Build RAG Chatbot with LangChain, Zilliz Cloud, Anthropic Claude 3 Sonnet, and Cohere embed-english-v3.0

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 Sonnet: This advanced model in the Claude 3 lineup specializes in creative and nuanced text generation. With a deep understanding of context and tone, it is well-suited for applications in creative writing, dialogue generation, and storytelling. Its ability to produce articulate and engaging prose makes it ideal for content creation and entertainment.
Cohere embed-english-v3.0: This model specializes in generating high-quality text embeddings for English language input. It is designed for tasks like semantic search, recommendation systems, and document similarity, providing robust performance due to its deep contextual understanding. Ideal for applications needing nuanced language comprehension and efficient information retrieval.

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 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-sonnet-20240229", model_provider="anthropic")

Step 3: Install and Set Up Cohere embed-english-v3.0

pip install -qU langchain-cohere

import getpass
import os

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

from langchain_cohere import CohereEmbeddings

embeddings = CohereEmbeddings(model="embed-english-v3.0")

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 Sonnet optimization tips

Claude 3 Sonnet provides a balance between speed and accuracy, making it a versatile choice for RAG systems. Optimize retrieval efficiency by limiting the number of retrieved documents per query to avoid excessive context window usage, which can slow down response times. Use semantic chunking techniques to break documents into meaningful sections, ensuring that only the most relevant information is passed to the model. Adjust temperature and sampling parameters dynamically based on the complexity of user queries, with lower values for factual responses and higher values when generating creative text. Implement structured prompting to guide the model toward more precise answers, reducing ambiguity in responses. If integrating multiple models, use Sonnet as an intermediate option for moderately complex queries while reserving Opus for high-stakes reasoning tasks. Utilize Claude’s API optimizations, such as streaming, to enhance response time and system efficiency.

Cohere embed-english-v3.0 optimization tips

Cohere embed-english-v3.0 is a robust embedding model tailored for English language text. To optimize retrieval, preprocess input text to eliminate irrelevant noise and focus on key terms or phrases that will drive relevant matches. Use approximate nearest neighbor (ANN) algorithms like HNSW for efficient retrieval in large-scale datasets. For resource management, employ dimensionality reduction or quantization techniques to compress embeddings, reducing storage requirements without sacrificing too much performance. Implement multi-threading for batch processing to accelerate embedding generation. Use caching to store frequently accessed embeddings and reduce redundant computations. Fine-tune the model on specific data to improve performance on domain-specific queries and ensure greater relevance in retrieval.

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? Wow, what an adventure we've taken together in this tutorial! You've dived into the dynamic world of building a cutting-edge RAG system, and I hope you’re just as excited about what you've accomplished as I am! By weaving together LangChain as your powerful framework, you've created a strong backbone to integrate all components seamlessly. This framework is fantastic for orchestrating how data flows and interactions occur within your application—kudos to you for mastering that!

Then, with the integration of Zilliz Cloud's vector database, you've set the stage for lightning-fast searches that can handle vast amounts of information. It’s truly amazing how this piece enables your system to quickly retrieve relevant data, making your applications responsive and user-friendly. With Anthropic Claude 3 Sonnet as your Large Language Model, you’ve brought conversational intelligence to your RAG system, allowing for nuanced, engaging interactions that can elevate any user experience.

And let’s not forget the embedding model, specifically Cohere embed-english-v3.0, which generates rich semantic representations to ensure that your system understands and finds meaning in the data. This is where the magic happens—when your system not only finds information but understands context!

Throughout our journey, we’ve also spotlighted optimization tips and even provided you with a handy cost calculator to fine-tune your projects economically. Now that you have this sophisticated toolkit at your fingertips, the possibilities for innovation are limitless!

So, what are you waiting for? Dive in! Start building, optimizing, and innovating your own RAG applications. The world is eager for your creativity—go forth and make amazing things happen!

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

We'd Love to Hear What You Think!

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If you like this tutorial, show your support by giving our Milvus GitHub repo a star ⭐—it means the world to us and inspires us to keep creating! 💖

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