Build RAG Chatbot with LangChain, Milvus, Google Vertex AI Claude 3 Sonnet, and Google Vertex AI text-embedding-005

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 Sonnet: This advanced AI model focuses on enhancing generative capabilities for creative applications. It merges powerful language understanding with customizable prompts to generate contextually relevant and coherent text. Ideal for marketing content, storytelling, and interactive AI experiences, it balances creativity with precision to meet diverse user needs.
Google Vertex AI text-embedding-005: This model produces high-quality text embeddings, facilitating nuanced semantic understanding and similarity comparisons. Its strength lies in efficiency and scalability, making it ideal for tasks like information retrieval, recommendation systems, and multi-language support. Perfect for developers seeking to enhance their applications with powerful contextual insights.

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 Sonnet

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-sonnet@20240229", model_provider="google_vertexai")

Step 3: Install and Set Up Google Vertex AI text-embedding-005

pip install -qU langchain-google-vertexai

from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings(model="text-embedding-005")

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 Sonnet optimization tips

Claude 3 Sonnet on Google Vertex AI balances efficiency and reasoning power, making it a great choice for general-purpose RAG setups. Optimize retrieval by dynamically adjusting the number of retrieved documents based on query complexity to maintain context quality. Keep prompts concise and well-structured, avoiding redundancy. Set temperature between 0.1 and 0.3 to balance factual consistency and response diversity. Cache commonly used queries to minimize latency and reduce token costs. Take advantage of Google’s autoscaling features to manage variable workloads. If deploying multiple models, position Sonnet as the mid-tier option, handling moderately complex queries without the computational overhead of Opus.

Google Vertex AI text-embedding-005 optimization tips

Google Vertex AI text-embedding-005 is a high-performing model, optimized for generating context-aware embeddings in RAG systems. Improve retrieval accuracy by leveraging a two-stage search process: first, filter using keyword matching, then re-rank results based on embedding similarity. Use batch embedding generation to minimize latency and streamline processing. When managing large datasets, implement hierarchical vector indexing to optimize memory usage and retrieval speed. Fine-tune embeddings with domain-specific data to improve retrieval relevance for niche use cases. For high-throughput systems, employ a distributed search infrastructure to scale efficiently while maintaining low query response times. Regularly evaluate the embeddings’ relevance and update the model with new training data for optimal 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 now.

Calculate your RAG cost

What Have You Learned?

Congratulations on making it to the finish line of this exciting tutorial! Isn’t it amazing to see how all these cutting-edge components can seamlessly work together to build a powerful Retrieval-Augmented Generation (RAG) system? By integrating the robust LangChain framework, you’ve learned how to create a sturdy backbone that holds everything together. The Milvus vector database was your key to unlocking ultra-fast searches, allowing you to explore vast datasets with ease and efficiency. You’ve also discovered how the Google Vertex AI Claude 3 Sonnet LLM can fuel conversational intelligence, empowering your applications to interact in natural and engaging ways. And let’s not forget the significance of embedding models like Google Vertex AI text-embedding-005, which allow you to generate rich, semantic representations of your data, paving the way for more insightful outcomes.

Throughout this journey, you’ve not only gained hands-on experience but also encountered optimization tips and a handy cost calculator to ensure your projects remain scalable and budget-friendly. Now that you’ve equipped yourself with these skills, the possibilities are truly endless. So, what’s next? It’s time to roll up your sleeves, dive deep into building, optimizing, and innovating your very own RAG applications! The world is waiting for your creativity and vision—go out there and make it happen!

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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