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https://github.com/antononcube/raku-llm-prompts

Raku package facilitating the creation, storage, retrieval, and curation of LLM prompts.
https://github.com/antononcube/raku-llm-prompts

database dsl large-language-models llm prompt-engineering raku rakulang

Last synced: 15 days ago
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Raku package facilitating the creation, storage, retrieval, and curation of LLM prompts.

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README 

        
# LLM::Prompts



## In brief



This repository is for a Raku (data) package facilitating the creation, storage, retrieval, and curation of 

[Large Language Models (LLM) prompts](https://en.wikipedia.org/wiki/Prompt_engineering).



Here is an example of using the prompt Domain Specific Language (DSL) in Jupyter chatbook, [AA2, AAp2]:



![](https://raw.githubusercontent.com/antononcube/Raku-LLM-Prompts/main/docs/Raku-LLM-Prompt-demo-Yoda-1.png)



----



## Installation



From Zef' ecosystem:



```

zef install LLM::Prompts

```



From GitHub:



```

zef install https://github.com/antononcube/Raku-LLM-Prompts.git

```



-----



## Usage examples



### Retrieval



Load the packages "LLM::Prompts", [AAp1], and "LLM::Functions", [AAp2]:



```perl6

use LLM::Prompts;

use LLM::Functions;

```



Show the record of the prompt named "FTFY":



```perl6

.say for |llm-prompt-data;

```



Here is an example of retrieval of prompt data with a regex that is applied over the prompt names:



```perl6

.say for llm-prompt-data(/Sc/)

```



More prompt retrieval examples are given in the section "Prompt data" below.



### LLM functions based on prompts



Make an LLM function from the prompt named "FTFY":



```perl6

my &f = llm-function(llm-prompt('FTFY'));

```



Use the LLM function to correct the grammar of sentence:



```perl6

&f('Where does he works now?')

```



Generate Raku code using the prompt "CodeWriter":



```perl6, output-lang=perl6, output-prompt=NONE

llm-synthesize([llm-prompt('CodeWriter'), "Simulate a random walk."])

```



### Prompt expansion



Prompt expansion using the chatbook prompt spec DSL described in [SW1] 

can be done using the function `llm-prompt-expand`:  



```perl6

llm-prompt-expand('What is an internal combustion engine? #ELI5')

```



Here we get the actual LLM answer:



```perl6

use Text::Utils :ALL;



'What is an internal combustion engine? #ELI5' 

        ==> llm-prompt-expand() 

        ==> llm-synthesize() 

        ==> wrap-paragraph() 

        ==> join("\n") 

```



Here is another example using a persona and two modifiers:



```perl6

my $prmt = llm-prompt-expand("@SouthernBelleSpeak What is light travel distance to Mars? #ELI5 #Moodified|sad")

```



Here we get the actual LLM answer:



```perl6

$prmt 

        ==> llm-prompt-expand() 

        ==> llm-synthesize()

        ==> wrap-paragraph()

        ==> join("\n") 

```



-----



## Prompt spec DSL



A more formal description of the Domain Specific Language (DSL) for specifying prompts

has the following elements: 



- Prompt personas can be "addressed" with "@". For example:



```

@Yoda Life can be easy, but some people instist for it to be difficult.

```



- One or several modifier prompts can be specified at the end of the prompt spec. For example:



```

Summer is over, school is coming soon. #HaikuStyled

```



```

Summer is over, school is coming soon. #HaikuStyled #Translated|Russian

```



- Functions can be specified to be applied "cell-wide" with "!" and placing the prompt spec at

  the start of the prompt spec to be expanded. For example:



```

!Translated|Portuguese Summer is over, school is coming soon

```



- Functions can be specified to be applied to "previous" messages with "!" and 

  placing just the prompt with one of the pointers "^" or "^^". 

  The former means "the last message", the latter means "all messages."

    - The messages can be provided with the option argument `:@messages` of `llm-prompt-expand`.

