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0.18 KB
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1 - Introduction
1 - What you should know - github.txt
0.10 KB
1 - What you should know.mp4
15.22 MB
10 - Deploy Chains as a RESTful API with LangServe
1 - Introducing LangServe Installation and setup.mp4
31.42 MB
2 - Create a server.mp4
6.50 MB
3 - Create the routes and the endpoints.mp4
50.80 MB
4 - Create a runnable to combine a prompt, a model, and output.mp4
37.40 MB
5 - Challenge Deploy a RESTful API.mp4
17.17 MB
6 - Solution Deploy a RESTful API.mp4
29.14 MB
11 - Finish Line Deploy to the Cloud and Share with the World
1 - Manage and deploy an app on Render.mp4
15.31 MB
2 - Create a GitHub repository and push your project.mp4
39.99 MB
3 - Deploy a new web service on Render.mp4
38.59 MB
2 - LangChain Basics Intro to Building LLM-Powered Apps
1 - Setup and installation.mp4
33.63 MB
2 - Create a chain and interface with an LLM.mp4
36.47 MB
3 - Define and structure a prompt.mp4
41.34 MB
4 - Create and invoke a chain (LCEL syntax).mp4
22.47 MB
5 - Work with output parsers.mp4
18.22 MB
3 - Adding Similarity Search and Context
1 - Quickstart Installation and setup.mp4
24.50 MB
2 - Create embeddings from text.mp4
10.87 MB
3 - Querying the vector store.mp4
11.22 MB
4 - Querying as a retriever.mp4
33.37 MB
4 - Leveraging LLMs with LangChain and RAG
1 - RAG Overview and architecture.mp4
21.97 MB
10 - Solution Add a chain with chat history.mp4
43.07 MB
11 - Solution Context- and history-aware chatbot.mp4
53.19 MB
2 - Breaking down the RAG pipeline.mp4
12.17 MB
3 - Project setup.mp4
31.44 MB
4 - Load and split documents into chunks.mp4
45.06 MB
5 - Initialize a vector store (Chroma) and ingest documents.mp4
49.33 MB
6 - Create the chain Prompt + model + parser.mp4
41.81 MB
7 - Create the chain Add context with a retriever.mp4
39.62 MB
8 - Pass data with RunnablePassthrough and query data.mp4
32.13 MB
9 - Challenge Create a custom agent with history.mp4
39.22 MB
5 - Create an Interactive Web App (Streamlit)
1 - Set up the Streamlit application.mp4
38.96 MB
2 - Build the layout with Streamlit components.mp4
39.50 MB
3 - Adding functionality with Streamlit.mp4
29.85 MB
4 - Challenge Deploy your Streamlit app.mp4
31.42 MB
5 - Solution Add app to GitHub.mp4
36.67 MB
6 - Solution Deploy your app.mp4
47.49 MB
6 - Build a Q&A Agent with Multiple Data Sources and Query Analysis
1 - Retrieval with query analysis.mp4
8.21 MB
2 - Connect to a data source and create an index.mp4
35.79 MB
3 - Set up query analysis to handle multiple data sources.mp4
51.14 MB
4 - Retrieval with query analysis.mp4
39.65 MB
5 - Challenge Retrieval with multiple data sources.mp4
35.45 MB
6 - Solution Q&A with multiple data sources.mp4
70.00 MB
7 - Perform Semantic Search Using MongoDB Atlas Vector Search and OpenAI
1 - Getting started with MongoDB Create an account.mp4
14.66 MB
2 - Build and deploy a free cluster.mp4
13.67 MB
3 - Set up the MongoDB environment and connect to the cluster.mp4
56.41 MB
4 - Create a secured database access.mp4
29.19 MB
5 - Load sample data and create the vector store.mp4
44.48 MB
6 - Create the Atlas Vector Search index.mp4
37.04 MB
7 - Run vector search queries.mp4
59.28 MB
8 - Interact with a NoSQL Database (MongoDB)
1 - Create a retrieval chain Define the prompt.mp4
26.33 MB
2 - Create a retrieval chain Define the context.mp4
53.46 MB
3 - Create a retrieval chain Parse and format results.mp4
18.19 MB
4 - Query documents and generate extended responses.mp4
34.04 MB
9 - LLM Fine-Tuning with the OpenAI Tools and Functions
1 - Using agents to perform actions in chains.mp4
15.79 MB
2 - Define tools.mp4
42.27 MB
3 - Select the perfect prompt.mp4
7.74 MB
4 - Bind tools and create agent.mp4
18.44 MB
5 - Create and run the agent executor.mp4
41.74 MB
6 - Challenge Create a multitask agent.mp4
45.48 MB
7 - Solution Define tools and functions.mp4
53.92 MB
Bonus Resources.txt
0.07 KB
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