Build RAG Chatbot with LangChain, LangChain vector store, NVIDIA BGE-M3, and Azure text-embedding-ada-002

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.
LangChain in-memory vector store: an in-memory, ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance. It is intended for demos and does not yet support ids or deletion. (If you want a much more scalable solution for your apps or even enterprise projects, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvusand 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.
Azure text-embedding-ada-002: This model specializes in generating high-quality text embeddings, enabling effective semantic search and natural language understanding. Its strengths lie in producing contextually rich representations that enhance tasks like text classification, clustering, and recommendation systems, making it ideal for applications in content discovery and personalized user experiences.

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 Azure text-embedding-ada-002

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 LangChain vector store

pip install -qU langchain-core

from langchain_core.vectorstores import InMemoryVectorStore

vector_store = InMemoryVectorStore(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.

LangChain in-memory vector store optimization tips

LangChain in-memory vector store is just an ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. It has very limited features and is only intended for demos. If you plan to build a functional or even production-level solution, 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 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.

Azure text-embedding-ada-002 Optimization Tips

To optimize the Azure text-embedding-ada-002 component in your Retrieval-Augmented Generation (RAG) setup, ensure that you batch your requests effectively to reduce latency and maximize throughput; try grouping up to 1000 tokens per request if possible. Preprocess your input data to remove unnecessary noise and stop words, which can improve embedding quality. Adjust the model’s parameters to suit your specific use case, focusing on the length of input sequences and maintaining token limits. Utilize caching mechanisms for frequently queried inputs to speed up retrieval, and regularly fine-tune your embeddings with domain-specific data to enhance relevance. Finally, monitor and analyze performance metrics continuously to make iterative adjustments that cater to any shifts in data patterns or retrieval needs.

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 to build a cutting-edge RAG system from scratch! You learned how LangChain acts as the backbone, orchestrating the flow between components with its intuitive framework. The LangChain vector store became your go-to for lightning-fast retrieval, efficiently storing and querying embeddings to find the most relevant context for your queries. You explored embedding models like NVIDIA’s BGE-M3, which shines with its multilingual prowess, and Azure’s text-embedding-ada-002, a versatile choice for semantic understanding—both transforming text into rich numerical representations. Together, these tools form a seamless pipeline: ingest data, embed it, store it, retrieve the best matches, and let your chosen LLM craft intelligent, context-aware responses. Along the way, you picked up pro tips like optimizing chunking strategies and balancing speed vs. accuracy, ensuring your RAG system isn’t just functional but efficient. And let’s not forget that handy RAG cost calculator—your secret weapon for budgeting and scaling projects without surprises!

Now, armed with this knowledge, you’re ready to create RAG applications that feel almost magical. Whether you’re enhancing customer support, building research tools, or experimenting with AI-driven storytelling, the possibilities are endless. So go ahead—tweak those parameters, mix and match models, and push the boundaries of what’s possible. Every line of code you write brings us closer to smarter, more responsive AI. The future of intelligent systems is in your hands… let’s build it together! 🚀

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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