Use LLMs inside Prolog!
pllm
is a minimal SWI-Prolog helper that exposes llm/2
. The predicate posts a prompt to an HTTP LLM endpoint and unifies the model's response text with the second argument.
The library currently supports any OpenAI-compatible chat/completions endpoint.
?- pack_install(pllm).
Some services require an API key for authentication.
Set the LLM_API_KEY
environment variable to your API key. You can do the following in your shell before starting SWI-Prolog:
echo LLM_API_KEY="sk-..." >> .env
set -a && source .env && set +a
Configure the endpoint and default model before calling llm/2
or llm/3
:
?- config("https://api.openai.com/v1/chat/completions", "gpt-4o-mini").
You can override the configured model per call with llm/3
options.
set -a && souce .env && set +a
swipl
?- [prolog/llm].
?- llm("Say hello in French.", Output).
Output = "Bonjour !".
?- llm("Say hello in French.", Output, [model("gpt-4o-mini"), timeout(30)]).
Output = "Bonjour !".
?- llm(Prompt, "Dog").
Prompt = "What animal is man's best friend?",
...
This library expects an OpenAI-compatible chat/completions endpoint. Below are common providers and endpoints you can try.
OpenAI
-
Endpoint:
https://api.openai.com/v1/chat/completions -
Example:
?- config("https://api.openai.com/v1/chat/completions", "gpt-4o-mini").
Ollama (local)
-
Endpoint:
http://localhost:11434/v1/chat/completions -
Example:
?- config("http://localhost:11434/v1/chat/completions", "llama3.1").
If you call llm/2
with an unbound first argument and a concrete response,
the library first asks the LLM to suggest a prompt that would (ideally)
produce that response, binds it to your variable, and then sends a second
request that wraps the suggested prompt in a hard constraint ("answer only with ..."
). This costs two API calls and is still best-effort; the model may ignore the constraint, in which case the predicate simply fails.