| 1 - Create a retrieval chain Define the prompt.mp4 | 26.3 MB | ||
| 1 - Getting started with MongoDB Create an account.mp4 | 14.7 MB | ||
| 1 - Introducing LangServe Installation and setup.mp4 | 31.4 MB | ||
| 1 - Manage and deploy an app on Render.mp4 | 15.3 MB | ||
| 1 - Quickstart Installation and setup.mp4 | 24.5 MB | ||
| 1 - RAG Overview and architecture.mp4 | 22 MB | ||
| 1 - Retrieval with query analysis.mp4 | 8.2 MB | ||
| 1 - Set up the Streamlit application.mp4 | 39 MB | ||
| 1 - Setup and installation.mp4 | 33.6 MB | ||
| 1 - Using agents to perform actions in chains.mp4 | 15.8 MB | ||
| 1 - What you should know - github.txt | 102.4 B | ||
| 1 - What you should know.mp4 | 15.2 MB | ||
| 10 - Solution Add a chain with chat history.mp4 | 43.1 MB | ||
| 11 - Solution Context- and history-aware chatbot.mp4 | 53.2 MB | ||
| 2 - Breaking down the RAG pipeline.mp4 | 12.2 MB | ||
| 2 - Build and deploy a free cluster.mp4 | 13.7 MB | ||
| 2 - Build the layout with Streamlit components.mp4 | 39.5 MB | ||
| 2 - Connect to a data source and create an index.mp4 | 35.8 MB | ||
| 2 - Create a GitHub repository and push your project.mp4 | 40 MB | ||
| 2 - Create a chain and interface with an LLM.mp4 | 36.5 MB | ||
| 2 - Create a retrieval chain Define the context.mp4 | 53.5 MB | ||
| 2 - Create a server.mp4 | 6.5 MB | ||
| 2 - Create embeddings from text.mp4 | 10.9 MB | ||
| 2 - Define tools.mp4 | 42.3 MB | ||
| 3 - Adding functionality with Streamlit.mp4 | 29.9 MB | ||
| 3 - Create a retrieval chain Parse and format results.mp4 | 18.2 MB | ||
| 3 - Create the routes and the endpoints.mp4 | 50.8 MB | ||
| 3 - Define and structure a prompt.mp4 | 41.3 MB | ||
| 3 - Deploy a new web service on Render.mp4 | 38.6 MB | ||
| 3 - Project setup.mp4 | 31.4 MB | ||
| 3 - Querying the vector store.mp4 | 11.2 MB | ||
| 3 - Select the perfect prompt.mp4 | 7.7 MB | ||
| 3 - Set up query analysis to handle multiple data sources.mp4 | 51.1 MB | ||
| 3 - Set up the MongoDB environment and connect to the cluster.mp4 | 56.4 MB | ||
| 4 - Bind tools and create agent.mp4 | 18.4 MB | ||
| 4 - Challenge Deploy your Streamlit app.mp4 | 31.4 MB | ||
| 4 - Create a runnable to combine a prompt, a model, and output.mp4 | 37.4 MB | ||
| 4 - Create a secured database access.mp4 | 29.2 MB | ||
| 4 - Create and invoke a chain (LCEL syntax).mp4 | 22.5 MB | ||
| 4 - Load and split documents into chunks.mp4 | 45.1 MB | ||
| 4 - Query documents and generate extended responses.mp4 | 34 MB | ||
| 4 - Querying as a retriever.mp4 | 33.4 MB | ||
| 4 - Retrieval with query analysis.mp4 | 39.6 MB | ||
| 5 - Challenge Deploy a RESTful API.mp4 | 17.2 MB | ||
| 5 - Challenge Retrieval with multiple data sources.mp4 | 35.4 MB | ||
| 5 - Create and run the agent executor.mp4 | 41.7 MB | ||
| 5 - Initialize a vector store (Chroma) and ingest documents.mp4 | 49.3 MB | ||
| 5 - Load sample data and create the vector store.mp4 | 44.5 MB | ||
| 5 - Solution Add app to GitHub.mp4 | 36.7 MB | ||
| 5 - Work with output parsers.mp4 | 18.2 MB | ||
| 6 - Challenge Create a multitask agent.mp4 | 45.5 MB | ||
| 6 - Create the Atlas Vector Search index.mp4 | 37 MB | ||
| 6 - Create the chain Prompt + model + parser.mp4 | 41.8 MB | ||
| 6 - Solution Deploy a RESTful API.mp4 | 29.1 MB | ||
| 6 - Solution Deploy your app.mp4 | 47.5 MB | ||
| 6 - Solution Q&A with multiple data sources.mp4 | 70 MB | ||
| 7 - Create the chain Add context with a retriever.mp4 | 39.6 MB | ||
| 7 - Run vector search queries.mp4 | 59.3 MB | ||
| 7 - Solution Define tools and functions.mp4 | 53.9 MB | ||
| 8 - Pass data with RunnablePassthrough and query data.mp4 | 32.1 MB | ||
| 9 - Challenge Create a custom agent with history.mp4 | 39.2 MB | ||
| Bonus Resources.txt | 102.4 B | ||
| Get Bonus Downloads Here.url | 204.8 B | ||
| ▲ 63 total files | |||
Level Up LLM App Development with LangChain & OpenAI
https://WebToolTip.com
Published 11/2025
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 51m | Size: 1.95 GB
Build LLM apps with LangChain, OpenAI, RAG, agents, vector search, and deploy full AI applications to the cloud.
What you'll learn
Build LLM-powered applications using LangChain and OpenAI APIs.
Implement Retrieval-Augmented Generation (RAG) to enhance model outputs.
Create intelligent agents with tools, functions, and multi-retriever workflows.
Deploy LLM apps to the cloud using LangServe, Streamlit, and Replit.
Requirements
Basic Python knowledge is helpful but not required. Beginners can follow along easily.
| torrent name | size | uploader | age | seed | leech |
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| 1.2 GB | freecoursewb | 7 months | 1 | 1 | |
| 1.6 GB | freecoursewb | 10 months | 0 | 0 | |
| 1.4 GB | freecoursewb | 10 months | 0 | 0 | |
| 1.5 GB | freecoursewb | 2 years | 0 | 0 | |
| 1.5 GB | Godonfox | 2 years | 3 | 1 |
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