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612 lines
22 KiB
Python
612 lines
22 KiB
Python
from __future__ import annotations
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import logging
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import os
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import sys
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from typing import (
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AbstractSet,
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Any,
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AsyncIterator,
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Collection,
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Dict,
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Iterator,
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List,
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Literal,
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Mapping,
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Optional,
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Set,
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Tuple,
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Union,
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)
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import openai
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import tiktoken
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.llms import BaseLLM
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from langchain_core.outputs import Generation, GenerationChunk, LLMResult
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from langchain_core.pydantic_v1 import Field, root_validator
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from langchain_core.utils import get_from_dict_or_env, get_pydantic_field_names
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from langchain_core.utils.utils import build_extra_kwargs
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logger = logging.getLogger(__name__)
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def _update_token_usage(
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keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
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) -> None:
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"""Update token usage."""
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_keys_to_use = keys.intersection(response["usage"])
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for _key in _keys_to_use:
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if _key not in token_usage:
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token_usage[_key] = response["usage"][_key]
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else:
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token_usage[_key] += response["usage"][_key]
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def _stream_response_to_generation_chunk(
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stream_response: Dict[str, Any],
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) -> GenerationChunk:
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"""Convert a stream response to a generation chunk."""
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if not stream_response["choices"]:
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return GenerationChunk(text="")
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return GenerationChunk(
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text=stream_response["choices"][0]["text"],
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generation_info=dict(
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finish_reason=stream_response["choices"][0].get("finish_reason", None),
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logprobs=stream_response["choices"][0].get("logprobs", None),
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),
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)
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class BaseOpenAI(BaseLLM):
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"""Base OpenAI large language model class."""
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"openai_api_key": "OPENAI_API_KEY"}
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@property
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def lc_attributes(self) -> Dict[str, Any]:
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attributes: Dict[str, Any] = {}
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if self.openai_api_base:
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attributes["openai_api_base"] = self.openai_api_base
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if self.openai_organization:
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attributes["openai_organization"] = self.openai_organization
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if self.openai_proxy:
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attributes["openai_proxy"] = self.openai_proxy
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return attributes
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client: Any = Field(default=None, exclude=True) #: :meta private:
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async_client: Any = Field(default=None, exclude=True) #: :meta private:
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model_name: str = Field(default="gpt-3.5-turbo-instruct", alias="model")
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"""Model name to use."""
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temperature: float = 0.7
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"""What sampling temperature to use."""
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max_tokens: int = 256
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"""The maximum number of tokens to generate in the completion.
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-1 returns as many tokens as possible given the prompt and
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the models maximal context size."""
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top_p: float = 1
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"""Total probability mass of tokens to consider at each step."""
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frequency_penalty: float = 0
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"""Penalizes repeated tokens according to frequency."""
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presence_penalty: float = 0
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"""Penalizes repeated tokens."""
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n: int = 1
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"""How many completions to generate for each prompt."""
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best_of: int = 1
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"""Generates best_of completions server-side and returns the "best"."""
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model_kwargs: Dict[str, Any] = Field(default_factory=dict)
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"""Holds any model parameters valid for `create` call not explicitly specified."""
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# When updating this to use a SecretStr
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# Check for classes that derive from this class (as some of them
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# may assume openai_api_key is a str)
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openai_api_key: Optional[str] = Field(default=None, alias="api_key")
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"""Automatically inferred from env var `OPENAI_API_KEY` if not provided."""
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openai_api_base: Optional[str] = Field(default=None, alias="base_url")
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"""Base URL path for API requests, leave blank if not using a proxy or service
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emulator."""
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openai_organization: Optional[str] = Field(default=None, alias="organization")
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"""Automatically inferred from env var `OPENAI_ORG_ID` if not provided."""
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# to support explicit proxy for OpenAI
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openai_proxy: Optional[str] = None
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batch_size: int = 20
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"""Batch size to use when passing multiple documents to generate."""
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request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
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default=None, alias="timeout"
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)
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"""Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or
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None."""
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logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict)
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"""Adjust the probability of specific tokens being generated."""
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max_retries: int = 2
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"""Maximum number of retries to make when generating."""
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streaming: bool = False
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"""Whether to stream the results or not."""
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allowed_special: Union[Literal["all"], AbstractSet[str]] = set()
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"""Set of special tokens that are allowed。"""
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disallowed_special: Union[Literal["all"], Collection[str]] = "all"
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"""Set of special tokens that are not allowed。"""
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tiktoken_model_name: Optional[str] = None
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"""The model name to pass to tiktoken when using this class.
