Build RAG Chatbot with LangChain, Zilliz Cloud, Google Vertex AI Claude 3 Sonnet, and Google Vertex AI textembedding-gecko@003

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.
Zilliz Cloud: a fully managed vector database-as-a-service platform built on top of the open-source Milvus, designed to handle high-performance vector data processing at scale. It enables organizations to efficiently store, search, and analyze large volumes of unstructured data, such as text, images, or audio, by leveraging advanced vector search technology. It 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 textembedding-gecko@003: This model specializes in generating high-quality text embeddings for diverse applications, including semantic search and content recommendation. It leverages advanced techniques for contextual understanding, ensuring accurate representations of intricate text. Ideal for integration into systems needing scalable and efficient NLP solutions, enhancing user experience in real-time applications.

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 textembedding-gecko@003

pip install -qU langchain-google-vertexai

from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings(model="textembedding-gecko@003")

Step 4: Install and Set Up Zilliz Cloud

pip install -qU langchain-milvus

from langchain_milvus import Zilliz

vector_store = Zilliz(
    embedding_function=embeddings,
    connection_args={
        "uri": ZILLIZ_CLOUD_URI,
        "token": ZILLIZ_CLOUD_TOKEN,
    },
)

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.

Zilliz Cloud optimization tips

Optimizing Zilliz Cloud for a RAG system involves efficient index selection, query tuning, and resource management. Use Hierarchical Navigable Small World (HNSW) indexing for high-speed, approximate nearest neighbor search while balancing recall and efficiency. Fine-tune ef_construction and M parameters based on your dataset size and query workload to optimize search accuracy and latency. Enable dynamic scaling to handle fluctuating workloads efficiently, ensuring smooth performance under varying query loads. Implement data partitioning to improve retrieval speed by grouping related data, reducing unnecessary comparisons. Regularly update and optimize embeddings to keep results relevant, particularly when dealing with evolving datasets. Use hybrid search techniques, such as combining vector and keyword search, to improve response quality. Monitor system metrics in Zilliz Cloud’s dashboard and adjust configurations accordingly to maintain low-latency, high-throughput performance.

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 textembedding-gecko@003 optimization tips

Google Vertex AI textembedding-gecko@003 is designed for advanced text understanding, making it ideal for high-accuracy RAG applications. Optimize embedding generation by removing noisy data and focusing on the most relevant content within documents. Use efficient vector search algorithms, such as FAISS with IVF or HNSW, to ensure fast and accurate document retrieval. Batch text embeddings for large volumes of data to speed up processing and minimize latency. Implement caching for high-frequency queries and periodically refresh embeddings to keep up with changes in the data landscape. Fine-tune the model on domain-specific tasks to improve relevance in specialized RAG applications. Consider deploying a multi-stage search strategy with semantic and keyword-based approaches for optimal accuracy and 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?

What an incredible journey we’ve been on together! In this tutorial, you’ve learned to harness the power of an innovative RAG (Retrieval-Augmented Generation) system by seamlessly integrating a robust framework, a high-performance vector database, cutting-edge large language models, and powerful embedding models. Each of these components plays a significant role in your RAG application, making it not only functional but also intelligent and responsive.

The framework acts as the backbone of your system, effortlessly coordinating the various elements to work in harmony. The vector database, leveraging Zilliz Cloud, ensures that you can perform lightning-fast searches through massive datasets, retrieving the most relevant information when you need it. Meanwhile, the Google Vertex AI Claude 3 Sonnet LLM elevates conversational intelligence, allowing your application to engage users in a natural and meaningful way. And let's not forget about the embedding model, such as the textembedding-gecko@003, which generates rich semantic representations that give context and depth to your queries.

Throughout our tutorial, you’ve also picked up some valuable optimization tips and even a fantastic free cost calculator to help keep your developments on track. Now it’s time to take the excitement and knowledge you’ve gained and put it into action! Start building your own RAG applications, optimize for the best performance, and don’t hesitate to let your creativity shine. The possibilities are endless, and I can’t wait to see what you create! Your journey in this cutting-edge field is just beginning—so let’s get innovating!

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! 💖