Build RAG Chatbot with LangChain, LangChain vector store, Google Vertex AI Claude 3 Opus, and IBM slate-125m-english-rtrvr-v2

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.
IBM slate-125m-english-rtrvr-v2: This AI model is designed for natural language understanding and text retrieval tasks. Leveraging advanced training techniques, it excels at processing and retrieving relevant information from large datasets, making it ideal for applications in chatbots, customer support, and content recommendation systems. Its strength lies in its ability to comprehend context and deliver accurate responses efficiently.

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 IBM slate-125m-english-rtrvr-v2

pip install -qU langchain-ibm

import getpass
import os

if not os.environ.get("WATSONX_APIKEY"):
  os.environ["WATSONX_APIKEY"] = getpass.getpass("Enter API key for IBM watsonx: ")

from langchain_ibm import WatsonxEmbeddings

embeddings = WatsonxEmbeddings(
    model_id="ibm/slate-125m-english-rtrvr-v2",
    url="https://us-south.ml.cloud.ibm.com",
    project_id="<WATSONX PROJECT_ID>",
)

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.

IBM slate-125m-english-rtrvr-v2 optimization tips

To optimize the IBM slate-125m-english-rtrvr-v2 in a Retrieval-Augmented Generation (RAG) setup, start by fine-tuning the model on domain-specific data relevant to your application, as it enhances response quality. Implement a caching mechanism for frequently accessed documents to reduce retrieval latency. Experiment with query reformulation techniques to improve retrieval accuracy by making the queries more aligned with the indexed content. Additionally, ensure that your indexing process is efficient by leveraging parallel computations, and regularly monitor the performance metrics to identify and address bottlenecks. Lastly, employ an ensemble approach by combining multiple models or retrieval strategies to boost the robustness and diversity of generated outputs.

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?

Wow, you’ve just unlocked the power to build a fully functional RAG system from scratch! By diving into this tutorial, you’ve learned how to seamlessly weave together LangChain as the orchestration backbone, LangChain’s vector store for lightning-fast semantic search, Google Vertex AI’s Claude 3 Opus as your creative LLM powerhouse, and IBM’s slate-125m-english-rtrvr-v2 embedding model to transform text into rich numerical representations. You now understand how these pieces interact: LangChain acts as the glue, managing data flow between the vector database (where your documents live as embeddings) and the LLM, which generates human-like responses using the retrieved context. The tutorial showed you how to ingest data, query it intelligently, and synthesize answers that feel both accurate and natural—like magic, but with code!

But wait, there’s more! You also picked up pro tips for optimizing your RAG pipeline, like tuning retrieval thresholds and balancing cost-performance tradeoffs. The free RAG cost calculator shared here is your secret weapon for estimating expenses upfront, so you can experiment fearlessly. Imagine what’s next—customizing this framework for your own datasets, fine-tuning embeddings for niche domains, or even combining multiple LLMs for specialized tasks. The tools are in your hands, and the possibilities are endless. So go ahead—build something bold, tweak it relentlessly, and watch your ideas come to life. The future of intelligent applications starts with you hitting “run” on that next script. Let’s get coding! 🚀

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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