Загрузка видео...

Не удалось загрузить видео

На главную

LangChain RAG App in Python – Chat with any PDF | Tutorial #python #coding #programming #ai #langchain Never upload sensitive documents to an AI chat tool. Instead, build your own PDF analyzer with Python, LangChain, and the Gemini API. In this short video we cover text extraction, vector embeddings,...

cod1ng_together's profile picture

Coding Together

33,200 subscribers

14,300 просмотров • 7 месяцев назад •via TikTok

Наука и технологии#python#coding#programming#ai#langchain
LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 0

Нет доступных комментариев

Здесь появятся комментарии из оригинального поста

Похожие видео

Build Your Own ChatGPT Bot in Python (in 60 Seconds) #python #coding #programming In this video you'll build a fully working ChatGPT bot from scratch using Python and the OpenAI API. Starting from a single API call, add user input function and then a full conversation loop with memory, all in under thirty lines of code. You'll learn how to initialize the OpenAI client, send messages to gpt-5.4-mini, and structure a growing message history so your bot actually remembers the conversation. All you need is Python and an OpenAI API key.
1:16

Build Your Own ChatGPT Bot in Python (in 60 Seconds) #python #coding #programming In this video you'll build a fully working ChatGPT bot from scratch using Python and the OpenAI API. Starting from a single API call, add user input function and then a full conversation loop with memory, all in under thirty lines of code. You'll learn how to initialize the OpenAI client, send messages to gpt-5.4-mini, and structure a growing message history so your bot actually remembers the conversation. All you need is Python and an OpenAI API key.

Coding Together

213,900 просмотров • 4 месяцев назад

A bit late in the day, but lets build a mini-chatgpt in a few lines of Python code👩🏾‍💻🐍✨ We'll use langchain-azure-ai to access Github models (which lets us build with llms for free), and then create a chat interface with streamlit💜 Did it it in *almost* 5mins😂 Code below!
7:31

A bit late in the day, but lets build a mini-chatgpt in a few lines of Python code👩🏾‍💻🐍✨ We'll use langchain-azure-ai to access Github models (which lets us build with llms for free), and then create a chat interface with streamlit💜 Did it it in *almost* 5mins😂 Code below!

Marlene Mhangami

30,590 просмотров • 1 год назад

Build a local LLM app with RAG to chat with Google Drive data using Mistral or Llama-3 running locally on your computer without wiring a single line of Python Code (100% free and without internet):
0:35

Build a local LLM app with RAG to chat with Google Drive data using Mistral or Llama-3 running locally on your computer without wiring a single line of Python Code (100% free and without internet):

Shubham Saboo

189,778 просмотров • 2 лет назад

🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️
1:37

🔥 New LangChain Academy Course: LangChain Essentials (Python & TypeScript) 🔥 Learn the basics of LangChain – our open source framework that makes it easy to start building agents with any model provider. Last week, we released LangChain 1.0. We’ve completely rewritten LangChain to be opinionated, focused, and powered by LangGraph’s runtime. It includes a new `create_agent` abstraction to build agents quickly, middleware for flexibility, and standard content blocks that work across any model provider. In this quickstart course, you'll learn how to: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug and test your agent with LangSmith Observability & Evaluation Enroll for free ➡️

LangChain

50,518 просмотров • 1 год назад

LangChain: Chat with Your Data, a new free short course created with Harrison Chase, is now available! In this 1 hour course, you’ll learn how to build one of the most requested LLM-based applications: Answering questions using information from a document or collection of documents (often called Retrieval Augmented Generation). You'll also learn how to use vector stores and embeddings to retrieve document chunks relevant to a query. I hope you enjoy the course!
3:17

LangChain: Chat with Your Data, a new free short course created with Harrison Chase, is now available! In this 1 hour course, you’ll learn how to build one of the most requested LLM-based applications: Answering questions using information from a document or collection of documents (often called Retrieval Augmented Generation). You'll also learn how to use vector stores and embeddings to retrieve document chunks relevant to a query. I hope you enjoy the course!

