"""OpenAI chat wrapper.""" from __future__ import annotations import base64 import json import logging import os import sys from io import BytesIO from math import ceil from operator import itemgetter from typing import ( Any, AsyncIterator, Callable, Dict, Iterator, List, Literal, Mapping, Optional, Sequence, Tuple, Type, TypedDict, TypeVar, Union, cast, overload, ) from urllib.parse import urlparse import openai import tiktoken from langchain_core.callbacks import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) from langchain_core.language_models import LanguageModelInput from langchain_core.language_models.chat_models import ( BaseChatModel, LangSmithParams, agenerate_from_stream, generate_from_stream, ) from langchain_core.messages import ( AIMessage, AIMessageChunk, BaseMessage, BaseMessageChunk, ChatMessage, ChatMessageChunk, FunctionMessage, FunctionMessageChunk, HumanMessage, HumanMessageChunk, InvalidToolCall, SystemMessage, SystemMessageChunk, ToolCall, ToolMessage, ToolMessageChunk, ) from langchain_core.messages.ai import UsageMetadata from langchain_core.output_parsers import JsonOutputParser, PydanticOutputParser from langchain_core.output_parsers.base import OutputParserLike from langchain_core.output_parsers.openai_tools import ( JsonOutputKeyToolsParser, PydanticToolsParser, make_invalid_tool_call, parse_tool_call, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.pydantic_v1 import BaseModel, Field, SecretStr, root_validator from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import ( convert_to_secret_str, get_from_dict_or_env, get_pydantic_field_names, ) from langchain_core.utils.function_calling import ( convert_to_openai_function, convert_to_openai_tool, ) from langchain_core.utils.utils import build_extra_kwargs logger = logging.getLogger(__name__) def _convert_dict_to_message(_dict: Mapping[str, Any]) -> BaseMessage: """Convert a dictionary to a LangChain message. Args: _dict: The dictionary. Returns: The LangChain message. """ role = _dict.get("role") name = _dict.get("name") id_ = _dict.get("id") if role == "user": return HumanMessage(content=_dict.get("content", ""), id=id_, name=name) elif role == "assistant": # Fix for azure # Also OpenAI returns None for tool invocations content = _dict.get("content", "") or "" additional_kwargs: Dict = {} if function_call := _dict.get("function_call"): additional_kwargs["function_call"] = dict(function_call) tool_calls = [] invalid_tool_calls = [] if raw_tool_calls := _dict.get("tool_calls"): additional_kwargs["tool_calls"] = raw_tool_calls for raw_tool_call in raw_tool_calls: try: tool_calls.append(parse_tool_call(raw_tool_call, return_id=True)) except Exception as e: invalid_tool_calls.append( make_invalid_tool_call(raw_tool_call, str(e)) ) return AIMessage( content=content, additional_kwargs=additional_kwargs, name=name, id=id_, tool_calls=tool_calls, invalid_tool_calls=invalid_tool_calls, ) elif role == "system": return SystemMessage(content=_dict.get("content", ""), name=name, id=id_) elif role == "function": return FunctionMessage( content=_dict.get("content", ""), name=cast(str, _dict.get("name")), id=id_ ) elif role == "tool": additional_kwargs = {} if "name" in _dict: additional_kwargs["name"] = _dict["name"] return ToolMessage( content=_dict.get("content", ""), tool_call_id=cast(str, _dict.get("tool_call_id")), additional_kwargs=additional_kwargs, name=name, id=id_, ) else: return ChatMessage(content=_dict.get("content", ""), role=role, id=id_) def _format_message_content(content: Any) -> Any: """Format message content.""" if content and isinstance(content, list): # Remove unexpected block types formatted_content = [] for block in content: if ( isinstance(block, dict) and "type" in block and block["type"] == "tool_use" ): continue else: formatted_content.append(block) else: formatted_content = content return formatted_content def _convert_message_to_dict(message: BaseMessage) -> dict: """Convert a LangChain message to a dictionary. Args: message: The LangChain message. Returns: The dictionary. """ message_dict: Dict[str, Any] = {"content": _format_message_content(message.content)} if (name := message.name or message.additional_kwargs.get("name")) is not None: message_dict["name"] = name # populate role and additional message data if isinstance(message, ChatMessage): message_dict["role"] = message.role elif isinstance(message, HumanMessage): message_dict["role"] = "user" elif isinstance(message, AIMessage): message_dict["role"] = "assistant" if "function_call" in message.additional_kwargs: message_dict["function_call"] = message.additional_kwargs["function_call"] if message.tool_calls or message.invalid_tool_calls: message_dict["tool_calls"] = [ _lc_tool_call_to_openai_tool_call(tc) for tc in message.tool_calls ] + [ _lc_invalid_tool_call_to_openai_tool_call(tc) for tc in message.invalid_tool_calls ] elif "tool_calls" in message.additional_kwargs: message_dict["tool_calls"] = message.additional_kwargs["tool_calls"] tool_call_supported_props = {"id", "type", "function"} message_dict["tool_calls"] = [ {k: v for k, v in tool_call.items() if k in tool_call_supported_props} for tool_call in message_dict["tool_calls"] ] else: pass # If tool calls present, content null value should be None not empty string. if "function_call" in message_dict or "tool_calls" in message_dict: message_dict["content"] = message_dict["content"] or None elif isinstance(message, SystemMessage): message_dict["role"] = "system" elif isinstance(message, FunctionMessage): message_dict["role"] = "function" elif isinstance(message, ToolMessage): message_dict["role"] = "tool" message_dict["tool_call_id"] = message.tool_call_id supported_props = {"content", "role", "tool_call_id"} message_dict = {k: v for k, v in message_dict.items() if k in supported_props} else: raise TypeError(f"Got unknown type {message}") return message_dict def _convert_delta_to_message_chunk( _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk] ) -> BaseMessageChunk: id_ = _dict.get("id") role = cast(str, _dict.get("role")) content = cast(str, _dict.get("content") or "") additional_kwargs: Dict = {} if _dict.get("function_call"): function_call = dict(_dict["function_call"]) if "name" in function_call and function_call["name"] is None: function_call["name"] = "" additional_kwargs["function_call"] = function_call tool_call_chunks = [] if raw_tool_calls := _dict.get("tool_calls"): additional_kwargs["tool_calls"] = raw_tool_calls try: tool_call_chunks = [ { "name": rtc["function"].get("name"), "args": rtc["function"].get("arguments"), "id": rtc.get("id"), "index": rtc["index"], } for rtc in raw_tool_calls ] except KeyError: pass if role == "user" or default_class == HumanMessageChunk: return HumanMessageChunk(content=content, id=id_) elif role == "assistant" or default_class == AIMessageChunk: return AIMessageChunk( content=content, additional_kwargs=additional_kwargs, id=id_, tool_call_chunks=tool_call_chunks, ) elif role == "system" or default_class == SystemMessageChunk: return SystemMessageChunk(content=content, id=id_) elif role == "function" or default_class == FunctionMessageChunk: return FunctionMessageChunk(content=content, name=_dict["name"], id=id_) elif role == "tool" or default_class == ToolMessageChunk: return ToolMessageChunk( content=content, tool_call_id=_dict["tool_call_id"], id=id_ ) elif role or default_class == ChatMessageChunk: return ChatMessageChunk(content=content, role=role, id=id_) else: return default_class(content=content, id=id_) # type: ignore class _FunctionCall(TypedDict): name: str _BM = TypeVar("_BM", bound=BaseModel) _DictOrPydanticClass = Union[Dict[str, Any], Type[_BM]] _DictOrPydantic = Union[Dict, _BM] class _AllReturnType(TypedDict): raw: BaseMessage parsed: Optional[_DictOrPydantic] parsing_error: Optional[BaseException] class BaseChatOpenAI(BaseChatModel): 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", alias="model") """Model name to use.""" temperature: float = 0.7 """What sampling temperature to use.""" 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 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.""" max_retries: int = 2 """Maximum number of retries to make when generating.""" streaming: bool = False """Whether to stream the results or not.""" n: int = 1 """Number of chat completions to generate for each prompt.""" max_tokens: Optional[int] = None """Maximum number of tokens to generate.""" 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.""" stop: Optional[Union[List[str], str]] = Field(default=None, alias="stop_sequences") """Default stop sequences.""" 