Build RAG Chatbot with LangChain, Zilliz Cloud, Anthropic Claude 3 Haiku, and Cohere embed-multilingual-v2.0

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: 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-v2.0: This model specializes in generating high-quality multilingual embeddings, enabling effective cross-lingual understanding and retrieval. Its strengths lie in capturing semantic relationships in diverse languages, making it suitable for applications such as multilingual search, recommendation systems, and global content analysis where language diversity is a critical factor.

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-v2.0

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-v2.0")

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 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-v2.0 optimization tips

Cohere embed-multilingual-v2.0 supports a variety of languages, making it ideal for cross-lingual RAG setups. To optimize efficiency, preprocess text to remove language-specific noise and handle encoding issues, ensuring clean input for embedding generation. Implement efficient ANN algorithms, like FAISS with hierarchical indexing, to support fast retrieval across multilingual datasets. Compress embeddings using techniques such as product quantization or HNSW to optimize storage and speed. Use language detection models to route queries to the appropriate language-specific embeddings, minimizing unnecessary computation. Batch embedding operations and take advantage of parallel processing to handle large amounts of multilingual data efficiently. Regularly update embeddings to ensure the model reflects any language shifts or evolving trends.

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? Well, you've just embarked on an exhilarating journey into the realm of information retrieval with the powerful RAG system! By integrating a framework like LangChain, you’ve discovered how to seamlessly connect different components—organizing and orchestrating everything to perform at its best. The vector database from Zilliz Cloud has empowered you to conduct lightning-fast, relevant searches, ensuring that your data retrieval is both efficient and effective.

And let’s not forget about Anthropic Claude 3 Haiku, your conversational AI ally! It adds that touch of natural understanding, allowing you to engage users in rich and meaningful dialogues. Meanwhile, the Cohere embed-multilingual-v2.0 model has sparked your imagination with its ability to generate deep semantic representations, breaking down language barriers and enhancing understanding across diverse contexts.

Throughout this tutorial, you’ve also picked up some nifty optimization tips and an invaluable cost calculator to help you make the most of your RAG applications! So now it’s your turn! Dive in, explore, and let your creativity run wild. Start building, optimizing, and innovating your own RAG applications, and who knows what breakthroughs you might discover? The sky's the limit—let’s see what you can 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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