Source code for langchain_openai.llms.base

from __future__ import annotations

import logging
import os
import sys
from typing import (
    AbstractSet,
    Any,
    AsyncIterator,
    Collection,
    Dict,
    Iterator,
    List,
    Literal,
    Mapping,
    Optional,
    Set,
    Tuple,
    Union,
)

import openai
import tiktoken
from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models.llms import BaseLLM
from langchain_core.outputs import Generation, GenerationChunk, LLMResult
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import (
    convert_to_secret_str,
    get_from_dict_or_env,
    get_pydantic_field_names,
)
from langchain_core.utils.utils import build_extra_kwargs

logger = logging.getLogger(__name__)


def _update_token_usage(
    keys: Set[str], response: Dict[str, Any], token_usage: Dict[str, Any]
) -> None:
    """Update token usage."""
    _keys_to_use = keys.intersection(response["usage"])
    for _key in _keys_to_use:
        if _key not in token_usage:
            token_usage[_key] = response["usage"][_key]
        else:
            token_usage[_key] += response["usage"][_key]


def _stream_response_to_generation_chunk(
    stream_response: Dict[str, Any],
) -> GenerationChunk:
    """Convert a stream response to a generation chunk."""
    if not stream_response["choices"]:
        return GenerationChunk(text="")
    return GenerationChunk(
        text=stream_response["choices"][0]["text"],
        generation_info=dict(
            finish_reason=stream_response["choices"][0].get("finish_reason", None),
            logprobs=stream_response["choices"][0].get("logprobs", None),
        ),
    )


