Build RAG Chatbot with Llamaindex, Faiss, Cohere Command R, 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.
- 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.)
Cohere Command R: A scalable enterprise AI model optimized for Retrieval-Augmented Generation (RAG), designed to handle complex workflows with high accuracy. Strengths include multilingual support, low-latency performance, and secure integration with business data. Ideal for automating customer support, data analysis, and generating context-aware insights from large datasets.
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 Cohere Command R

%pip install llama-index-llms-cohere

from llama_index.llms.cohere import Cohere

llm = Cohere(model="command-r", api_key=api_key)

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 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.

Cohere Command R optimization tips

To optimize Cohere Command R in a RAG setup, fine-tune prompts for clarity and specificity, using explicit instructions to guide context-aware responses. Limit input context to relevant chunks (e.g., 256-512 tokens) to reduce noise and computational overhead. Adjust temperature and top-p values to balance creativity and factual accuracy—lower values enhance precision for retrieval tasks. Implement query augmentation (e.g., synonyms, rephrasing) to improve retrieval alignment. Use Cohere’s built-in reranking to prioritize high-confidence documents. Regularly validate outputs against source data to minimize hallucinations and ensure consistency. Profile latency and batch requests where possible for scalability.

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?

Wow, what an incredible journey you’ve just been on! In this tutorial, we’ve explored the thrilling world of building a Retrieval-Augmented Generation (RAG) system by seamlessly integrating a powerful framework like LlamaIndex, an efficient vector database like Faiss, a cutting-edge large language model (LLM) using Cohere Command R, and a robust embedding model with Ollama mxbai-embed-large. You’ve learned how each of these components plays a vital role in enhancing the capabilities of your RAG pipeline, enabling you to retrieve relevant information and generate meaningful responses with unmatched accuracy. The best part? You now have the tools to optimize and fine-tune your setup, ensuring top-notch performance!

But that's not all! This tutorial also equipped you with handy optimization tips and a free RAG cost calculator to help you manage your resources effectively. Just think about the amazing applications you can build, whether for personal projects or professional implementations! Now that you’ve gained this knowledge, it’s time to roll up your sleeves and dive into your own RAG ventures. You are fully equipped to innovate, experiment, and discover new possibilities. So, go on—start building, optimizing, and infusing your creativity into your projects! Who knows what groundbreaking applications you’ll create? The future is bright, and it’s yours for the taking!

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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