Build RAG Chatbot with LangChain, Milvus, Anthropic Claude 3.5 Sonnet, and voyage-3-lite

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.
Milvus: An open-source vector database optimized to store, index, and search large-scale vector embeddings efficiently, perfect for use cases like RAG, semantic search, and recommender systems. If you hate to manage your own infrastructure, we recommend using Zilliz Cloud, which is a fully managed vector database service built on Milvus and offers a free tier supporting up to 1 million vectors.
Anthropic Claude 3.5 Sonnet: This advanced model in the Claude 3 family is designed for nuanced understanding and creative language generation. With enhanced prompt comprehension and contextual awareness, it excels in complex dialogue, creative writing, and sophisticated content creation. Ideal for applications where deep engagement and high-quality output are paramount.
Voyage-3-Lite: This model is designed for resource-constrained environments, offering a lightweight and efficient solution for various NLP tasks. It excels in applications such as chatbots, content generation, and real-time text analysis, providing quick responses while maintaining a balance between performance and computational efficiency. Ideal for developers needing high throughput with limited resources.

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 Sonnet

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-sonnet-latest", model_provider="anthropic")

Step 3: Install and Set Up voyage-3-lite

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-lite")

Step 4: Install and Set Up Milvus

pip install -qU langchain-milvus

from langchain_milvus import Milvus

vector_store = Milvus(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.

Milvus optimization tips

Milvus serves as a highly efficient vector database, critical for retrieval tasks in a RAG system. To optimize its performance, ensure that indexes are properly built to balance speed and accuracy; consider utilizing HNSW (Hierarchical Navigable Small World) for efficient nearest neighbor search where response time is crucial. Partitioning data based on usage patterns can enhance query performance and reduce load times, enabling better scalability. Regularly monitor and adjust cache settings based on query frequency to avoid latency during data retrieval. Employ batch processing for vector insertions, which can minimize database lock contention and enhance overall throughput. Additionally, fine-tune the model parameters by experimenting with the dimensionality of the vectors; higher dimensions can improve retrieval accuracy but may increase search time, necessitating a balance tailored to your specific use case and hardware infrastructure.

Anthropic Claude 3.5 Sonnet optimization tips

Claude 3.5 Sonnet delivers enhanced reasoning while maintaining a balance between speed and complexity. Optimize by refining retrieval pipelines to prioritize top-ranked context, reducing unnecessary token consumption. Use adaptive chunking methods to provide structured and concise context, ensuring that the model receives only the most relevant information. Implement a dynamic query selection approach, routing simpler questions to Claude 3.5 Haiku for cost and speed efficiency while reserving Sonnet for deeper reasoning. Optimize API request handling with batch processing and response caching to lower latency. Fine-tune temperature and sampling settings, keeping them low for deterministic outputs in retrieval-based tasks. Use system instructions to guide responses, improving consistency and reducing hallucinations. If deploying at scale, leverage parallel query execution and real-time monitoring to dynamically adjust workloads based on system performance metrics.

voyage-3-lite optimization tips

voyage-3-lite is optimized for speed and efficiency, making it a strong choice for low-latency RAG applications. Improve retrieval by minimizing the number of retrieved documents while maintaining relevance using adaptive filtering techniques. Keep prompts concise and to the point, avoiding redundant context to reduce processing overhead. Set temperature between 0.1 and 0.2 to prioritize factual accuracy and prevent unnecessary response variation. Use caching to reduce repeated API calls for common queries. Implement response streaming to improve user experience in real-time applications. Optimize resource usage by running voyage-3-lite in high-throughput scenarios where speed is prioritized over deep reasoning, reserving larger models for more complex analysis.

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 fantastic journey it has been! In this tutorial, you’ve gained a solid understanding of how to seamlessly integrate a framework like LangChain with a powerful vector database such as Milvus, sophisticated conversational capabilities through Anthropic Claude 3.5 Sonnet, and nuanced semantic representations provided by an embedding model like voyage-3-lite. Each of these components plays a vital role in crafting an advanced Retrieval-Augmented Generation (RAG) system. The framework acts as the glue that holds everything together, making data flow smooth and efficient. Meanwhile, Milvus ensures your searches are lightning-fast, allowing you to retrieve relevant information in a blink. With Claude 3.5 Sonnet at the helm, your application can engage users in dynamic conversations, adapting and responding to their needs like a seasoned expert. And let's not overlook the embedding model that generates rich semantic representations, unlocking deeper insights from your data.

Plus, the optimization tips you learned and the handy free cost calculator will empower you to refine your RAG applications for maximum performance and efficiency. Now it’s time for you to take the plunge! Get started on building, optimizing, and innovating your own RAG applications. The possibilities are endless, and I can't wait to see the incredible solutions you’ll create! So roll up your sleeves, dive in, and let your imagination soar!

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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