Build RAG Chatbot with LangChain, Milvus, Google Vertex AI Gemini 1.5 Flash, 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.
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 Gemini 1.5 Flash: An advanced AI model designed for real-time applications, Gemini 1.5 Flash is optimized for speed and responsiveness in predictive analytics and NLP tasks. Its strengths lie in generating insights rapidly while ensuring accuracy, making it ideal for use cases in customer service automation and dynamic content generation.
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 Gemini 1.5 Flash

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 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 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 Gemini 1.5 Flash optimization tips

Gemini 1.5 Flash is optimized for low-latency inference, making it ideal for real-time RAG applications. Improve efficiency by filtering out low-relevance documents before retrieval, ensuring optimal token usage. Use structured prompts with clear formatting to enhance response clarity. Keep temperature between 0.1 and 0.2 for accuracy, fine-tuning top-p as needed. Leverage Google’s AI infrastructure for dynamic scaling to handle fluctuating workloads. Implement caching for common queries to reduce redundant processing. In a multi-model deployment, use Gemini 1.5 Flash for fast responses while reserving Pro models for deeper reasoning.

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 have you learned? Wow, what a journey we’ve just been on! Throughout this tutorial, we’ve rolled up our sleeves and explored how to integrate a powerful framework like LangChain with a vector database such as Milvus, harnessing the amazing capabilities of the Google Vertex AI Gemini 1.5 Flash LLM and the cutting-edge embedding model textembedding-gecko@003. Together, these components form a robust RAG system that truly pushes the boundaries of what’s possible.

You’ve seen how the LangChain framework ties everything together seamlessly, allowing you to manage workflows and processes effortlessly. The vector database empowers lightning-fast searches for relevant information, making sure that your application delivers results at breakneck speed. Meanwhile, the LLM brings conversational flair and sophistication, ensuring that interactions are not just functional but also engaging. Last but not least, the embedding model provides rich semantic representations, enhancing the overall understanding of context and meaning.

We also shared some nifty optimization tips and introduced a free cost calculator to help you strategize your project budgets. So, what’s next? It’s time to dive in! Start building, optimizing, and innovating your own RAG applications. The sky's the limit, and I can’t wait to see the incredible solutions you’ll create! Let your creativity flow and make your mark in the world of AI-powered applications!

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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