Build RAG Chatbot with LangChain, Faiss, Google Vertex AI Gemini 2.0 Flash Thinking, and Cohere embed-multilingual-v3.0

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.
Faiss: also known as Facebook AI Similarity Search, is an open-source vector search library that allows developers to quickly search for semantically similar multimedia data within a massive dataset of unstructured data. (If you want a much more scalable solution or hate to manage your own infrastructure, 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 2.0 Flash Thinking: This advanced AI model is designed for rapid, high-quality cognitive analysis and decision-making. It leverages enhanced natural language understanding and generation, enabling seamless interactions. Ideal for real-time applications in customer service, content creation, and intelligent automation, it excels in scenarios that demand quick, insightful responses.
Cohere embed-multilingual-v3.0: This model provides high-quality multilingual text embeddings, enabling effective semantic understanding across diverse languages. Its strengths lie in capturing nuanced meanings and facilitating cross-lingual search and analysis. Ideal for applications in global customer support, content recommendation, and multilingual data analysis, it enhances multilingual communication and insight extraction.

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 2.0 Flash Thinking

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-2.0-flash-thinking-exp-01-21", model_provider="google_vertexai")

Step 3: Install and Set Up Cohere embed-multilingual-v3.0

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-multilingual-v3.0")

Step 4: Install and Set Up Faiss

pip install -qU langchain-community

from langchain_community.vectorstores import FAISS

vector_store = FAISS(embedding_function=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.

Faiss Optimization Tips

To enhance the performance of the Faiss library in a Retrieval-Augmented Generation (RAG) system, begin by selecting the appropriate index type based on your data volume and query speed requirements; for example, using an IVF (Inverted File) index can significantly speed up queries on large datasets by reducing the search space. Optimize your indexing process by using the nlist parameter to partition data into smaller clusters and set an appropriate number of probes (nprobe) during retrieval to balance between speed and accuracy. Ensure the vectors are properly normalized and consider using 16-bit or 8-bit quantization during indexing to reduce memory footprints for large datasets while maintaining reasonable retrieval accuracy. Additionally, consider leveraging GPU acceleration if available, as Faiss highly benefits from parallel processing, leading to faster nearest neighbor searches. Continuous fine-tuning and benchmarking with varying parameters and configurations can guide you in finding the most efficient setup specific to your data characteristics and retrieval requirements.

Google Vertex AI Gemini 2.0 Flash Thinking optimization tips

Gemini 2.0 Flash Thinking is designed for rapid yet thoughtful responses in RAG systems. Optimize retrieval by focusing on high-confidence document matching, reducing unnecessary data processing. Keep prompts structured, providing key details upfront while avoiding excessive context. Adjust temperature settings (0.1–0.3) to fine-tune response diversity. Use caching to reduce API overhead for repeated queries. Take advantage of Google Cloud’s GPU-accelerated processing to improve throughput. If using Flash Thinking alongside larger models, assign it to medium-complexity reasoning tasks that require faster turnaround than Pro models but more depth than standard Flash.

Cohere embed-multilingual-v3.0 optimization tips

Cohere embed-multilingual-v3.0 is designed for multilingual support, making it highly useful in global RAG systems. To optimize performance, preprocess multilingual input by handling language-specific quirks, such as tokenization and special characters, to maintain consistency across different languages. Implement language detection models to filter and route queries to the appropriate language embeddings, improving both speed and relevance. Use indexing structures like FAISS or HNSW to speed up search across multilingual datasets. Compress embeddings using techniques like quantization to optimize storage while ensuring quality. To handle scalability, leverage distributed storage systems for efficient management of multilingual embeddings. Continuously retrain and update embeddings to reflect new languages or evolving language models.

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, what an incredible journey you've just completed! You’ve taken a deep dive into the world of Retrieval-Augmented Generation (RAG) systems, and you've now gained hands-on experience integrating some truly powerful components. From utilizing LangChain to architect the entire framework and ensuring all these elements work together seamlessly, to employing a vector database like Faiss for super-fast searches—this tutorial has shown you how these cutting-edge technologies can be combined to create intelligent applications.

You’ve also harnessed the conversational prowess of Google Vertex AI Gemini 2.0 Flash Thinking to enable engaging interactions, while the Cohere embedding model has empowered you to generate rich semantic representations that enhance your application’s context understanding. Each piece plays a vital role, and together, they create an exciting ecosystem of possibilities!

Along the way, you’ve picked up nifty optimization tips and gained access to a free cost calculator, making it easier than ever to scale your projects. Now it’s time to put all this new knowledge into action! I encourage you to start building your own RAG applications and let your creativity flow. Experiment, innovate, and watch your ideas transform into reality. The future is bright, and with your newfound skills, the sky's the limit! So, what are you waiting for? Dive in and start creating!

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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