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93 lines
3.2 KiB
Python
93 lines
3.2 KiB
Python
import json, uuid, requests
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from ..typing import Any, CreateResult
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from .base_provider import BaseProvider
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class H2o(BaseProvider):
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url = "https://gpt-gm.h2o.ai"
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working = True
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supports_stream = True
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model = "h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v1"
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@staticmethod
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def create_completion(
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model: str,
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messages: list[dict[str, str]],
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stream: bool, **kwargs: Any) -> CreateResult:
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conversation = ""
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for message in messages:
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conversation += "%s: %s\n" % (message["role"], message["content"])
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conversation += "assistant: "
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session = requests.Session()
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headers = {"Referer": "https://gpt-gm.h2o.ai/r/jGfKSwU"}
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data = {
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"ethicsModalAccepted" : "true",
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"shareConversationsWithModelAuthors": "true",
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"ethicsModalAcceptedAt" : "",
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"activeModel" : model,
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"searchEnabled" : "true",
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}
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session.post("https://gpt-gm.h2o.ai/settings",
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headers=headers, data=data)
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headers = {"Referer": "https://gpt-gm.h2o.ai/"}
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data = {"model": model}
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response = session.post("https://gpt-gm.h2o.ai/conversation",
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headers=headers, json=data).json()
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if "conversationId" not in response:
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return
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data = {
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"inputs": conversation,
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"parameters": {
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"temperature" : kwargs.get("temperature", 0.4),
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"truncate" : kwargs.get("truncate", 2048),
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"max_new_tokens" : kwargs.get("max_new_tokens", 1024),
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"do_sample" : kwargs.get("do_sample", True),
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"repetition_penalty": kwargs.get("repetition_penalty", 1.2),
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"return_full_text" : kwargs.get("return_full_text", False),
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},
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"stream" : True,
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"options": {
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"id" : kwargs.get("id", str(uuid.uuid4())),
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"response_id" : kwargs.get("response_id", str(uuid.uuid4())),
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"is_retry" : False,
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"use_cache" : False,
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"web_search_id": "",
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},
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}
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response = session.post(f"https://gpt-gm.h2o.ai/conversation/{response['conversationId']}",
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headers=headers, json=data)
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response.raise_for_status()
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response.encoding = "utf-8"
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generated_text = response.text.replace("\n", "").split("data:")
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generated_text = json.loads(generated_text[-1])
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yield generated_text["generated_text"]
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@classmethod
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@property
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def params(cls):
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params = [
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("model", "str"),
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("messages", "list[dict[str, str]]"),
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("stream", "bool"),
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("temperature", "float"),
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("truncate", "int"),
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("max_new_tokens", "int"),
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("do_sample", "bool"),
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("repetition_penalty", "float"),
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("return_full_text", "bool"),
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]
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param = ", ".join([": ".join(p) for p in params])
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return f"g4f.provider.{cls.__name__} supports: ({param})"
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