2.8 KiB
2.8 KiB
Lesson 03 | data types
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