Build RAG Chatbot with LangChain, OpenSearch, Google Vertex AI Claude 3.7 Sonnet, and Cohere embed-english-light-v3.0

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 Claude 3.7 Sonnet: Google Vertex AI Claude 3.7 Sonnet: Google’s Vertex AI integrates Claude 3.7 Sonnet, bringing advanced NLP capabilities to the Google Cloud ecosystem. It supports the creation and deployment of secure, customized language models with high precision in natural language understanding and generation. Ideal for enterprises looking to leverage AI for chatbots, document processing, and AI-assisted customer interactions with a focus on scalability and cloud-native solutions.
Cohere embed-english-light-v3.0: This model specializes in generating high-quality embeddings for English text, optimized for efficiency and low resource usage. It excels in tasks such as semantic search, clustering, and recommendation systems. Ideal for developers needing fast and scalable text representation capabilities in resource-constrained environments.

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 Claude 3.7 Sonnet

pip install -qU "langchain[google-vertexai]"

# Ensure your VertexAI credentials are configured

from langchain.chat_models import init_chat_model

llm = init_chat_model("claude-3-7-sonnet@20250219", model_provider="google_vertexai")

Step 3: Install and Set Up Cohere embed-english-light-v3.0

pip install -qU langchain-cohere

import getpass
import os

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

from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-english-light-v3.0")

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 Claude 3.7 Sonnet Optimization Tips

In a Retrieval-Augmented Generation (RAG) setup with Google Vertex AI Claude 3.7 Sonnet, focus on fine-tuning the model to your domain-specific data for enhanced response accuracy. Use Vertex AI’s integrated tools to scale document retrieval, ensuring that your knowledge base is well-structured and efficiently indexed. Adjust retrieval parameters such as embedding vectors and similarity thresholds to improve the relevance of documents pulled into the generation process. Monitor response times and reduce latency by optimizing batch processing and utilizing Google Cloud’s low-latency storage. Additionally, regularly test and adjust hyperparameters like temperature and top-p to balance response creativity with factual correctness.

Cohere embed-english-light-v3.0 optimization tips

Cohere embed-english-light-v3.0 is optimized for lighter, faster embeddings in English language tasks. To maximize efficiency, preprocess input text by stripping out unnecessary words and standardizing formatting, which reduces computational load and speeds up embedding generation. Leverage vector compression techniques to reduce storage costs and improve retrieval performance. When managing large volumes of data, implement efficient search strategies like HNSW or product quantization to reduce time spent querying. Batch embedding operations to process multiple texts at once, minimizing the overhead from frequent API calls. Periodically update the embeddings to capture any changes in the dataset, ensuring freshness and accuracy in search results while optimizing storage.

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 magic of building a powerful RAG system from scratch using cutting-edge tools! You discovered how LangChain acts as the glue that ties everything together, orchestrating workflows between your vector database (OpenSearch) and large language model (Google Vertex AI’s Claude 3.7 Sonnet). With Cohere’s lightning-fast embed-english-light-v3.0 model, you learned to transform messy text into meaningful vectors, enabling OpenSearch to retrieve context with surgical precision. Claude 3.7 Sonnet then steps in like a creative genius, synthesizing that retrieved knowledge into human-like responses that feel both natural and deeply informed. You saw firsthand how these pieces form a seamless pipeline—ingesting data, optimizing storage, and generating answers that balance speed and accuracy. Plus, those optimization tips (like chunking strategies and hybrid search configurations) gave you the tools to fine-tune performance, while the free RAG cost calculator empowered you to balance quality and budget like a pro!

But this is just the beginning—you’re now equipped to build smarter, faster, and more cost-effective AI applications. Imagine what you’ll create next: chatbots that feel eerily knowledgeable, research assistants that surface golden insights, or tools that transform industries. The RAG framework you’ve mastered today is a launchpad for innovation. So go ahead—experiment with different models, tweak retrieval parameters, and push the boundaries of what’s possible. The world needs your ideas, and now you’ve got the toolkit to bring them to life. Let’s get building! 🚀

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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