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Level Up LLM App Development with LangChain and OpenAI

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Uploader:

freecoursewb

Source:

1337x

Downloads:

831

Type:

Tutorials

Language:

English

Category:

Other

Size:

2.0 GB

Added:

March 1, 2026, 8:41 a.m.

Peers:

Seeders: 3, Leechers: 3 (Last updated: 1 month, 1 week ago)

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Files:

Get Bonus Downloads Here.url 180 bytes
1 - What you should know - github.txt 107 bytes
1 - What you should know.mp4 15.2 MB
1 - Introducing LangServe Installation and setup.mp4 31.4 MB
2 - Create a server.mp4 6.5 MB
3 - Create the routes and the endpoints.mp4 50.8 MB
4 - Create a runnable to combine a prompt, a model, and output.mp4 37.4 MB
5 - Challenge Deploy a RESTful API.mp4 17.2 MB
6 - Solution Deploy a RESTful API.mp4 29.1 MB
1 - Manage and deploy an app on Render.mp4 15.3 MB
2 - Create a GitHub repository and push your project.mp4 40.0 MB
3 - Deploy a new web service on Render.mp4 38.6 MB
1 - Setup and installation.mp4 33.6 MB
2 - Create a chain and interface with an LLM.mp4 36.5 MB
3 - Define and structure a prompt.mp4 41.3 MB
4 - Create and invoke a chain (LCEL syntax).mp4 22.5 MB
5 - Work with output parsers.mp4 18.2 MB
1 - Quickstart Installation and setup.mp4 24.5 MB
2 - Create embeddings from text.mp4 10.9 MB
3 - Querying the vector store.mp4 11.2 MB
4 - Querying as a retriever.mp4 33.4 MB
1 - RAG Overview and architecture.mp4 22.0 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
3 - Project setup.mp4 31.4 MB
4 - Load and split documents into chunks.mp4 45.1 MB
5 - Initialize a vector store (Chroma) and ingest documents.mp4 49.3 MB
6 - Create the chain Prompt + model + parser.mp4 41.8 MB
7 - Create the chain Add context with a retriever.mp4 39.6 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
1 - Set up the Streamlit application.mp4 39.0 MB
2 - Build the layout with Streamlit components.mp4 39.5 MB
3 - Adding functionality with Streamlit.mp4 29.9 MB
4 - Challenge Deploy your Streamlit app.mp4 31.4 MB
5 - Solution Add app to GitHub.mp4 36.7 MB
6 - Solution Deploy your app.mp4 47.5 MB
1 - Retrieval with query analysis.mp4 8.2 MB
2 - Connect to a data source and create an index.mp4 35.8 MB
3 - Set up query analysis to handle multiple data sources.mp4 51.1 MB
4 - Retrieval with query analysis.mp4 39.6 MB
5 - Challenge Retrieval with multiple data sources.mp4 35.4 MB
6 - Solution Q&A with multiple data sources.mp4 70.0 MB
1 - Getting started with MongoDB Create an account.mp4 14.7 MB
2 - Build and deploy a free cluster.mp4 13.7 MB
3 - Set up the MongoDB environment and connect to the cluster.mp4 56.4 MB
4 - Create a secured database access.mp4 29.2 MB
5 - Load sample data and create the vector store.mp4 44.5 MB
6 - Create the Atlas Vector Search index.mp4 37.0 MB
7 - Run vector search queries.mp4 59.3 MB
1 - Create a retrieval chain Define the prompt.mp4 26.3 MB
2 - Create a retrieval chain Define the context.mp4 53.5 MB
3 - Create a retrieval chain Parse and format results.mp4 18.2 MB
4 - Query documents and generate extended responses.mp4 34.0 MB
1 - Using agents to perform actions in chains.mp4 15.8 MB
2 - Define tools.mp4 42.3 MB
3 - Select the perfect prompt.mp4 7.7 MB
4 - Bind tools and create agent.mp4 18.4 MB
5 - Create and run the agent executor.mp4 41.7 MB
6 - Challenge Create a multitask agent.mp4 45.5 MB
7 - Solution Define tools and functions.mp4 53.9 MB
Bonus Resources.txt 70 bytes

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