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:* LangChain: An open-source framework that helps you orchestrate the interaction between LLMs, vector stores, embedding models, etc, making it easier to integrate a RAG pipeline.
- Milvus: An open-source vector database optimized to store, index, and search large-scale vector embeddings efficiently, perfect for use cases like RAG, semantic search, and recommender systems. If you hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on Milvus and offers a free tier supporting up to 1 million vectors.
- Mistral AI Pixtral: Pixtral is a cutting-edge image generation model designed for high-quality visual content creation. With a focus on artistic style transfer and detail accuracy, it excels in transforming text prompts into vibrant images. Ideal for applications in design, marketing, and creative fields, Pixtral enhances workflows with its versatility and aesthetic precision.
- NVIDIA bge-m3: The NVIDIA bge-m3 is a state-of-the-art generative model designed for high-performance multi-modal tasks, particularly in natural language processing and computer vision. Its strengths lie in real-time data processing and scalability, making it ideal for applications in interactive AI systems, creative content generation, and advanced analytical tools in various industries.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 LangChain
%pip install --quiet --upgrade langchain-text-splitters langchain-community langgraph
Step 2: Install and Set Up Mistral AI Pixtral
pip install -qU "langchain[mistralai]"
import getpass
import os
if not os.environ.get("MISTRAL_API_KEY"):
os.environ["MISTRAL_API_KEY"] = getpass.getpass("Enter API key for Mistral AI: ")
from langchain.chat_models import init_chat_model
llm = init_chat_model("pixtral-12b-2409", model_provider="mistralai")
Step 3: Install and Set Up NVIDIA bge-m3
pip install -qU langchain-nvidia-ai-endpoints
import getpass
import os
if not os.environ.get("NVIDIA_API_KEY"):
os.environ["NVIDIA_API_KEY"] = getpass.getpass("Enter API key for NVIDIA: ")
from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings
embeddings = NVIDIAEmbeddings(model="baai/bge-m3")
Step 4: Install and Set Up Milvus
pip install -qU langchain-milvus
from langchain_milvus import Milvus
vector_store = Milvus(embedding_function=embeddings)
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 bs4
from langchain import hub
from langchain_community.document_loaders import WebBaseLoader
from langchain_core.documents import Document
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langgraph.graph import START, StateGraph
from typing_extensions import List, TypedDict
# Load and chunk contents of the blog
loader = WebBaseLoader(
web_paths=("https://milvus.io/docs/overview.md",),
bs_kwargs=dict(
parse_only=bs4.SoupStrainer(
class_=("doc-style doc-post-content")
)
),
)
docs = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
all_splits = text_splitter.split_documents(docs)
# Index chunks
_ = vector_store.add_documents(documents=all_splits)
# Define prompt for question-answering
prompt = hub.pull("rlm/rag-prompt")
# Define state for application
class State(TypedDict):
question: str
context: List[Document]
answer: str
# Define application steps
def retrieve(state: State):
retrieved_docs = vector_store.similarity_search(state["question"])
return {"context": retrieved_docs}
def generate(state: State):
docs_content = "nn".join(doc.page_content for doc in state["context"])
messages = prompt.invoke({"question": state["question"], "context": docs_content})
response = llm.invoke(messages)
return {"answer": response.content}
# Compile application and test
graph_builder = StateGraph(State).add_sequence([retrieve, generate])
graph_builder.add_edge(START, "retrieve")
graph = graph_builder.compile()
Test the Chatbot
Yeah! You've built your own chatbot. Let's ask the chatbot a question. response = graph.invoke({"question": "What data types does Milvus support?"})
print(response["answer"])
Example Output
Milvus supports various data types including sparse vectors, binary vectors, JSON, and arrays. Additionally, it handles common numerical and character types, making it versatile for different data modeling needs. This allows users to manage unstructured or multi-modal data efficiently.
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.
