👀 What is llm-linker?

llm-linker hope to connect natural language requests with relevant structured actions or responses through the semantic understanding ability of LLM.

graph LR
task_prompt-->|embedding| index_map --> actions.->command.-> image_gen
command.-> cv_task
command.-> program
command.-> ...
query_prompt-->|embedding| index_map 
actions.->reply
actions.->regen .-> more_context

Loading

TODOs

• support requests type, for directly link to OpenAPIs.
• support linkDB, to store links locally.
• combine with Decision Tree ?

👉 Try it

# Get Apikey on https://llmapi.io

python3 run.py --apikey=your_api_key --links=links.txt --query='what is the time?'

# some.sh will be executed

python3 run.py --links=links.txt --query="写一个登录页面" --apikey=********************************

result:

🎨 Linker schema

During runtime，user input query prompt, prompt encoded as embedding，then match with linker's task，finally you can do anything through actions with simple language prompt.

User's query prompt is always provided as a parameter to actions.

The following linker description as a example:

{
    "task" : "play music 'ten years'",
    "actions" : [
        {
            "type" : "command",
            "content" : "./play.sh",
            "input" : "_NONE_"
        }
     ]
}

The task in linker is a prompt to be encoded as embedding，then the embedding index map with actions. The actions described in detail what to do when user's query matched task. The type in actions should be reply command or regen:

• reply: you can pin the content of replies when task was matched.
• command: you can run a set of scripts or local programs when task was matched.
• regen: you can re-ask LLM (such as chatgpt) based on task input to get new responses when task was matched. The content in actions is your reply content or shell or prompt for regen. The input in actions means if your content need a input when proccessing:_NONE_,_PROMPT_,_OUTPUT_.

some linker demos

1. Using reply type

{
    "task" : "tell your name", "actions" : [{ "type" : "reply", "content" : "I'm llm-linker!","input":"_NONE_"}]
}

user's input: what's your name?

linker's output: I'm llm-linker!

2. Using command type

{
    "task" : "get current time", "actions" : [{ "type" : "command", "content" : "./get_time.sh","input":"_NONE_"}]
}

get_time.sh:

#! /bin/sh
date +"%Y-%m-%d %H:%M:%S"

user's input: what's the time?

linker's output: 2023-04-11 11:22:33

2. Using regen type

{
    "task" : "do math addition", "actions" : [{ "type" : "regen", "content" : "Calculate step by step:","input":"_PROMPT_"}]
}

user's input: 1+1=?

linker's output(use chatgpt):

Sure, I'd be happy to help you calculate step by step!
1 + 1 = 2
Explanation:
1. Start with the leftmost number: 1 + 1
2. Add the two numbers together: 1 + 1
3. The answer is 2. 

📜 Interface description

Very simple, only TWO api

The only thing you need to use is the Linker class.

Interface in Linker:

1. add (link:dict)

use this interface to dynamic add linkers:

link = Linker(apikey='xxx') # apikey can get on llmapi.io, use it to talk with LLMs online.
link.add({"task" : "tell your name", "actions" : [{"type" : "reply", "content" : "I'm llm-linker!", "input":"_NONE_"} ]})

# Done!

2. act (prompt:str, match_thresh:float = 0.7)

use this interface to match task and execute actions for user's query:

match_thresh is the threshold for embedding similarity

link.act('what is your name?')

# Done!

🌟 If you like this project, please follow us and star it, let's play together! ❤️