Build RAG Chatbot with LangChain, Faiss, AWS Bedrock Claude 3 Haiku, and Google Vertex AI text-embedding-004

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 Haiku: This model harnesses the power of Claude 3 within AWS's Bedrock service, facilitating scalable AI solutions. It excels in natural language understanding and generation tasks, making it ideal for customer support, content creation, and conversational agents. Its integration with cloud infrastructure ensures flexibility and ease of deployment for enterprises.
Google Vertex AI text-embedding-004: This model specializes in creating high-quality text embeddings for diverse natural language processing tasks. Its strength lies in capturing semantic meaning and relationships effectively, making it suitable for applications such as semantic search, clustering, and recommendation systems. Ideal for developers seeking to enhance AI-driven insights from textual data.

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 Haiku

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-haiku-20240307-v1:0", model_provider="bedrock_converse")

Step 3: Install and Set Up Google Vertex AI text-embedding-004

pip install -qU langchain-google-vertexai

from langchain_google_vertexai import VertexAIEmbeddings

embeddings = VertexAIEmbeddings(model="text-embedding-004")

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 Haiku optimization tips

Claude 3 Haiku on AWS Bedrock is designed for speed and efficiency, making it ideal for real-time RAG applications. Optimize retrieval by prioritizing high-precision embeddings and filtering out low-relevance documents before passing them to the model. Keep prompts structured with clear formatting to help the model focus on the most critical context. Use temperature settings between 0.1 and 0.3 for factual accuracy and adjust top-p to balance diversity. For latency-sensitive applications, leverage AWS Bedrock’s scaling capabilities to ensure smooth performance under heavy loads. Implement caching for frequently accessed queries to reduce API calls and improve response times. If used in a multi-model setup, reserve Haiku for fast preliminary filtering or summarization before escalating complex queries to larger models.

Google Vertex AI text-embedding-004 optimization tips

Google Vertex AI text-embedding-004 offers high-quality embeddings suitable for a wide range of RAG applications. To improve retrieval efficiency, reduce redundancy in input text by preprocessing data and focusing on key concepts and relevant context. For large-scale deployments, utilize batch processing to generate embeddings in parallel, reducing latency. Optimize search performance by implementing hybrid search strategies that combine traditional keyword matching with dense vector similarity. Fine-tune temperature settings to balance between creativity and precision, and adjust the model’s top-k and top-p parameters to control the variability of results. Cache embeddings for high-demand queries to reduce unnecessary processing, and refresh embeddings periodically to maintain relevance as new data is ingested.

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 exciting tutorial, you’ve not only become familiar with key components but also mastered the seamless integration of a framework, vector database, large language model (LLM), and an embedding model to build a cutting-edge retrieval-augmented generation (RAG) system! You've witnessed how LangChain elegantly ties everything together, creating a cohesive pipeline that manages workflows effortlessly. With the power of Faiss, you saw how a vector database can deliver lightning-fast searches, enabling your application to retrieve relevant information in an instant.

The LLM, exemplified by Claude 3 from AWS Bedrock, breathed life into your system, allowing for dynamic and engaging conversations while handling complex queries. You also explored the capabilities of Google’s Vertex AI text-embedding-004, which generates rich semantic representations of data, making it easier than ever to understand and utilize context in your interactions.

Along the way, we shared optimization tips to refine your implementation and even introduced a free cost calculator to empower you as you explore scaling your applications. So, take a moment to appreciate your achievements! Now, it's time to unleash your creativity: start building, optimizing, and innovating your own RAG applications. The possibilities are endless, and the future is bright—embrace the journey ahead!

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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