Build RAG Chatbot with LangChain, Zilliz Cloud, Azure GPT-4o mini, 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.
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.
Azure GPT-4o Mini: A compact version of the powerful GPT-4 architecture, designed for efficient processing in resource-constrained environments. It delivers robust performance in natural language understanding and generation, making it suitable for chatbots, customer support, and content creation. Ideal for applications where speed and scalability are essential without compromising on quality.
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 Azure GPT-4o mini

pip install -qU "langchain[openai]"

import getpass
import os

if not os.environ.get("AZURE_OPENAI_API_KEY"):
  os.environ["AZURE_OPENAI_API_KEY"] = getpass.getpass("Enter API key for Azure: ")

from langchain_openai import AzureChatOpenAI

llm = AzureChatOpenAI(
    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
    azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
)

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 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.

Azure GPT-4o mini optimization tips

Azure GPT-4o mini is a cost-efficient, low-latency model optimized for fast RAG applications. Improve retrieval by ensuring only the top-ranked, most relevant documents are included in the context to minimize unnecessary token consumption. Structure prompts with bullet points or numbered lists for clarity. Adjust temperature settings between 0.1 and 0.2 for precision, modifying top-p as needed for response diversity. To enhance performance, batch multiple API requests and implement caching for frequently queried information. Azure’s infrastructure allows for auto-scaling, so configure dynamic scaling to handle varying workloads efficiently. Stream responses for improved real-time performance, ensuring fast and interactive user experiences. If used in a pipeline, assign GPT-4o mini to preliminary filtering or summarization while reserving larger models for complex tasks.

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?

What have you learned? Throughout this tutorial, you’ve embarked on an exciting journey to integrate four transformative components: a robust framework, a lightning-fast vector database, a conversational dynamo of an LLM, and a sophisticated embedding model. Each part plays a unique role, creating a seamless and cutting-edge RAG system that pushes the boundaries of what's possible in information retrieval and conversational AI. You discovered how the framework intelligently orchestrates these components, ensuring they work in harmony to deliver a smooth user experience.

The vector database provides you with the speed you crave, enabling rapid searches that no longer feel like a chore. Meanwhile, the LLM, powered by Azure GPT-4o mini, breathes life into your applications, enabling them to communicate naturally and intuitively. You've also learned how the Google Vertex AI text-embedding-005 model transforms textual data into rich semantic representations, enhancing the depth of understanding your system can achieve.

Don’t forget the extra features! We shared optimization tips that will make your RAG system even more efficient and a handy cost calculator to help you budget your innovations. So, what’s next? Dive into your own projects! Start building your RAG applications, experiment with optimizations, and let your creativity ignite new possibilities. The tools are in your hands—go forth and innovate with confidence!

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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