Build RAG Chatbot with LangChain, Zilliz Cloud, AWS Bedrock Claude 3 Haiku, and voyage-3

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.
Zilliz Cloud: a fully managed vector database-as-a-service platform built on top of the open-source Milvus, designed to handle high-performance vector data processing at scale. It enables organizations to efficiently store, search, and analyze large volumes of unstructured data, such as text, images, or audio, by leveraging advanced vector search technology. It 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.
Voyage-3: Designed for AI-powered navigation and journey planning, Voyage-3 optimizes route efficiency while providing real-time traffic updates and data insights. Its strengths lie in predictive analysis and adaptive learning, making it ideal for logistics, delivery services, and travel apps that demand reliable and intelligent navigation solutions.

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 voyage-3

pip install -qU langchain-voyageai

import getpass
import os

if not os.environ.get("VOYAGE_API_KEY"):
  os.environ["VOYAGE_API_KEY"] = getpass.getpass("Enter API key for Voyage AI: ")

from langchain-voyageai import VoyageAIEmbeddings

embeddings = VoyageAIEmbeddings(model="voyage-3")

Step 4: Install and Set Up Zilliz Cloud

pip install -qU langchain-milvus

from langchain_milvus import Zilliz

vector_store = Zilliz(
    embedding_function=embeddings,
    connection_args={
        "uri": ZILLIZ_CLOUD_URI,
        "token": ZILLIZ_CLOUD_TOKEN,
    },
)

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.

Zilliz Cloud optimization tips

Optimizing Zilliz Cloud for a RAG system involves efficient index selection, query tuning, and resource management. Use Hierarchical Navigable Small World (HNSW) indexing for high-speed, approximate nearest neighbor search while balancing recall and efficiency. Fine-tune ef_construction and M parameters based on your dataset size and query workload to optimize search accuracy and latency. Enable dynamic scaling to handle fluctuating workloads efficiently, ensuring smooth performance under varying query loads. Implement data partitioning to improve retrieval speed by grouping related data, reducing unnecessary comparisons. Regularly update and optimize embeddings to keep results relevant, particularly when dealing with evolving datasets. Use hybrid search techniques, such as combining vector and keyword search, to improve response quality. Monitor system metrics in Zilliz Cloud’s dashboard and adjust configurations accordingly to maintain low-latency, high-throughput performance.

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.

voyage-3 optimization tips

voyage-3 is a versatile model suitable for balanced performance in RAG systems, making efficient retrieval strategies crucial for maintaining low latency and high accuracy. Improve retrieval by leveraging embedding-based similarity search with reranking to ensure relevant context is included. Structure prompts with clear context separation and concise instructions to maximize response accuracy. Set temperature between 0.1 and 0.3 for controlled output while tuning top-k and top-p for flexibility. Implement response caching for frequently queried data to minimize redundant processing and API calls. Utilize parallel processing and request batching to optimize resource efficiency. For multi-model deployments, assign voyage-3 to mid-tier complexity tasks while using larger models for deeper analysis and smaller models for real-time, low-latency queries.

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 an incredible journey you’ve just completed! Throughout this tutorial, you've harnessed the power of four remarkable components — a robust framework, a vector database, a cutting-edge language model (LLM), and an innovative embedding model — to build a state-of-the-art Retrieval-Augmented Generation (RAG) system.

By integrating LangChain, you’ve provided a seamless way to connect all the dots, ensuring a smooth interchange between your components. Meanwhile, the vector database, powered by Zilliz Cloud, has equipped you with lightning-fast search capabilities, letting you retrieve relevant information in a flash. The conversational intelligence fueled by AWS Bedrock Claude 3 Haiku has transformed your application into a dynamic experience, capable of engaging users in meaningful dialogues. And let’s not forget how the embedding model has enriched your system with deep semantic representations, enhancing understanding and connection.

Remember those optimization tips and the free cost calculator we discussed? They’re your secret weapons for fine-tuning and managing your RAG applications efficiently. Now that you've acquired these essential skills, the sky's the limit! So, don’t just stop here—go out there, build your version of RAG applications, innovate with new ideas, and let your creativity flow. The future is in your hands; let’s see where your unique vision takes you!

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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