Build RAG Chatbot with LangChain, OpenSearch, Google Vertex AI Gemini 1.5 Flash, and Azure text-embedding-3-large

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 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.
Azure text-embedding-3-large: This powerful AI model specializes in generating high-quality text embeddings for natural language processing tasks. With its advanced understanding of context, it excels in applications like semantic search, recommendation systems, and clustering. Ideal for developers seeking to enhance text analysis and retrieval in complex datasets while ensuring robust accuracy and performance.

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 Azure text-embedding-3-large

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 AzureOpenAIEmbeddings

embeddings = AzureOpenAIEmbeddings(
    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 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 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.

Azure text-embedding-3-large optimization tips

Azure text-embedding-3-large is a powerful model for generating high-quality text embeddings. Optimize efficiency by preprocessing input text to remove redundant content and ensure the embeddings focus on the most important concepts. Leverage approximate nearest neighbor (ANN) search algorithms like FAISS or HNSW for fast retrieval across large datasets. When dealing with high-throughput applications, implement caching strategies to store frequently accessed embeddings, reducing API call overhead. Use multi-threading or parallel processing to handle batch requests and reduce latency in large-scale systems. Consider dimensionality reduction techniques, such as PCA or quantization, to reduce storage requirements and improve retrieval speed. Regularly update embeddings to reflect new data and ensure your system’s search results are accurate.

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 diving into this tutorial, you’ve unlocked the power of combining cutting-edge tools to build a robust RAG system from scratch! You learned how LangChain acts as the glue that ties everything together, orchestrating workflows between components with ease. OpenSearch stepped in as your vector database superhero, efficiently storing and retrieving embeddings while handling complex similarity searches at scale. Then came Google Vertex AI Gemini 1.5 Flash, the LLM powerhouse that transformed retrieved context into coherent, human-like answers—fast and cost-effectively. And let’s not forget Azure’s text-embedding-3-large, which turned raw text into rich numerical representations, giving your system the “eyes” to understand semantic relationships in your data. Together, these pieces formed a seamless pipeline: ingest data, generate embeddings, store them intelligently, retrieve context, and generate answers that feel almost magical. You even picked up pro tips like balancing chunk sizes for accuracy, optimizing hybrid search strategies, and leveraging OpenSearch’s filtering capabilities to fine-tune performance. Plus, that free RAG cost calculator? A game-changer for budgeting and scaling your projects wisely!

Now that you’ve seen how these tools harmonize, the world of intelligent applications is yours to conquer. Whether you’re building chatbots, research assistants, or custom knowledge bases, you’ve got the blueprint to innovate. Experiment with different models, tweak retrieval parameters, or plug in domain-specific data—your creativity is the only limit. Remember, every iteration makes your system smarter, faster, and more aligned with real-world needs. So fire up your IDE, spin up those cloud resources, and start building. The future of AI-driven solutions is bright, and you’re already holding the tools to shape it. Go make something amazing—we can’t wait to see what you create next! 🚀

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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