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Tiktoken is used to count the number of tokens in documents to constrain
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them to be under a certain limit. By default, when set to None, this will
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be the same as the embedding model name. However, there are some cases
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where you may want to use this Embedding class with a model name not
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supported by tiktoken. This can include when using Azure embeddings or
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when using one of the many model providers that expose an OpenAI-like
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API but with different models. In those cases, in order to avoid erroring
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when tiktoken is called, you can specify a model name to use here."""
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default_headers: Union[Mapping[str, str], None] = None
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default_query: Union[Mapping[str, object], None] = None
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# Configure a custom httpx client. See the
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# [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
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http_client: Union[Any, None] = None
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"""Optional httpx.Client."""
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class Config:
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"""Configuration for this pydantic object."""
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allow_population_by_field_name = True
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@root_validator(pre=True)
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def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
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"""Build extra kwargs from additional params that were passed in."""
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all_required_field_names = get_pydantic_field_names(cls)
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extra = values.get("model_kwargs", {})
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values["model_kwargs"] = build_extra_kwargs(
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extra, values, all_required_field_names
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)
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return values
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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if values["n"] < 1:
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raise ValueError("n must be at least 1.")
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if values["streaming"] and values["n"] > 1:
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raise ValueError("Cannot stream results when n > 1.")
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if values["streaming"] and values["best_of"] > 1:
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raise ValueError("Cannot stream results when best_of > 1.")
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values["openai_api_key"] = get_from_dict_or_env(
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values, "openai_api_key", "OPENAI_API_KEY"
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)
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values["openai_api_base"] = values["openai_api_base"] or os.getenv(
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"OPENAI_API_BASE"
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)
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values["openai_proxy"] = get_from_dict_or_env(
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values,
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"openai_proxy",
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"OPENAI_PROXY",
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default="",
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)
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values["openai_organization"] = (
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values["openai_organization"]
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or os.getenv("OPENAI_ORG_ID")
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or os.getenv("OPENAI_ORGANIZATION")
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)
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client_params = {
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"api_key": values["openai_api_key"],
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"organization": values["openai_organization"],
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"base_url": values["openai_api_base"],
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"timeout": values["request_timeout"],
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"max_retries": values["max_retries"],
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"default_headers": values["default_headers"],
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"default_query": values["default_query"],
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"http_client": values["http_client"],
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}
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if not values.get("client"):
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values["client"] = openai.OpenAI(**client_params).completions
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if not values.get("async_client"):
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values["async_client"] = openai.AsyncOpenAI(**client_params).completions
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return values
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@property
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def _default_params(self) -> Dict[str, Any]:
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"""Get the default parameters for calling OpenAI API."""
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normal_params: Dict[str, Any] = {
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"temperature": self.temperature,
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"top_p": self.top_p,
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"frequency_penalty": self.frequency_penalty,
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"presence_penalty": self.presence_penalty,
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"n": self.n,
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"logit_bias": self.logit_bias,
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}
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if self.max_tokens is not None:
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normal_params["max_tokens"] = self.max_tokens
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# Azure gpt-35-turbo doesn't support best_of
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# don't specify best_of if it is 1
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if self.best_of > 1:
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normal_params["best_of"] = self.best_of
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return {**normal_params, **self.model_kwargs}
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def _stream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[GenerationChunk]:
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params = {**self._invocation_params, **kwargs, "stream": True}
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self.get_sub_prompts(params, [prompt], stop) # this mutates params
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for stream_resp in self.client.create(prompt=prompt, **params):
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if not isinstance(stream_resp, dict):
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stream_resp = stream_resp.dict()
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chunk = _stream_response_to_generation_chunk(stream_resp)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(
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chunk.text,
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chunk=chunk,
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verbose=self.verbose,
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logprobs=chunk.generation_info["logprobs"]
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if chunk.generation_info
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else None,
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)
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async def _astream(
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self,
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prompt: str,
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[GenerationChunk]:
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params = {**self._invocation_params, **kwargs, "stream": True}
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self.get_sub_prompts(params, [prompt], stop) # this mutates params
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async for stream_resp in await self.async_client.create(
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prompt=prompt, **params
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):
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if not isinstance(stream_resp, dict):
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stream_resp = stream_resp.dict()
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chunk = _stream_response_to_generation_chunk(stream_resp)
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yield chunk
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if run_manager:
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await run_manager.on_llm_new_token(
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chunk.text,
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chunk=chunk,
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verbose=self.verbose,
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logprobs=chunk.generation_info["logprobs"]
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if chunk.generation_info
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else None,
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)
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def _generate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> LLMResult:
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"""Call out to OpenAI's endpoint with k unique prompts.
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Args:
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prompts: The prompts to pass into the model.
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stop: Optional list of stop words to use when generating.
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Returns:
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The full LLM output.
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Example:
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.. code-block:: python
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response = openai.generate(["Tell me a joke."])
