Build RAG Chatbot with LangChain, Faiss, AWS Bedrock Claude 3.7 Sonnet, and Ollama paraphrase-multilingual

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.
Ollama Paraphrase-Multilingual: This AI model specializes in generating paraphrases across multiple languages, enhancing content diversity and accessibility. Its strength lies in understanding context while altering sentence structure, making it ideal for translation services, content creation, and multilingual learning applications.

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 Ollama paraphrase-multilingual

pip install -qU langchain-ollama

from langchain_ollama import OllamaEmbeddings

embeddings = OllamaEmbeddings(model="paraphrase-multilingual")

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.

Ollama Paraphrase-Multilingual Optimization Tips

To optimize the Ollama paraphrase-multilingual component in your Retrieval-Augmented Generation (RAG) setup, ensure that your training dataset is diverse and representative of the languages and dialects you intend to support, as this enhances paraphrasing accuracy across contexts. Use transfer learning with domain-specific data to improve performance on niche topics. Adjust hyperparameters such as learning rate and batch size based on validation results to enhance convergence. Implement a caching mechanism for frequently accessed paraphrases to reduce response time during retrieval. Monitor and analyze performance metrics regularly to identify bottlenecks, and consider fine-tuning the model periodically based on user feedback and new datasets to adapt to evolving language use.

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 building a cutting-edge RAG system from scratch! You learned how LangChain acts as the glue that binds everything together, orchestrating workflows between components like a pro. Faiss stepped in as your vector database superhero, enabling lightning-fast similarity searches across your data, while Ollama’s paraphrase-multilingual model gave your system a global voice, generating rich embeddings that capture meaning across languages. And let’s not forget AWS Bedrock’s Claude 3.7 Sonnet—the LLM powerhouse that transformed retrieved context into coherent, human-like responses. Together, these tools formed a seamless pipeline: ingesting data, retrieving relevant snippets, and generating answers that feel almost magical. You also picked up pro tips for optimizing performance, like tuning retrieval thresholds and balancing cost-efficiency, and even discovered tools like the free RAG cost calculator to keep your projects budget-friendly.

Now, armed with this knowledge, you’re ready to revolutionize how information is accessed and used! Whether you’re building chatbots, research assistants, or multilingual knowledge bases, you’ve got the blueprint. Experiment with different datasets, tweak parameters for your use case, and explore how small optimizations can lead to big wins. The world of RAG is yours to shape—so go ahead, start building, iterate fearlessly, and watch your ideas come to life. The future of intelligent applications is in your hands, and there’s no limit to what you’ll create next. Let’s get coding! 🚀

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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