mirror of
https://github.com/Hopiu/llm.git
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429 lines
15 KiB
Python
429 lines
15 KiB
Python
from llm import EmbeddingModel, Model, hookimpl
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import llm
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from llm.utils import dicts_to_table_string
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import click
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import datetime
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import openai
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import os
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try:
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from pydantic import field_validator, Field # type: ignore
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except ImportError:
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from pydantic.fields import Field
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from pydantic.class_validators import validator as field_validator # type: ignore [no-redef]
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import requests
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from typing import List, Iterable, Iterator, Optional, Union
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import json
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import yaml
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if os.environ.get("LLM_OPENAI_SHOW_RESPONSES"):
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def log_response(response, *args, **kwargs):
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click.echo(response.text, err=True)
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return response
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openai.requestssession = requests.Session()
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openai.requestssession.hooks["response"].append(log_response)
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@hookimpl
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def register_models(register):
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register(Chat("gpt-3.5-turbo"), aliases=("3.5", "chatgpt"))
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register(Chat("gpt-3.5-turbo-16k"), aliases=("chatgpt-16k", "3.5-16k"))
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register(Chat("gpt-4"), aliases=("4", "gpt4"))
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register(Chat("gpt-4-1106-preview"), aliases=("gpt-4-turbo", "4-turbo", "4t"))
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register(Chat("gpt-4-32k"), aliases=("4-32k",))
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register(
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Completion("gpt-3.5-turbo-instruct", default_max_tokens=256),
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aliases=("3.5-instruct", "chatgpt-instruct"),
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)
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# Load extra models
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extra_path = llm.user_dir() / "extra-openai-models.yaml"
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if not extra_path.exists():
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return
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with open(extra_path) as f:
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extra_models = yaml.safe_load(f)
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for extra_model in extra_models:
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model_id = extra_model["model_id"]
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aliases = extra_model.get("aliases", [])
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model_name = extra_model["model_name"]
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api_base = extra_model.get("api_base")
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api_type = extra_model.get("api_type")
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api_version = extra_model.get("api_version")
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api_engine = extra_model.get("api_engine")
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headers = extra_model.get("headers")
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if extra_model.get("completion"):
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klass = Completion
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else:
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klass = Chat
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chat_model = klass(
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model_id,
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model_name=model_name,
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api_base=api_base,
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api_type=api_type,
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api_version=api_version,
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api_engine=api_engine,
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headers=headers,
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)
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if api_base:
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chat_model.needs_key = None
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if extra_model.get("api_key_name"):
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chat_model.needs_key = extra_model["api_key_name"]
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register(
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chat_model,
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aliases=aliases,
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)
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@hookimpl
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def register_embedding_models(register):
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register(Ada002(), aliases=("ada",))
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class Ada002(EmbeddingModel):
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model_id = "ada-002"
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needs_key = "openai"
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key_env_var = "OPENAI_API_KEY"
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batch_size = 100 # Maybe this should be 2048
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def embed_batch(self, items: Iterable[Union[str, bytes]]) -> Iterator[List[float]]:
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results = openai.Embedding.create(
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input=items, model="text-embedding-ada-002", api_key=self.get_key()
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)["data"]
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return ([float(r) for r in result["embedding"]] for result in results)
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@hookimpl
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def register_commands(cli):
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@cli.group(name="openai")
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def openai_():
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"Commands for working directly with the OpenAI API"
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@openai_.command()
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@click.option("json_", "--json", is_flag=True, help="Output as JSON")
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@click.option("--key", help="OpenAI API key")
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def models(json_, key):
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"List models available to you from the OpenAI API"
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from llm.cli import get_key
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api_key = get_key(key, "openai", "OPENAI_API_KEY")
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response = requests.get(
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"https://api.openai.com/v1/models",
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headers={"Authorization": f"Bearer {api_key}"},
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)
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if response.status_code != 200:
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raise click.ClickException(
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f"Error {response.status_code} from OpenAI API: {response.text}"
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)
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models = response.json()["data"]
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if json_:
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click.echo(json.dumps(models, indent=4))
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else:
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to_print = []
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for model in models:
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# Print id, owned_by, root, created as ISO 8601
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created_str = datetime.datetime.utcfromtimestamp(
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model["created"]
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).isoformat()
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to_print.append(
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{
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"id": model["id"],
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"owned_by": model["owned_by"],
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"created": created_str,
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}
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)
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done = dicts_to_table_string("id owned_by created".split(), to_print)
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print("\n".join(done))
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class Chat(Model):
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needs_key = "openai"
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key_env_var = "OPENAI_API_KEY"
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can_stream: bool = True
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default_max_tokens = None
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class Options(llm.Options):
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temperature: Optional[float] = Field(
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description=(
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"What sampling temperature to use, between 0 and 2. Higher values like "
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"0.8 will make the output more random, while lower values like 0.2 will "
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"make it more focused and deterministic."
