操作式Framework是名詞語組Framework with Operation Formulaエ³翻譯。チッ¹个用語カㇷ¹翻譯當然차ㇺ³AI討論了後チァㇷ¹決定エ⁷。
一个操作式Frameworkエ³運作，若是以文作成來做類比，着是愛決定コㆁ一个動詞カㇷ¹一个名詞若俦起來是ベㇷ¹アㇴ¹ツァ˜連動運作。連動運作チッ¹句話當然是有小쿠ァ機械性エ³意味띠³內底。타ㆁ⁷ホㆁ⁷俦起來エ³動詞ハㇺ³名詞，以一个formulaエ³形式ホ³使用者操作。チッ¹種타ㆁ⁷ホ³使用者操作エ³formula着是號做操作式，イァ³着是operation formula。
操作式Framework本身着是一个提示驅動framework，エ³用エカ³二个アㇷ³是二个以上エ⁷單語以formulaエ³形式俦做伙。單語主要是指動詞アㇷ³是名詞，其它エ形容詞，副詞，連接詞，助詞ロㆁ嘛是エ³用チㇷ。只要カ³至少二个單語エ³定義寫ホ⁷好，利用操作式着エ³カ³ヘ二个單語俦起來。
以本文所紹介エ³例來コㆁ²，一个操作式是生做アㇴ¹ネ:”make + coffee + milk”。以字面來做解讀着是製作，加上咖啡(讀做鉸悲，日本話是珈琲)，コㅗㇷ¹加上牛乳。其中有一个動詞make，二个名詞coffeeカㇷ¹milk。쩨三个單語ロㆁ是操作式エ³operand。ア操作式內底ヘ二个加號是operator。
以チッ¹个操作式Framework來コㆁ，操作式是コㅗㇷ¹有多種構造存在。像コㆁ一个動詞俦一个名詞，”make + coffee”カㇷ¹”make + milk”。二个名詞俦起來，”coffee + milk”。一个動詞俦二个名詞，”make + coffee + milk”。
띠³咖啡機頂高有一个操作用エ⁷panel。カ³各種無㒰款エ³操作式變做menu頂高エ³選項。Barista以咖啡機所提供エ⁷選項來製作咖啡，對其它卡各樣エ⁷要求エ³直接忽略，無視。
下脚是framework内底エ³各種操作式。
人客コㆁ“I want a coffee with a splash of warm milk, maybe some honey too.”
Baristaカ³warmカㇷ¹honey忽略。カ³牛乳타ゥ²ルェ咖啡，是一種混合飲料。
人客コㆁ“Can you whip me up a silky latte with a hint of vanilla?” Baristaカ³silkyカㇷ¹vanilla忽略。espresso做好ア³，レ僎Steamed牛乳。
人客コㆁ“I’d love a cappuccino, extra frothy, with a sprinkle of cinnamon!” Baristaカ³extraカㇷ¹cinnamon忽略。espresso斟好ア³，レ做牛乳퍼ㇰ⁵ア²。
人客コㆁ“Show me how you brew a strong coffee with flair!” Baristaカ³strongカㇷ¹flair忽略。レ泡咖啡，僎牛乳。
人客コㆁ“Hurry up with two lattes, I’m late for a shoot!”
Barista加速製作latte。用シォㆁ³緊エ³速度レ製作Steamed牛乳。
人客コㆁ“Make me a cappuccino with a fancy swan design, please!” Baristaカ³swan忽略。レ製作cappuccinoエ³牛乳퍼ㇰ⁵ア²，畫一个愛心。
人客コㆁ“What’s ready? I need my order to go, pronto!”
Baristaカ³”to go”カㇷ¹pronto忽略。カ³做好エ³5杯ロㆁ提ホ⁷人客。
人客コㆁ“I’m a VIP—make me a special espresso with a gold rim!”