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["n"] > 1 and values["streaming"]: raise ValueError("n must be 1 when streaming.") values["openai_api_key"] = convert_to_secret_str( get_from_dict_or_env(values, "openai_api_key", "OPENAI_API_KEY") ) # Check OPENAI_ORGANIZATION for backwards compatibility. values["openai_organization"] = ( values["openai_organization"] or os.getenv("OPENAI_ORG_ID") or os.getenv("OPENAI_ORGANIZATION") ) 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="" ) 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"], } openai_proxy = values["openai_proxy"] if not values.get("client"): if openai_proxy and not values["http_client"]: try: import httpx except ImportError as e: raise ImportError( "Could not import httpx python package. " "Please install it with `pip install httpx`." ) from e values["http_client"] = httpx.Client(proxy=openai_proxy) sync_specific = {"http_client": values["http_client"]} values["client"] = openai.OpenAI( **client_params, **sync_specific ).chat.completions if not values.get("async_client"): if openai_proxy and not values["http_async_client"]: try: import httpx except ImportError as e: raise ImportError( "Could not import httpx python package. " "Please install it with `pip install httpx`." ) from e values["http_async_client"] = httpx.AsyncClient(proxy=openai_proxy) async_specific = {"http_client": values["http_async_client"]} values["async_client"] = openai.AsyncOpenAI( **client_params, **async_specific ).chat.completions return values @property def _default_params(self) -> Dict[str, Any]: """Get the default parameters for calling OpenAI API.""" params = { "model": self.model_name, "stream": self.streaming, "n": self.n, "temperature": self.temperature, **self.model_kwargs, } if self.max_tokens is not None: params["max_tokens"] = self.max_tokens if self.stop: params["stop"] = self.stop return params def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict: overall_token_usage: dict = {} system_fingerprint = None for output in llm_outputs: if output is None: # Happens in streaming continue token_usage = output["token_usage"] if token_usage is not None: for k, v in token_usage.items(): if k in overall_token_usage: overall_token_usage[k] += v else: overall_token_usage[k] = v if system_fingerprint is None: system_fingerprint = output.get("system_fingerprint") combined = {"token_usage": overall_token_usage, "model_name": self.model_name} if system_fingerprint: combined["system_fingerprint"] = system_fingerprint return combined def _stream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> Iterator[ChatGenerationChunk]: message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk with self.client.create(messages=message_dicts, **params) as response: for chunk in response: if not isinstance(chunk, dict): chunk = chunk.model_dump() if len(chunk["choices"]) == 0: if token_usage := chunk.get("usage"): usage_metadata = UsageMetadata( input_tokens=token_usage.get("prompt_tokens", 0), output_tokens=token_usage.get("completion_tokens", 0), total_tokens=token_usage.get("total_tokens", 0), ) generation_chunk = ChatGenerationChunk( message=default_chunk_class( content="", usage_metadata=usage_metadata ) ) logprobs = None else: continue else: choice = chunk["choices"][0] if choice["delta"] is None: continue message_chunk = _convert_delta_to_message_chunk( choice["delta"], default_chunk_class ) generation_info = {} if finish_reason := choice.get("finish_reason"): generation_info["finish_reason"] = finish_reason if model_name := chunk.get("model"): generation_info["model_name"] = model_name if system_fingerprint := chunk.get("system_fingerprint"): generation_info["system_fingerprint"] = system_fingerprint logprobs = choice.get("logprobs") if logprobs: generation_info["logprobs"] = logprobs default_chunk_class = message_chunk.__class__ generation_chunk = ChatGenerationChunk( message=message_chunk, generation_info=generation_info or None ) if run_manager: run_manager.on_llm_new_token( generation_chunk.text, chunk=generation_chunk, logprobs=logprobs ) yield generation_chunk def _generate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[CallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: if self.streaming: stream_iter = self._stream( messages, stop=stop, run_manager=run_manager, **kwargs ) return generate_from_stream(stream_iter) message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs} response = self.client.create(messages=message_dicts, **params) return self._create_chat_result(response) def _create_message_dicts( self, messages: List[BaseMessage], stop: Optional[List[str]] ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]: params = self._default_params if stop is not None: params["stop"] = stop message_dicts = [_convert_message_to_dict(m) for m in messages] return message_dicts, params def _create_chat_result( self, response: