#+title: Lesson 03 | data types
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* Links
#+attr_html: :class links
- [[../toc.org][TOC | Build n8n ai agents 8 hr course]]
- [[https://www.youtube.com/watch?v=Ey18PDiaAYI][Course Video]]

Curr: https://youtu.be/Ey18PDiaAYI?si=OFqJ3CX3m2AHLWdV&t=3270

*** timestamps
:PROPERTIES:
:CUSTOM_ID: timestamp
:END:

#+attr_html: :class playlist
- [[https://www.youtube.com/watch?v=Ey18PDiaAYI&t=2445s][0:40:45 n8n n8n Data Types]]
- [[https://www.youtube.com/watch?v=Ey18PDiaAYI&t=2905s][0:48:25 23 Workflow Templates FREE]]
- [[https://www.youtube.com/watch?v=Ey18PDiaAYI&t=2949s][0:49:09 Step by Step AI Workflows]]
- [[https://www.youtube.com/watch?v=Ey18PDiaAYI&t=3085s][0:51:25 1) Rag Pipeline and Chatbot]]
- [[https://www.youtube.com/watch?v=Ey18PDiaAYI&t=4262s][1:11:02 2) Customer Support Workflow]]
- [[https://www.youtube.com/watch?v=Ey18PDiaAYI&t=4926s][1:22:06 3) LinkedIn Content Creator Workflow]]

* Notes
** set field
- can also name the fields and data type for each field

*** data types
| data type | symbol | example             |
|-----------+--------+---------------------|
| String    | A      | "blah"              |
| Number    | #      | 50                  |
| Boolean   | ✓      | true                |
| Array     | ☰     | [1, "one", "three"] |
| Object    | 3d box | {"blah": 33}        |

** 3 AI workflows
*** 1. RAG pipeline & chatbot
**** tools
- pinecone :: vector database
- google drive :: data storage
- google docs ::
- open router :: lets us connect to ai models like openai's or anthropics

*** 2. Customer support
**** purpose
- build off prev workflow with pinecone db
- respond to customer support related emails

**** tools
- pinecone
- gmail
- n8n agent
- open router

*** 3. LinkedIn Content Creation
**** tools
- tavily :: search the web
- google sheets :: store content ideas, and write content ideas to it

** Workflow #1 - Rag Pipeline and Chatbot
*** RAG
- stands for :: retrieval, augmented, generation
- looks inside database for the answer

**** Vector Database
- multidimensional graph of points
- vector is placed based on meaning of vector
  - ie, wolf and dog will be close
  - banana, apple will be close

**** how it works
- we have a document
- break it into chunks
- run it through an 'embeddings model'
  - this puts the chunks into a vector model

**** Query
- run the query through the embeddings model
- see where it lands in vectors, grabs back the nearest 4 or 5 vectors and returns it to us

*** process
- our trigger is any changes in folder on google drive

**** get Google Drive Credentials
***** create OAuth credentials