- For example:



```

!ShortLineIt^

```



- Here is a table of prompt expansion specs (more or less the same as the one in [SW1]):



| Spec               | Interpretation                                      |

|:-------------------|:----------------------------------------------------|

| @*name*            | Direct chat to a persona                            |

| #*name*            | Use modifier prompts                                |

| !*name*            | Use function prompt with the input of current cell  |

| !*name*>           | *«same as above»*                                   |

| &*name*>           | *«same as above»*                                   |

| !*name*^           | Use function prompt with previous chat message      |

| !*name*^^          | Use function prompt with all previous chat messages |

| !*name*￨*param*... | Include parameters for prompts                      |



**Remark:** The function prompts can have both sigils "!" and "&".



**Remark:** Prompt expansion make the usage of LLM-chatbooks much easier.

See "Jupyter::Chatbook", [AAp3].



-----



## Prompt data



Here is how the prompt data can be obtained:



```perl6

llm-prompt-data.elems

```



Here is an example of retrieval of prompt data with a regex that is applied over the prompt names:



```perl6

.say for llm-prompt-data(/Em/, fields => )

```



In many cases it is better to have the prompt data -- or any data -- in long format.

Prompt data in long format can be obtained with the function `llm-prompt-dataset`:



```perl6

use Data::Reshapers;

use Data::Summarizers;



llm-prompt-dataset.pick(6)

        ==> to-pretty-table(align => 'l', field-names => )

```



Here is a breakdown of the prompts categories:



```perl6

select-columns(llm-prompt-dataset, ).grep({ $_ eq 'Categories' }) ==> records-summary

```



Here are obtained all modifier prompts in compact format:



```perl6

llm-prompt-dataset():modifiers:compact ==> to-pretty-table(field-names => , align => 'l')

```



**Remark:** The adverbs `:functions`, `:modifiers`, and `:personas` mean 

that *only* the prompts with the corresponding categories will be returned.



**Remark:** The adverbs `:compact`, `:functions`, `:modifiers`, and `:personas` 

have the respective shortcuts `:c`, `:f`, `:m`, and `:p`.



-----



## Implementation notes



### Prompt collection



The original (for this package) collection of prompts was a (not small) sample of the prompt texts

hosted at [Wolfram Prompt Repository](https://resources.wolframcloud.com/PromptRepository/) (WPR), [SW2].

All prompts from WPR in the package have the corresponding contributors and URLs to the corresponding WPR pages.  



Example prompts from Google/Bard/PaLM and ~~OpenAI/ChatGPT~~ are added using the format of WPR. 



### Extending the prompt collection



It is essential to have the ability to programmatically add new prompts.

(Not implemented yet -- see the TODO section below.)



### Prompt expansion



Initially prompt DSL grammar and corresponding expansion actions were implemented.

Having a grammar is most likely not needed, though, and it is better to use "prompt expansion" (via regex-based substitutions.)



Prompts can be "just expanded" using the sub `llm-prompt-expand`. 



### Usage in chatbooks



Here is a flowchart that summarizes prompt parsing and expansion in chat cells of Jupyter chatbooks, [AAp3]:



```mermaid

flowchart LR

    OpenAI{{OpenAI}}

    PaLM{{PaLM}}

    LLMFunc[[LLM::Functions]]

    LLMProm[[LLM::Prompts]]

    CODB[(Chat objects)]

    PDB[(Prompts)]

    CCell[/Chat cell/]

    CRCell[/Chat result cell/]

    CIDQ{Chat ID
specified?}

    CIDEQ{Chat ID
exists in DB?}

    RECO[Retrieve existing
chat object]

    COEval[Message
evaluation]

    PromParse[Prompt
DSL spec parsing]

    KPFQ{Known
prompts
found?}

    PromExp[Prompt
expansion]

    CNCO[Create new
chat object]

    CIDNone["Assume chat ID
is 'NONE'"] 

    subgraph Chatbook frontend    

        CCell

        CRCell

    end

    subgraph Chatbook backend

        CIDQ

        CIDEQ

        CIDNone

        RECO

        CNCO

        CODB

    end

    subgraph Prompt processing

        PDB

        LLMProm

        PromParse

        KPFQ

        PromExp 

    end

    subgraph LLM interaction

      COEval

      LLMFunc

      PaLM

      OpenAI

    end

    CCell --> CIDQ

    CIDQ --> |yes| CIDEQ

    CIDEQ --> |yes| RECO

    RECO --> PromParse

    COEval --> CRCell

    CIDEQ -.- CODB

    CIDEQ --> |no| CNCO

    LLMFunc -.- CNCO -.- CODB

    CNCO --> PromParse --> KPFQ

    KPFQ --> |yes| PromExp

    KPFQ --> |no| COEval

    PromParse -.- LLMProm 

    PromExp -.- LLMProm

    PromExp --> COEval 

    LLMProm -.- PDB

    CIDQ --> |no| CIDNone

    CIDNone --> CIDEQ

    COEval -.- LLMFunc

    LLMFunc <-.-> OpenAI

    LLMFunc <-.-> PaLM