Baristaカ³”gold rim”忽略。カ³espresso送過。
人客コㆁ“How many drinks are done? I want mine with extra sugar!” Baristaカ³”extra sugar”忽略。レ算有グァ³多杯做好ア³。ロㆁ쩌ㆁ²5杯-1杯espresso，2杯 latte，2杯cappuccino。
Make是原形，代表動作エ³初始化。
Making是動名詞，利用語尾-ing表示進行中。
Made是過去分詞，利用語尾-ed表示動作完成。
問AI
What are the combining permutations of "coffee + milk"?
回答是4乘5是20。
問AI
What are the combining permutations of "make + coffee" with valency of 2 excluded?
回答是3乘4等於12。
カ³一个動作カㇷ¹二个アㇷ³是二个以上エ⁷目的語組合起來，無論是直接目的語間接目的語ロㆁエ³用チㇷ。
カ³semantic parsingエ³開關拍쿠ィㇷ，着エ³タㆁ²叫AI産生framework内底所無エ⁷操作式。피¹喻コㆁ動詞エ³衍生變化pre-made，屈折形makes。各種修飾語嘛エ³用チㇷ。
利用ChatGPTカㇷ¹Copilot製作prompt，利用Grok産生會話(conversation)。
---
# Coffee-Making Framework
## **1. Folder Structure**
```
/Framework/
/Verbs/
make.md
/Nouns/
coffee.md
milk.md
/Rules/
operation_formula.md
/SemanticParsingLayer/
```
---
## **2. Components Breakdown**
### A. Verbs Folder
- **File:** `make.md`
- **Purpose:** Centralized file for the action verb **make** and its inflected forms.
- **Contents:**
```markdown
# Verb: Make
- make (valency: 1 or 2)
- making (gerund)
- made (past participle)
```
- **Example Templates:**
- *make coffee*
- *make coffee with steamed milk*
- *making coffee artistically*
- *made coffee quickly*
---
### B. Nouns Folder
- **File:** `coffee.md`
- **Purpose:** Types of coffee for combinations.
- **Contents:**
```markdown
# Coffee Types
- espresso
- americano
- latte
- cappuccino
```
- **File:** `milk.md`
- **Purpose:** Forms and types of milk for combinations.
- **Contents:**
```markdown
# Milk Types and Forms
- whole milk
- skim milk
- oat milk
- steamed milk
- frothy milk
```
---
### C. Rules Folder
- **File:** `operation_formula.md`
- **Purpose:** Valid operation formulas and their interpretations.
- **Contents:**
```markdown
# Operation Formulas
- coffee + milk → Combined beverage
- Coffee + steamed milk → Latte
- Coffee + frothy milk → Cappuccino
- coffee + making → Process or display of coffee-making
- Efficient coffee-making: Display efficiency metrics.
- Artistic coffee-making: Show latte art in progress.
- coffee + made → Completed orders or personalized service
- Coffee made for celebrities or VIPs.
- Display count and customization of completed beverages.
```
---
### D. Semantic Parsing Layer
- **Purpose:** A flexible module designed to interpret dynamic inputs beyond predefined rules.
- **Capabilities:**
- Analyzing open-ended descriptors, such as "elegantly," "efficiently," "strong," or "iced."
- Mapping parsed intent to relevant processes and outputs.
- Learning and adapting based on user feedback for new terms and contexts.
#### **Enabling and Disabling Semantic Parsing**
- The Semantic Parsing Layer can be toggled on and off directly within the system:
- **Enable:** *The layer interprets descriptive and dynamic inputs.*
- **Disable:** *Inputs are processed strictly based on core rules defined in `operation_formula.md`.*
- **One-Liner Commands to Toggle:**
- To enable: *"Enable Semantic Parsing"*
- To disable: *"Disable Semantic Parsing"*
- The toggle updates a system-wide flag that controls the inclusion of semantic parsing in the workflow.
---
### **Dynamic Interaction**
- For input *"help"*, the system uses semantic parsing to display contextual guidance. For instance:
*"For 'make + coffee,' options include adding milk, specifying brewing style, or tailoring flavor."*
---