Level Up LLM App Development with LangChain and OpenAI
Seeders : 4 Leechers : 0
| Torrent Hash : | 62AE6C545DFF7884EE3AAE64F81FDF731117680A |
| Torrent Added : | at March 1, 2026, 8:41 a.m. in Other |
| Torrent Size : | 2.0 GB |
Knox
Level Up LLM App Development with LangChain and OpenAI
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Level Up LLM App Development with LangChain and OpenAI
Get Bonus Downloads Here.url -
1 - What you should know - github.txt -
1 - What you should know.mp4 -
1 - Introducing LangServe Installation and setup.mp4 -
2 - Create a server.mp4 -
3 - Create the routes and the endpoints.mp4 -
4 - Create a runnable to combine a prompt, a model, and output.mp4 -
5 - Challenge Deploy a RESTful API.mp4 -
6 - Solution Deploy a RESTful API.mp4 -
1 - Manage and deploy an app on Render.mp4 -
2 - Create a GitHub repository and push your project.mp4 -
3 - Deploy a new web service on Render.mp4 -
1 - Setup and installation.mp4 -
2 - Create a chain and interface with an LLM.mp4 -
3 - Define and structure a prompt.mp4 -
4 - Create and invoke a chain (LCEL syntax).mp4 -
5 - Work with output parsers.mp4 -
1 - Quickstart Installation and setup.mp4 -
2 - Create embeddings from text.mp4 -
3 - Querying the vector store.mp4 -
4 - Querying as a retriever.mp4 -
1 - RAG Overview and architecture.mp4 -
10 - Solution Add a chain with chat history.mp4 -
11 - Solution Context- and history-aware chatbot.mp4 -
2 - Breaking down the RAG pipeline.mp4 -
3 - Project setup.mp4 -
4 - Load and split documents into chunks.mp4 -
5 - Initialize a vector store (Chroma) and ingest documents.mp4 -
6 - Create the chain Prompt + model + parser.mp4 -
7 - Create the chain Add context with a retriever.mp4 -
8 - Pass data with RunnablePassthrough and query data.mp4 -
9 - Challenge Create a custom agent with history.mp4 -
1 - Set up the Streamlit application.mp4 -
2 - Build the layout with Streamlit components.mp4 -
3 - Adding functionality with Streamlit.mp4 -
4 - Challenge Deploy your Streamlit app.mp4 -
5 - Solution Add app to GitHub.mp4 -
6 - Solution Deploy your app.mp4 -
1 - Retrieval with query analysis.mp4 -
2 - Connect to a data source and create an index.mp4 -
3 - Set up query analysis to handle multiple data sources.mp4 -
4 - Retrieval with query analysis.mp4 -
5 - Challenge Retrieval with multiple data sources.mp4 -
6 - Solution Q&A with multiple data sources.mp4 -
1 - Getting started with MongoDB Create an account.mp4 -
2 - Build and deploy a free cluster.mp4 -
3 - Set up the MongoDB environment and connect to the cluster.mp4 -
4 - Create a secured database access.mp4 -
5 - Load sample data and create the vector store.mp4 -
6 - Create the Atlas Vector Search index.mp4 -
7 - Run vector search queries.mp4 -
1 - Create a retrieval chain Define the prompt.mp4 -
2 - Create a retrieval chain Define the context.mp4 -
3 - Create a retrieval chain Parse and format results.mp4 -
4 - Query documents and generate extended responses.mp4 -
1 - Using agents to perform actions in chains.mp4 -
2 - Define tools.mp4 -
3 - Select the perfect prompt.mp4 -
4 - Bind tools and create agent.mp4 -
5 - Create and run the agent executor.mp4 -
6 - Challenge Create a multitask agent.mp4 -
7 - Solution Define tools and functions.mp4 -
Bonus Resources.txt -
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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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