Build RAG Chatbot with LangChain, Faiss, AWS Bedrock Claude 3.7 Sonnet, and Nomic Nomic Embed

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.
Faiss: also known as Facebook AI Similarity Search, is an open-source vector search library that allows developers to quickly search for semantically similar multimedia data within a massive dataset of unstructured data. (If you want a much more scalable solution or hate to manage your own infrastructure, 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: 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.
Nomic Nomic Embed: Nomic Embed is an advanced AI model designed for generating high-dimensional embeddings that capture semantic relationships within textual data. Its strengths lie in providing robust text representation, enabling superior performance in natural language understanding tasks such as information retrieval, sentiment analysis, and recommendation systems. Ideal for applications in content personalization and knowledge discovery, Nomic Embed streamlines the process of deriving insights from large datasets.

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 Nomic Nomic Embed

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-embed-text-v1")

Step 4: Install and Set Up Faiss

pip install -qU langchain-community

from langchain_community.vectorstores import FAISS

vector_store = FAISS(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.

Faiss Optimization Tips

To enhance the performance of the Faiss library in a Retrieval-Augmented Generation (RAG) system, begin by selecting the appropriate index type based on your data volume and query speed requirements; for example, using an IVF (Inverted File) index can significantly speed up queries on large datasets by reducing the search space. Optimize your indexing process by using the nlist parameter to partition data into smaller clusters and set an appropriate number of probes (nprobe) during retrieval to balance between speed and accuracy. Ensure the vectors are properly normalized and consider using 16-bit or 8-bit quantization during indexing to reduce memory footprints for large datasets while maintaining reasonable retrieval accuracy. Additionally, consider leveraging GPU acceleration if available, as Faiss highly benefits from parallel processing, leading to faster nearest neighbor searches. Continuous fine-tuning and benchmarking with varying parameters and configurations can guide you in finding the most efficient setup specific to your data characteristics and retrieval requirements.

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.

Nomic Nomic Embed optimization tips

To optimize the Nomic Nomic Embed component in a Retrieval-Augmented Generation (RAG) setup, focus on fine-tuning your embedding model with domain-specific data to enhance contextual relevance. Implement efficient indexing strategies, such as using FAISS or Annoy, to speed up retrieval times without compromising accuracy. Experiment with dimensionality reduction techniques, like PCA or t-SNE, to decrease computational load while retaining essential semantic information. Regularly clean and preprocess your corpus to eliminate noise and improve embedding quality. Lastly, monitor embedding drift over time and update your embeddings periodically to ensure they reflect the latest knowledge in your target domain.

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 power of combining cutting-edge tools to create a functional and efficient RAG system! You learned how LangChain acts as the glue, orchestrating the entire pipeline with its flexible framework—connecting data sources, managing workflows, and streamlining interactions between components. Faiss, the lightning-fast vector database, stepped in to handle semantic search, turning dense embeddings from Nomic’s Embed model into actionable insights by retrieving the most relevant context. Speaking of embeddings, Nomic’s robust model transformed your raw text into rich numerical representations, capturing meaning in ways that make retrieval accurate and nuanced. And then there’s AWS Bedrock’s Claude 3.7 Sonnet, the LLM superstar, which synthesized that retrieved context into coherent, human-like responses, demonstrating how powerful generative AI becomes when grounded in real data. Along the way, you picked up optimization tricks like tuning retrieval thresholds and balancing cost-performance tradeoffs, and even discovered how the free RAG cost calculator can help you budget smarter as you scale.

Now that you’ve seen how these pieces fit together seamlessly, imagine the possibilities! You’re not just building chatbots or search tools—you’re crafting intelligent systems that learn, adapt, and deliver value. Whether you’re enhancing customer support, automating research, or creating personalized experiences, you’ve got the toolkit to innovate. So go ahead—experiment with different datasets, tweak those parameters, and push the boundaries of what RAG can do. The future of AI-powered applications is yours to shape, and this tutorial is just the launchpad. Start building, optimize fearlessly, and let your creativity turn these concepts into solutions that wow users and solve real-world problems. The next breakthrough? It could be yours! 🚀

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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