FastAPI for AI Applications: Async, Auto-Documentation, and Performance

I built snapconfig to speed up loading large configs across AI worker processes. Inspired by uv's use of rkyv, it compiles JSON/YAML/TOML/INI/env files to a binary cache and then memory-maps them for zero-copy reads. Cold loads are slower than Python's json module, but cached loads are 10-1000x faster. Perfect for Celery workers, serverless functions, AI workflows, or any setup where multiple processes read the same config. Visit our GitHub repo for benchmark results and learn more about SnapConfig. #LaravelCompany, #PythonPerformance, #RustProgramming, #EfficientConfigLoading, #FastAIWorkflows

🚀 Why I Choose FastAPI Over Flask & Django When building modern APIs, my priority is performance, clarity, and developer productivity—and that’s where FastAPI consistently stands out for me. ✔️ High performance powered by Starlette ✔️ Async-first design for scalable applications ✔️ Type safety & validation with Pydantic ✔️ Automatic API documentation (Swagger & ReDoc out of the box) Flask and Django are excellent frameworks, but for API-centric systems where speed, scalability, and clean design matter, FastAPI has become my go-to choice. Curious to hear from others—what framework are you using for your APIs, and why? #FastAPI #BackendDevelopment #APIs #Python #WebDevelopment #SoftwareEngineering #MachineLearning #AI #ArtificalIntellegence #KSA #Mozn

I cleaned up local scripts that I had been repeatedly using and I'm releasing it as a new python package named `RetroFlow`. It makes beautiful, minimalist ASCII flow charts from simple strings like the following. It supports title banners, complex cycles, and more. simple_diagram = """ A -> B B -> C C -> D C -> E E -> A """ Beyond their cool, retro look straight out of the "golden age of computing" era, they have a real advantage now that agentic software engineering has become the norm: These ASCII, pure text diagrams can happily live inline with: PRs and other technical settings. Yes, agents can read images, but in my experience they more easily internalize these tiny representations (vs an image or full deck). Yes, you could just have agents read from things like the code block above but this is a visual format both people and agents can easily understand. Let me know what you think! Blog post: https://lnkd.in/euPzKegk GitHub: https://lnkd.in/ekw-RAEg PyPi: https://lnkd.in/etPvPvVU #Python #AI #DataVisualization #OpenSource

Build Your Own PDF RAG Agent: Python, Streamlit, ChromaDB, Neo4j & Groq RAG agents answer questions from your documents. Upload a PDF, ask questions, get answers with sources. This tutorial builds one from scratch. You’ll understand why each piece exists. How they connect. Where things can break. All the parts that tutorials usually skip. What RAG Actually Does: ——————————————— Normal chatbot: Guesses answers from training data. Gets things wrong. Makes up facts. RAG agent: Searches your documents first. Finds relevant sections. Uses those to answer. Cites sources. The difference is grounding. Answers come from real data, not imagination. The Four Components (How They Connect): 1. ChromaDB stores document chunks as vectors. Vectors are just number lists that represent meaning. “Dog” and “puppy” have similar vectors. “Dog” and “car” don’t.  This lets you search by concept, not just keywords. 2. Neo4j tracks relationships between chunks. Chunk 5 connects to Chunk 6. Both mention “authentication.” When you retrieve Chunk 5, Neo4j knows Chunk 6 adds context. Grabs both. 3. Groq runs the LLM (Llama 3.3 70B). Takes your question plus retrieved chunks. Generates an answer. Does this fast — under 1 second. Free tier handles thousands of queries. 4. Streamlit builds the web interface. Python code becomes a working app. No HTML, CSS, or JavaScript needed. Each component handles one job. ChromaDB = storage and search. Neo4j = relationships. Groq = generation. Streamlit = UI. Clean separation makes debugging easy. Why This Stack ————————— Groq is fast. Other APIs take 3–5 seconds. Groq responds in under 1 second. Same model quality. Free tier is generous. ChromaDB runs locally. No server setup. No cloud costs. Just works. Data stays on your machine. Neo4j adds context. Vector search finds similar chunks. Graph search connects related chunks. Together they’re stronger than either alone. Streamlit ships fast. Write Python. Get web app. Deploy in minutes. #rag #ragagent #ai #aiagent #groq #neo4j https://lnkd.in/gqT3p9E9

