Build RAG Chatbot with Llamaindex, HNSWlib, Mistral Pixtral, and Cohere embed-multilingual-v3.0
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.
- Mixtral: A high-performance, sparse mixture-of-experts (MoE) language model designed for efficient text generation and comprehension. Excelling in multilingual support and domain adaptability, it balances speed and accuracy, making it ideal for enterprise-scale applications, real-time chatbots, and resource-constrained environments requiring cost-effective AI solutions.
- Cohere embed-multilingual-v3.0: A multilingual text embedding model designed to convert text in over 100 languages into high-dimensional vectors (1024 dimensions), excelling in semantic understanding and cross-lingual tasks. Its strengths include robust multilingual alignment and nuanced context capture, ideal for cross-language semantic search, multilingual document clustering, and enhancing NLP applications like recommendation systems in diverse linguistic environments.
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
%pip install llama-index-llms-mistralai
from llama_index.llms.mistralai import MistralAI
llm = MistralAI(model="pixtral-12b-2409")
Step 3: Install and Set Up Cohere embed-multilingual-v3.0
%pip install llama-index-embeddings-cohere
from llama_index.embeddings.cohere import CohereEmbedding
embed_model = CohereEmbedding(
api_key=cohere_api_key,
model_name="embed-multilingual-v3.0",
)
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 optimization tips
To optimize Mistral Pixtral in a RAG setup, fine-tune its prompt engineering by using clear, structured instructions to guide context integration. Limit retrieved documents to the most relevant chunks (e.g., 3-5) to reduce noise and improve response quality. Adjust temperature (0.2-0.5) for balanced creativity and precision. Use dynamic batching for parallel processing to accelerate inference. Implement quantization (e.g., 4-bit) to reduce memory usage without significant performance loss. Regularly evaluate retrieval alignment with domain-specific benchmarks to refine embedding and reranking strategies.
Cohere embed-multilingual-v3.0 optimization tips
To optimize Cohere embed-multilingual-v3.0 in RAG, preprocess text by normalizing casing, removing redundant whitespace, and filtering low-relevance content. Use appropriate chunk sizes (200–500 tokens) to balance context retention and embedding quality. Batch embedding requests to reduce latency. Leverage its multilingual strength by aligning input language with supported locales and applying language-specific stopword filtering. Fine-tune retrieval with hybrid search (semantic + keyword) and metadata filters. Regularly update embeddings to reflect new data and test retrieval accuracy using diverse multilingual queries to ensure robust cross-lingual performance.
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?
Wow, what a journey we’ve had together! In this tutorial, you’ve learned how to harness the power of a robust framework, vector database, large language model (LLM), and an embedding model to build a state-of-the-art Retrieval-Augmented Generation (RAG) system. By integrating LlamaIndex for seamless data management, HNSWlib for efficient indexing and retrieval, Mistral Pixtral as your LLM for generating human-like text, and Cohere’s multilingual embedding model that allows you to embrace global accessibility, you’re now equipped with the knowledge and tools to create sophisticated RAG applications. We also covered optimization tips to enhance performance and scalability, ensuring that your system can handle real-world demands with ease.
And guess what? You have the fantastic bonus of a free RAG cost calculator to help you evaluate your project’s budget and resource allocation, paving the way for smarter decision-making in your RAG endeavors. With all these components and features at your fingertips, the possibilities are endless! So why wait? Your newfound skills empower you to build, optimize, and innovate RAG applications that could transform industries or solve real-world problems. Embrace your creativity, dive in, and start turning your ideas into reality today—who knows what amazing solutions you might create!
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!
We’d love to hear your thoughts! 🌟 Leave your questions or comments below or join our vibrant Milvus Discord community to share your experiences, ask questions, or connect with thousands of AI enthusiasts. Your journey matters to us!
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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 Pixtral
- Step 3: Install and Set Up Cohere embed-multilingual-v3.0
- 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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