, and the Cohere embed-english-light-v3.0 embedding model. Each piece plays a vital role in your RAG pipeline—LlamaIndex seamlessly connects your data with the LLM, while Faiss efficiently manages your vector storage to retrieve information in a flash. The use of the embedding model ensures that your queries and data are well-represented for optimal performance. You’ve also learned some valuable optimization tips to enhance your system’s efficiency and effectiveness, along with access to a free RAG cost calculator to assist you in budgeting your projects!

Now that you have this powerful toolkit at your fingertips, the possibilities are endless! Whether you're looking to build a chatbot, enhance an information retrieval system, or innovate new applications in various domains, you're equipped to make it happen. The next step is yours: start experimenting with these components, optimize your setup, and don’t hesitate to get creative! Dive into the world of RAG applications and watch your ideas take flight. Remember, innovation starts with you—so get building today!

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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