[docs]class BaseOpenAI(BaseLLM): """Base OpenAI large language model class.""" client: Any = Field(default=None, exclude=True) #: :meta private: async_client: Any = Field(default=None, exclude=True) #: :meta private: model_name: str = Field(default="gpt-3.5-turbo-instruct", alias="model") """Model name to use.""" temperature: float = 0.7 """What sampling temperature to use.""" max_tokens: int = 256 """The maximum number of tokens to generate in the completion. -1 returns as many tokens as possible given the prompt and the models maximal context size.""" top_p: float = 1 """Total probability mass of tokens to consider at each step.""" frequency_penalty: float = 0 """Penalizes repeated tokens according to frequency.""" presence_penalty: float = 0 """Penalizes repeated tokens.""" n: int = 1 """How many completions to generate for each prompt.""" best_of: int = 1 """Generates best_of completions server-side and returns the "best".""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" openai_api_key: Optional[SecretStr] = Field(default=None, alias="api_key") """Automatically inferred from env var `OPENAI_API_KEY` if not provided.""" openai_api_base: Optional[str] = Field(default=None, alias="base_url") """Base URL path for API requests, leave blank if not using a proxy or service emulator.""" openai_organization: Optional[str] = Field(default=None, alias="organization") """Automatically inferred from env var `OPENAI_ORG_ID` if not provided.""" # to support explicit proxy for OpenAI openai_proxy: Optional[str] = None batch_size: int = 20 """Batch size to use when passing multiple documents to generate.""" request_timeout: Union[float, Tuple[float, float], Any, None] = Field( default=None, alias="timeout" ) """Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or None.""" logit_bias: Optional[Dict[str, float]] = Field(default_factory=dict) """Adjust the probability of specific tokens being generated.""" max_retries: int = 2 """Maximum number of retries to make when generating.""" streaming: bool = False """Whether to stream the results or not.""" allowed_special: Union[Literal["all"], AbstractSet[str]] = set() """Set of special tokens that are allowed。""" disallowed_special: Union[Literal["all"], Collection[str]] = "all" """Set of special tokens that are not allowed。""" tiktoken_model_name: Optional[str] = None """The model name to pass to tiktoken when using this class. Tiktoken is used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name. However, there are some cases where you may want to use this Embedding class with a model name not supported by tiktoken. This can include when using Azure embeddings or when using one of the many model providers that expose an OpenAI-like API but with different models. In those cases, in order to avoid erroring when tiktoken is called, you can specify a model name to use here.""" default_headers: Union[Mapping[str, str], None] = None default_query: Union[Mapping[str, object], None] = None # Configure a custom httpx client. See the # [httpx documentation](https://www.python-httpx.org/api/#client) for more details. http_client: Union[Any, None] = None """Optional httpx.Client. Only used for sync invocations. Must specify http_async_client as well if you'd like a custom client for async invocations. """ http_async_client: Union[Any, None] = None """Optional httpx.AsyncClient. Only used for async invocations. Must specify http_client as well if you'd like a custom client for sync invocations.""" class Config: """Configuration for this pydantic object.""" allow_population_by_field_name = True @root_validator(pre=True) def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: """Build extra kwargs from additional params that were passed in.""" all_required_field_names = get_pydantic_field_names(cls) extra = values.get("model_kwargs", {}) values["model_kwargs"] = build_extra_kwargs( extra, values, all_required_field_names ) return values @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" if values["n"] < 1: raise ValueError("n must be at least 1.") if values["streaming"] and values["n"] > 1: raise ValueError("Cannot stream results when n > 1.") if values["streaming"] and values["best_of"] > 1: raise ValueError("Cannot stream results when best_of > 1.") openai_api_key = get_from_dict_or_env( values, "openai_api_key", "OPENAI_API_KEY" ) values["openai_api_key"] = ( convert_to_secret_str(openai_api_key) if openai_api_key else None ) values["openai_api_base"] = values["openai_api_base"] or os.getenv( "OPENAI_API_BASE" ) values["openai_proxy"] = get_from_dict_or_env( values, "openai_proxy", "OPENAI_PROXY", default="", ) values["openai_organization"] = ( values["openai_organization"] or os.getenv("OPENAI_ORG_ID") or os.getenv("OPENAI_ORGANIZATION") ) client_params = { "api_key": ( values["openai_api_key"].get_secret_value() if values["openai_api_key"] else None ), "organization": values["openai_organization"], "base_url": values["openai_api_base"], "timeout": values["request_timeout"], "max_retries": values["max_retries"], "default_headers": values["default_headers"], "default_query": values["default_query"], } if not values.get("client"): sync_specific = {"http_client": values["http_client"]} values["client"] = openai.OpenAI( **client_params, **sync_specific ).completions if not values.get("async_client"): async_specific = {"http_client": values["http_async_client"]} values["async_client"] = openai.AsyncOpenAI( **client_params, **async_specific ).completions return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling OpenAI API.""" normal_params: Dict[str, Any] = { "temperature": self.temperature, "top_p": self.top_p, "frequency_penalty": self.frequency_penalty, "presence_penalty": self.presence_penalty, "n": self.n, "logit_bias": self.logit_bias, } if self.max_tokens is not None: normal_params["max_tokens"] = self.max_tokens # Azure gpt-35-turbo doesn't support best_of # don't specify best_of if it is 1 if self.best_of > 1: normal_params["best_of"] = self.best_of return {**normal_params, **self.model_kwargs} def _stream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[GenerationChunk]: params = {**self._invocation_params, **kwargs, "stream": True} self.get_sub_prompts(params, [prompt], stop) # this mutates params for stream_resp in self.client.create(prompt=prompt, **params): if not isinstance(stream_resp, dict): stream_resp = stream_resp.model_dump() chunk = _stream_response_to_generation_chunk(stream_resp) if run_manager: run_manager.on_llm_new_token( chunk.text, chunk=chunk, verbose=self.verbose, logprobs=( chunk.generation_info["logprobs"] if chunk.generation_info else None ), ) yield chunk async def _astream( self, prompt: str, stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[GenerationChunk]: params = {**self._invocation_params, **kwargs, "stream": True} self.get_sub_prompts(params, [prompt], stop) # this mutates params async for stream_resp in await self.async_client.create( prompt=prompt, **params ): if not isinstance(stream_resp, dict): stream_resp = stream_resp.model_dump() chunk = _stream_response_to_generation_chunk(stream_resp) if run_manager: await