Build a RAG-Powered AI Chatbot with LangChain, FastAPI, Next.js & ChromaDB
TemiTope Kayode
Ever wished your internal company chatbot actually knew things? Tired of generic answers or "Sorry, I can't help with that"? What if you could build a chatbot that uses your company's actual documentation, wikis, or knowledge base to provide accurate, context-aware answers?
That’s exactly what we’ll build today, a powerful AI chatbot using the Retrieval-Augmented Generation (RAG) pattern. With the help of LangChain, we’ll orchestrate a seamless pipeline that connects Large Language Models (LLMs), a fast FastAPI backend, a sleek Next.js frontend, and ChromaDB for vector storage, all working together to deliver smart, context-aware responses grounded in your own data.
This post will guide you step-by-step, providing code you can adapt and run. By the end, you'll have a functional RAG system that can answer questions based on your provided documents.
What We'll Cover:
What is RAG? (A quick primer)
System Architecture: How the pieces fit together.
Setting up the Backend: FastAPI, ChromaDB, Langchain, and the LLM connection.
Ingesting Data: Loading and preparing documents for retrieval.
Building the RAG Chain: The core logic for retrieving and generating answers.
Creating the API Endpoint: Exposing our RAG chain via FastAPI.
Setting up the Frontend: A simple Next.js interface to interact with our chatbot.
Putting it all Together: Running the system.
Prerequisites:
Python 3.8+ and pip installed.
Node.js and npm/yarn installed.
Basic understanding of Python, FastAPI, React (Next.js), and APIs.
An OpenAI API key (or access to another LLM provider supported by Langchain). We'll use OpenAI for this example due to its widespread use and Langchain integration. Remember LLM usage can incur costs.
Ready? Let's dive in!
1. What is RAG? (The 60-Second Explanation)
RAG stands for Retrieval-Augmented Generation. It's a technique to make Large Language Models (LLMs) more factual and context-aware.
Instead of just relying on its pre-trained (and potentially outdated or generic) knowledge, an LLM in a RAG system works like this:
Retrieve: When you ask a question, the system first searches a specific knowledge base (in our case, documents stored in ChromaDB) for relevant information snippets.
Augment: These relevant snippets (the "context") are then added to your original question.
Generate: This combined prompt (context + question) is sent to the LLM, which generates an answer based primarily on the provided context.
Why RAG?
Reduces Hallucinations: LLMs are less likely to make things up if they have relevant facts handy.
Uses Custom/Recent Data: Answers questions based on your specific, up-to-date documents.
More Control: You curate the knowledge base the LLM uses.
2. System Architecture
Here's a high-level view of our system:
+-----------+ +------------+ +-------------+ +-----------+ +------+ +-------------+
| User | --> | Next.js | --> | FastAPI | --> | ChromaDB | --> | LLM | --> | FastAPI |
| (Browser) | | Frontend | | Backend | | Vector DB | | | | Formatter |
+-----------+ +------------+ +-------------+ +-----------+ +------+ +-------------+
^ |
|---------------------------------------------------------------------------------------------|
Final response returned to user via Next.js
User: Types a question into the Next.js frontend.
Next.js Frontend: Sends the question to the FastAPI backend API endpoint.
FastAPI Backend:
Receives the question.
Uses an embedding model to turn the question into a vector.
Queries ChromaDB with the question vector to find relevant document chunks.
Constructs a prompt containing the retrieved chunks (context) and the original question.
Sends the augmented prompt to the LLM (e.g., OpenAI's GPT).
Receives the generated answer from the LLM.
Formats the response (including a fallback "I don't know" if no context was found).
Sends the response back to the Next.js frontend.
Next.js Frontend: Displays the answer received from the backend to the user.
ChromaDB: Stores vector representations (embeddings) of our company documents, allowing efficient similarity search.
LLM: The language model that generates the final answer based on the context provided by the retriever.
(Side Note: Could we use Gradio? Yes! Gradio is excellent for quickly building simple UIs for machine learning models, especially for demos. However, building separate FastAPI and Next.js components gives us a more scalable and customizable structure, closer to a production setup.)
3. Setting up the Backend (FastAPI, ChromaDB, Langchain)
Let's create our backend project.
