Build RAG Chatbot with LangChain, OpenSearch, Google Vertex AI Gemini 2.0 Flash, and NVIDIA nv-embed-v1

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.
OpenSearch: An open-source search and analytics suite derived from Elasticsearch. It offers robust full-text search and real-time analytics, with vector search available as an add-on for similarity-based queries, extending its capabilities to handle high-dimensional data. Since it is just a vector search add-on rather than a purpose-built vector database, it lacks scalability and availability and many other advanced features required by enterprise-level applications. Therefore, if you prefer a much more scalable solution or hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvus and offers a free tier supporting up to 1 million vectors.)
Google Vertex AI Gemini 2.0 Flash: This model is designed for rapid deployment and high-performance machine learning tasks. With enhanced capabilities for natural language processing and image recognition, it excels in real-time analytics and automated decision-making. Ideal for businesses seeking quick insights and efficient model training across various applications, including chatbots and data analysis.
NVIDIA nv-embed-v1: This model specializes in generating high-quality embeddings for various applications, such as image and text processing. Its strength lies in efficiently capturing semantic similarities and contextual information. Ideal for tasks like semantic search, recommendation systems, and natural language understanding, it facilitates advanced AI solutions across diverse industries.

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

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-001", model_provider="google_vertexai")

Step 3: Install and Set Up NVIDIA nv-embed-v1

pip install -qU langchain-nvidia-ai-endpoints

import getpass
import os

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

from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings

embeddings = NVIDIAEmbeddings(model="nvidia/nv-embed-v1")

Step 4: Install and Set Up OpenSearch

pip install --upgrade --quiet  opensearch-py langchain-community

from langchain_community.vectorstores import OpenSearchVectorSearch
opensearch_vector_search = OpenSearchVectorSearch(
    "http://localhost:9200",
    "embeddings",
    embedding_function
)

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.

OpenSearch optimization tips

To optimize OpenSearch in a Retrieval-Augmented Generation (RAG) setup, fine-tune indexing by enabling efficient mappings and reducing unnecessary stored fields. Use HNSW for vector search to speed up similarity queries while balancing recall and latency with appropriate ef_search and ef_construction values. Leverage shard and replica settings to distribute load effectively, and enable caching for frequent queries. Optimize text-based retrieval with BM25 tuning and custom analyzers for better relevance. Regularly monitor cluster health, index size, and query performance using OpenSearch Dashboards and adjust configurations accordingly.

Google Vertex AI Gemini 2.0 Flash optimization tips

Gemini 2.0 Flash is built for ultra-fast RAG applications, making it crucial to optimize efficiency and speed. Improve retrieval by minimizing the number of retrieved documents to avoid unnecessary token consumption. Structure prompts concisely, using bullet points or numbered lists for clarity. Set temperature to 0.1–0.2 for factual responses, fine-tuning top-p and top-k for variation control. Implement response caching for frequently queried topics to reduce latency. Take advantage of Google Cloud’s auto-scaling and GPU acceleration to maintain smooth performance under load. If using multiple models, leverage Flash for quick summarization and preliminary analysis before escalating queries to larger models.

NVIDIA nv-embed-v1 Optimization Tips

To optimize the NVIDIA nv-embed-v1 within a Retrieval-Augmented Generation (RAG) setup, ensure you fine-tune your models with domain-specific data to improve relevance and accuracy. Utilize mixed precision training to speed up training times and reduce memory usage. Implement efficient batching techniques to handle larger datasets, maximizing throughput. Leverage NVIDIA’s TensorRT for inference optimization, enabling faster response times during retrieval. Lastly, monitor GPU utilization and experiment with various hyperparameters, such as learning rates and dropout rates, for optimal performance tailored to your application needs.

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?

By now, you’ve unlocked the magic of building a powerful RAG system from the ground up! You’ve seen how LangChain acts as the glue, orchestrating the entire workflow with its flexible framework, connecting your data pipeline to the AI models seamlessly. OpenSearch steps in as your trusty vector database, storing and retrieving embeddings at lightning speed, ensuring your system can handle real-time queries without breaking a sweat. The NVIDIA nv-embed-v1 model blew your mind by transforming raw text into rich, meaningful embeddings, giving your data the superpower of context-aware understanding. And let’s not forget Google Vertex AI Gemini 2.0 Flash—the star of the show—generating human-like responses that feel natural and insightful, all while staying efficient and scalable. Together, these tools form a RAG pipeline that’s not just functional but thrillingly capable, turning mountains of unstructured data into actionable insights and dynamic conversations.

But wait—there’s more! You’ve also picked up pro tips for optimizing your system, like fine-tuning retrieval parameters and balancing speed with accuracy, ensuring your RAG application runs like a well-oiled machine. The free RAG cost calculator shared in the tutorial? That’s your secret weapon for budgeting and scaling smartly, so you can experiment without breaking the bank. Imagine what you’ll create next: chatbots that feel alive, search engines that anticipate needs, or tools that transform industries. The world of AI is your playground now, and you’ve got the skills to build, tweak, and innovate. So go ahead—dive in, iterate fearlessly, and let your curiosity lead the way. The future of intelligent applications is literally at your fingertips. Let’s make it happen! 🚀

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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