Union[dict, openai.BaseModel] ) -> ChatResult: generations = [] if not isinstance(response, 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")) token_usage = response.get("usage", {}) for res in response["choices"]: message = _convert_dict_to_message(res["message"]) if token_usage and isinstance(message, AIMessage): message.usage_metadata = { "input_tokens": token_usage.get("prompt_tokens", 0), "output_tokens": token_usage.get("completion_tokens", 0), "total_tokens": token_usage.get("total_tokens", 0), } generation_info = dict(finish_reason=res.get("finish_reason")) if "logprobs" in res: generation_info["logprobs"] = res["logprobs"] gen = ChatGeneration(message=message, generation_info=generation_info) generations.append(gen) llm_output = { "token_usage": token_usage, "model_name": response.get("model", self.model_name), "system_fingerprint": response.get("system_fingerprint", ""), } return ChatResult(generations=generations, llm_output=llm_output) async def _astream( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> AsyncIterator[ChatGenerationChunk]: message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs, "stream": True} default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk response = await self.async_client.create(messages=message_dicts, **params) async with response: async for chunk in response: if not isinstance(chunk, dict): chunk = chunk.model_dump() if len(chunk["choices"]) == 0: if token_usage := chunk.get("usage"): usage_metadata = UsageMetadata( input_tokens=token_usage.get("prompt_tokens", 0), output_tokens=token_usage.get("completion_tokens", 0), total_tokens=token_usage.get("total_tokens", 0), ) generation_chunk = ChatGenerationChunk( message=default_chunk_class( content="", usage_metadata=usage_metadata ) ) logprobs = None else: continue else: choice = chunk["choices"][0] if choice["delta"] is None: continue message_chunk = _convert_delta_to_message_chunk( choice["delta"], default_chunk_class ) generation_info = {} if finish_reason := choice.get("finish_reason"): generation_info["finish_reason"] = finish_reason if model_name := chunk.get("model"): generation_info["model_name"] = model_name if system_fingerprint := chunk.get("system_fingerprint"): generation_info["system_fingerprint"] = system_fingerprint logprobs = choice.get("logprobs") if logprobs: generation_info["logprobs"] = logprobs default_chunk_class = message_chunk.__class__ generation_chunk = ChatGenerationChunk( message=message_chunk, generation_info=generation_info or None ) if run_manager: await run_manager.on_llm_new_token( token=generation_chunk.text, chunk=generation_chunk, logprobs=logprobs, ) yield generation_chunk async def _agenerate( self, messages: List[BaseMessage], stop: Optional[List[str]] = None, run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, **kwargs: Any, ) -> ChatResult: if self.streaming: stream_iter = self._astream( messages, stop=stop, run_manager=run_manager, **kwargs ) return await agenerate_from_stream(stream_iter) message_dicts, params = self._create_message_dicts(messages, stop) params = {**params, **kwargs} response = await self.async_client.create(messages=message_dicts, **params) return self._create_chat_result(response) @property def _identifying_params(self) -> Dict[str, Any]: """Get the identifying parameters.""" return {"model_name": self.model_name, **self._default_params} def _get_invocation_params( self, stop: Optional[List[str]] = None, **kwargs: Any ) -> Dict[str, Any]: """Get the parameters used to invoke the model.""" return { "model": self.model_name, **super()._get_invocation_params(stop=stop), **self._default_params, **kwargs, } def _get_ls_params( self, stop: Optional[List[str]] = None, **kwargs: Any ) -> LangSmithParams: """Get standard params for tracing.""" params = self._get_invocation_params(stop=stop, **kwargs) ls_params = LangSmithParams( ls_provider="openai", ls_model_name=self.model_name, ls_model_type="chat", ls_temperature=params.get("temperature", self.temperature), ) if ls_max_tokens := params.get("max_tokens", self.max_tokens): ls_params["ls_max_tokens"] = ls_max_tokens if ls_stop := stop or params.get("stop", None): ls_params["ls_stop"] = ls_stop return ls_params @property def _llm_type(self) -> str: """Return type of chat model.""" return "openai-chat" def _get_encoding_model(self) -> Tuple[str, tiktoken.Encoding]: if self.tiktoken_model_name is not None: model = self.tiktoken_model_name else: model = self.model_name try: encoding = tiktoken.encoding_for_model(model) except