lambdaprompt 0.0.2

A functional programming interface for building AI systems

λprompt - Turn prompts into functions

UNDER CONSTRUCTION

pip install lambdaprompt

lambdaprompt is a python package, ...

• minimalistic API
• functional helpers
• create complex and emergent behavior

For using openAI, set up API keys as environment variables or set after importing (also easy to just make a .env file, since this uses dotenv package)

OPENAI_API_KEY=...

import lambdaprompt; lambdaprompt.setup(openai_api_key=’...’)

Creating a prompt

Prompts use JINJA templating to create a string, the string is passed to the LLM for completion.

from lambdaprompt import asyncGPT3Prompt

deeper_website_choice = asyncGPT3Prompt(
    "deeper_website_choice",
    """
For the question [{{ question }}], the search results are
{{ search_results }}
In order to answer the question, which three page indices (0-9 from above) should be further investigated? (eg. [2, 7, 9])
[""",
    stop="]",
)

You can also turn any function into a prompt (useful for composing prompts, or creating programs out of prompts.

from lambdaprompt import prompt

@prompt
def standard_function(text_input):
    res = is_a_question(text_input)
    if res.lower().startswith('yes'):
        return answer_the_question(text_input)
    else:
        return "That was not a question, please try again"

Using a prompt

await deeper_website_choice(question="What is the capital of France?", search_results="0: result 0, 1: france, 2: another thing, ...")

Some special properties

1. For prompts with only a single variable, can directly call with the variable as args (no need to define in kwarg)

basic_qa = asyncGPT3Prompt("basic_qa", """What is the answer to the question [{{ question }}]?""")

await basic_qa("Is it safe to eat pizza with chopsticks?")

2. You can use functional primatives to create more complex prompts

print(*map(basic_qa, ["Is it safe to eat pizza with chopsticks?", "What is the capital of France?"]))

3. You can apply these to pandas dataframes to do analytics quickly using LLMs

import pandas as pd
from lambdaprompt import GPT3Prompt

df = pd.DataFrame({'country': ["France", "Japan", "USA"]})
df['capitals'] = df.apply(GPT3Prompt("basic_qa", """What is the capital of {{ country }}?"""), axis=1)

Bonus

There is also a GPT3Edit class, that can be used to edit text.

Advanced usage

Pre-and-post call hooks (tracing and logging) --> This is just on all the time right now... it makes a sqlitedb.

lambdaprompt.register(pre=print, post=print)

Lightweight web-server for calling prompts (useful for attaching to JS webapps)

uvicorn lambdaprompt.promptapi:app --reload

Design Patterns

• Response Optimization
  • Ideation, Scoring and Selection
  • Error Correcting Language Loops
• Summarization and Aggregations
  • Rolling
  • k-tree
• Meta-Prompting

Contributions are welcome

Contributing