LangChain optimization tips
To optimize LangChain, focus on minimizing redundant operations in your workflow by structuring your chains and agents efficiently. Use caching to avoid repeated computations, speeding up your system, and experiment with modular design to ensure that components like models or databases can be easily swapped out. This will provide both flexibility and efficiency, allowing you to quickly scale your system without unnecessary delays or complications.
Milvus optimization tips
Milvus serves as a highly efficient vector database, critical for retrieval tasks in a RAG system. To optimize its performance, ensure that indexes are properly built to balance speed and accuracy; consider utilizing HNSW (Hierarchical Navigable Small World) for efficient nearest neighbor search where response time is crucial. Partitioning data based on usage patterns can enhance query performance and reduce load times, enabling better scalability. Regularly monitor and adjust cache settings based on query frequency to avoid latency during data retrieval. Employ batch processing for vector insertions, which can minimize database lock contention and enhance overall throughput. Additionally, fine-tune the model parameters by experimenting with the dimensionality of the vectors; higher dimensions can improve retrieval accuracy but may increase search time, necessitating a balance tailored to your specific use case and hardware infrastructure.
Mistral AI Pixtral optimization tips
Pixtral is optimized for multimodal RAG applications, requiring careful management of both textual and visual data retrieval. Improve retrieval efficiency by using specialized embeddings for different modalities—vector search for text and CLIP-based embeddings for images. Implement a multimodal ranking system to prioritize the most contextually relevant passages and images. Optimize model performance by structuring input prompts effectively, ensuring text and visual information are well-integrated without unnecessary repetition. Fine-tune temperature settings based on response requirements—lower values (0.1–0.2) for accuracy-driven applications, higher values for creative outputs. If deploying at scale, use parallel inference for handling large multimodal datasets efficiently. Streamline inference by leveraging batching and caching strategies, especially when handling frequently queried images and text pairs.
NVIDIA bge-m3 optimization tips
To optimize the NVIDIA bge-m3 in a Retrieval-Augmented Generation (RAG) setup, ensure you're using the latest driver and CUDA toolkit for improved performance. Fine-tune the model hyperparameters such as learning rate and batch size based on your specific dataset to enhance efficiency. Employ mixed precision training to speed up computations and reduce memory usage. Utilize data augmentation techniques to increase the variability of your training dataset, helping the model generalize better. Additionally, streamline your retrieval process by implementing efficient indexing methods and caching frequently accessed data, which can significantly reduce latency during inference. Finally, monitor resource utilization with NVIDIA’s profiling tools to identify and address bottlenecks dynamically.
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?
By diving into this tutorial, you’ve unlocked the power of combining cutting-edge tools to build a robust RAG system from scratch! You learned how LangChain acts as the glue, orchestrating the entire pipeline by seamlessly connecting your data sources, retrieval logic, and generative AI. With Milvus as your vector database, you saw firsthand how to store and query dense embeddings at scale, ensuring lightning-fast similarity searches that pull the most relevant context for your queries. Then came Mistral AI’s Mixtral, the LLM powerhouse that transforms retrieved snippets into coherent, human-like answers—showcasing its knack for multilingual understanding and creative problem-solving. And let’s not forget NVIDIA’s bge-m3, the embedding model that turns text into rich, multidimensional vectors, capturing semantic nuances so your system understands exactly what users are asking for. Together, these tools form a dynamic quartet, turning raw data into actionable insights with precision and flair.
But this tutorial didn’t stop at the basics—you also picked up pro tips for optimizing performance, like tweaking chunk sizes for better retrieval or fine-tuning prompts to guide Mixtral’s outputs. The cherry on top? That free RAG cost calculator you explored, which helps you balance accuracy and expenses as you scale. Now, imagine what you can build next! Whether it’s a customer support bot, a research assistant, or a personalized learning tool, you’ve got the blueprint to innovate. So fire up your IDE, experiment with these tools, and let your creativity run wild. The future of intelligent applications is in your hands—go build something amazing, share it with the world, and keep pushing the boundaries of what RAG can do! 🚀
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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