Andrew Ng

384,564 просмотров • 3 лет назад

🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️
2:25

🔗 New LangChain Academy Course: Introduction to LangChain (Python) 🔗 Learn how to build with LangChain – our open source framework that makes it easy to start building agents with any model provider. In this course, you’ll create agents that can reason, use tools, and take action, and learn how to debug their behavior with LangSmith. Along the way, you’ll: - Build an agent with the `create_agent` abstraction - Use LangChain’s core building blocks: Models, Messages, Memory, and Tools - Customize your agent with middleware - Debug your agent with LangSmith Observability & Studio By the end of the course, you’ll have assembled a full team of personal assistants. Enroll for free ➡️

LangChain

41,241 просмотров • 10 месяцев назад

Build AI Agents with RAG without writing a single line of Python Code. 100% Opensource and Nocode.
0:42

Build AI Agents with RAG without writing a single line of Python Code. 100% Opensource and Nocode.

Unwind AI

70,756 просмотров • 1 год назад

See Google’s Gemini 2.5 Pro in action with the Box AI API! We put it to the test on complex invoice calculations and analyzing 10-K financial documents. Leverage Gemini 2.5 Pro via the Box AI API for advanced document processing, data extraction, and Q&A in your applications.
4:01

See Google’s Gemini 2.5 Pro in action with the Box AI API! We put it to the test on complex invoice calculations and analyzing 10-K financial documents. Leverage Gemini 2.5 Pro via the Box AI API for advanced document processing, data extraction, and Q&A in your applications.

Box

489,097 просмотров • 1 год назад

🔗 Announcing LangChain OSS Skills LangChain has the most popular frameworks for building AI agents — and now your coding agent can be an expert in it. We're excited to release the first iteration of LangChain OSS Skills, giving your agent expertise in our open source frameworks. The skills include guidance on how to use langchain, langgraph, and deepagents to effectively build agents. ➡️ Install our OSS skills for your coding agent here: ➡️ Read more:
0:43

🔗 Announcing LangChain OSS Skills LangChain has the most popular frameworks for building AI agents — and now your coding agent can be an expert in it. We're excited to release the first iteration of LangChain OSS Skills, giving your agent expertise in our open source frameworks. The skills include guidance on how to use langchain, langgraph, and deepagents to effectively build agents. ➡️ Install our OSS skills for your coding agent here: ➡️ Read more:

LangChain OSS

65,840 просмотров • 8 месяцев назад

Build agents with LangChain + Browserbase. Give your Deep Agents search, fetch, and browser subagents to access the full web. All with full observability with the Browserbase dashboard.
0:26

Build agents with LangChain + Browserbase. Give your Deep Agents search, fetch, and browser subagents to access the full web. All with full observability with the Browserbase dashboard.

LangChain

48,004 просмотров • 6 месяцев назад

GLM 4.6 in Claude Code to build a python app Took me around 3 mins to make this python app with a Streamlit web ui and llamacpp python bindings The app lets you run an inference via a simple chat interface plus configurable params 👇 (video 4x speed)
0:52

GLM 4.6 in Claude Code to build a python app Took me around 3 mins to make this python app with a Streamlit web ui and llamacpp python bindings The app lets you run an inference via a simple chat interface plus configurable params 👇 (video 4x speed)

Melvin Vivas

21,834 просмотров • 1 год назад

Here's how to build your first MCP tool and server with less than 10 Lines of Python code🐍✨ Tried to make this video as short as possible and show the code, the json config and running the server in vs code with github copilot💜
3:43

Here's how to build your first MCP tool and server with less than 10 Lines of Python code🐍✨ Tried to make this video as short as possible and show the code, the json config and running the server in vs code with github copilot💜

Marlene Mhangami

14,630 просмотров • 1 год назад

I built an app that lets you create custom chatbots trained on your own documents or websites! Built with Langchain, and Redis for vector store and chat memory. Backend (Adonis) - Frontend (Nuxt 3) - #opensource #openai #ChatGPT
1:13

I built an app that lets you create custom chatbots trained on your own documents or websites! Built with Langchain, and Redis for vector store and chat memory. Backend (Adonis) - Frontend (Nuxt 3) - #opensource #openai #ChatGPT