Big news from HumemAI: we just released ArcadeDB Embedded Python Bindings. 🚀 If you build in Python but want a serious database engine underneath, this is a new way to work: ArcadeDB runs embedded inside your Python process. 🐍⚡️ No driver hop. No separate DB service to manage. Much lower latency for local-first workloads. 🧠📍 You can simply install it with: `uv pip install arcadedb-embedded` 📦✅ Why we built it: A lot of “AI memory” isn’t just embeddings. You need structure, relationships, transactions, and fast retrieval. ArcadeDB gives tables + documents + graphs + vectors in one engine, and we wanted it to feel natural from Python. 🧩🔗🔎 What you get: - Python-first API for database + schema + transactions 🧱 - SQL and OpenCypher when you want them 🗣️ - HNSW vector search via JVector for nearest-neighbor retrieval 🧠➡️🧠 - A truly standalone wheel: lightweight JVM 25 (jlink) + required JARs + JPype bridge ☕️🔧 Repo: https://lnkd.in/eSNxpD6W Docs: https://lnkd.in/eTh6xdjs Video: https://lnkd.in/enSszpQy 🎥 If you’re building local-first AI apps, agent memory, or hybrid graph + vector retrieval, I’d love feedback and contributions. 🙌 #Python #ArcadeDB #OpenSource #Vectors #GraphDatabase #EmbeddedDatabase

𝗣𝘆𝘁𝗵𝗼𝗻 𝘀𝗰𝗿𝗮𝗽𝗶𝗻𝗴 𝗶𝘀𝗻'𝘁 𝗱𝗲𝗮𝗱. It just evolved past BeautifulSoup. Here's what's actually working in 2026: → Playwright + Python = headless browser magic 🎭 → ScrapingBee API = bypass anti-bot walls instantly → Scrapy + Splash = JavaScript-heavy sites solved Real example? I scraped 50K product listings from a React-heavy e-commerce site last week. BeautifulSoup? Failed. Selenium? Too slow. Playwright? Done in 12 minutes. ⚡ The shift: • Static HTML scraping → Dynamic content extraction • Single-threaded → Async/await patterns • Manual parsing → AI-powered data structuring Bottom line? Your scraping stack determines your data quality. (♻️ Repost if this helped) What's your go-to Python scraping tool right now? Drop it below. 👇 #PythonDevelopment #WebScraping #DevTools

a MCP-powered agent in ~70 lines of code NEW: tiny-agents now supports AGENTS.md standard. 🥳 Inspired by Tiny Agents in JS, we ported the idea to Python 🐍 and extended the huggingface_hub client SDK to act as a MCP Client so it can pull tools from MCP servers and pass them to the LLM during inference. MCP (Model Context Protocol) is an open protocol that standardizes how Large Language Models (LLMs) interact with external tools and APIs....

