Build RAG Chatbot with LangChain, Zilliz Cloud, Anthropic Claude 3.5 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.
Anthropic Claude 3.5 Haiku: This model builds upon Claude 3's capabilities with enhanced understanding and generation of nuanced language. It excels in creative writing, conversational AI, and complex query handling. Best suited for tasks where clarity and depth of response are paramount, Claude 3.5 balances efficiency with sophisticated insights.
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 Anthropic Claude 3.5 Haiku

pip install -qU "langchain[anthropic]"

import getpass
import os

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

from langchain.chat_models import init_chat_model

llm = init_chat_model("claude-3-5-haiku-latest", model_provider="anthropic")

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.

Anthropic Claude 3.5 Haiku optimization tips

Claude 3.5 Haiku offers improved efficiency and accuracy over its predecessor, making it ideal for latency-sensitive RAG workflows. Optimize by leveraging structured prompts that minimize token waste while maintaining clarity. Use adaptive retrieval strategies where simpler queries receive fewer context documents, preventing excessive computation. Implement embeddings-based reranking to ensure only the most relevant information is passed to the model, improving both speed and response quality. Reduce API calls by caching high-traffic queries and employing response summarization techniques to streamline outputs. Tune temperature and nucleus sampling to ensure responses remain factual and well-structured, typically keeping temperature around 0.1-0.2 for strict accuracy. Optimize batch processing for large-scale retrieval tasks, reducing the overhead of multiple individual queries. Use Claude 3.5 Haiku in combination with higher-end models strategically, allowing for cost-effective scaling in production RAG systems.

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 a journey we've embarked on together! Throughout this tutorial, you've witnessed the seamless integration of powerful components: a robust framework like LangChain that knits everything together, a swift vector database like Zilliz Cloud that enhances our searching capabilities, the conversational prowess of the LLM with Anthropic Claude 3.5 Haiku, and an embedding model that crafts rich semantic representations of data. Each piece plays a vital role in crafting a cutting-edge Retrieval-Augmented Generation (RAG) system that not only meets but exceeds expectations in intelligent data handling.

We explored how the framework lays the foundation and ensures smooth interactions among components, while the vector database freaks out with its lightning-fast retrieval. We also saw how Claude 3.5 adds that magical touch of conversational intelligence, allowing users to engage with data in an incredibly human-like manner. And let’s not forget the embedding model, which helps the system understand context and nuance to deliver the most pertinent responses.

In addition to this solid foundation, we sprinkled in some optimization tips and even a free cost calculator to streamline your journey in building your own applications.

Now, the real fun begins! You're armed with tools and knowledge—so go forth, get creative, and start building your own RAG applications. Push the boundaries, innovate, and make your mark! The sky's the limit, and I can’t wait to see what you create!

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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