Build RAG Chatbot with LangChain, Zilliz Cloud, Google Vertex AI Gemini 2.0 Flash-Lite, and Google Vertex AI text-embedding-004

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-Lite: This model streamlines AI deployment with an emphasis on low-latency and cost-efficient solutions. It excels in real-time applications like chatbots and interactive tools, combining high performance with seamless integration across various frameworks. Ideal for businesses looking to enhance user engagement without compromising efficiency.
Google Vertex AI text-embedding-004: This model specializes in creating high-quality text embeddings for diverse natural language processing tasks. Its strength lies in capturing semantic meaning and relationships effectively, making it suitable for applications such as semantic search, clustering, and recommendation systems. Ideal for developers seeking to enhance AI-driven 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-Lite

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-1.5-flash", model_provider="google_vertexai")

Step 3: Install and Set Up Google Vertex AI text-embedding-004

pip install -qU langchain-google-vertexai

from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings(model="text-embedding-004")

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-Lite optimization tips

Gemini 2.0 Flash-Lite is a lightweight, fast-response model suited for cost-efficient RAG applications. Improve retrieval by using high-precision embeddings to minimize irrelevant context. Structure prompts efficiently, keeping them short and well-organized. Adjust temperature (0.1–0.2) for accuracy, tuning top-p for output variety when needed. Cache frequent queries to reduce API usage and improve performance. Use Google’s auto-scaling infrastructure to handle demand spikes seamlessly. If deploying multiple models, utilize Flash-Lite for initial filtering and summarization while reserving larger models for in-depth reasoning.

Google Vertex AI text-embedding-004 optimization tips

Google Vertex AI text-embedding-004 offers high-quality embeddings suitable for a wide range of RAG applications. To improve retrieval efficiency, reduce redundancy in input text by preprocessing data and focusing on key concepts and relevant context. For large-scale deployments, utilize batch processing to generate embeddings in parallel, reducing latency. Optimize search performance by implementing hybrid search strategies that combine traditional keyword matching with dense vector similarity. Fine-tune temperature settings to balance between creativity and precision, and adjust the model’s top-k and top-p parameters to control the variability of results. Cache embeddings for high-demand queries to reduce unnecessary processing, and refresh embeddings periodically to maintain relevance as new data is ingested.

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? Throughout this tutorial, you've embarked on an exciting journey to integrate powerful technologies to build a cutting-edge Retrieval-Augmented Generation (RAG) system! You’ve seen firsthand how the LangChain framework weaves these components together seamlessly, acting as the backbone that connects everything and allows for smooth operations. With Zilliz Cloud's vector database, you discovered how it accelerates your search capabilities, enabling lightning-fast retrieval of relevant information that enhances your application's responsiveness and efficiency.

Furthermore, you explored how Google Vertex AI Gemini 2.0 Flash-Lite unleashes the potential of a large language model, imbuing your system with conversational intelligence that can engage users in meaningful interactions. By leveraging the Google Vertex AI text-embedding-004 model, you learned to generate rich semantic representations—transforming raw data into valuable insights!

And let’s not forget the extra features you picked up along the way, like optimization tips and a handy cost calculator to help you navigate your RAG system’s budget effectively. Now, the real magic begins. You have the tools and knowledge to start building, optimizing, and innovating your own RAG applications! Let your creativity soar, push the boundaries of what you can achieve, and bring your visionary ideas to life. The world of RAG awaits your brilliance—dive in and make your mark!

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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