Build RAG Chatbot with LangChain, Zilliz Cloud, Google Vertex AI Gemini 2.0 Flash Thinking, and Google Vertex AI textembedding-gecko@001

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 Gemini 2.0 Flash Thinking: This advanced AI model is designed for rapid, high-quality cognitive analysis and decision-making. It leverages enhanced natural language understanding and generation, enabling seamless interactions. Ideal for real-time applications in customer service, content creation, and intelligent automation, it excels in scenarios that demand quick, insightful responses.
Google Vertex AI textembedding-gecko@001: This AI model specializes in generating high-quality text embeddings, facilitating superior semantic understanding and context capturing. Its strengths lie in efficient processing and scalability, making it ideal for applications like search, recommendation systems, and natural language understanding tasks that demand precise insights from textual data.

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 Gemini 2.0 Flash Thinking

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

# Ensure your VertexAI credentials are configured

from langchain.chat_models import init_chat_model

llm = init_chat_model("gemini-2.0-flash-thinking-exp-01-21", model_provider="google_vertexai")

Step 3: Install and Set Up Google Vertex AI textembedding-gecko@001

pip install -qU langchain-google-vertexai

from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings(model="textembedding-gecko@001")

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 Gemini 2.0 Flash Thinking optimization tips

Gemini 2.0 Flash Thinking is designed for rapid yet thoughtful responses in RAG systems. Optimize retrieval by focusing on high-confidence document matching, reducing unnecessary data processing. Keep prompts structured, providing key details upfront while avoiding excessive context. Adjust temperature settings (0.1–0.3) to fine-tune response diversity. Use caching to reduce API overhead for repeated queries. Take advantage of Google Cloud’s GPU-accelerated processing to improve throughput. If using Flash Thinking alongside larger models, assign it to medium-complexity reasoning tasks that require faster turnaround than Pro models but more depth than standard Flash.

Google Vertex AI textembedding-gecko@001 optimization tips

Google Vertex AI textembedding-gecko@001 provides strong semantic understanding suitable for a variety of RAG workflows. To optimize retrieval, preprocess text to remove non-essential words and structure content to highlight key information. Use nearest neighbor search with techniques like HNSW or FAISS to enhance retrieval speed without sacrificing accuracy. Optimize batch processing by grouping multiple text queries together, reducing API call overhead and increasing throughput. Fine-tune temperature settings to ensure consistent responses, and adjust top-k or top-p parameters based on the desired level of output diversity. Cache embeddings for frequently used text and set up periodic updates to ensure embedding freshness. Use dimensionality reduction to manage memory usage and storage costs effectively.

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 have you learned? Well, if you've followed along in this tutorial, you've unlocked the incredible potential of building a cutting-edge Retrieval-Augmented Generation (RAG) system by seamlessly integrating four powerful components: a flexible framework, a high-performance vector database, a state-of-the-art language model, and a dynamic embedding model. Together, these tools create a robust architecture that not only provides speed and efficiency but also enables rich and contextually aware interactions.

You’ve seen how the framework serves as the backbone, effortlessly tying all components together and allowing for smooth collaboration. The vector database, powered by Zilliz Cloud, equips your system with lightning-fast search capabilities, ensuring that you can retrieve relevant information in the blink of an eye. With Google Vertex AI's Gemini 2.0 Flash Thinking, your conversational intelligence soars, bringing a level of interaction that feels both natural and intelligent. Meanwhile, the embedding model translates complex semantic relationships into rich representations that enhance understanding and contextual relevance.

We've also shared handy optimization tips and even a free cost calculator, empowering you to refine your system to perfection. Now, armed with this knowledge and inspiration, it’s your turn to take action! Start building, optimizing, and innovating your own RAG applications. The possibilities are endless, and your journey into this exciting technology landscape is just beginning. Embrace the challenge, and let your creativity flow!

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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