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Build a SMART Portfolio Website (Next.js 14, Langchain, Vercel AI SDK, OpenAI API, Tailwind CSS)
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The coolest portfolio website you can build to impress recruiters and friends! It has an AI chatbot that is trained on YOUR data. The AI can answer any question about you and scan your website for relevant information & links.
You will learn about vector embeddings and vector databases, LLMs, prompting, API requests, response streaming, markdown formatting, and more.
You can use the same code to build any other kind of website chatbot, like a smart documentation page. This is the technology you'll need as a developer in the future.
Project files:
📣 Follow Coding in Flow on social media:
Timestamps:
0:00 - What you will build
21:58 - Portfolio website design & metadata
56:41 - Dark theme with next-themes
1:04:41 - Chat box window + useChat (Vercel AI SDK)
1:47:24 - OpenAI ChatGPT API request
1:57:14 - Langchain basics (ChatOpenAI, templates, stream, pipe, invoke)
2:08:16 - Document loading & splitting (DirectoryLoader, TextLoader, RecursiveCharacterTextSplitter)
2:32:20 - Vector embeddings + vector store (AstraDB)
2:48:35 - Langchain vector retriever, createstuffDocumentsChain, createRetrievalChain
3:01:11 - chat_history + createHistoryAwareRetriever (rephrase search prompt)
3:11:59 - Langchain caching (Upstash Redis)
3:19:42 - Deployment (Vercel)
You will learn about vector embeddings and vector databases, LLMs, prompting, API requests, response streaming, markdown formatting, and more.
You can use the same code to build any other kind of website chatbot, like a smart documentation page. This is the technology you'll need as a developer in the future.
Project files:
📣 Follow Coding in Flow on social media:
Timestamps:
0:00 - What you will build
21:58 - Portfolio website design & metadata
56:41 - Dark theme with next-themes
1:04:41 - Chat box window + useChat (Vercel AI SDK)
1:47:24 - OpenAI ChatGPT API request
1:57:14 - Langchain basics (ChatOpenAI, templates, stream, pipe, invoke)
2:08:16 - Document loading & splitting (DirectoryLoader, TextLoader, RecursiveCharacterTextSplitter)
2:32:20 - Vector embeddings + vector store (AstraDB)
2:48:35 - Langchain vector retriever, createstuffDocumentsChain, createRetrievalChain
3:01:11 - chat_history + createHistoryAwareRetriever (rephrase search prompt)
3:11:59 - Langchain caching (Upstash Redis)
3:19:42 - Deployment (Vercel)
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