Build RAG Chatbot with LangChain, LangChain vector store, Google Vertex AI Gemini 1.5 Pro, 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 Gemini 1.5 Pro: This advanced AI model combines powerful machine learning capabilities with enhanced multimodal support, enabling seamless processing of text, images, and audio. Its strengths lie in scalability and customization, making it ideal for enterprise applications such as content generation, data analysis, and customer interaction automation.
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 Gemini 1.5 Pro

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

# Ensure your VertexAI credentials are configured

from langchain.chat_models import init_chat_model

llm = init_chat_model("gemini-1.5-pro", 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 Gemini 1.5 Pro optimization tips

Gemini 1.5 Pro is a robust model designed for complex RAG applications requiring deep reasoning. Optimize retrieval by implementing advanced ranking algorithms to ensure highly relevant documents are included. Structure prompts with clear logical sections to maximize response coherence. Keep temperature between 0.1 and 0.3 for factual precision, fine-tuning top-k/top-p as needed. Use Google’s AI infrastructure to batch API calls, reducing processing overhead. Implement caching strategies to optimize frequently queried topics. Streaming responses can enhance real-time applications, improving perceived responsiveness. If deploying multiple models, reserve Gemini 1.5 Pro for high-value analytical tasks while using lighter models for simpler retrieval-based queries.

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?

By diving into this tutorial, you’ve unlocked the magic of building a RAG system from scratch using cutting-edge tools! You learned how LangChain acts as the glue that binds everything together, orchestrating workflows between your data, models, and user queries. The LangChain Vector Store became your go-to for lightning-fast semantic search, transforming raw text into retrievable knowledge by leveraging the IBM Slate-125M-English-RTRVR-v2 embedding model—a lightweight yet powerful tool that captures the essence of your data. Then, Google Vertex AI Gemini 1.5 Pro stepped in as the creative powerhouse, synthesizing retrieved information into coherent, context-rich responses. Together, these components form a seamless pipeline: chunking data, embedding it, storing it for quick access, and generating answers that feel almost human. Along the way, you picked up pro tips like optimizing chunk sizes for accuracy and using metadata filters to refine results, ensuring your RAG system isn’t just functional but efficient. And let’s not forget the free RAG cost calculator—your secret weapon for balancing performance and budget as you scale!

Now it’s your turn to take these tools and run with them! Whether you’re building a chatbot, a research assistant, or a knowledge hub, you’ve got the blueprint to make it happen. Experiment with different embedding models, tweak retrieval strategies, and let Gemini’s creativity surprise you. Remember, every iteration brings you closer to a system that’s uniquely yours. The world of AI is evolving fast, and you’re not just keeping up—you’re leading the charge. So fire up your IDE, embrace the trial and error, and build something that blows your own mind. The future of intelligent applications is in your hands—let’s make it incredible! 🚀

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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