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),
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ge=0,
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le=2,
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default=None,
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)
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max_tokens: Optional[int] = Field(
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description="Maximum number of tokens to generate.", default=None
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)
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top_p: Optional[float] = Field(
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description=(
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"An alternative to sampling with temperature, called nucleus sampling, "
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"where the model considers the results of the tokens with top_p "
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"probability mass. So 0.1 means only the tokens comprising the top "
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"10% probability mass are considered. Recommended to use top_p or "
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"temperature but not both."
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),
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ge=0,
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le=1,
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default=None,
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)
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frequency_penalty: Optional[float] = Field(
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description=(
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"Number between -2.0 and 2.0. Positive values penalize new tokens based "
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"on their existing frequency in the text so far, decreasing the model's "
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"likelihood to repeat the same line verbatim."
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),
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ge=-2,
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le=2,
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default=None,
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)
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presence_penalty: Optional[float] = Field(
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description=(
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"Number between -2.0 and 2.0. Positive values penalize new tokens based "
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"on whether they appear in the text so far, increasing the model's "
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"likelihood to talk about new topics."
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),
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ge=-2,
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le=2,
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default=None,
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)
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stop: Optional[str] = Field(
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description=("A string where the API will stop generating further tokens."),
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default=None,
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)
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logit_bias: Optional[Union[dict, str]] = Field(
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description=(
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"Modify the likelihood of specified tokens appearing in the completion. "
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'Pass a JSON string like \'{"1712":-100, "892":-100, "1489":-100}\''
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),
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default=None,
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)
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seed: Optional[int] = Field(
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description="Integer seed to attempt to sample deterministically",
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default=None,
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)
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@field_validator("logit_bias")
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def validate_logit_bias(cls, logit_bias):
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if logit_bias is None:
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return None
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if isinstance(logit_bias, str):
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try:
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logit_bias = json.loads(logit_bias)
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except json.JSONDecodeError:
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raise ValueError("Invalid JSON in logit_bias string")
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validated_logit_bias = {}
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for key, value in logit_bias.items():
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try:
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int_key = int(key)
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int_value = int(value)
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if -100 <= int_value <= 100:
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validated_logit_bias[int_key] = int_value
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else:
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raise ValueError("Value must be between -100 and 100")
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except ValueError:
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raise ValueError("Invalid key-value pair in logit_bias dictionary")
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return validated_logit_bias
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def __init__(
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self,
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model_id,
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key=None,
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model_name=None,
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api_base=None,
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api_type=None,
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api_version=None,
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api_engine=None,
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headers=None,
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):
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self.model_id = model_id
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self.key = key
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self.model_name = model_name
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self.api_base = api_base
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self.api_type = api_type
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self.api_version = api_version
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self.api_engine = api_engine
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self.headers = headers
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def __str__(self):
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return "OpenAI Chat: {}".format(self.model_id)
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def execute(self, prompt, stream, response, conversation=None):
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messages = []
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current_system = None
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if conversation is not None:
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for prev_response in conversation.responses:
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if (
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prev_response.prompt.system
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and prev_response.prompt.system != current_system
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):
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messages.append(
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{"role": "system", "content": prev_response.prompt.system}
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)
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current_system = prev_response.prompt.system
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messages.append(
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{"role": "user", "content": prev_response.prompt.prompt}
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)
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messages.append({"role": "assistant", "content": prev_response.text()})
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if prompt.system and prompt.system != current_system:
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messages.append({"role": "system", "content": prompt.system})
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messages.append({"role": "user", "content": prompt.prompt})
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response._prompt_json = {"messages": messages}
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kwargs = self.build_kwargs(prompt)
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if stream:
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completion = openai.ChatCompletion.create(
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model=self.model_name or self.model_id,
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messages=messages,
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stream=True,
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**kwargs,
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)
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chunks = []
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for chunk in completion:
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chunks.append(chunk)
