Build RAG Chatbot with LangChain, Milvus, AWS Bedrock Claude 3 Sonnet, and Nomic Embed Text V2

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.
AWS Bedrock Claude 3 Sonnet: This model is a powerful version of Claude 3 tailored for seamless integration within AWS Bedrock. It combines advanced natural language processing with robust reasoning skills, ideal for conversational AI, content generation, and personalized customer interactions. Its design emphasizes scalability and efficiency for enterprise-level applications.
Nomic Embed Text V2: An open-source, multilingual text embedding model using a Mixture-of-Experts (MoE) architecture for efficient processing. Trained on 1.6 billion text pairs, it excels in retrieval tasks, supports flexible embedding dimensions, and optimizes storage and compute costs. Its training data and code are fully open-sourced for transparency.

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 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-sonnet-20240229-v1:0", model_provider="bedrock_converse")

Step 3: Install and Set Up Nomic Embed Text V2

pip install -qU langchain-nomic

import getpass
import os

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

from langchain_nomic import NomicEmbeddings

embeddings = NomicEmbeddings(model="nomic-ai/nomic-embed-text-v2-moe")

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.

AWS Bedrock Claude 3 Sonnet optimization tips

Claude 3 Sonnet on AWS Bedrock offers a balance between efficiency and reasoning power, making it a strong choice for general-purpose RAG systems. Optimize retrieval by dynamically adjusting the number of retrieved documents based on query complexity, ensuring relevance without overloading context windows. Structure prompts clearly, keeping key facts upfront to maximize comprehension. Use temperature settings around 0.1–0.3 for high factual accuracy while adjusting top-k and top-p to fine-tune output diversity. Implement caching for frequently used data to improve speed and reduce token costs. Use AWS Bedrock’s scaling options to handle variable traffic efficiently, distributing requests intelligently. If using Sonnet alongside Opus or Haiku, position it as a middle-tier model—handling moderately complex queries without the computational overhead of Opus.

Nomic Embed Text V2 optimization tips

Nomic Embed Text V2 is a versatile embedding model suited for general-purpose text search tasks in RAG systems. Optimize efficiency by preprocessing input data to remove irrelevant noise and focus on the most meaningful content, which can help reduce computational overhead. For faster retrieval, employ approximate nearest neighbor (ANN) search algorithms like HNSW or FAISS to speed up the search process while maintaining accuracy. Consider implementing vector quantization techniques to reduce storage space without significantly affecting retrieval quality. Utilize batching to process multiple texts in parallel and minimize API latency. If working with large datasets, regularly update embeddings to reflect the most recent information, ensuring that your retrieval system remains relevant and effective.

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?

What have you learned? Throughout this tutorial, you’ve ventured into the exciting world of building a cutting-edge Retrieval-Augmented Generation (RAG) system by seamlessly integrating a powerful framework, a high-performance vector database, a conversational language model, and an advanced embedding model. Isn’t it thrilling to see how the LangChain framework ties everything together, orchestrating smooth interactions between components? With Milvus at your disposal, you’ve unlocked the ability to conduct lightning-fast searches through vast data sets, enabling incredibly efficient retrieval of information—an essential part of your RAG system!

But that’s not all! The AWS Bedrock Claude 3 Sonnet brings conversational intelligence to life, allowing your RAG application to engage with users in meaningful ways that feel natural and intuitive. And with the Nomic Embed Text V2 creating rich semantic representations, the context of your data and queries is preserved, allowing for deeper understanding and more accurate results.

Don’t forget the handy optimization tips and the free cost calculator provided in this tutorial! These tools are designed to help you refine your RAG applications and manage resources wisely. Now, it’s your turn! Dive in, start building, optimizing, and innovating your own RAG applications. The possibilities are endless, and I can’t wait to see what incredible projects you create! So go ahead, unleash your creativity, and make your mark in the world of RAG technology!

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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