Build RAG Chatbot with Llamaindex, Faiss, Mistral Ministral 3B, and jina-clip-v2

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.)
Mistral Ministral 3B: A compact, high-efficiency language model optimized for fast inference and low-resource environments. With 3 billion parameters, it balances performance and scalability, excelling in text generation, summarization, and question-answering tasks. Ideal for edge computing, real-time applications, and cost-sensitive deployments requiring reliable NLP capabilities without heavy computational demands.
Jina-CLIP-V2: A multimodal AI model designed to seamlessly connect text and visual data, excelling in cross-modal retrieval tasks. Strengths include high accuracy in image-text matching, multilingual support, and scalable architecture. Ideal for semantic image search, content moderation, and personalized recommendations in e-commerce or media platforms.

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 Mistral Ministral 3B

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="ministral-3b-latest")

Step 3: Install and Set Up jina-clip-v2

%pip install llama-index-embeddings-jinaai

You may also need other packages that do not come direcly with llama-index.

!pip install Pillow

from llama_index.embeddings.jinaai import JinaEmbedding

embed_model = JinaEmbedding(
    api_key=jinaai_api_key,
    model="jina-clip-v2",
    # choose `retrieval.passage` to get passage embeddings
    task="retrieval.passage",
)

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.

Mistral Ministral 3B optimization tips

To optimize Mistral Ministral 3B in RAG, fine-tune it on domain-specific data to improve retrieval relevance and response accuracy. Use 4-bit or 8-bit quantization to reduce memory usage while maintaining performance. Implement dynamic batching during inference to handle multiple queries efficiently. Prune redundant layers or apply LoRA for lightweight adaptation. Cache frequent retrieval outputs to minimize recomputation. Optimize prompt engineering to reduce input token length, and leverage FlashAttention for faster processing. Monitor latency and adjust context window sizes based on use-case requirements to balance speed and coherence.

Jina-CLIP-v2 optimization tips

To optimize Jina-CLIP-v2 in a RAG setup, preprocess inputs by cleaning text, normalizing formats, and truncating overly long documents to reduce noise. Use batch inference for embeddings to leverage GPU parallelism, and ensure model weights are quantized or pruned for faster inference. Cache frequently accessed embeddings to avoid redundant computations. Fine-tune the model on domain-specific data to improve retrieval relevance. Pair with efficient vector indexes (e.g., FAISS) for low-latency similarity searches, and monitor embedding quality via recall metrics to balance speed and accuracy. Regularly update the index with fresh data.

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 embarked on an exciting journey exploring how to build a Retrieval-Augmented Generation (RAG) system by seamlessly integrating LlamaIndex, a powerful vector database, with the amazing capabilities of the Mistral Ministral 3B language model and the jina-clip-v2 embedding model. By combining these components, you've learned how to effectively store and retrieve data while leveraging the strengths of LLMs to generate coherent and contextually relevant responses. The tutorial laid out step-by-step methods to connect these tools, showcasing how they can transform raw data into an intelligent, responsive system that enhances user experiences.

But that’s not all! You’ve also picked up valuable tips for optimizing your RAG pipeline, ensuring you can streamline performance and improve efficiency. And let’s not forget the handy RAG cost calculator, which equips you to estimate and manage your project budgets effectively. With these newfound skills and tools, the possibilities are endless! So why wait? It's time to grab the proverbial bull by the horns! Start building, optimizing, and innovating your very own RAG applications, and remember—the journey you’re on not only boosts your knowledge but also has the potential to create truly impactful solutions for tomorrow. Happy building!

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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