KeyError: model = "cl100k_base" encoding = tiktoken.get_encoding(model) return model, encoding def get_token_ids(self, text: str) -> List[int]: """Get the tokens present in the text with tiktoken package.""" if self.custom_get_token_ids is not None: return self.custom_get_token_ids(text) # tiktoken NOT supported for Python 3.7 or below if sys.version_info[1] <= 7: return super().get_token_ids(text) _, encoding_model = self._get_encoding_model() return encoding_model.encode(text) # TODO: Count bound tools as part of input. def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int: """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package. **Requirements**: You must have the ``pillow`` installed if you want to count image tokens if you are specifying the image as a base64 string, and you must have both ``pillow`` and ``httpx`` installed if you are specifying the image as a URL. If these aren't installed image inputs will be ignored in token counting. OpenAI reference: https://github.com/openai/openai-cookbook/blob/ main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb""" if sys.version_info[1] <= 7: return super().get_num_tokens_from_messages(messages) model, encoding = self._get_encoding_model() if model.startswith("gpt-3.5-turbo-0301"): # every message follows {role/name}\n{content}\n tokens_per_message = 4 # if there's a name, the role is omitted tokens_per_name = -1 elif model.startswith("gpt-3.5-turbo") or model.startswith("gpt-4"): tokens_per_message = 3 tokens_per_name = 1 else: raise NotImplementedError( f"get_num_tokens_from_messages() is not presently implemented " f"for model {model}. See " "https://platform.openai.com/docs/guides/text-generation/managing-tokens" # noqa: E501 " for information on how messages are converted to tokens." ) num_tokens = 0 messages_dict = [_convert_message_to_dict(m) for m in messages] for message in messages_dict: num_tokens += tokens_per_message for key, value in message.items(): # This is an inferred approximation. OpenAI does not document how to # count tool message tokens. if key == "tool_call_id": num_tokens += 3 continue if isinstance(value, list): # content or tool calls for val in value: if isinstance(val, str) or val["type"] == "text": text = val["text"] if isinstance(val, dict) else val num_tokens += len(encoding.encode(text)) elif val["type"] == "image_url": if val["image_url"].get("detail") == "low": num_tokens += 85 else: image_size = _url_to_size(val["image_url"]["url"]) if not image_size: continue num_tokens += _count_image_tokens(*image_size) # Tool/function call token counting is not documented by OpenAI. # This is an approximation. elif val["type"] == "function": num_tokens += len( encoding.encode(val["function"]["arguments"]) ) num_tokens += len(encoding.encode(val["function"]["name"])) else: raise ValueError( f"Unrecognized content block type\n\n{val}" ) elif not value: continue else: # Cast str(value) in case the message value is not a string # This occurs with function messages num_tokens += len(encoding.encode(value)) if key == "name": num_tokens += tokens_per_name # every reply is primed with assistant num_tokens += 3 return num_tokens def bind_functions( self, functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], function_call: Optional[ Union[_FunctionCall, str, Literal["auto", "none"]] ] = None, **kwargs: Any, ) -> Runnable[LanguageModelInput, BaseMessage]: """Bind functions (and other objects) to this chat model. Assumes model is compatible with OpenAI function-calling API. NOTE: Using bind_tools is recommended instead, as the `functions` and `function_call` request parameters are officially marked as deprecated by OpenAI. Args: functions: A list of function definitions to bind to this chat model. Can be a dictionary, pydantic model, or callable. Pydantic models and callables will be automatically converted to their schema dictionary representation. function_call: Which function to require the model to call. Must be the name of the single provided function or "auto" to automatically determine which function to call (if any). **kwargs: Any additional parameters to pass to the :class:`~langchain.runnable.Runnable` constructor. """ formatted_functions = [convert_to_openai_function(fn) for fn in functions] if function_call is not None: function_call = ( {"name": function_call} if isinstance(function_call, str) and function_call not in ("auto", "none") else function_call ) if isinstance(function_call, dict) and len(formatted_functions) != 1: