Download Level Up LLM App Development with LangChain and OpenAI Torrent

Torrent Contents Size: 2 GB

Level Up LLM App Development with LangChain and OpenAI

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1 - Create a retrieval chain Define the prompt.mp4	MP4	26.3 MB
1 - Getting started with MongoDB Create an account.mp4	MP4	14.7 MB
1 - Introducing LangServe Installation and setup.mp4	MP4	31.4 MB
1 - Manage and deploy an app on Render.mp4	MP4	15.3 MB
1 - Quickstart Installation and setup.mp4	MP4	24.5 MB
1 - RAG Overview and architecture.mp4	MP4	22 MB
1 - Retrieval with query analysis.mp4	MP4	8.2 MB
1 - Set up the Streamlit application.mp4	MP4	39 MB
1 - Setup and installation.mp4	MP4	33.6 MB
1 - Using agents to perform actions in chains.mp4	MP4	15.8 MB
1 - What you should know - github.txt	TXT	102.4 B
1 - What you should know.mp4	MP4	15.2 MB
10 - Solution Add a chain with chat history.mp4	MP4	43.1 MB
11 - Solution Context- and history-aware chatbot.mp4	MP4	53.2 MB
2 - Breaking down the RAG pipeline.mp4	MP4	12.2 MB
2 - Build and deploy a free cluster.mp4	MP4	13.7 MB
2 - Build the layout with Streamlit components.mp4	MP4	39.5 MB
2 - Connect to a data source and create an index.mp4	MP4	35.8 MB
2 - Create a GitHub repository and push your project.mp4	MP4	40 MB
2 - Create a chain and interface with an LLM.mp4	MP4	36.5 MB
2 - Create a retrieval chain Define the context.mp4	MP4	53.5 MB
2 - Create a server.mp4	MP4	6.5 MB
2 - Create embeddings from text.mp4	MP4	10.9 MB
2 - Define tools.mp4	MP4	42.3 MB
3 - Adding functionality with Streamlit.mp4	MP4	29.9 MB
3 - Create a retrieval chain Parse and format results.mp4	MP4	18.2 MB
3 - Create the routes and the endpoints.mp4	MP4	50.8 MB
3 - Define and structure a prompt.mp4	MP4	41.3 MB
3 - Deploy a new web service on Render.mp4	MP4	38.6 MB
3 - Project setup.mp4	MP4	31.4 MB
3 - Querying the vector store.mp4	MP4	11.2 MB
3 - Select the perfect prompt.mp4	MP4	7.7 MB
3 - Set up query analysis to handle multiple data sources.mp4	MP4	51.1 MB
3 - Set up the MongoDB environment and connect to the cluster.mp4	MP4	56.4 MB
4 - Bind tools and create agent.mp4	MP4	18.4 MB
4 - Challenge Deploy your Streamlit app.mp4	MP4	31.4 MB
4 - Create a runnable to combine a prompt, a model, and output.mp4	MP4	37.4 MB
4 - Create a secured database access.mp4	MP4	29.2 MB
4 - Create and invoke a chain (LCEL syntax).mp4	MP4	22.5 MB
4 - Load and split documents into chunks.mp4	MP4	45.1 MB
4 - Query documents and generate extended responses.mp4	MP4	34 MB
4 - Querying as a retriever.mp4	MP4	33.4 MB
4 - Retrieval with query analysis.mp4	MP4	39.6 MB
5 - Challenge Deploy a RESTful API.mp4	MP4	17.2 MB
5 - Challenge Retrieval with multiple data sources.mp4	MP4	35.4 MB
5 - Create and run the agent executor.mp4	MP4	41.7 MB
5 - Initialize a vector store (Chroma) and ingest documents.mp4	MP4	49.3 MB
5 - Load sample data and create the vector store.mp4	MP4	44.5 MB
5 - Solution Add app to GitHub.mp4	MP4	36.7 MB
5 - Work with output parsers.mp4	MP4	18.2 MB
6 - Challenge Create a multitask agent.mp4	MP4	45.5 MB
6 - Create the Atlas Vector Search index.mp4	MP4	37 MB
6 - Create the chain Prompt + model + parser.mp4	MP4	41.8 MB
6 - Solution Deploy a RESTful API.mp4	MP4	29.1 MB
6 - Solution Deploy your app.mp4	MP4	47.5 MB
6 - Solution Q&A with multiple data sources.mp4	MP4	70 MB
7 - Create the chain Add context with a retriever.mp4	MP4	39.6 MB
7 - Run vector search queries.mp4	MP4	59.3 MB
7 - Solution Define tools and functions.mp4	MP4	53.9 MB
8 - Pass data with RunnablePassthrough and query data.mp4	MP4	32.1 MB
9 - Challenge Create a custom agent with history.mp4	MP4	39.2 MB
Bonus Resources.txt	TXT	102.4 B
Get Bonus Downloads Here.url	URL	204.8 B

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Level Up LLM App Development with LangChain and OpenAI

Torrent Contents Size: 2 GB

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