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"""
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# TODO: write a unit test for this
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params = self._invocation_params
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params = {**params, **kwargs}
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sub_prompts = self.get_sub_prompts(params, prompts, stop)
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choices = []
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token_usage: Dict[str, int] = {}
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# Get the token usage from the response.
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# Includes prompt, completion, and total tokens used.
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_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
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system_fingerprint: Optional[str] = None
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for _prompts in sub_prompts:
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if self.streaming:
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if len(_prompts) > 1:
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raise ValueError("Cannot stream results with multiple prompts.")
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generation: Optional[GenerationChunk] = None
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for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs):
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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choices.append(
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{
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"text": generation.text,
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"finish_reason": generation.generation_info.get("finish_reason")
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if generation.generation_info
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else None,
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"logprobs": generation.generation_info.get("logprobs")
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if generation.generation_info
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else None,
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}
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)
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else:
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response = self.client.create(prompt=_prompts, **params)
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if not isinstance(response, dict):
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# V1 client returns the response in an PyDantic object instead of
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# dict. For the transition period, we deep convert it to dict.
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response = response.dict()
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choices.extend(response["choices"])
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_update_token_usage(_keys, response, token_usage)
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if not system_fingerprint:
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system_fingerprint = response.get("system_fingerprint")
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return self.create_llm_result(
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choices,
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prompts,
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params,
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token_usage,
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system_fingerprint=system_fingerprint,
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)
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async def _agenerate(
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self,
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prompts: List[str],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> LLMResult:
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"""Call out to OpenAI's endpoint async with k unique prompts."""
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params = self._invocation_params
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params = {**params, **kwargs}
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sub_prompts = self.get_sub_prompts(params, prompts, stop)
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choices = []
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token_usage: Dict[str, int] = {}
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# Get the token usage from the response.
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# Includes prompt, completion, and total tokens used.
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_keys = {"completion_tokens", "prompt_tokens", "total_tokens"}
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system_fingerprint: Optional[str] = None
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for _prompts in sub_prompts:
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if self.streaming:
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if len(_prompts) > 1:
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raise ValueError("Cannot stream results with multiple prompts.")
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generation: Optional[GenerationChunk] = None
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async for chunk in self._astream(
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_prompts[0], stop, run_manager, **kwargs
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):
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if generation is None:
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generation = chunk
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else:
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generation += chunk
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assert generation is not None
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choices.append(
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{
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"text": generation.text,
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"finish_reason": generation.generation_info.get("finish_reason")
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if generation.generation_info
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else None,
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"logprobs": generation.generation_info.get("logprobs")
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if generation.generation_info
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else None,
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}
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)
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else:
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response = await self.async_client.create(prompt=_prompts, **params)
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if not isinstance(response, dict):
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response = response.dict()
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choices.extend(response["choices"])
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_update_token_usage(_keys, response, token_usage)
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return self.create_llm_result(
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choices,
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prompts,
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params,
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token_usage,
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system_fingerprint=system_fingerprint,
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)
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def get_sub_prompts(
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self,
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params: Dict[str, Any],
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prompts: List[str],
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stop: Optional[List[str]] = None,
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) -> List[List[str]]:
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"""Get the sub prompts for llm call."""
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if stop is not None:
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if "stop" in params:
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raise ValueError("`stop` found in both the input and default params.")
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params["stop"] = stop
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if params["max_tokens"] == -1:
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if len(prompts) != 1:
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raise ValueError(
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"max_tokens set to -1 not supported for multiple inputs."
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)
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params["max_tokens"] = self.max_tokens_for_prompt(prompts[0])
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sub_prompts = [
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prompts[i : i + self.batch_size]
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for i in range(0, len(prompts), self.batch_size)
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]
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return sub_prompts
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def create_llm_result(
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self,
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choices: Any,
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prompts: List[str],
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params: Dict[str, Any],
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token_usage: Dict[str, int],
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*,
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system_fingerprint: Optional[str] = None,
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) -> LLMResult:
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"""Create the LLMResult from the choices and prompts."""
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generations = []
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n = params.get("n", self.n)
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for i, _ in enumerate(prompts):
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sub_choices = choices[i * n : (i + 1) * n]
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generations.append(
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[
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Generation(
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text=choice["text"],
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generation_info=dict(
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finish_reason=choice.get("finish_reason"),
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logprobs=choice.get("logprobs"),
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),
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)
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for choice in sub_choices
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]
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)
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llm_output = {"token_usage": token_usage, "model_name": self.model_name}
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if system_fingerprint:
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llm_output["system_fingerprint"] = system_fingerprint
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return LLMResult(generations=generations, llm_output=llm_output)
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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"""Get the parameters used to invoke the model."""
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return self._default_params
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|
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@property
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|
def _identifying_params(self) -> Mapping[str, Any]:
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"""Get the identifying parameters."""
