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