# Create a project directory
mkdir rag-backend
cd rag-backend
# Create and activate a virtual environment (recommended)
python -m venv venv
source venv/bin/activate # On Windows use `venv\Scripts\activate`
# Install dependencies
pip install fastapi uvicorn python-dotenv langchain langchain-openai chromadb tiktoken pydantic langchain-community
fastapi: The web framework.uvicorn: The ASGI server to run FastAPI.python-dotenv: To manage environment variables (like API keys).langchain,langchain-openai,langchain-community: Core Langchain library, OpenAI integration, and community loaders/splitters.chromadb: The vector database client.tiktoken: Used by Langchain/OpenAI for token counting.pydantic: For data validation (used by FastAPI).
Now, create a file named .env in the rag-backend directory to store your OpenAI API key:
# .env
OPENAI_API_KEY="your_openai_api_key_here"
Replace your_openai_api_key_here with your actual key.
Important:
Do not commit your .env file (or any file containing secrets or API keys) to Git or any version control system.
To protect your keys, add .env to your .gitignore file:
# .gitignore
.env
This prevents you from accidentally exposing your secrets. Keeping your API keys private keeps your app secure!
Create a file named main.py. This will contain our core backend logic.
# main.py
import os
import textwrap
from typing import List, Any
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
from dotenv import load_dotenv
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
from langchain_core.documents import Document
from langchain.document_loaders import TextLoader
from langchain.prompts import ChatPromptTemplate
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.output_parser import StrOutputParser
# --- Configuration & Setup ---
# Load environment variables (especially OPENAI_API_KEY)
load_dotenv()
# Check for API key
if not os.getenv("OPENAI_API_KEY"):
raise ValueError(
"OPENAI_API_KEY not found in environment variables. Please set it in the .env file."
)
CHROMA_DB_PATH: str = "./chroma_db"
COLLECTION_NAME: str = "company_docs"
MODEL_NAME: str = "gpt-4o-mini" # Or choose another model
# --- Data Loading & Processing (Run Once or as Needed) ---
DUMMY_DOC_PATH: str = "company_info.txt"
if not os.path.exists(DUMMY_DOC_PATH):
with open(DUMMY_DOC_PATH, "w") as f:
f.write(
textwrap.dedent(
"""\
Temi Technologies - Internal FAQ
About Temi Technologies:
Temi Technologies is a forward-thinking software company founded by TemiTope Kayode, a passionate engineer and technology leader. Based in the UK and operating globally, we specialise in building cutting-edge digital solutions for businesses, educators, and individuals.
Our mission is to empower our clients by turning bold ideas into robust, scalable products—whether it’s AI-powered chatbots, cloud platforms, business automation, or custom applications. We combine deep technical expertise with a genuine desire to help organisations and communities thrive in the digital age.
About the Company Chatbot:
This chatbot was developed and is continually improved by Temi Technologies, led by our founder TemiTope Kayode. It’s designed to provide instant, accurate answers to questions about our company, services, and processes—making information more accessible for everyone in the organisation.
Who created the chatbot?
The chatbot was designed and built by TemiTope Kayode, the founder and CEO of Temi Technologies. With over a decade of experience in software engineering, product leadership, and AI, TemiTope brings a hands-on approach to every project—ensuring all our solutions are reliable, user-friendly, and truly impactful.
Q: What is the standard process for requesting vacation time?
A: Employees should submit vacation requests through the HR portal at least two weeks in advance. Requests are subject to manager approval based on team availability.
Q: How do I reset my corporate password?
A: You can reset your password via the self-service tool at https://temitechnologies.com/password_reset. If you encounter issues, please contact the IT Help Desk at x5555.
Q: What are the office hours?
A: Standard office hours are 9:00 AM to 5:00 PM, Monday through Friday. Some departments may have different schedules; please check with your manager.
Q: Does Temi Technologies offer remote work options?
A: Temi Technologies offers hybrid and fully remote work options for eligible positions. Eligibility depends on the role, team needs, and manager approval. Refer to the Remote Work Policy document on the company intranet for details.
Q: Who is TemiTope Kayode?
A: TemiTope Kayode is the founder and CEO of Temi Technologies. As a seasoned software engineer, technical architect, and business leader, TemiTope has driven innovation in multiple industries—from education to enterprise software. He is committed to mentoring the next generation of tech talent and delivering technology solutions that create lasting value.
"""
)
)
def load_and_store_documents() -> Chroma:
"""
Loads documents from file, splits them, creates embeddings, and stores them in ChromaDB.
