Build RAG Chatbot with LangChain, OpenSearch, NVIDIA Qwen2.5-7B-Instruct, and IBM multilingual-e5-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.)
NVIDIA Qwen2.5-7B-Instruct: This advanced language model is designed for instruction-following tasks, leveraging the capabilities of 7 billion parameters to comprehend and generate diverse text responses. Its strengths lie in natural language understanding and contextual adaptability, making it ideal for applications in tutoring, conversational agents, and automated content generation across various domains.
IBM multilingual-e5-large: This advanced AI model excels in natural language processing across multiple languages. Designed for tasks such as text generation, translation, and sentiment analysis, it exhibits strong contextual understanding and fluency. Ideal for global enterprises seeking to enhance customer interaction and automate multilingual communication.

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 NVIDIA Qwen2.5-7B-Instruct

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.chat_models import init_chat_model

llm = init_chat_model("qwen/qwen2.5-7b-instruct", model_provider="nvidia")

Step 3: Install and Set Up IBM multilingual-e5-large

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="intfloat/multilingual-e5-large",
    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.

NVIDIA Qwen2.5-7B-Instruct Optimization Tips

To optimize the NVIDIA Qwen2.5-7B-Instruct model in a Retrieval-Augmented Generation (RAG) setup, consider implementing mixed precision training to reduce memory footprint and accelerate training times. Fine-tune the model on domain-specific data to enhance relevancy in generated responses while adjusting the retrieval component's cosine similarity threshold to balance precision and recall. Utilize an efficient caching mechanism to store frequently accessed data, ensuring low-latency responses. Experiment with varying the number of retrievals based on query complexity, and leverage batch processing during inference to maximize throughput. Finally, keep an eye on hardware utilization metrics to adjust configurations and achieve optimal performance.

IBM multilingual-e5-large optimization tips

To optimize the IBM multilingual-e5-large model for use in a Retrieval-Augmented Generation (RAG) setup, consider fine-tuning the model on domain-specific data to improve relevance and coherence during generation. Maintain balanced retrieval by leveraging diverse and high-quality datasets to enhance the quality of context provided to the model. Implement caching mechanisms for commonly requested queries to minimize latency and improve response times. Monitor prompt design closely, ensuring they are concise and context-rich to guide the model effectively. Lastly, integrate user feedback to iteratively refine the retrieval and generation processes, thereby enhancing overall performance and user satisfaction.

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 seen firsthand how combining LangChain, OpenSearch, NVIDIA’s Qwen2.5-7B-Instruct, and IBM’s multilingual-e5-large creates a powerhouse RAG system! You learned how LangChain acts as the glue, orchestrating the entire pipeline—connecting your data sources, managing retrieval, and guiding the LLM to generate precise, context-aware answers. OpenSearch shines as your vector database, efficiently storing and retrieving embeddings, while IBM’s multilingual-e5-large ensures your system understands and processes queries across multiple languages, breaking down barriers for global applications. And let’s not forget NVIDIA’s Qwen2.5-7B-Instruct, the LLM that turns retrieved context into human-like responses, blending creativity with accuracy. Together, these tools transform raw data into actionable insights, whether you’re building a multilingual chatbot, a research assistant, or an enterprise knowledge base. You even picked up pro tips for optimizing performance, like tweaking chunking strategies or fine-tuning models, and discovered how the free RAG cost calculator helps you balance quality and budget—so you can scale smartly.

This tutorial didn’t just teach you how to build a RAG system—it gave you the tools to innovate. You’ve seen how each component’s strengths amplify the others, creating solutions that are greater than the sum of their parts. Now it’s your turn! Experiment with different datasets, refine your retrieval logic, or explore hybrid search setups. Whether you’re optimizing for speed, cost, or multilingual reach, you’ve got the foundation to tackle it all. The world of generative AI is moving fast, but you’re already ahead of the curve. So go build something amazing, share your breakthroughs, and keep pushing what’s possible with RAG. Your next big idea is just a few lines of code away—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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