Build RAG Chatbot with LangChain, Milvus, Mistral AI Mistral 7B, and NVIDIA nv-embed-v1

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's Mistral 7B: A highly efficient and powerful language model with 7 billion parameters. It is designed to offer robust performance for a wide range of natural language processing tasks, delivering high-quality responses while maintaining a balance between speed and computational resource usage. Its architecture is optimized for both flexibility and scalability in AI applications.
NVIDIA nv-embed-v1: This model specializes in generating high-quality embeddings for various applications, such as image and text processing. Its strength lies in efficiently capturing semantic similarities and contextual information. Ideal for tasks like semantic search, recommendation systems, and natural language understanding, it facilitates advanced AI solutions across diverse 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 Mistral AI Mistral 7B

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-7b", model_provider="mistralai")

Step 3: Install and Set Up NVIDIA nv-embed-v1

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="nvidia/nv-embed-v1")

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 7B optimization tips

Mistral 7B is a dense transformer-based model optimized for efficiency, making it a strong choice for RAG applications with constrained resources. Improve retrieval by using high-quality embeddings and reranking strategies to ensure only the most relevant documents are included in the context window. Optimize token usage by structuring input prompts concisely, eliminating redundant details while preserving key information. Use temperature (0.1–0.3) for consistent factual accuracy and adjust sampling techniques based on the required response diversity. Deploy Mistral 7B in a scalable infrastructure by leveraging quantization techniques (such as 4-bit or 8-bit precision) to improve inference speed without significantly impacting accuracy. Implement batch processing for handling multiple queries efficiently, reducing overall computational load. When working in real-time applications, consider response caching to minimize redundant API calls.

NVIDIA nv-embed-v1 Optimization Tips

To optimize the NVIDIA nv-embed-v1 within a Retrieval-Augmented Generation (RAG) setup, ensure you fine-tune your models with domain-specific data to improve relevance and accuracy. Utilize mixed precision training to speed up training times and reduce memory usage. Implement efficient batching techniques to handle larger datasets, maximizing throughput. Leverage NVIDIA’s TensorRT for inference optimization, enabling faster response times during retrieval. Lastly, monitor GPU utilization and experiment with various hyperparameters, such as learning rates and dropout rates, for optimal performance tailored to your application 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 just unlocked the power of combining cutting-edge tools to build a RAG system that bridges knowledge and creativity! You learned how LangChain acts as the brain of your application, orchestrating workflows and seamlessly connecting your data pipeline to the Mistral AI Mistral 7B LLM, which brings human-like reasoning and text generation to the table. The magic starts with NVIDIA’s nv-embed-v1, transforming raw text into rich numerical embeddings—turning words into a language machines truly understand. These embeddings then find their home in Milvus, a blazing-fast vector database that retrieves the most relevant context in milliseconds, ensuring your LLM has the freshest, most accurate information to work with. Together, these tools form a dynamic pipeline that supercharges AI responses with precision and depth, all while staying scalable and efficient. Plus, you picked up pro tips like optimizing chunk sizes for embeddings and balancing speed-accuracy tradeoffs in retrieval—skills that’ll save you time and resources. And don’t forget that handy free RAG cost calculator to keep your projects budget-friendly!

Now that you’ve seen how these pieces fit together, imagine the possibilities ahead. You’re not just building a RAG system—you’re crafting solutions that could transform how people interact with data, automate tasks, or even spark new forms of creativity. Whether you’re tweaking the pipeline for faster performance, experimenting with different embedding models, or exploring Milvus’s advanced indexing features, you’re equipped to innovate. Ready to take the next leap? Start experimenting, iterate fearlessly, and let your curiosity guide you. The tools are in your hands, the concepts are clear, and the AI landscape is yours to explore. Go build something amazing—and remember, every line of code you write brings us closer to the future of intelligent apps! 🚀

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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