Returns:
Chroma: The created Chroma vector store containing embedded document chunks.
"""
print("Loading and processing documents...")
loader: TextLoader = TextLoader(DUMMY_DOC_PATH)
documents: List[Document] = loader.load()
text_splitter: RecursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(
chunk_size=500, chunk_overlap=50
)
docs: List[Document] = text_splitter.split_documents(documents)
print(f"Split into {len(docs)} chunks.")
embeddings: OpenAIEmbeddings = (
OpenAIEmbeddings()
) # Defaults to "text-embedding-ada-002"
# Create Chroma vector store (or connect to existing)
vectorstore: Chroma = Chroma.from_documents(
docs,
embeddings,
collection_name=COLLECTION_NAME,
persist_directory=CHROMA_DB_PATH,
)
print(
f"Documents stored in ChromaDB collection '{COLLECTION_NAME}' at {CHROMA_DB_PATH}"
)
return vectorstore
# --- Initialise Vector Store ---
try:
print("Attempting to load existing vector store...")
vectorstore: Chroma = Chroma(
persist_directory=CHROMA_DB_PATH,
embedding_function=OpenAIEmbeddings(),
collection_name=COLLECTION_NAME,
)
print("Vector store loaded successfully.")
except Exception as e: # Be more specific with exceptions in production
print(f"Failed to load existing vector store: {e}. Creating a new one...")
vectorstore = load_and_store_documents()
# Create a retriever to fetch relevant document chunks for a given question.
# 'k' determines how many top-matching chunks are returned as context for the answer.
# Increasing 'k' can improve the chances of including all relevant information,
# especially for complex or open-ended questions, but may make the context longer
# (potentially exceeding model input limits or introducing less-relevant info).
# Decreasing 'k' will keep responses more focused but may miss some necessary details.
retriever = vectorstore.as_retriever(
search_kwargs={"k": 3}
) # Retrieve top 3 most relevant chunks (adjust 'k' as needed)
# --- Initialise LLM ---
llm = ChatOpenAI(
model_name=MODEL_NAME, temperature=0
) # Low temperature for factual answers
# --- Define RAG Chain Prompt Template ---
template: str = (
"You are Temi Technologies' intelligent assistant, here to provide clear and accurate answers to questions about our company, services, and policies.\n"
"Rely only on the provided context below to answer. If the answer cannot be found in the context, say you don't know.\n"
"Keep your response brief—no more than three sentences.\n\n"
"Context:\n{context}\n\n"
"Question:\n{question}\n\n"
"Answer:"
)
# Create a prompt template for the RAG chain, instructing the LLM how to answer
prompt: ChatPromptTemplate = ChatPromptTemplate.from_template(template)
def format_docs(docs: List[Any]) -> str:
"""
Formats a list of document objects into a single string, separated by double newlines.
Args:
docs (list): List of document objects, each with a 'page_content' attribute.
Returns:
str: All document contents joined together, ready to be used as context in the prompt.
"""
return "\n\n".join(doc.page_content for doc in docs)
# --- Define the RAG Chain ---
rag_chain = (
{"context": retriever | format_docs, "question": RunnablePassthrough()}
| prompt
| llm
| StrOutputParser()
)
# --- FastAPI Application ---
app = FastAPI(
title="Temi Technologies RAG Chatbot API",
description="API for answering questions based on Temi Technologies documents.",
)
# Add CORS middleware to allow requests from our Next.js frontend
from fastapi.middleware.cors import CORSMiddleware
app.add_middleware(
CORSMiddleware,
allow_origins=[
"*"
], # Allow all origins for simplicity (Please restrict in production!)
allow_credentials=True,
allow_methods=["*"], # Allow all methods
allow_headers=["*"], # Allow all headers
)
# --- Pydantic Models ---
class QueryRequest(BaseModel):
question: str
class QueryResponse(BaseModel):
answer: str
# Optional: Add sources/context if needed
# sources: List[str] = []
# --- Chat Endpoint ---
@app.post("/chat", response_model=QueryResponse)
async def chat_endpoint(request: QueryRequest) -> QueryResponse:
"""
Receives a question, processes it through the RAG chain, and returns the answer.
Args:
request (QueryRequest): The request containing the user's question.
Returns:
QueryResponse: The answer generated by the RAG pipeline.
