Build RAG Chatbot with LangChain, LangChain vector store, Google Vertex AI Gemini 1.5 Pro, and IBM all-minilm-l6-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 all-minilm-l6-v2: This model is a compact, efficient transformer-based language representation model optimized for tasks requiring fast inferencing. It excels in natural language understanding tasks such as sentiment analysis and information retrieval, making it ideal for applications in chatbots, search engines, and data annotation.

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 all-minilm-l6-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="sentence-transformers/all-minilm-l6-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 all-minilm-l6-v2 optimization tips

To optimize the performance of IBM all-minilm-l6-v2 in a Retrieval-Augmented Generation (RAG) setup, consider implementing streamlined query preprocessing to remove stop words and normalize text, ensuring that input queries are concise and relevant. Layering caching strategies on frequently retrieved results can significantly reduce latency, while fine-tuning the model with domain-specific data enhances relevance and accuracy. Additionally, experiment with batch processing during inference to leverage parallelization, and monitor and adjust hyperparameters like learning rates and maximum token counts to refine model responses. Lastly, ensure that your retrieval system is seamlessly integrated with the generation process to maintain context and coherence in 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 power of combining cutting-edge tools to build a fully functional RAG system from scratch! You learned how LangChain acts as the glue, orchestrating the entire pipeline by seamlessly connecting your data, retrieval logic, and language model. The IBM all-minilm-l6-v2 embedding model transformed your raw text into rich numerical representations, enabling the LangChain Vector Store to efficiently index and retrieve the most relevant information. Then, Google Vertex AI Gemini 1.5 Pro stepped in as the brain, synthesizing those retrieved snippets into coherent, context-aware responses that feel almost human. Along the way, you saw how these components work in harmony—turning unstructured data into actionable insights while balancing speed, accuracy, and scalability. Plus, those optimization tips (like tweaking chunk sizes and experimenting with metadata filters) gave you a toolkit to fine-tune performance, and the free RAG cost calculator helped you estimate expenses upfront—no surprises!

Now that you’ve seen the magic of integrating frameworks, databases, LLMs, and embeddings, it’s time to make this knowledge your own. Imagine the applications you could build: smarter chatbots, hyper-personalized recommendation engines, or research assistants that digest mountains of data in seconds. The tools are here, the blueprint is clear, and your creativity is the only limit. Start small, iterate often, and don’t shy away from experimenting with different models or optimization strategies. Every tweak you make brings you closer to building something truly groundbreaking. So fire up your IDE, load up your dataset, and let your RAG-powered ideas come to life—the future of intelligent applications is yours to shape! 🚀

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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