```



Here is an example of prompt expansion in a generic LLM chat cell and chat meta cell 

showing the content of the corresponding chat object:



![](https://raw.githubusercontent.com/antononcube/Raku-LLM-Prompts/main/docs/Raku-LLM-Prompt-demo-Yoda-2.png)



-------



## Command Line Interface



### Playground access



The package provides a Command Line Interface (CLI) script:



```shell

llm-prompt --help

```



Here is an example with a prompt name:



```shell

llm-prompt NothingElse RAKU

```



Here is an example with a regex:



```shell

llm-prompt 'rx/ ^ N .* /'

```



-----



## TODO



- [ ] TODO Implementation

  - [X] DONE Prompt retrieval adverbs

  - [X] DONE Prompt DSL grammar and actions

  - [X] DONE Prompt spec expansion

  - [X] DONE CLI for prompt retrieval

  - [ ] MAYBE CLI for prompt dataset

  - [ ] TODO Addition of user/local prompts 

    - [X] DONE Using XDG data directory.

    - [X] DONE Prompt stencil

    - [X] DONE User prompt ingestion and addition to the main prompts

    - [ ] TODO By modifying existing prompts.

    - [ ] TODO Automatic prompt template fill-in.

    - [ ] TODO Guided template fill-in.

      - [ ] TODO DSL based

      - [ ] TODO LLM based

- [X] DONE Add more prompts

  - [X] DONE Google's Bard example prompts

  - [X] CANCELED OpenAI's ChatGPT example prompts

  - [X] [ProfSynapse prompt](https://github.com/ProfSynapse/Synapse_CoR)

  - [X] Google [OR-Tools](https://developers.google.com/optimization) prompt

- [ ] TODO Documentation

  - [X] DONE Querying (ingested) prompts

  - [X] DONE Prompt DSL

  - [X] DONE Daily joke via CLI

  - [ ] TODO Prompt format

  - [ ] TODO On hijacking prompts

  - [ ] TODO Diagrams

    - [X] DONE Chatbook usage 

    - [ ] Typical usage



-----



## References



### Articles



[AA1] Anton Antonov,

["Workflows with LLM functions"](https://rakuforprediction.wordpress.com/2023/08/01/workflows-with-llm-functions/),

(2023),

[RakuForPrediction at WordPress](https://rakuforprediction.wordpress.com).



[SW1] Stephen Wolfram,

["The New World of LLM Functions: Integrating LLM Technology into the Wolfram Language"](https://writings.stephenwolfram.com/2023/05/the-new-world-of-llm-functions-integrating-llm-technology-into-the-wolfram-language/),

(2023),

[Stephen Wolfram Writings](https://writings.stephenwolfram.com).



[SW2] Stephen Wolfram,

["Prompts for Work & Play: Launching the Wolfram Prompt Repository"](https://writings.stephenwolfram.com/2023/06/prompts-for-work-play-launching-the-wolfram-prompt-repository/),

(2023),

[Stephen Wolfram Writings](https://writings.stephenwolfram.com).



### Packages, paclets, repositories



[AAp1] Anton Antonov,

[LLM::Prompts Raku package](https://github.com/antononcube/Raku-LLM-Prompts),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp2] Anton Antonov,

[LLM::Functions Raku package](https://github.com/antononcube/Raku-LLM-Functions),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[AAp3] Anton Antonov,

[Jupyter::Chatbook Raku package](https://github.com/antononcube/Raku-Jupyter-Chatbook),

(2023),

[GitHub/antononcube](https://github.com/antononcube).



[WRIr1] Wolfram Research, Inc.,

[Wolfram Prompt Repository](https://resources.wolframcloud.com/PromptRepository)