Build RAG Chatbot with LangChain, Faiss, NVIDIA BGE-M3, and NVIDIA bge-m3

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.
Faiss: also known as Facebook AI Similarity Search, is an open-source vector search library that allows developers to quickly search for semantically similar multimedia data within a massive dataset of unstructured data. (If you want 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 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.
NVIDIA bge-m3: The NVIDIA bge-m3 is a state-of-the-art generative model designed for high-performance multi-modal tasks, particularly in natural language processing and computer vision. Its strengths lie in real-time data processing and scalability, making it ideal for applications in interactive AI systems, creative content generation, and advanced analytical tools in various industries.

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 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_nvidia_ai_endpoints import NVIDIAEmbeddings

embeddings = NVIDIAEmbeddings(model="baai/bge-m3")

Step 4: Install and Set Up Faiss

pip install -qU langchain-community

from langchain_community.vectorstores import FAISS

vector_store = FAISS(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.

Faiss Optimization Tips

To enhance the performance of the Faiss library in a Retrieval-Augmented Generation (RAG) system, begin by selecting the appropriate index type based on your data volume and query speed requirements; for example, using an IVF (Inverted File) index can significantly speed up queries on large datasets by reducing the search space. Optimize your indexing process by using the nlist parameter to partition data into smaller clusters and set an appropriate number of probes (nprobe) during retrieval to balance between speed and accuracy. Ensure the vectors are properly normalized and consider using 16-bit or 8-bit quantization during indexing to reduce memory footprints for large datasets while maintaining reasonable retrieval accuracy. Additionally, consider leveraging GPU acceleration if available, as Faiss highly benefits from parallel processing, leading to faster nearest neighbor searches. Continuous fine-tuning and benchmarking with varying parameters and configurations can guide you in finding the most efficient setup specific to your data characteristics and retrieval requirements.

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.

NVIDIA bge-m3 optimization tips

To optimize the NVIDIA bge-m3 in a Retrieval-Augmented Generation (RAG) setup, ensure you're using the latest driver and CUDA toolkit for improved performance. Fine-tune the model hyperparameters such as learning rate and batch size based on your specific dataset to enhance efficiency. Employ mixed precision training to speed up computations and reduce memory usage. Utilize data augmentation techniques to increase the variability of your training dataset, helping the model generalize better. Additionally, streamline your retrieval process by implementing efficient indexing methods and caching frequently accessed data, which can significantly reduce latency during inference. Finally, monitor resource utilization with NVIDIA’s profiling tools to identify and address bottlenecks dynamically.

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 of combining cutting-edge tools to build a Retrieval-Augmented Generation (RAG) system from the ground up! You learned how LangChain acts as the glue, seamlessly orchestrating workflows between components like a pro. Faiss, your vector database superhero, stepped in to handle lightning-fast similarity searches, making it easy to retrieve the most relevant information from massive datasets. Then came NVIDIA’s BGE-M3 embedding model, transforming text into rich, meaningful vectors that capture the nuances of language—supercharging your system’s ability to understand context. Finally, the large language model (LLM) took center stage, synthesizing retrieved data into coherent, human-like responses. Together, these pieces form a dynamic pipeline where retrieval and generation work hand-in-hand to deliver accurate, context-aware answers. You also picked up pro tips for optimizing performance, like tuning chunk sizes and balancing speed with precision, and even discovered a free RAG cost calculator to keep your projects budget-friendly!

But this is just the beginning! You’re now equipped to experiment, iterate, and push boundaries. Maybe you’ll tweak the embedding model for domain-specific tasks or integrate real-time data streams for fresher insights. The tools you’ve mastered today are your launchpad. So go ahead—build something that wows users, solves real problems, or even disrupts industries. Remember, every line of code you write brings us closer to smarter, more responsive AI. The future of intelligent applications is yours to shape. Let’s get building, optimizing, and innovating—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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