Build RAG Chatbot with LangChain, LangChain vector store, AWS Bedrock Claude 3.7 Sonnet, and NVIDIA nv-embedqa-e5-v5

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.)
AWS Bedrock Claude 3.7 Sonnet: AWS Bedrock Claude 3.7 Sonnet: Built on Anthropic's Claude 3.7 Sonnet, AWS Bedrock integrates Claude's capabilities into AWS services, enabling seamless AI model deployment across industries. It offers scalable, high-performance natural language processing for enterprises, enhancing tasks like content creation, chatbots, and language-based AI solutions while leveraging AWS’s cloud infrastructure for ease of integration and scalability.
NVIDIA nv-embedqa-e5-v5: This model combines advanced natural language processing with deep learning to perform efficient question answering. It excels in understanding context and providing accurate responses from embedded knowledge sources. Ideal for applications in customer support, chatbots, and interactive learning environments, it enhances user engagement through intuitive interactions.

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 AWS Bedrock Claude 3.7 Sonnet

pip install -qU "langchain[aws]"

# Ensure your AWS credentials are configured

from langchain.chat_models import init_chat_model

llm = init_chat_model("anthropic.claude-3-7-sonnet-20250219-v1:0", model_provider="bedrock_converse")

Step 3: Install and Set Up NVIDIA nv-embedqa-e5-v5

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-embedqa-e5-v5")

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.)

AWS Bedrock Claude 3.7 Sonnet Optimization Tips

When integrating AWS Bedrock Claude 3.7 Sonnet in a RAG setup, optimize the retrieval pipeline by leveraging AWS’s scalable infrastructure for high-performance searches. Index documents based on relevance to ensure fast and accurate retrievals, and consider using AWS services like Lambda for dynamic scaling. Fine-tune Claude 3.7 Sonnet on specific industry or application data to enhance contextual relevance. Minimize costs and latency by adjusting the batch sizes for queries and optimizing the use of cloud-based storage solutions. Ensure that model hyperparameters, such as temperature and beam width, are fine-tuned to maintain both creativity and accuracy in generated responses.

NVIDIA nv-embedqa-e5-v5 optimization tips

To optimize the NVIDIA nv-embedqa-e5-v5 in a Retrieval-Augmented Generation (RAG) setup, first ensure that your model is correctly fine-tuned with a diverse training dataset to enhance retrieval accuracy. Utilize mixed precision training to boost computational efficiency and reduce memory usage. Implement batching for queries and responses to leverage parallel processing capabilities. Regularly monitor and adjust the learning rate and other hyperparameters for optimal training performance. Finally, incorporate caching mechanisms for frequently accessed embeddings and results to significantly speed up retrieval times.

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 now, you’ve unlocked the magic of building a powerful RAG system from the ground up! You’ve seen how LangChain acts as the glue that binds everything together, orchestrating workflows and simplifying complex interactions between components. With its intuitive framework, you learned to seamlessly connect the LangChain vector store—your dynamic knowledge vault—to efficiently store and retrieve relevant information. Then came the star of the show: AWS Bedrock’s Claude 3.7 Sonnet, which transformed those retrieved snippets into human-like, context-aware responses, showcasing the raw power of modern LLMs. And let’s not forget the NVIDIA nv-embedqa-e5-v5 embedding model, which turned your text into rich numerical vectors, making semantic search lightning-fast and accurate. Together, these pieces form a RAG pipeline that’s greater than the sum of its parts, giving you the tools to turn unstructured data into actionable insights.

But this tutorial didn’t stop at the basics! You also picked up pro tips for optimizing your system, like fine-tuning retrieval thresholds and balancing cost-performance tradeoffs. The free RAG cost calculator you explored is your secret weapon for budgeting smarter, ensuring your projects stay scalable and cost-effective. Now imagine what’s next—personalizing chatbots, supercharging research tools, or revolutionizing customer support. The skills you’ve gained today are just the beginning. So go ahead, fire up your IDE, experiment with different datasets, and tweak those parameters. The world of AI-driven applications is yours to shape, and your RAG-powered creations could be the next big thing. Let’s build, optimize, and innovate—your journey as a RAG wizard starts now! 🚀

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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