Build RAG Chatbot with LangChain, OpenSearch, Google Vertex AI Gemini 1.5 Pro, and IBM granite-embedding-278m-multilingual

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 Pro: This advanced AI model combines powerful machine learning capabilities with enhanced multimodal support, enabling seamless processing of text, images, and audio. Its strengths lie in scalability and customization, making it ideal for enterprise applications such as content generation, data analysis, and customer interaction automation.
IBM granite-embedding-278m-multilingual: This advanced AI model specializes in generating multilingual text embeddings, making it highly effective for tasks such as cross-linguistic information retrieval and translation. With its strength in understanding diverse languages, it excels in applications involving global datasets and multilingual customer engagement.

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 Pro

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-pro", model_provider="google_vertexai")

Step 3: Install and Set Up IBM granite-embedding-278m-multilingual

pip install -qU langchain-ibm

import getpass
import os

if not os.environ.get("WATSONX_APIKEY"):
  os.environ["WATSONX_APIKEY"] = getpass.getpass("Enter API key for IBM watsonx: ")

from langchain_ibm import WatsonxEmbeddings

embeddings = WatsonxEmbeddings(
    model_id="ibm/granite-embedding-278m-multilingual",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="<WATSONX PROJECT_ID>",
)

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 Pro optimization tips

Gemini 1.5 Pro is a robust model designed for complex RAG applications requiring deep reasoning. Optimize retrieval by implementing advanced ranking algorithms to ensure highly relevant documents are included. Structure prompts with clear logical sections to maximize response coherence. Keep temperature between 0.1 and 0.3 for factual precision, fine-tuning top-k/top-p as needed. Use Google’s AI infrastructure to batch API calls, reducing processing overhead. Implement caching strategies to optimize frequently queried topics. Streaming responses can enhance real-time applications, improving perceived responsiveness. If deploying multiple models, reserve Gemini 1.5 Pro for high-value analytical tasks while using lighter models for simpler retrieval-based queries.

IBM granite-embedding-278m-multilingual optimization tips

To optimize the IBM granite-embedding-278m-multilingual for your Retrieval-Augmented Generation (RAG) setup, consider fine-tuning the model on domain-specific data relevant to your use case, which helps improve accuracy in embeddings. Use mini-batches when processing queries to balance memory efficiency and speed, ensuring you leverage GPU acceleration. Implement a caching mechanism for frequently accessed documents to reduce retrieval latency, and experiment with different similarity metrics to find the most effective one for your data. Regularly monitor the performance and iterate over hyperparameters such as learning rates and embedding dimensions to further enhance your retrieval capabilities.

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 learned how to weave together cutting-edge tools to create a powerful RAG system that’s ready to tackle real-world challenges! You now understand how LangChain acts as the glue, orchestrating the entire pipeline—connecting your data sources, managing interactions with the IBM granite-embedding-278m-multilingual model to transform text into rich embeddings, and seamlessly querying OpenSearch to retrieve the most relevant context. Speaking of OpenSearch, you’ve seen how its lightning-fast vector search capabilities make it the perfect partner for storing and retrieving embeddings at scale. Then there’s Google Vertex AI Gemini 1.5 Pro, the LLM powerhouse that takes those retrieved snippets and crafts coherent, context-aware responses, showcasing its knack for understanding nuance and generating human-like text. Along the way, you picked up optimization tricks like tuning chunk sizes for embeddings and balancing latency with accuracy, not to mention that handy free RAG cost calculator to keep your projects budget-friendly!

But this isn’t just about following steps—it’s about unlocking creativity. You’ve got the blueprint to build smarter applications, whether it’s chatbots that truly “get” user intent or search engines that feel almost psychic. Imagine tweaking the pipeline for multilingual support using IBM’s embedding model or experimenting with Gemini’s advanced reasoning for complex queries. The tools are yours now. So go ahead—iterate, optimize, and innovate! The world of RAG is evolving fast, and you’re already at the forefront. Start building, play with new datasets, and watch your ideas come to life. The future of AI-driven solutions is in your hands, and there’s no limit to what you’ll create next. Let’s get coding! 🚀

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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