Build RAG Chatbot with LangChain, LangChain vector store, NVIDIA Qwen2.5-7B-Instruct, 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.
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 Qwen2.5-7B-Instruct: This advanced language model is designed for instruction-following tasks, leveraging the capabilities of 7 billion parameters to comprehend and generate diverse text responses. Its strengths lie in natural language understanding and contextual adaptability, making it ideal for applications in tutoring, conversational agents, and automated content generation across various domains.
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 NVIDIA Qwen2.5-7B-Instruct

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("qwen/qwen2.5-7b-instruct", model_provider="nvidia")

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 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 Qwen2.5-7B-Instruct Optimization Tips

To optimize the NVIDIA Qwen2.5-7B-Instruct model in a Retrieval-Augmented Generation (RAG) setup, consider implementing mixed precision training to reduce memory footprint and accelerate training times. Fine-tune the model on domain-specific data to enhance relevancy in generated responses while adjusting the retrieval component's cosine similarity threshold to balance precision and recall. Utilize an efficient caching mechanism to store frequently accessed data, ensuring low-latency responses. Experiment with varying the number of retrievals based on query complexity, and leverage batch processing during inference to maximize throughput. Finally, keep an eye on hardware utilization metrics to adjust configurations and achieve optimal performance.

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?

You’ve just unlocked the power to build a cutting-edge RAG system from scratch! By diving into this tutorial, you’ve learned how to seamlessly weave together LangChain’s flexible orchestration framework, a lightning-fast vector database for storing and retrieving contextual data, NVIDIA’s powerhouse Qwen2.5-7B-Instruct LLM for generating intelligent responses, and the precision of NVIDIA’s arctic-embed-l model to transform text into rich, semantic embeddings. Each piece plays a critical role: LangChain acts as the glue, the vector database serves as your dynamic memory, the embedding model maps meaning into numbers, and the LLM turns it all into human-like answers. Together, they form a pipeline that’s not just functional but smart—capable of understanding nuanced queries and delivering accurate, context-aware results. Plus, you picked up pro tips for optimizing performance, like tuning retrieval parameters and balancing cost-efficiency with quality, and even discovered a free RAG cost calculator to help you experiment without breaking the bank.

Now that you’ve seen how these tools harmonize, the world of AI-driven applications is yours to explore! Whether you’re building chatbots, knowledge assistants, or custom solutions for real-time data, you’re equipped to innovate. Don’t stop here—tweak your pipeline, experiment with hybrid retrieval strategies, or integrate domain-specific data to make your system even smarter. The best part? You’re not just following steps; you’re shaping the future of how humans and machines interact. So fire up your IDE, play with those embeddings, and let your creativity run wild. Every line of code you write brings us closer to smarter, more intuitive AI. Ready to make an impact? Let’s build something amazing—your RAG journey has only just begun! 🚀✨

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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