Build RAG Chatbot with Llamaindex, HNSWlib, Mistral Large, and OpenAI text-embedding-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.
- 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 Large: A state-of-the-art language model optimized for advanced reasoning, multilingual tasks, and high-stakes decision-making. It excels in code generation, complex analysis, and cross-lingual understanding, offering scalability, efficiency, and high accuracy for enterprise solutions, AI-driven research, and global customer interaction platforms.
- OpenAI text-embedding-3-large: A state-of-the-art embedding model designed to convert text into high-dimensional vectors, capturing deep semantic relationships. Renowned for its accuracy, scalability, and ability to handle long contexts (up to 8192 tokens), it excels in semantic search, retrieval-augmented generation (RAG), recommendation systems, and multilingual NLP tasks requiring nuanced 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 Large
%pip install llama-index-llms-mistralai
from llama_index.llms.mistralai import MistralAI
llm = MistralAI(model="mistral-large-latest")
Step 3: Install and Set Up OpenAI text-embedding-3-large
%pip install llama-index-embeddings-openai
from llama_index.embeddings.openai import OpenAIEmbedding
embed_model = OpenAIEmbedding(
model="text-embedding-3-large",
)
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 Large optimization tips
To enhance Mistral Large’s performance in RAG systems, prioritize efficient context handling by truncating or summarizing retrieved documents to fit its token limit while retaining key information. Fine-tune prompts to explicitly guide the model to reference retrieved content, using phrases like “based on the provided context.” Adjust temperature settings (lower for factuality, higher for creativity) and max token limits to balance output quality and length. Implement caching for frequent queries, and use parallel processing to speed up document retrieval. Regularly evaluate retrieval relevance scores to ensure high-quality inputs, and experiment with chunk sizes/overlaps during indexing to optimize context granularity.
OpenAI text-embedding-3-large optimization tips
Optimize OpenAI text-embedding-3-large in RAG by adjusting the dimensions
parameter to balance accuracy and efficiency—lower values reduce latency and cost while retaining semantic relevance. Batch embedding requests to maximize throughput, preprocess text to remove noise (e.g., truncate to 8191 tokens, normalize whitespace), and cache frequent queries. Use cosine similarity for retrieval alignment, validate embeddings with domain-specific benchmarks, and fine-tune hybrid search strategies (e.g., combining sparse/dense vectors) to improve recall. Monitor API rate limits and leverage asynchronous calls for scalability.
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
What Have You Learned?
Congratulations on making it through the tutorial! You've taken an exciting step into the world of Retrieval-Augmented Generation (RAG) systems by integrating some powerful tools: LlamaIndex for structured information management, HNSWlib for efficient vector searches, Mistral Large for generating human-like text, and OpenAI’s text-embedding-3-large model for superior text understanding. Each of these components serves a unique purpose, and together they can unlock incredible capabilities when it comes to building intelligent applications that leverage vast amounts of data. You’ve learned how to create a seamless pipeline where data retrieval enhances generation, providing users with relevant and contextually rich information. Plus, we shared some handy optimization tips to fine-tune your system's performance and even offered a free RAG cost calculator to help you strategize your resource allocation!
Now that you have this knowledge, the possibilities are truly endless! You’re not just equipped with a basic understanding of these technologies; you’ve got the keys to transform ideas into reality. Think of your own applications that could benefit from RAG—for instance, personal assistants, chatbots, or even unique content generators. It’s your turn to take this knowledge and build something remarkable! Start creating, optimizing, and innovating your RAG applications today. Dive in with enthusiasm, experiment with new features, and who knows? You might uncover groundbreaking solutions that could change the way we interact with information. Go ahead and make your mark in this exciting field!
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
We'd Love to Hear What You Think!
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- Introduction to RAG
- Key Components We'll Use for This RAG Chatbot
- Step 1: Install and Set Up Llamaindex
- Step 2: Install and Set Up Mistral Large
- Step 3: Install and Set Up OpenAI text-embedding-3-large
- Step 4: Install and Set Up HNSWlib
- Step 5: Build a RAG Chatbot
- Optimization Tips
- RAG Cost Calculator: A Free Tool to Calculate Your Cost in Seconds
- What Have You Learned?
- Further Resources
- We'd Love to Hear What You Think!
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