Build RAG Chatbot with Llamaindex, HNSWlib, Mistral Pixtral Large, and Ollama all-minilm

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.
HNSWlib: a high-performance C++ and Python library for approximate nearest neighbor (ANN) search using the Hierarchical Navigable Small World (HNSW) algorithm. It provides fast, scalable, and efficient similarity search in high-dimensional spaces, making it ideal for vector databases and AI applications.
Mistral Pixtral Large: A high-performance language model optimized for advanced natural language processing tasks, excelling in multilingual understanding, contextual accuracy, and scalable deployment. Its efficiency in processing complex queries and real-time data makes it ideal for enterprise applications like AI-driven analytics, dynamic content generation, and multilingual customer support automation.
Ollama all-minilm: A compact language model optimized for efficient NLP tasks, offering fast inference and low resource consumption. Strengths include scalability, versatility in text processing, and seamless integration. Ideal for real-time applications, edge deployments, and scenarios requiring lightweight yet robust 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 Mistral Pixtral Large

%pip install llama-index-llms-mistralai

from llama_index.llms.mistralai import MistralAI

llm = MistralAI(model="pixtral-large-latest")

Step 3: Install and Set Up Ollama all-minilm

%pip install llama-index-embeddings-ollama

from llama_index.embeddings.ollama import OllamaEmbedding

embed_model = OllamaEmbedding(
    model_name="all-minilm",
)

Step 4: Install and Set Up HNSWlib

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

from llama_index.vector_stores.hnswlib import HnswlibVectorStore
from llama_index.core import (
    VectorStoreIndex,
    StorageContext,
    SimpleDirectoryReader,
)

vector_store = HnswlibVectorStore.from_params(
    space="ip",
    dimension=embed_model._model.get_sentence_embedding_dimension(),
    max_elements=1000,
)

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.

HNSWlib optimization tips

To optimize HNSWlib for a Retrieval-Augmented Generation (RAG) setup, fine-tune the M parameter (number of connections per node) to balance accuracy and memory usage—higher values improve recall but increase indexing time. Adjust ef_construction (search depth during indexing) to enhance retrieval quality. During queries, set ef_search dynamically based on latency vs. accuracy trade-offs. Use multi-threading for faster indexing and querying. Ensure vectors are properly normalized for consistent similarity comparisons. If working with large datasets, periodically rebuild the index to maintain efficiency. Store the index on disk and load it efficiently for persistence in production environments. Monitor query performance and tweak parameters to achieve optimal speed-recall balance.

Mistral Pixtral Large optimization tips

To optimize Mistral Pixtral Large in a RAG setup, prioritize chunk sizing for retrieval—experiment with 256-512 token chunks to balance context and relevance. Use dense embeddings (e.g., SBERT) paired with hybrid search (ANN + keyword) for efficient document retrieval. Fine-tune the model on domain-specific data to enhance response accuracy. Adjust generation parameters: lower temperature (0.2-0.4) for factual consistency and limit top-k to 50-100. Implement query caching for repetitive inputs and use metadata filtering to prune irrelevant documents. Regularly evaluate retrieval hit rate and latency to refine thresholds and indexing strategies.

Ollama all-minilm optimization tips

Optimize Ollama all-minilm in RAG by preprocessing input text into concise, semantically meaningful chunks (256-512 tokens) to align with its context window. Use metadata filtering during retrieval to prioritize relevant documents and reduce noise. Fine-tune the model on domain-specific data to enhance answer relevance. Adjust temperature (0.2-0.5) and top-p (0.85-0.95) for balanced creativity and accuracy. Cache frequent queries to reduce latency, and employ batch processing for parallel inference. Quantize the model to 8-bit for faster inference with minimal accuracy loss. Regularly evaluate retrieval hit rates and answer quality to iteratively refine the pipeline.

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 just unlocked the secrets of building a Retrieval-Augmented Generation (RAG) system by skillfully weaving together an impressive array of components: a robust framework with LlamaIndex, a powerful vector database like HNSWlib, an advanced language model such as Mistral Pixtral Large, and an efficient embedding model like Ollama all-minilm. Through this hands-on journey, you've seen how each piece plays a crucial role in enhancing information retrieval and generating insights, boosting your application's performance and user experience. Understanding how these components work together sets you on the path to becoming a maestro in RAG development!

But that’s not all; you've also picked up some handy optimization tips along the way, which will save you time and resources as you dive deeper into your applications. Plus, you got a sneak peek at the free RAG cost calculator — an invaluable tool to help you evaluate and manage your project’s expenses effectively. Now, armed with this knowledge and the confidence to experiment, it's time to start building your own RAG applications. Remember, the only limit is your imagination! Go ahead, innovate, optimize, and transform your ideas into reality; you’re on the brink of amazing discoveries and impactful applications. Keep pushing boundaries, and let's see what incredible creations you come up with!

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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