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# System prepended metadata

title: Domain AI 應用：透過智能自動化改變行業

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# Domain AI 應用：透過智能自動化改變行業

:crown: Speaker: <font color="#00bfff">**Leo Chu @Unition**</font>

{%hackmd 3_LMFJLvQV-rMOZRjKILnQ %}

:raised_hands: Slido: https://app.sli.do/event/62My7psHmtEaaKDXG8baBZ

###### ✦✦✦✦✦ Here we go ! ✦✦✦✦✦ 

## Movtivation 
Traditional Customer Service - 客服
因客戶愈多, 所需要的客服愈多. 想提供線上AI客服

###  Customer Service
Adv:
Problem-solving 
Interpersonal interaction
Credibility - 有些客戶比較喜歡真人, 不會冷冰冰的

Dis-adv:
High Cost
Low Efficiency
Inconsistency
Limited Working Hours 

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### **AI** Customer Service 
Adv:
Cost-effectiveness
Real-Time responses
Scalability
Automation

Dis-adv:
Lack of emotions and personalization
Limited responses
 > 只能回應訓練的資識
 
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 ## How to feed knowledge to GPT Model
 1.Weight
 2.Input
 
 ### Via model weights - fine-tuning 
 1. Like a lone term memory 
 2. High cost on prepare data 
 3. Fine-tuning is better suited to teaching specialized task or styles , and is less reliable for factual 


### Via model inputs 
1. message inputs are like short-term memory
2. open book exam


demo : ask with article 


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## Tokens 

沒法一次給這麼多token , 就算是有也是挺花錢的. 

What is token? : 處理文本的最小單位, 使用者輸入內容. e.g. 一個中文字2個token 

Token Limit:
GPT3.5: 4,096 tokens 
GPT4 
GPT3.5-16k
GPT4-32K


## Embedding Data 
1. Convert document/input to vector
  做向量化時保持原本的特徵, 然後做比較.
2. Calculate similarity 
  我們怎麼做比較的呢? Consine Similarity
  
  比對相似度的概念: 
  RBG model
  Red : [255,0,0] , Pink [255,105, 180]
  Consine =  0.77 
  > Consine做完的range  0 ~ 1, 0.77 算高的了. 意思是紅跟粉紅很像
  
  
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## Embedding Search 
1. Prepare search data
2. Search
3. Ask

### Prepare search data 
  1. Collect 
  2. Chunk
  3. Embed 
  4. Store 

### Search 
 1. Given a user question
 2. Generate an embedding for the query
 3. Rank the text sections
 4. Insert the question and the most relevant sections 
 5. Return GPT's answer
 
Demo : Failed , Not working 來個掌聲.

在DB就2個欄 , content & 向量

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# Prompt

需要精準的描述, 才能完成合理的任務

OpnenAI Prompt Types 
1. System Role
2. Assistant Role
3. User Role


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# Open AI 

Why Azure Open AI 


|  |Embedding Search | Cognitive Search|
| -------- | -------- | -------- |
| Multi-lang      | X     | V     |
| Cost for preprocessing Data     | V     | X     |
| Support Differnt Type input     | X     | v     |


Security 

How Azure Open AI work 

 flow : Questoin -> Query from DB as `Knowledge` -> Prompt + `Knowledge`


Demo : Fail (請自行想像)

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有提供API 做Embed 嗎? 
  yes , 且可以自己選擇等級 Ada or 達文西
  可以處理一段文字. 
  
  
Leo : 建議在演講前, 記得先在現場試過Demo先










  
  
  