run_manager.on_llm_new_token( chunk.text, chunk=chunk, verbose=self.verbose, logprobs=( chunk.generation_info["logprobs"] if chunk.generation_info else None ), ) yield chunk def _generate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Call out to OpenAI's endpoint with k unique prompts. Args: prompts: The prompts to pass into the model. stop: Optional list of stop words to use when generating. Returns: The full LLM output. Example: .. code-block:: python response = openai.generate(["Tell me a joke."]) """ # TODO: write a unit test for this params = self._invocation_params params = {**params, **kwargs} sub_prompts = self.get_sub_prompts(params, prompts, stop) choices = [] token_usage: Dict[str, int] = {} # Get the token usage from the response. # Includes prompt, completion, and total tokens used. _keys = {"completion_tokens", "prompt_tokens", "total_tokens"} system_fingerprint: Optional[str] = None for _prompts in sub_prompts: if self.streaming: if len(_prompts) > 1: raise ValueError("Cannot stream results with multiple prompts.") generation: Optional[GenerationChunk] = None for chunk in self._stream(_prompts[0], stop, run_manager, **kwargs): if generation is None: generation = chunk else: generation += chunk assert generation is not None choices.append( { "text": generation.text, "finish_reason": ( generation.generation_info.get("finish_reason") if generation.generation_info else None ), "logprobs": ( generation.generation_info.get("logprobs") if generation.generation_info else None ), } ) else: response = self.client.create(prompt=_prompts, **params) if not isinstance(response, dict): # V1 client returns the response in an PyDantic object instead of # dict. For the transition period, we deep convert it to dict. response = response.model_dump() # Sometimes the AI Model calling will get error, we should raise it. # Otherwise, the next code 'choices.extend(response["choices"])' # will throw a "TypeError: 'NoneType' object is not iterable" error # to mask the true error. Because 'response["choices"]' is None. if response.get("error"): raise ValueError(response.get("error")) choices.extend(response["choices"]) _update_token_usage(_keys, response, token_usage) if not system_fingerprint: system_fingerprint = response.get("system_fingerprint") return self.create_llm_result( choices, prompts, params, token_usage, system_fingerprint=system_fingerprint, ) async def _agenerate( self, prompts: List[str], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> LLMResult: """Call out to OpenAI's endpoint async with k unique prompts.""" params = self._invocation_params params = {**params, **kwargs} sub_prompts = self.get_sub_prompts(params, prompts, stop) choices = [] token_usage: Dict[str, int] = {} # Get the token usage from the response. # Includes prompt, completion, and total tokens used. _keys = {"completion_tokens", "prompt_tokens", "total_tokens"} system_fingerprint: Optional[str] = None for _prompts in sub_prompts: if self.streaming: if len(_prompts) > 1: raise ValueError("Cannot stream results with multiple prompts.") generation: Optional[GenerationChunk] = None async for chunk in self._astream( _prompts[0], stop, run_manager, **kwargs ): if generation is None: generation = chunk else: generation += chunk assert generation is not None choices.append( { "text": generation.text, "finish_reason": ( generation.generation_info.get("finish_reason") if generation.generation_info else None ), "logprobs": ( generation.generation_info.get("logprobs") if generation.generation_info else None ), } ) else: response = await self.async_client.create(prompt=_prompts, **params) if not isinstance(response, dict): response = response.model_dump() choices.extend(response["choices"]) _update_token_usage(_keys, response, token_usage) return self.create_llm_result( choices, prompts, params, token_usage, system_fingerprint=system_fingerprint, )
[docs] def get_sub_prompts( self, params: Dict[str, Any], prompts: List[str], stop: Optional[List[str]] = None, ) -> List[List[str]]: """Get the sub prompts for llm call.""" if stop is not None: if "stop" in params: raise ValueError("`stop` found in both the input and default params.") params["stop"] = stop if params["max_tokens"] == -1: if len(prompts) != 1: raise ValueError( "max_tokens set to -1 not supported for multiple inputs." ) params["max_tokens"] = self.max_tokens_for_prompt(prompts[0]) sub_prompts = [ prompts[i : i + self.batch_size] for i in range(0, len(prompts), self.batch_size) ] return sub_prompts
[docs] def create_llm_result( self, choices: Any, prompts: List[str], params: Dict[str, Any], token_usage: Dict[str, int], *, system_fingerprint: Optional[str] = None, ) -> LLMResult: """Create the LLMResult from the choices and prompts.""" generations = [] n = params.get("n", self.n) for i, _ in enumerate(prompts): sub_choices = choices[i * n : (i + 1) * n] generations.append( [ Generation( text=choice["text"], generation_info=dict( finish_reason=choice.get("finish_reason"), logprobs=choice.get("logprobs"), ), ) for choice in sub_choices ] ) llm_output = {"token_usage": token_usage, "model_name": self.model_name} if system_fingerprint: llm_output["system_fingerprint"] = system_fingerprint return LLMResult(generations=generations, llm_output=llm_output)
@property def _invocation_params(self) -> Dict[str, Any]: """Get the parameters used to invoke the model.""" return self._default_params @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" return {**{"model_name": self.model_name}, **self._default_params} @property def _llm_type(self) -> str: """Return type of llm.""" return "openai"
[docs] def get_token_ids(self, text: str) -> List[int]: """Get the token IDs using the tiktoken package.""" if self.custom_get_token_ids is not None: return self.custom_get_token_ids(text) # tiktoken NOT supported for Python < 3.8 if sys.version_info[1] < 8: return super().get_num_tokens(text) model_name = self.tiktoken_model_name or self.model_name try: enc = tiktoken.encoding_for_model(model_name) except KeyError: enc = tiktoken.get_encoding("cl100k_base") return enc.encode( text, allowed_special=self.allowed_special, disallowed_special=self.disallowed_special, )
[docs] @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)
[docs] 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
[docs]class OpenAI(BaseOpenAI): """OpenAI large language models. To use, you should have the environment variable ``OPENAI_API_KEY`` set with your API key, or pass it as a named parameter to the constructor. 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_openai import OpenAI model = OpenAI(model_name="gpt-3.5-turbo-instruct") """
[docs] @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "llms", "openai"]
[docs] @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} @property def lc_secrets(self) -> Dict[str, str]: return {"openai_api_key": "OPENAI_API_KEY"} @property def lc_attributes(self) -> Dict[str, Any]: attributes: Dict[str, Any] = {} if self.openai_api_base: attributes["openai_api_base"] = self.openai_api_base if self.openai_organization: attributes["openai_organization"] = self.openai_organization if self.openai_proxy: attributes["openai_proxy"] = self.openai_proxy return attributes