"""
question: str = request.question
if not question:
raise HTTPException(status_code=400, detail="Question cannot be empty")
try:
# Check if context is found *before* calling the full chain
# This helps ensure we only answer based on retrieved docs
retrieved_docs = retriever.invoke(question)
if not retrieved_docs:
print("No relevant documents found.")
answer = "I couldn't find any relevant information in the provided documents to answer your question."
else:
print(f"Found {len(retrieved_docs)} relevant document chunks.")
# If we found docs, proceed with the full chain
answer = rag_chain.invoke(question)
print(f"LLM Answer: {answer}")
return QueryResponse(answer=answer)
except Exception as e:
print(f"Error processing question: {e}")
# Consider more specific error handling here
raise HTTPException(
status_code=500,
detail="Internal server error while processing the question.",
)
# --- Run the Application (for local development) ---
if __name__ == "__main__":
import uvicorn
# Ensure data is loaded/checked on startup when running directly
if not os.path.exists(os.path.join(CHROMA_DB_PATH, COLLECTION_NAME)):
print("Initialising Chroma DB as it doesn't exist...")
load_and_store_documents()
print("Starting FastAPI server...")
uvicorn.run(app, host="0.0.0.0", port=8000)
Explanation:
Imports & Setup: Import necessary libraries, load environment variables, define constants.
Data Loading (load_and_store_documents):
Uses TextLoader (you can swap this for
PDFMinerLoader,WebBaseLoader, etc., fromlangchain_community.document_loaders).Uses
RecursiveCharacterTextSplitterto break documents into smaller, manageable chunks.Initialises OpenAIEmbeddings to convert text chunks into vectors.
Uses
Chroma.from_documentsto embed the chunks and store them in a persistent ChromaDB collection (./chroma_db directory).
Initialise Vector Store: Tries to load the existing ChromaDB collection. If it fails (e.g., first run), it calls load_and_store_documents.
Retriever: Creates a retriever from the vector store. search_kwargs={"k": 3} means it will fetch the top 3 most relevant document chunks for a given query.
Initialise LLM: Sets up the ChatOpenAI model. temperature=0 makes the output more deterministic and factual.
RAG Chain: This is the core Langchain Expression Language (LCEL) chain:
{"context": retriever | format_docs, "question": RunnablePassthrough()}: Takes the input question. Runs the retriever to get docs. Formats the docs into a single string. Passes the original question through unchanged. Creates a dictionary{"context": formatted_docs, "question": original_question}.| prompt: Feeds this dictionary into our ChatPromptTemplate.| llm: Sends the formatted prompt to the LLM.| StrOutputParser(): Parses the LLM's chat message output into a simple string.
FastAPI App:
Initialises the FastAPI app.
CORS Middleware: Crucial for allowing the Next.js frontend (running on a different port) to communicate with the backend. Note:
allow_origins=["*"]is insecure for production. You should restrict it to your frontend's specific domain.Pydantic Models:
QueryRequestdefines the expected input structure,QueryResponsedefines the output./chatEndpoint:Takes a
QueryRequest.Retrieves relevant documents first.
Crucially: If retrieved_docs is empty, it immediately returns the "I don't know" message without calling the LLM, enforcing the RAG principle.
If documents are found, it invokes the full rag_chain.
Includes basic error handling.
Run: Standard
Uvicornsetup to run the server locally.
To Run the Backend:
Make sure you're in the
rag-backenddirectory with your virtual environment activated.Ensure you have your
.envfile with theOPENAI_API_KEY.Run:
pythonmain.py
The first time you run it, it should print messages about loading/processing documents and storing them in ChromaDB. Subsequent runs should load the existing database. You'll see INFO: Uvicorn running on http://0.0.0.0:8000.
You can test it using tools like curl or Postman/Insomnia:
curl -X POST http://localhost:8000/chat \
-H "Content-Type: application/json" \
-d '{"question": "How do I request a vacation?"}'
You should get a JSON response like: {“answer": To request a vacation, you should submit your request through the HR portal at least two weeks in advance. The request will be subject to manager approval based on team availability.”} (or similar, depending on the LLM).
Try asking something not in the document, like "What's the cafeteria menu?":
curl -X POST http://localhost:8000/chat \
-H "Content-Type: application/json" \
-d '{"question": "What is the cafeteria menu today?"}'
You should get the fallback response like I do not know or I couldn't find any relevant information in the provided documents to answer your question depending.
