The Python, AI & Data series goes from collecting and cleaning data to machine learning, deep learning, Vietnamese language processing, LLMs, RAG, agents and running models in production, with runnable Python code ...
Model Context Protocol is an open protocol that lets AI applications (clients) connect to external tools and data (servers) through one common standard, instead of every application writing its own ad...
The hand-written tool calling loop from the previous article is enough for a simple agent, but it has one weakness: all state lives in local variables of a running Python function. The more branches t...
An LLM only produces text. To let it look up an order, calculate shipping or issue a refund, you have to give it a way to ask your application to do that, and the standard mechanism today is tool call...
Getting the HTML from a crawler is only half the job. The other half is structured data extraction: turning a page into typed records you can load into a database or a report. The classic approach to...
You edit the prompt, change how chunks are split, swap the embedding model, and the chatbot "seems better". But better by how much, on which questions, and which questions are quietly getting worse? W...
The previous article ended on an uncomfortable number: out of 22 questions, 4 never got the right passage into the top 3. When the right passage is not in the prompt, the model makes things up or refu...
A language model answers fluently about your store's return policy, but the "14 days" it quotes appears in none of your documents. That is why RAG (Retrieval-Augmented Generation) has become the defau...
After Embeddings and semantic search: sentence-transformers and FAISS, every project hits the same next question: where do the vectors live once the data grows, gets edited, gets deleted and has to be...
The search box on most websites still matches keywords: a customer types "drop my order" (the colloquial Vietnamese phrase), the system finds no such word in the article "How to cancel an order," and...
Sending all of your customer-support content to an outside API is not always allowed, and token bills are not always cheap. Running a local LLM means the model sits on your own laptop or server: the d...
Calling an LLM API from Python sounds simple: send a list of messages, get text back. Once LLM APIs ship in a product, three questions show up right away. How do users see words appear instead of wait...
Most beginners write prompts by feel: change a few words, try one or two inputs, and ship it if it looks fine. That breaks down once the prompt sits inside a product that handles thousands of messages...
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