𝗣𝗠 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗠 𝗜 𝗰𝗵𝗼𝘀𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝗜 𝗹𝗼𝘃𝗲 𝘀𝗻𝗮𝗸𝗲𝘀. No, really. I had snakes such as pythons (plus other reptiles) as pets growing up. When I saw the name `Python`, I thought "yeah this is for me". Only to learn from my analyst that it's named after Monty Python, not actual snakes. The logo is still snakes though, so 𝘐'𝘮 𝘤𝘰𝘶𝘯𝘵𝘪𝘯𝘨 𝘪𝘵 𝘢𝘴 𝘢 𝘸𝘪𝘯. Python (or coding in general) felt logical to me. You `def` your functions. You use them. Of course there's more to it, but the gist of it is here. Though I did spend an embarrassing amount of time wondering that I needed a physical printer to code with Python because of `print` but I digress. We all start somewhere. When I moved past "Hello World" scripts, I discovered Streamlit. 𝗦𝘁𝗿𝗲𝗮𝗺𝗹𝗶𝘁 𝗶𝘀 𝗮𝗻 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝘁𝗵𝗮𝘁 𝘁𝘂𝗿𝗻𝘀 𝗰𝗼𝗱𝗲 𝗶𝗻𝘁𝗼 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗨𝗜. First impression? The simplicity of it all. Fell in love immediately. Building ELI5+++ on it was pure joy. Clean. Fast. Intuitive. Here's why it's perfect for PMs learning to code: • Free and open-source • Transforms Python into interactive UI • Constraints = Creativity (you can't over-build) • "Good enough" becomes obvious • Perfect for prototypes and hobby projects Yes, there are limitations. But for most use cases, it's more than enough. If you're on a low-code journey with AI and interested in Python, spend some time with Streamlit. It'll simplify everything. 𝗖𝗼𝗺𝗶𝗻𝗴 𝘂𝗽: Next week: Building ELI5+++ walkthrough Week after: Deploying for free Questions? Ask away 👇

FastAPI is a modern Python framework designed for building high-performance APIs, increasingly utilized for AI and ML workloads. Recently, I developed a Retrieval-Augmented Generation (RAG) API that operates entirely on my local machine. In this project, I: - Set up a Python workspace and virtual environment, installing FastAPI, Uvicorn, ChromaDB, and Ollama as the core stack. - Configured Ollama with the tinyllama model, enabling the LLM to run locally without external API calls, ensuring zero costs and complete data privacy. - Created a knowledge base from text files and generated embeddings with Chroma, allowing the API to search by meaning rather than just keywords. - Built a FastAPI RAG endpoint that utilizes Chroma to retrieve relevant context and tinyllama (via Ollama) to generate grounded answers. - Tested the entire setup end-to-end using both curl and the automatically generated Swagger UI at /docs to verify requests, responses, and error handling. Key observations: - RAG proves to be more beneficial than standard LLM calls, as answers remain connected to the knowledge base embeddings stored in Chroma. - Running the LLM through Ollama facilitated keeping the entire system local while still achieving quick responses from tinyllama. - FastAPI’s type hints and automatic documentation provided a “production-style” feel, even for a learning project. This project was part of the “Build a RAG API with FastAPI” initiative on NextWork, aimed at deepening my understanding of backend development, vector databases, and AI integration. If you are also developing AI APIs with FastAPI, Chroma, or Ollama—particularly in local or containerized environments—I would be interested in seeing your architectures and deployment strategies. https://lnkd.in/gKAY3SyX #FastAPI #RAG #AI #Python #API #NextWork

🚀 Full Stack Journey Day 62: Validating Indian Mobile Numbers with Python Regex! 📱🇮🇳 Day 62 of my #FullStackDevelopment series is all about precision! Today, I’s applying everything I’ve learned about Regular Expressions to a real-world challenge: Mobile Number Validation. 🔍🐍 Validation is the first line of defense in any application. For Indian mobile numbers, the logic needs to be specific to ensure data integrity. Here’s how I tackled it: Generic Mobile Validation: Understanding the pattern for a standard 10-digit number using [0-9]{10} or \d{10}. 🔢 Indian Mobile Number Specifics: In India, mobile numbers follow specific rules: They are 10 digits long. They must start with 6, 7, 8, or 9. They may or may not have a prefix like 0 or +91. Using the re.compile() method, I created a reusable Regex Object. This is much more efficient when you need to validate a whole list of numbers (like in a CSV or database) because the pattern is compiled only once! ⚡ Pattern Breakdown: ^[6-9]\d{9}$ ^ and $ ensure the entire string is exactly 10 digits. [6-9] ensures it starts with the correct Indian mobile series. \d{9} ensures 9 more digits follow. 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/g_XzMtHx #Python #AdvancedPython #Regex #DataValidation #WebDevelopment #FullStackDeveloper #BackendDevelopment #IndiaTech #CodingLife #Day62 #SoftwareEngineering LinkedIn Samruddhi P.