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content = chunk["choices"][0].get("delta", {}).get("content")
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if content is not None:
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yield content
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response.response_json = combine_chunks(chunks)
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else:
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completion = openai.ChatCompletion.create(
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model=self.model_name or self.model_id,
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messages=messages,
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stream=False,
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**kwargs,
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)
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response.response_json = completion.to_dict_recursive()
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yield completion.choices[0].message.content
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def build_kwargs(self, prompt):
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kwargs = dict(not_nulls(prompt.options))
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if "max_tokens" not in kwargs and self.default_max_tokens is not None:
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kwargs["max_tokens"] = self.default_max_tokens
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if self.api_base:
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kwargs["api_base"] = self.api_base
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if self.api_type:
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kwargs["api_type"] = self.api_type
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if self.api_version:
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kwargs["api_version"] = self.api_version
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if self.api_engine:
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kwargs["engine"] = self.api_engine
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if self.needs_key:
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if self.key:
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kwargs["api_key"] = self.key
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else:
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# OpenAI-compatible models don't need a key, but the
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# openai client library requires one
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kwargs["api_key"] = "DUMMY_KEY"
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if self.headers:
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kwargs["headers"] = self.headers
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return kwargs
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class Completion(Chat):
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class Options(Chat.Options):
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logprobs: Optional[int] = Field(
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description="Include the log probabilities of most likely N per token",
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default=None,
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le=5,
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)
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def __init__(self, *args, default_max_tokens=None, **kwargs):
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super().__init__(*args, **kwargs)
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self.default_max_tokens = default_max_tokens
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def __str__(self):
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return "OpenAI Completion: {}".format(self.model_id)
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def execute(self, prompt, stream, response, conversation=None):
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if prompt.system:
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raise NotImplementedError(
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"System prompts are not supported for OpenAI completion models"
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)
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messages = []
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if conversation is not None:
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for prev_response in conversation.responses:
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messages.append(prev_response.prompt.prompt)
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messages.append(prev_response.text())
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messages.append(prompt.prompt)
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response._prompt_json = {"messages": messages}
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kwargs = self.build_kwargs(prompt)
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if stream:
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completion = openai.Completion.create(
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model=self.model_name or self.model_id,
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prompt="\n".join(messages),
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stream=True,
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**kwargs,
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)
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chunks = []
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for chunk in completion:
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chunks.append(chunk)
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content = chunk["choices"][0].get("text") or ""
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if content is not None:
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yield content
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response.response_json = combine_chunks(chunks)
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else:
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completion = openai.Completion.create(
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model=self.model_name or self.model_id,
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prompt="\n".join(messages),
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stream=False,
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**kwargs,
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)
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response.response_json = completion.to_dict_recursive()
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yield completion.choices[0]["text"]
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def not_nulls(data) -> dict:
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return {key: value for key, value in data if value is not None}
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def combine_chunks(chunks: List[dict]) -> dict:
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content = ""
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role = None
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finish_reason = None
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# If any of them have log probability, we're going to persist
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# those later on
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logprobs = []
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for item in chunks:
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for choice in item["choices"]:
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if (
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"logprobs" in choice
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and "text" in choice
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and isinstance(choice["logprobs"], dict)
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and "top_logprobs" in choice["logprobs"]
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):
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logprobs.append(
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{
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"text": choice["text"],
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"top_logprobs": choice["logprobs"]["top_logprobs"],
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}
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)
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if "text" in choice and "delta" not in choice:
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content += choice["text"]
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continue
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if "role" in choice["delta"]:
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role = choice["delta"]["role"]
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if "content" in choice["delta"]:
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content += choice["delta"]["content"]
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if choice.get("finish_reason") is not None:
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finish_reason = choice["finish_reason"]
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# Imitations of the OpenAI API may be missing some of these fields
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combined = {
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"content": content,
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"role": role,
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"finish_reason": finish_reason,
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}
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if logprobs:
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combined["logprobs"] = logprobs
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for key in ("id", "object", "model", "created", "index"):
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if key in chunks[0]:
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combined[key] = chunks[0][key]
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return combined
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