Build RAG Chatbot with LangChain, Zilliz Cloud, Anthropic Claude 3.7 Sonnet, 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.
Zilliz Cloud: a fully managed vector database-as-a-service platform built on top of the open-source Milvus, designed to handle high-performance vector data processing at scale. It enables organizations to efficiently store, search, and analyze large volumes of unstructured data, such as text, images, or audio, by leveraging advanced vector search technology. It offers a free tier supporting up to 1 million vectors.
Anthropic Claude 3.7 Sonnet: Anthropic Claude 3.7 Sonnet: Claude 3.7 Sonnet is an advanced AI language model developed by Anthropic, designed to offer enhanced reasoning, alignment, and safety. It excels in tasks requiring sophisticated conversational abilities, providing users with natural, context-aware responses while maintaining ethical and safe outputs. Ideal for applications in customer service, content generation, and dialogue systems where safety and clarity are paramount.
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 Anthropic Claude 3.7 Sonnet

pip install -qU "langchain[anthropic]"

import getpass
import os

if not os.environ.get("ANTHROPIC_API_KEY"):
  os.environ["ANTHROPIC_API_KEY"] = getpass.getpass("Enter API key for Anthropic: ")

from langchain.chat_models import init_chat_model

llm = init_chat_model("claude-3-7-sonnet-latest", model_provider="anthropic")

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 Zilliz Cloud

pip install -qU langchain-milvus

from langchain_milvus import Zilliz

vector_store = Zilliz(
    embedding_function=embeddings,
    connection_args={
        "uri": ZILLIZ_CLOUD_URI,
        "token": ZILLIZ_CLOUD_TOKEN,
    },
)

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.

Zilliz Cloud optimization tips

Optimizing Zilliz Cloud for a RAG system involves efficient index selection, query tuning, and resource management. Use Hierarchical Navigable Small World (HNSW) indexing for high-speed, approximate nearest neighbor search while balancing recall and efficiency. Fine-tune ef_construction and M parameters based on your dataset size and query workload to optimize search accuracy and latency. Enable dynamic scaling to handle fluctuating workloads efficiently, ensuring smooth performance under varying query loads. Implement data partitioning to improve retrieval speed by grouping related data, reducing unnecessary comparisons. Regularly update and optimize embeddings to keep results relevant, particularly when dealing with evolving datasets. Use hybrid search techniques, such as combining vector and keyword search, to improve response quality. Monitor system metrics in Zilliz Cloud’s dashboard and adjust configurations accordingly to maintain low-latency, high-throughput performance.

Anthropic Claude 3.7 Sonnet Optimization Tips

To optimize the use of Anthropic Claude 3.7 Sonnet in a Retrieval-Augmented Generation (RAG) setup, focus on ensuring high-quality, relevant retrievals from your document store. Preprocess and index your knowledge base effectively by removing redundancy and structuring content for easy retrieval. Additionally, fine-tune the model on domain-specific data to improve response relevance. Consider batching requests for efficiency and adjusting the temperature and top-k parameters to balance creativity and accuracy. Monitor performance closely and adjust query embeddings to fine-tune the retrieval pipeline, ensuring low-latency and high-accuracy answers.

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 unlocked the foundational magic of building a RAG system from the ground up! You discovered how LangChain acts as the glue that elegantly orchestrates your entire pipeline, seamlessly connecting your data, models, and logic. With Zilliz Cloud’s blazing-fast vector database, you learned to store and retrieve embeddings at scale, turning unstructured data into queryable knowledge. The power of Anthropic’s Claude 3.7 Sonnet then took center stage, showcasing its ability to generate insightful, context-aware responses by synthesizing retrieved information with natural language mastery. And let’s not forget NVIDIA’s arctic-embed-l model—your trusty sidekick for transforming text into rich numerical representations, ensuring your system understands the nuances of language. Together, these tools form a dynamic quartet that breathes life into intelligent applications, whether you’re building chatbots, research assistants, or creative AI co-pilots.

But wait—there’s more! You also picked up pro tips for optimizing performance, like tuning chunk sizes for embeddings and balancing speed with accuracy in retrieval. The included free RAG cost calculator became your secret weapon for budgeting resources without sacrificing quality. Now, armed with this knowledge and these tools, you’re ready to create systems that don’t just answer questions but truly understand context. Imagine what’s next: personalized learning platforms, hyper-targeted search engines, or AI that anticipates user needs. The possibilities are as limitless as your curiosity. So fire up your IDE, experiment with new datasets, and start shaping the future of intelligent applications. Your RAG journey has just begun—go build something extraordinary! 🚀

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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