Build RAG Chatbot with LangChain, LangChain vector store, Google Vertex AI Claude 3 Haiku, and Cohere embed-english-light-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.
LangChain in-memory vector store: an in-memory, ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance. It is intended for demos and does not yet support ids or deletion. (If you want a much more scalable solution for your apps or even enterprise projects, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvusand offers a free tier supporting up to 1 million vectors.)
Google Vertex AI Claude 3 Haiku: A versatile model within Google’s Vertex AI ecosystem, it is designed for seamless integration and enhanced generative capabilities. It excels in natural language processing tasks, such as text generation, summarization, and conversational AI. Ideal for businesses seeking to leverage AI for robust, scalable applications in various domains.
Cohere embed-english-light-v2.0: This lightweight embedding model is designed for efficient English text encoding, providing high-quality representations for various natural language processing tasks. It excels in scenarios requiring embedding generation with minimal computational resources, such as document similarity, clustering, and recommendation systems, ensuring fast performance without compromising accuracy.

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 Google Vertex AI Claude 3 Haiku

pip install -qU "langchain[google-vertexai]"

# Ensure your VertexAI credentials are configured

from langchain.chat_models import init_chat_model

llm = init_chat_model("claude-3-haiku@20240307", model_provider="google_vertexai")

Step 3: Install and Set Up Cohere embed-english-light-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-english-light-v2.0")

Step 4: Install and Set Up LangChain vector store

pip install -qU langchain-core

from langchain_core.vectorstores import InMemoryVectorStore

vector_store = InMemoryVectorStore(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.

LangChain in-memory vector store optimization tips

LangChain in-memory vector store is just an ephemeral vector store that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. It has very limited features and is only intended for demos. If you plan to build a functional or even production-level solution, we recommend using Zilliz Cloud, which is a fully managed vector database service built on the open-source Milvus and offers a free tier supporting up to 1 million vectors.)

Google Vertex AI Claude 3 Haiku optimization tips

Claude 3 Haiku on Google Vertex AI is designed for low-latency RAG applications requiring fast retrieval and generation. Optimize retrieval by using efficient vector search techniques to minimize irrelevant data in the context. Keep prompts short and structured to maximize clarity while reducing token usage. Set temperature between 0.1 and 0.3 for fact-based queries to maintain accuracy. Use Google Cloud’s auto-scaling and caching features to handle peak loads effectively. If deploying in a multi-model setup, reserve Haiku for fast, high-volume tasks while offloading complex reasoning to larger models. Implement response streaming for real-time applications to reduce latency and improve interactivity.

Cohere embed-english-light-v2.0 optimization tips

Cohere embed-english-light-v2.0 is designed for faster, more resource-efficient embedding generation in English-language tasks. To optimize processing, preprocess text by removing stopwords, punctuation, and unnecessary formatting to minimize the complexity of the input. Leverage vector compression techniques, such as quantization or PCA, to reduce storage requirements without sacrificing significant accuracy. For retrieval, implement hybrid search strategies that combine keyword search with dense vector search to achieve faster and more relevant results. Use multi-threading or parallel processing to handle large batches of embeddings efficiently. Apply caching for frequently queried embeddings to minimize re-processing and reduce query latency, especially for high-traffic RAG systems.

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 a journey we've taken together! Through this tutorial, you've gained a holistic understanding of how to build an advanced RAG (Retrieve-Activate-Generate) system by seamlessly integrating four powerful components: a robust framework, a dynamic vector database, a state-of-the-art LLM, and an innovative embedding model.

We kicked things off with the framework, which is the backbone that ties everything together—organizing your workflows and making the integration feel like a breeze! Next, we explored the vector database, which enables lightning-fast searches through your data, ensuring that ready-to-go information is just a click away. You saw how the LLM, fueled by the incredible capabilities of Google Vertex AI Claude 3 Haiku, brings your application to life with conversational intelligence, allowing for a rich, engaging interaction with users. And let’s not forget about the embedding model from Cohere that crafts deep, meaningful semantic representations, making your data not only accessible but also intuitively understandable.

As a bonus, you grabbed some optimization tips and a handy cost calculator, which means you are not just learning but applying your knowledge to streamline processes and manage resources efficiently. So, what now? It’s time to roll up your sleeves, dive into the code, and start building your very own RAG applications! Imagine the world of possibilities at your fingertips—optimizing, innovating, and taking your projects to new heights! Embrace the excitement of creation and let your ideas flourish. 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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