nostrdvm/nostr_dvm/tasks/textgeneration_llmlite.py

125 lines
4.7 KiB
Python

import json
import os
from nostr_sdk import Kind
from nostr_dvm.interfaces.dvmtaskinterface import DVMTaskInterface, process_venv
from nostr_dvm.utils.admin_utils import AdminConfig
from nostr_dvm.utils.definitions import EventDefinitions
from nostr_dvm.utils.dvmconfig import DVMConfig, build_default_config
from nostr_dvm.utils.nip88_utils import NIP88Config
from nostr_dvm.utils.nip89_utils import NIP89Config, check_and_set_d_tag
"""
This File contains a Module to generate Text, based on a prompt using a LLM (local or API) (Ollama, custom model, chatgpt)
Accepted Inputs: Prompt (text)
Outputs: Generated text
"""
class TextGenerationLLMLite(DVMTaskInterface):
KIND: Kind = EventDefinitions.KIND_NIP90_GENERATE_TEXT
TASK: str = "text-to-text"
FIX_COST: float = 0
dependencies = [("nostr-dvm", "nostr-dvm"),
("litellm", "litellm==1.12.3")]
async def init_dvm(self, name, dvm_config: DVMConfig, nip89config: NIP89Config, nip88config: NIP88Config = None,
admin_config: AdminConfig = None, options=None):
dvm_config.SCRIPT = os.path.abspath(__file__)
async def is_input_supported(self, tags, client=None, dvm_config=None):
for tag in tags:
if tag.as_vec()[0] == 'i':
input_value = tag.as_vec()[1]
input_type = tag.as_vec()[2]
if input_type != "text":
return False
return True
async def create_request_from_nostr_event(self, event, client=None, dvm_config=None):
request_form = {"jobID": event.id().to_hex() + "_" + self.NAME.replace(" ", "")}
prompt = ""
if self.options.get("default_model") and self.options.get("default_model") != "":
model = self.options['default_model']
else:
model = "gpt-3.5-turbo" # "gpt-4-1106-preview" # This will call chatgpt and requires an OpenAI API Key set in .env
if self.options.get("server") and self.options.get("server") != "":
server = self.options['server']
else:
server = "http://localhost:11434" # default ollama server. This will only be used for ollama models.
for tag in event.tags().to_vec():
if tag.as_vec()[0] == 'i':
input_type = tag.as_vec()[2]
if input_type == "text":
prompt = tag.as_vec()[1]
options = {
"prompt": prompt,
"model": model,
"server": server
}
request_form['options'] = json.dumps(options)
return request_form
async def process(self, request_form):
from litellm import completion
options = self.set_options(request_form)
try:
if options["model"].startswith("ollama"):
response = completion(
model=options["model"],
messages=[{"content": options["prompt"], "role": "user"}],
api_base=options["server"],
stream=False
)
print(response.choices[0].message.content)
return response.choices[0].message.content
else:
response = completion(
model=options["model"],
messages=[{"content": options["prompt"], "role": "user"}],
)
print(response.choices[0].message.content)
return response.choices[0].message.content
except Exception as e:
print("Error in Module: " + str(e))
raise Exception(e)
# We build an example here that we can call by either calling this file directly from the main directory,
# or by adding it to our playground. You can call the example and adjust it to your needs or redefine it in the
# playground or elsewhere
def build_example(name, identifier, admin_config):
dvm_config = build_default_config(identifier)
admin_config.LUD16 = dvm_config.LN_ADDRESS
options = {'default_model': "ollama/llama2-uncensored", 'server': "http://localhost:11434"}
nip89info = {
"name": name,
"picture": "https://image.nostr.build/c33ca6fc4cc038ca4adb46fdfdfda34951656f87ee364ef59095bae1495ce669.jpg",
"about": "I use a LLM connected via OLLAMA",
"supportsEncryption": True,
"acceptsNutZaps": dvm_config.ENABLE_NUTZAP,
"nip90Params": {}
}
nip89config = NIP89Config()
nip89config.DTAG = check_and_set_d_tag(identifier, name, dvm_config.PRIVATE_KEY, nip89info["picture"])
nip89config.CONTENT = json.dumps(nip89info)
return TextGenerationLLMLite(name=name, dvm_config=dvm_config, nip89config=nip89config, admin_config=admin_config,
options=options)
if __name__ == '__main__':
process_venv(TextGenerationLLMLite)