Build RAG Chatbot with LangChain, Milvus, Mistral AI Mistral Nemo, and NVIDIA arctic-embed-l

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.
Mistral AI Mistral Nemo: This model is designed for high-performance natural language processing, emphasizing interpretability and adaptability. It excels in tasks involving text generation, dialogue systems, and content creation. Ideal for industries like marketing and entertainment, Mistral Nemo delivers rich, coherent narratives while allowing for fine-tuning to specific domain requirements.
NVIDIA arctic-embed-l: This AI model is designed for optimizing embedded systems with a focus on low-latency processing and energy efficiency. It excels in real-time applications, making it ideal for edge computing in smart devices, IoT applications, and automated systems, enabling advanced analytics and decision-making on-site.

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 Mistral AI Mistral Nemo

pip install -qU "langchain[mistralai]"

import getpass
import os

if not os.environ.get("MISTRAL_API_KEY"):
  os.environ["MISTRAL_API_KEY"] = getpass.getpass("Enter API key for Mistral AI: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("open-mistral-nemo", model_provider="mistralai")

Step 3: Install and Set Up NVIDIA arctic-embed-l

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="snowflake/arctic-embed-l")

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.

Mistral AI Mistral Nemo optimization tips

Mistral Nemo is designed for domain-specific knowledge retrieval and response generation, making retrieval efficiency a top priority. Optimize performance by fine-tuning retrieval pipelines to ensure domain-relevant embeddings are used for similarity searches. Use a ranking layer to prioritize the most contextually relevant passages before passing them to the model, reducing token waste. Adjust temperature settings (0.1–0.3) to maintain consistent, structured responses. In latency-sensitive applications, use token streaming to provide incremental responses rather than waiting for full completion. If running Nemo in a production environment, consider distributed inference techniques to manage large-scale workloads effectively. Monitor retrieval accuracy continuously and fine-tune embeddings periodically to keep responses aligned with domain-specific updates.

NVIDIA arctic-embed-l optimization tips

To optimize the NVIDIA arctic-embed-l component in your Retrieval-Augmented Generation (RAG) setup, consider implementing a multi-threading approach to parallelize data processing, which can significantly enhance throughput. Make use of mixed precision training to speed up the model's computations while minimizing memory usage. Regularly fine-tune your embeddings with domain-specific data to improve their relevance and accuracy. Additionally, leverage batch processing techniques to reduce latency and ensure efficient GPU utilization. Monitor your inference times and adjust the cache size dynamically based on workload patterns to balance speed and resource consumption effectively.

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 just unlocked the power to build a cutting-edge RAG system from scratch! You learned how LangChain acts as the glue that binds everything together, orchestrating the flow of data between your vector database, embedding model, and large language model (LLM). With Milvus, you saw how lightning-fast vector similarity searches can retrieve the most relevant context from vast datasets, turning unstructured data into actionable insights. Mistral AI’s capabilities then stepped in to generate thoughtful, context-aware responses, showcasing how LLMs can transform raw information into coherent, human-like answers. And let’s not forget NVIDIA’s arctic-embed-l, which turned your text into rich numerical embeddings—the secret sauce that makes semantic search possible. Together, these tools form a seamless pipeline that elevates AI applications from generic chatbots to intelligent, knowledge-driven assistants.

But this isn’t just about plugging in tools—it’s about optimizing and innovating. You discovered practical tips like fine-tuning retrieval parameters, balancing speed with accuracy, and using the free RAG cost calculator to estimate expenses before scaling up. Whether you’re building customer support bots, research assistants, or creative writing aids, you now have the blueprint to adapt this system to your needs. The possibilities are endless, and the best part? You’re equipped to experiment, tweak, and push boundaries. So go ahead—fire up your code editor, tweak those prompts, and let your creativity run wild. The future of AI-powered applications is in your hands, and this tutorial is just the beginning. Build something amazing, share it with the world, and watch your ideas come to life! 🚀

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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