4. Setting up the Frontend (Next.js)
Now, let's create a simple interface to interact with our backend.
# Navigate outside the backend directory
cd ..
# Create a new Next.js app (using App Router and TypeScript recommended)
npx create-next-app@latest rag-frontend --typescript --tailwind --eslint --app
# Enter the frontend directory
cd rag-frontend
# Install axios (optional, you can use fetch)
npm install axios # or yarn add axios
Replace the content of app/page.tsx with the following:
// app/page.tsx
"use client";
import { useState, FormEvent } from "react";
import axios from "axios";
export default function Home() {
const [query, setQuery] = useState<string>("");
const [answer, setAnswer] = useState<string>("");
const [isLoading, setIsLoading] = useState<boolean>(false);
const [error, setError] = useState<string | null>(null);
const handleSubmit = async (event: FormEvent<HTMLFormElement>) => {
event.preventDefault();
if (!query.trim()) return;
setIsLoading(true);
setAnswer("");
setError(null);
const backendUrl =
process.env.NEXT_PUBLIC_BACKEND_URL || "http://localhost:8000/chat";
try {
const response = await axios.post(
backendUrl,
{ question: query },
{ headers: { "Content-Type": "application/json" } }
);
if (response.data && response.data.answer) {
setAnswer(response.data.answer);
} else {
setError(
"Received an unexpected response format from the server."
);
}
// eslint-disable-next-line @typescript-eslint/no-explicit-any
} catch (err: any) {
console.error("API call failed:", err);
if (axios.isAxiosError(err) && err.response) {
setError(`Error: ${err.response.data?.detail || err.message}`);
} else {
setError(
`An error occurred: ${err.message || "Unknown error"}`
);
}
} finally {
setIsLoading(false);
}
};
const handleClear = () => {
setQuery("");
setAnswer("");
setError(null);
};
return (
<main className="flex min-h-screen flex-col items-center justify-center p-4 sm:p-8 bg-gradient-to-br from-zinc-900 to-zinc-800">
<div className="w-full max-w-3xl bg-zinc-900/50 backdrop-blur-md p-6 sm:p-8 rounded-2xl shadow-2xl border border-zinc-700/50">
<h1 className="text-3xl sm:text-4xl font-bold mb-6 text-center text-zinc-100 font-sans tracking-tight drop-shadow-md">
Temi Technologies Chatbot
</h1>
<form onSubmit={handleSubmit} className="mb-6 space-y-4">
<div className="relative">
<input
type="text"
value={query}
onChange={(e) => setQuery(e.target.value)}
placeholder="Ask a question about Temi Technologies..."
className="w-full px-4 py-3 bg-zinc-800 border border-zinc-600 rounded-lg focus:outline-none focus:ring-2 focus:ring-amber-400 text-zinc-100 placeholder-zinc-400 transition-all duration-200"
disabled={isLoading}
aria-label="Chat input"
/>
{query && (
<button
type="button"
onClick={handleClear}
className="absolute right-3 top-1/2 -translate-y-1/2 text-zinc-400 hover:text-amber-400 transition-colors"
aria-label="Clear input"
>
✕
</button>
)}
</div>
<button
type="submit"
disabled={isLoading}
className={`w-full px-4 py-3 rounded-lg text-zinc-100 font-semibold transition-all duration-200 ease-in-out transform hover:scale-105 ${
isLoading
? "bg-zinc-600 cursor-not-allowed"
: "bg-amber-400 text-zinc-900 hover:bg-amber-500"
}`}
>
{isLoading ? (
<span className="flex items-center justify-center">
<span className="animate-pulse">Thinking</span>
<span className="ml-2 flex space-x-1">
<span className="w-1 h-1 bg-zinc-100 rounded-full animate-bounce"></span>
<span className="w-1 h-1 bg-zinc-100 rounded-full animate-bounce delay-100"></span>
<span className="w-1 h-1 bg-zinc-100 rounded-full animate-bounce delay-200"></span>
</span>
</span>
) : (
"Ask"
)}
</button>
</form>
{error && (
<div className="mt-4 p-4 bg-rose-500/10 text-rose-500 border border-rose-500/50 rounded-lg animate-fade-in">
{error}
</div>
)}
{answer && (
<div className="mt-6 p-4 bg-zinc-800/50 border border-emerald-500/50 rounded-lg animate-fade-in">
<h2 className="text-lg font-semibold mb-2 text-emerald-500">
Answer:
</h2>
<p className="text-zinc-100 whitespace-pre-wrap">
{answer}
</p>
</div>
)}
</div>
<footer className="mt-8 text-center text-zinc-300 text-sm">
Powered by LangChain, RAG, FastAPI, Next.js, ChromaDB & Tailwind
CSS
<span className="mx-2">•</span>
<a
href="https://blog.codewithtemi.site/build-a-rag-powered-ai-chatbot-with-langchain-fastapi-nextjs-and-chromadb"
target="_blank"
rel="noopener noreferrer"
className="underline decoration-amber-400/50 hover:decoration-amber-400 transition-all duration-200"
aria-label="Read the blog post about this chatbot"
>
Read the Blog Post
</a>
</footer>
</main>
);
}
Explanation:
'use client';: Necessary inNext.jsApp Router to use hooks like useState.State: Manages the user's input (query), the chatbot's answer, loading status (isLoading), and potential error messages.
