Build RAG Chatbot with LangChain, LangChain vector store, Google Vertex AI Claude 3 Opus, and HuggingFace all-MiniLM-L12-v1

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 Opus: This advanced AI model integrates Google's cloud capabilities with Claude's robust language understanding. It excels in generating coherent and contextually relevant content across diverse applications, including chatbots, content creation, and data analysis. Ideal for organizations seeking scalable, high-performance solutions for natural language processing tasks.
HuggingFace all-MiniLM-L12-v1: This model is a lightweight transformer designed for efficient natural language understanding and generation tasks. It excels in providing high-quality embeddings for various applications, including search, clustering, and conversational AI, while maintaining a small footprint for faster inference and deployment. Ideal for resource-constrained environments or mobile applications, it offers a balance between performance and efficiency.

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 Opus

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-opus@20240229", model_provider="google_vertexai")

Step 3: Install and Set Up HuggingFace all-MiniLM-L12-v1

pip install -qU langchain-huggingface

from langchain_huggingface import HuggingFaceEmbeddings

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L12-v1")

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 Opus optimization tips

Claude 3 Opus on Google Vertex AI is a high-performance model suited for complex reasoning tasks in RAG applications. Improve retrieval by employing multi-step document ranking to ensure only the most relevant data is passed to the model. Structure prompts effectively, placing key facts upfront to enhance response quality. Keep temperature low (0.1–0.2) for factual accuracy and fine-tune top-k/top-p for nuanced control. Utilize Google Vertex AI’s resource scaling to manage workload surges efficiently. Implement response caching for frequently accessed queries to optimize cost and speed. If using Opus alongside smaller models, deploy it selectively for queries requiring deep analytical capabilities while using lighter models for routine tasks.

HuggingFace all-MiniLM-L12-v1 optimization tips

To optimize the HuggingFace all-MiniLM-L12-v1 model for your Retrieval-Augmented Generation (RAG) setup, consider implementing mixed precision training to speed up computations and reduce memory usage, enabling you to handle larger batch sizes. Experiment with layer freezing during fine-tuning to preserve certain parameters while optimizing others, ensuring faster convergence. Use an efficient data preprocessing pipeline to reduce input bottlenecks, and implement caching mechanisms for frequently accessed data. Furthermore, leverage model distillation techniques to create smaller, faster versions of the model that maintain comparable performance, and experiment with different pooling strategies to find the most effective way to condense retrieved documents for better context input. Lastly, regularly monitor and fine-tune hyperparameters such as learning rate and batch size based on validation performance to achieve optimal results.

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?

By diving into this tutorial, you’ve unlocked the power of combining cutting-edge tools to build a RAG system from the ground up! You’ve seen how LangChain acts as the glue, seamlessly orchestrating workflows between components while handling document loading, chunking, and pipeline logic. The LangChain vector store stepped in as your trusty retrieval engine, transforming text into searchable embeddings using HuggingFace’s all-MiniLM-L12-v1 model—proving that even compact models can pack a punch in capturing semantic meaning. Then came the star of the show: Google Vertex AI’s Claude 3 Opus, which turned retrieved context into human-like responses, showcasing how modern LLMs can synthesize information with startling coherence. Along the way, you learned optimization tricks like balancing chunk sizes for accuracy vs. speed and leveraging metadata filtering to sharpen results. And let’s not forget that handy RAG cost calculator—your secret weapon for estimating expenses before scaling up!

Now you’re equipped to create AI applications that don’t just answer questions but understand context, pulling insights from vast data lakes like a pro. Whether you’re building chatbots, research tools, or enterprise search systems, you’ve got the blueprint to mix and match these components creatively. Imagine tailoring retrieval strategies for niche domains or fine-tuning embeddings for your specific use case—the possibilities are endless! So fire up your IDE, experiment with different models, and start crafting RAG solutions that wow users. The future of intelligent apps is yours to shape, one query at a time. Let’s build something amazing! 🚀

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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