Build RAG Chatbot with Llamaindex, Faiss, DeepSeek R1, and voyage-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.)
DeepSeek R1: DeepSeek R1 is an open-weight large language model (LLM) designed for high-performance natural language processing, featuring a 128K context window for long-document understanding. It excels in reasoning, coding, and text generation, making it ideal for research, commercial applications, and AI-driven workflows requiring extended context retention and adaptability.
Voyage-3-Large: This model is designed for generative tasks, offering enhanced creativity and contextual understanding. With robust training on diverse datasets, it excels in producing coherent narratives and dialogue, making it ideal for applications in storytelling, content creation, and interactive experiences where imaginative output is essential.

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 DeepSeek R1

%pip install llama-index-llms-deepseek

from llama_index.llms.deepseek import DeepSeek

# you can also set DEEPSEEK_API_KEY in your environment variables
llm = DeepSeek(model="deepseek-reasoner", api_key="you_api_key")

# You might also want to set deepseek as your default llm
# from llama_index.core import Settings
# Settings.llm = llm

Step 3: Install and Set Up voyage-3-large

%pip install llama-index-embeddings-voyageai

from llama_index.embeddings.voyageai import VoyageEmbedding

embed_model = VoyageEmbedding(
    voyage_api_key="",
    model_name="voyage-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.

DeepSeek R1 optimization tips

To optimize DeepSeek R1 in a RAG setup, ensure that retrieval delivers high-quality, semantically relevant documents by fine-tuning your embedding model and search strategies. Use hybrid retrieval (vector + keyword search) to improve accuracy, especially for complex queries. Keep document chunks concise but rich in context to avoid exceeding token limits. Leverage caching to reduce redundant queries and enhance response speed. Experiment with prompt templates to maximize the model’s reasoning and comprehension capabilities. Monitor API latency and adjust query batching to improve efficiency. Fine-tune temperature settings to maintain consistency in generated responses.

voyage-3-large optimization tips

voyage-3-large provides enhanced reasoning capabilities, making it ideal for complex RAG tasks requiring deep contextual understanding. Optimize retrieval by implementing a multi-step ranking system that prioritizes highly relevant documents while filtering out lower-quality information. Use structured prompts with clearly delineated context and user queries to improve comprehension. Adjust temperature (0.1–0.3) and fine-tune top-k and top-p settings to maintain accuracy and prevent excessive variability. Take advantage of parallelized inference and request batching to improve processing efficiency. Leverage caching for high-frequency queries to reduce costs and latency. In multi-model setups, deploy voyage-3-large for intricate reasoning tasks while using smaller models for less complex queries to balance cost and performance effectively.

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 journeying through this tutorial! You’ve equipped yourself with a solid understanding of how to integrate four powerful components to build an innovative Retrieval-Augmented Generation (RAG) system. By exploring LlamaIndex as your framework, you learned how to efficiently manage and organize your data for seamless interaction with your models. You discovered how Faiss serves as a robust vector database, efficiently handling high-dimensional embeddings and allowing for quick similarity searches. And with the integration of DeepSeek R1 and the voyage-3-large LLM, you've tapped into the capabilities of advanced language models for generating meaningful responses based on the retrieved information.

But that's not all! Remember the optimization tips we shared throughout the tutorial? They’re your secret weapon for creating a smoother pipeline, whether you’re fine-tuning parameters or utilizing the special features of a free RAG cost calculator to manage deployment expenses. You now have the knowledge and tools to build, optimize, and innovate your own RAG applications! So, get excited and dive into your projects—experiment, iterate, and don’t be afraid to push the boundaries of what’s possible. The world of RAG systems is vast and full of opportunities for you to explore. Start today, and who knows what amazing solutions you’ll come up with tomorrow!

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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