Build RAG Chatbot with LangChain, Milvus, Google Vertex AI Claude 3 Haiku, and NVIDIA nv-embedqa-e5-v5

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.
• Google Vertex AI Claude 3 Haiku: A versatile model within Google’s Vertex AI ecosystem, it is designed for seamless integration and enhanced generative capabilities. It excels in natural language processing tasks, such as text generation, summarization, and conversational AI. Ideal for businesses seeking to leverage AI for robust, scalable applications in various domains.
• NVIDIA nv-embedqa-e5-v5: This model combines advanced natural language processing with deep learning to perform efficient question answering. It excels in understanding context and providing accurate responses from embedded knowledge sources. Ideal for applications in customer support, chatbots, and interactive learning environments, it enhances user engagement through intuitive interactions.

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 Google Vertex AI Claude 3 Haiku

pip install -qU "langchain[google-vertexai]"

# Ensure your VertexAI credentials are configured

from langchain.chat_models import init_chat_model

llm = init_chat_model("claude-3-haiku@20240307", model_provider="google_vertexai")

Step 3: Install and Set Up NVIDIA nv-embedqa-e5-v5

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="nvidia/nv-embedqa-e5-v5")

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 = "\n\n".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.

Google Vertex AI Claude 3 Haiku optimization tips

Claude 3 Haiku on Google Vertex AI is designed for low-latency RAG applications requiring fast retrieval and generation. Optimize retrieval by using efficient vector search techniques to minimize irrelevant data in the context. Keep prompts short and structured to maximize clarity while reducing token usage. Set temperature between 0.1 and 0.3 for fact-based queries to maintain accuracy. Use Google Cloud’s auto-scaling and caching features to handle peak loads effectively. If deploying in a multi-model setup, reserve Haiku for fast, high-volume tasks while offloading complex reasoning to larger models. Implement response streaming for real-time applications to reduce latency and improve interactivity.

NVIDIA nv-embedqa-e5-v5 optimization tips

To optimize the NVIDIA nv-embedqa-e5-v5 in a Retrieval-Augmented Generation (RAG) setup, first ensure that your model is correctly fine-tuned with a diverse training dataset to enhance retrieval accuracy. Utilize mixed precision training to boost computational efficiency and reduce memory usage. Implement batching for queries and responses to leverage parallel processing capabilities. Regularly monitor and adjust the learning rate and other hyperparameters for optimal training performance. Finally, incorporate caching mechanisms for frequently accessed embeddings and results to significantly speed up retrieval times.

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 to build a sophisticated RAG system from the ground up! You learned how LangChain acts as the glue, orchestrating workflows between components with its flexible framework, while Milvus stepped in as your high-performance vector database, storing and retrieving embeddings at lightning speed. With Google Vertex AI’s Claude 3 Haiku, you saw firsthand how a cutting-edge LLM generates human-like responses by synthesizing retrieved context, and NVIDIA’s nv-embed-qa-e5-v5 transformed raw text into rich semantic embeddings, ensuring your system understands nuance and intent. Together, these tools formed a seamless pipeline—ingesting data, chunking it intelligently, embedding it for meaning, and delivering answers that feel almost magical. Plus, you picked up optimization tricks like tuning chunk sizes and hybrid search strategies to balance speed and accuracy, and even discovered how to use the free RAG cost calculator to budget your projects wisely!

Now that you’ve seen how these pieces fit together, the real adventure begins. Imagine the applications you’ll create—chatbots that truly understand users, search engines that surface exactly what’s needed, or AI assistants that feel like collaborators. You’ve got the tools, the know-how, and even the cost-saving hacks. So why wait? Start experimenting, tweak those parameters, and let your creativity run wild. The world of RAG is yours to explore, optimize, and redefine. Go build something brilliant—you’ve got this! 🚀✨

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!

If you like this tutorial, show your support by giving our Milvus GitHub repo a star ⭐—it means the world to us and inspires us to keep creating! 💖