The Artist

20,829 просмотров • 3 лет назад

How to Create a Spinning Globe 🌍 with Your Own Data Layers (No Coding Required) Learn how to create a spinning 3D globe map without writing any code using a simple web-based tool. In this step-by-step geospatial tutorial, you'll see how to build an interactive spinning globe, overlay raster and vector data layers, and visualize datasets directly in your browser or in a Jupyter Notebook environment. Web-based demo: Jupyter notebook demo: Video tutorial: #geospatial #opensource #python #jupyter #maplibre
0:29

How to Create a Spinning Globe 🌍 with Your Own Data Layers (No Coding Required) Learn how to create a spinning 3D globe map without writing any code using a simple web-based tool. In this step-by-step geospatial tutorial, you'll see how to build an interactive spinning globe, overlay raster and vector data layers, and visualize datasets directly in your browser or in a Jupyter Notebook environment. Web-based demo: Jupyter notebook demo: Video tutorial: #geospatial #opensource #python #jupyter #maplibre

Qiusheng Wu

12,745 просмотров • 8 месяцев назад

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️
2:36

LangChain Academy is live! Our first course — Introduction to LangGraph — teaches you the in-and-outs of building a reliable AI agent. In this course, you’ll learn how to: 🛠️ Build agents with LangGraph's graph-based workflows 🔄 Use memory + human-in-the-loop for smarter, self-corrective agents 📚 Create your own AI assistant that can perform knowledge tasks Enroll now for free ➡️ Bring LangChain Academy to your company ➡️

LangChain

105,536 просмотров • 2 лет назад

RustGIS is now on PyPI: pip install rustgis RustGIS is a fast, free and open-source GIS in pure Rust for mapping, analyzing and sharing geospatial data. It runs natively on Windows, macOS and Linux, and in your web browser. Your data stays on your machine unless you send it to an AI model you choose, and every action is a command you can script. The new rustgis Python package brings the RustGIS engine into Python and the full RustGIS app into your Jupyter notebook, with no server to run and nothing else to install. What you can do - The engine in-process: open the same data the app opens, run any of RustGIS's commands and geoprocessing tools, style layers and render maps, all from Python. - GeoPandas in and out: turn any layer into a GeoDataFrame, or add a GeoDataFrame as a layer. - The whole app in a notebook cell: shows the ribbon, panes and map right below your code. - Two-way sync: what Python adds or styles appears in the app, and what you pan, edit or add in the app comes back to Python. - Projects that travel: save a .rgis project from Python and open it as it is in the desktop app or the web app. - Runs where you work: JupyterLab, Jupyter Notebook, VS Code and Google Colab, with prebuilt wheels for Windows, macOS and Linux. Get started in three lines: import rustgis m = m Try it out - Open the getting-started notebook in Colab: - PyPI: - Python guide: - Launch the web app: - GitHub: #GIS #Rust #Python #Jupyter #OpenSource #Geospatial
0:38

RustGIS is now on PyPI: pip install rustgis RustGIS is a fast, free and open-source GIS in pure Rust for mapping, analyzing and sharing geospatial data. It runs natively on Windows, macOS and Linux, and in your web browser. Your data stays on your machine unless you send it to an AI model you choose, and every action is a command you can script. The new rustgis Python package brings the RustGIS engine into Python and the full RustGIS app into your Jupyter notebook, with no server to run and nothing else to install. What you can do - The engine in-process: open the same data the app opens, run any of RustGIS's commands and geoprocessing tools, style layers and render maps, all from Python. - GeoPandas in and out: turn any layer into a GeoDataFrame, or add a GeoDataFrame as a layer. - The whole app in a notebook cell: shows the ribbon, panes and map right below your code. - Two-way sync: what Python adds or styles appears in the app, and what you pan, edit or add in the app comes back to Python. - Projects that travel: save a .rgis project from Python and open it as it is in the desktop app or the web app. - Runs where you work: JupyterLab, Jupyter Notebook, VS Code and Google Colab, with prebuilt wheels for Windows, macOS and Linux. Get started in three lines: import rustgis m = m Try it out - Open the getting-started notebook in Colab: - PyPI: - Python guide: - Launch the web app: - GitHub: #GIS #Rust #Python #Jupyter #OpenSource #Geospatial

Qiusheng Wu

13,852 просмотров • 29 дней назад