handleSubmit:Triggered when the form is submitted.
Sets loading state.
Uses
axiosto make a POST request to our FastAPI backend (http://localhost:8000/chat) with the user's question.Handles the response, updating the answer state or setting an error.
Resets loading state in the finally block.
UI:
A simple form with an input field and a submit button.
Conditionally displays loading indicators, error messages, and the final answer.
Basic styling using Tailwind CSS (included with create-next-app).
To Run the Frontend:
Make sure you're in the rag-frontend directory.
Run:
npm run dev(oryarn dev)Open your browser to
http://localhost:3000(or whatever portNext.jsindicates).
5. Putting it all Together
Start the Backend:
Open a terminal in the rag-backend directory.
Activate the virtual environment (source
venv/bin/activateorvenv\Scripts\activate).Run python
main.py. Wait until you see Uvicorn running onhttp://0.0.0.0:8000.
Start the Frontend:
Open a separate terminal in the rag-frontend directory.
Run
npm run dev(oryarn dev).
Interact:
Go to
http://localhost:3000in your browser.Ask questions that are covered in the
company_info.txtfile (e.g., "How to reset password?", "What are office hours?"). You should get concise answers based only on that text.Ask questions that are not covered (e.g., "Tell me about project Phoenix?"). You should get the "I don’t know" response.
Congratulations! You’ve built a sleek RAG chatbot powered by LangChain, FastAPI, Next.js, ChromaDB, and Tailwind CSS. This powerful system blends cutting-edge retrieval with a stunning frontend. Great work on creating an impressive application!
6. Limitations & Next Steps
This is a foundational example. Real-world applications would need more sophistication:
Better Document Loading: Handle various formats (PDF, DOCX, HTML), maybe from multiple sources.
Advanced Retrieval: Explore different retriever settings (e.g., MMR for diversity) or techniques like HyDE (Hypothetical Document Embeddings).
More Robust Error Handling: Implement more specific error catching and user feedback.
Chat History: Implement context windows to remember previous turns in the conversation.
Scalability: Consider asynchronous processing for data ingestion, more robust database solutions if ChromaDB's limits are reached, and proper deployment strategies.
UI/UX: Build a more feature-rich chat interface.
In our next post, we'll explore how to make this chatbot even more powerful by adding Tools (also known as Function Calling). This allows the LLM to interact with other APIs or databases to fetch real-time information or perform actions, going beyond just retrieving static document content. Stay tuned!
I hope this detailed guide helps you build your own RAG-powered applications. Let me know in the comments if you have questions or built something cool with this! Happy coding!
#RAG #LLM #AI #FastAPI #Python #NextJS #React #JavaScript #ChromaDB #VectorDatabase #Chatbot #WebDevelopment
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Written by
TemiTope Kayode
TemiTope Kayode
Seasoned software engineer, technical founder, and mentor with deep expertise in web and mobile development, enterprise applications, DevOps, and modern AI/LLM technologies. I build robust, scalable platforms using Python (Django, FastAPI), JavaScript/TypeScript (React, Next.js), and Dart (Flutter). With a strong academic background and extensive real-world experience, I’m passionate about delivering impactful solutions and guiding the next generation of developers. I love exploring where software, AI, and technology intersect with everyday life. Outside of tech, I enjoy quality time with family, learning new things, and helping others grow.