Build RAG Chatbot with LangChain, Milvus, Anthropic Claude 3 Haiku, and Cohere embed-multilingual-light-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.
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: This advanced AI language model from Anthropic focuses on safety and alignment, capable of generating coherent and context-aware text. It excels in creative writing, conversational AI, and insightful summarization. Ideal for creating engaging content while ensuring adherence to ethical standards and user intent.
Cohere embed-multilingual-light-v3.0: This model is designed for efficient multilingual text embedding, enabling robust semantic search and similarity tasks across various languages. It excels in scenarios requiring rapid comprehension and matching of diverse linguistic datasets, making it ideal for global applications in content recommendation, categorization, and cross-language 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 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-haiku-20240307", model_provider="anthropic")

Step 3: Install and Set Up Cohere embed-multilingual-light-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-multilingual-light-v3.0")

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 Haiku optimization tips

Claude 3 Haiku is designed for efficiency, making it a great choice for low-latency RAG applications. Optimize token usage by structuring prompts concisely, removing redundant text, and leveraging system messages effectively to guide responses. Use function calling when applicable to offload structured processing tasks and improve response reliability. Batch process queries where possible to reduce API overhead and enhance throughput. If latency is critical, consider caching frequent queries and pre-generating responses for common questions. Fine-tune response control with temperature and top-p sampling; lower temperature values (e.g., 0.2-0.3) help maintain consistency in factual retrieval tasks. Use streaming mode for real-time applications to get faster partial responses while processing large prompts. Regularly evaluate and adjust model parameters based on performance benchmarks to balance speed and accuracy in your RAG pipeline.

Cohere embed-multilingual-light-v3.0 optimization tips

Cohere embed-multilingual-light-v3.0 is optimized for faster and more efficient multilingual embeddings. Reduce input data size by filtering out non-essential elements and focusing on key phrases or terms that provide the most value in cross-lingual search scenarios. Implement approximate nearest neighbor search methods, such as FAISS, to ensure that retrieval remains fast and accurate even in large datasets. Use vector compression techniques to save on storage space while maintaining retrieval quality. Employ caching strategies to store commonly used embeddings and reduce unnecessary recomputation. Optimize search performance by organizing embeddings into a hierarchical structure based on languages or topics, enabling quicker retrieval from specific language groups.

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?

Congratulations on reaching the end of this tutorial! You’ve embarked on an incredible journey into the world of Retrieval-Augmented Generation (RAG) systems, and it's exciting to see how all the components come together to create something so powerful and innovative. We explored how LangChain provides the essential framework, seamlessly integrating every part of your system and making it easy to manage complex workflows. The Milvus vector database topped it all off with its ability to perform rapid, scalable searches—an absolute game-changer for handling vast amounts of data efficiently!

With Anthropic Claude 3 Haiku at your disposal, you now have a sophisticated LLM that genuinely elevates the conversational dynamics of your applications, providing rich, human-like interactions that can amaze your users. Plus, the embedding model, Cohere’s embed-multilingual-light-v3.0, transforms raw data into nuanced semantic representations, ensuring your searches are not just fast but profoundly relevant, catering to the context of your queries.

You also discovered handy optimization tips and even a free cost calculator to help manage your resources better, ensuring your projects are both efficient and cost-effective! Now that you've seen how all these pieces fit together, it’s time for you to roll up those sleeves and start building your own RAG applications! Dive in, innovate, and let your creativity shine—who knows? You might just create the next big thing! Happy building!

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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