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return {**{"model_name": self.model_name}, **self._default_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "openai"
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def get_token_ids(self, text: str) -> List[int]:
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"""Get the token IDs using the tiktoken package."""
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# tiktoken NOT supported for Python < 3.8
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if sys.version_info[1] < 8:
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return super().get_num_tokens(text)
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model_name = self.tiktoken_model_name or self.model_name
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try:
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enc = tiktoken.encoding_for_model(model_name)
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except KeyError:
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logger.warning("Warning: model not found. Using cl100k_base encoding.")
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model = "cl100k_base"
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enc = tiktoken.get_encoding(model)
|
|
|
|
return enc.encode(
|
|
text,
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|
allowed_special=self.allowed_special,
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|
disallowed_special=self.disallowed_special,
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|
)
|
|
|
|
@staticmethod
|
|
def modelname_to_contextsize(modelname: str) -> int:
|
|
"""Calculate the maximum number of tokens possible to generate for a model.
|
|
|
|
Args:
|
|
modelname: The modelname we want to know the context size for.
|
|
|
|
Returns:
|
|
The maximum context size
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
max_tokens = openai.modelname_to_contextsize("gpt-3.5-turbo-instruct")
|
|
"""
|
|
model_token_mapping = {
|
|
"gpt-4": 8192,
|
|
"gpt-4-0314": 8192,
|
|
"gpt-4-0613": 8192,
|
|
"gpt-4-32k": 32768,
|
|
"gpt-4-32k-0314": 32768,
|
|
"gpt-4-32k-0613": 32768,
|
|
"gpt-3.5-turbo": 4096,
|
|
"gpt-3.5-turbo-0301": 4096,
|
|
"gpt-3.5-turbo-0613": 4096,
|
|
"gpt-3.5-turbo-16k": 16385,
|
|
"gpt-3.5-turbo-16k-0613": 16385,
|
|
"gpt-3.5-turbo-instruct": 4096,
|
|
"text-ada-001": 2049,
|
|
"ada": 2049,
|
|
"text-babbage-001": 2040,
|
|
"babbage": 2049,
|
|
"text-curie-001": 2049,
|
|
"curie": 2049,
|
|
"davinci": 2049,
|
|
"text-davinci-003": 4097,
|
|
"text-davinci-002": 4097,
|
|
"code-davinci-002": 8001,
|
|
"code-davinci-001": 8001,
|
|
"code-cushman-002": 2048,
|
|
"code-cushman-001": 2048,
|
|
}
|
|
|
|
# handling finetuned models
|
|
if "ft-" in modelname:
|
|
modelname = modelname.split(":")[0]
|
|
|
|
context_size = model_token_mapping.get(modelname, None)
|
|
|
|
if context_size is None:
|
|
raise ValueError(
|
|
f"Unknown model: {modelname}. Please provide a valid OpenAI model name."
|
|
"Known models are: " + ", ".join(model_token_mapping.keys())
|
|
)
|
|
|
|
return context_size
|
|
|
|
@property
|
|
def max_context_size(self) -> int:
|
|
"""Get max context size for this model."""
|
|
return self.modelname_to_contextsize(self.model_name)
|
|
|
|
def max_tokens_for_prompt(self, prompt: str) -> int:
|
|
"""Calculate the maximum number of tokens possible to generate for a prompt.
|
|
|
|
Args:
|
|
prompt: The prompt to pass into the model.
|
|
|
|
Returns:
|
|
The maximum number of tokens to generate for a prompt.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
max_tokens = openai.max_token_for_prompt("Tell me a joke.")
|
|
"""
|
|
num_tokens = self.get_num_tokens(prompt)
|
|
return self.max_context_size - num_tokens
|
|
|
|
|
|
class OpenAI(BaseOpenAI):
|
|
"""OpenAI large language models.
|
|
|
|
To use, you should have the ``openai`` python package installed, and the
|
|
environment variable ``OPENAI_API_KEY`` set with your API key.
|
|
|
|
Any parameters that are valid to be passed to the openai.create call can be passed
|
|
in, even if not explicitly saved on this class.
|
|
|
|
Example:
|
|
.. code-block:: python
|
|
|
|
from langchain_community.llms import OpenAI
|
|
openai = OpenAI(model_name="gpt-3.5-turbo-instruct")
|
|
"""
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "llms", "openai"]
|
|
|
|
@classmethod
|
|
def is_lc_serializable(cls) -> bool:
|
|
"""Return whether this model can be serialized by Langchain."""
|
|
return True
|
|
|
|
@property
|
|
def _invocation_params(self) -> Dict[str, Any]:
|
|
return {**{"model": self.model_name}, **super()._invocation_params}
|