Build RAG Chatbot with LangChain, Milvus, NVIDIA BGE-M3, and Ollama paraphrase-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.
Milvus: An open-source vector database optimized to store, index, and search large-scale vector embeddings efficiently, perfect for use cases like RAG, semantic search, and recommender systems. If you hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on Milvus and offers a free tier supporting up to 1 million vectors.
NVIDIA BGE-M3: The NVIDIA BGE-M3 is a state-of-the-art language model designed to generate human-like text, making it suitable for a variety of natural language processing tasks. Its strength lies in its ability to produce coherent and contextually relevant responses, making it ideal for applications in chatbots, content creation, and virtual assistants. BGE-M3 excels in understanding nuanced input, providing users with highly accurate and engaging interactions, thereby enhancing automated communication systems across multiple industries.
Ollama Paraphrase-Multilingual: This AI model specializes in generating paraphrases across multiple languages, enhancing content diversity and accessibility. Its strength lies in understanding context while altering sentence structure, making it ideal for translation services, content creation, and multilingual learning applications.

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 BGE-M3

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("baai/bge-m3", model_provider="nvidia")

Step 3: Install and Set Up Ollama paraphrase-multilingual

pip install -qU langchain-ollama

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(model="paraphrase-multilingual")

Step 4: Install and Set Up Milvus

pip install -qU langchain-milvus

from langchain_milvus import Milvus

vector_store = Milvus(embedding_function=embeddings)

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.

Milvus optimization tips

Milvus serves as a highly efficient vector database, critical for retrieval tasks in a RAG system. To optimize its performance, ensure that indexes are properly built to balance speed and accuracy; consider utilizing HNSW (Hierarchical Navigable Small World) for efficient nearest neighbor search where response time is crucial. Partitioning data based on usage patterns can enhance query performance and reduce load times, enabling better scalability. Regularly monitor and adjust cache settings based on query frequency to avoid latency during data retrieval. Employ batch processing for vector insertions, which can minimize database lock contention and enhance overall throughput. Additionally, fine-tune the model parameters by experimenting with the dimensionality of the vectors; higher dimensions can improve retrieval accuracy but may increase search time, necessitating a balance tailored to your specific use case and hardware infrastructure.

NVIDIA BGE-M3 optimization tips

To optimize the NVIDIA BGE-M3 in a Retrieval-Augmented Generation (RAG) setup, ensure that your data retrieval system is finely tuned for reusability and relevance—consider implementing a caching mechanism to store frequently accessed documents. Additionally, experimenting with the retrieval parameters, such as k-value in nearest neighbor searches, can yield better results for your specific tasks. Utilize mixed-precision training to enhance throughput and reduce memory usage without compromising model performance. Regularly monitor and fine-tune hyperparameters through validation tests to achieve the best balance between speed and accuracy. Lastly, leverage NVIDIA's TensorRT or ONNX optimization tools for model deployment to maximize inference efficiency.

Ollama Paraphrase-Multilingual Optimization Tips

To optimize the Ollama paraphrase-multilingual component in your Retrieval-Augmented Generation (RAG) setup, ensure that your training dataset is diverse and representative of the languages and dialects you intend to support, as this enhances paraphrasing accuracy across contexts. Use transfer learning with domain-specific data to improve performance on niche topics. Adjust hyperparameters such as learning rate and batch size based on validation results to enhance convergence. Implement a caching mechanism for frequently accessed paraphrases to reduce response time during retrieval. Monitor and analyze performance metrics regularly to identify bottlenecks, and consider fine-tuning the model periodically based on user feedback and new datasets to adapt to evolving language use.

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 modern RAG system from the ground up! You learned how LangChain acts as the glue that ties everything together, orchestrating the flow of data between components with its flexible framework. Milvus stepped in as your powerhouse vector database, efficiently storing and retrieving embeddings at scale, while the NVIDIA BGE-M3 embedding model transformed text into rich, multidimensional vectors, capturing semantic nuances for smarter search. Then came Ollama’s paraphrase-multilingual model, the multilingual maestro that generates human-like responses by synthesizing retrieved information into coherent answers. Along the way, you discovered how these pieces fit into a seamless pipeline—ingesting data, chunking it, embedding it, querying it, and generating answers—all while balancing speed and accuracy. You even picked up pro tips for optimizing performance, like tuning chunk sizes and leveraging metadata filtering, and got a sneak peek into managing costs with the free RAG cost calculator to keep your projects budget-friendly.

Now you’re equipped to turn raw data into intelligent, responsive applications! Whether you’re building multilingual chatbots, research assistants, or domain-specific knowledge bases, you’ve seen firsthand how combining these tools opens endless doors. The best part? You’re not just following steps—you’re understanding the why behind each layer of the stack. So go ahead, experiment with different models, tweak retrieval strategies, and push the boundaries of what RAG can do. The world of AI-driven applications is yours to shape, and every line of code you write brings your ideas closer to reality. Ready to build something amazing? Let’s go—your next breakthrough is waiting! 🚀

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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