raise ValueError( "When specifying `function_call`, you must provide exactly one " "function." ) if ( isinstance(function_call, dict) and formatted_functions[0]["name"] != function_call["name"] ): raise ValueError( f"Function call {function_call} was specified, but the only " f"provided function was {formatted_functions[0]['name']}." ) kwargs = {**kwargs, "function_call": function_call} return super().bind(functions=formatted_functions, **kwargs) def bind_tools( self, tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]], *, tool_choice: Optional[ Union[dict, str, Literal["auto", "none", "required", "any"], bool] ] = None, **kwargs: Any, ) -> Runnable[LanguageModelInput, BaseMessage]: """Bind tool-like objects to this chat model. Assumes model is compatible with OpenAI tool-calling API. Args: tools: A list of tool definitions to bind to this chat model. Can be a dictionary, pydantic model, callable, or BaseTool. Pydantic models, callables, and BaseTools will be automatically converted to their schema dictionary representation. tool_choice: Which tool to require the model to call. Options are: name of the tool (str): calls corresponding tool; "auto": automatically selects a tool (including no tool); "none": does not call a tool; "any" or "required": force at least one tool to be called; True: forces tool call (requires `tools` be length 1); False: no effect; or a dict of the form: {"type": "function", "function": {"name": <>}}. **kwargs: Any additional parameters to pass to the :class:`~langchain.runnable.Runnable` constructor. """ formatted_tools = [convert_to_openai_tool(tool) for tool in tools] if tool_choice: if isinstance(tool_choice, str): # tool_choice is a tool/function name if tool_choice not in ("auto", "none", "any", "required"): tool_choice = { "type": "function", "function": {"name": tool_choice}, } # 'any' is not natively supported by OpenAI API. # We support 'any' since other models use this instead of 'required'. if tool_choice == "any": tool_choice = "required" elif isinstance(tool_choice, bool): tool_choice = "required" elif isinstance(tool_choice, dict): tool_names = [ formatted_tool["function"]["name"] for formatted_tool in formatted_tools ] if not any( tool_name == tool_choice["function"]["name"] for tool_name in tool_names ): raise ValueError( f"Tool choice {tool_choice} was specified, but the only " f"provided tools were {tool_names}." ) else: raise ValueError( f"Unrecognized tool_choice type. Expected str, bool or dict. " f"Received: {tool_choice}" ) kwargs["tool_choice"] = tool_choice return super().bind(tools=formatted_tools, **kwargs) # TODO: Fix typing. @overload # type: ignore[override] def with_structured_output( self, schema: Optional[_DictOrPydanticClass] = None, *, method: Literal["function_calling", "json_mode"] = "function_calling", include_raw: Literal[True] = True, **kwargs: Any, ) -> Runnable[LanguageModelInput, _AllReturnType]: ... @overload def with_structured_output( self, schema: Optional[_DictOrPydanticClass] = None, *, method: Literal["function_calling", "json_mode"] = "function_calling", include_raw: Literal[False] = False, **kwargs: Any, ) -> Runnable[LanguageModelInput, _DictOrPydantic]: ... def with_structured_output( self, schema: Optional[_DictOrPydanticClass] = None, *, method: Literal["function_calling", "json_mode"] = "function_calling", include_raw: bool = False, **kwargs: Any, ) -> Runnable[LanguageModelInput, _DictOrPydantic]: """Model wrapper that returns outputs formatted to match the given schema. Args: schema: The output schema as a dict or a Pydantic class. If a Pydantic class then the model output will be an object of that class. If a dict then the model output will be a dict. With a Pydantic class the returned attributes will be validated, whereas with a dict they will not be. If `method` is "function_calling" and `schema` is a dict, then the dict must match the OpenAI function-calling spec or be a valid JSON schema with top level 'title' and 'description' keys specified. method: The method for steering model generation, either "function_calling" or "json_mode". If "function_calling" then the schema will be converted to an OpenAI function and the returned model will make use of the function-calling API. If "json_mode" then OpenAI's JSON mode will be used. Note that if using "json_mode" then you must include instructions for formatting the output into the desired schema into the model call. include_raw: If False then only the parsed structured output is returned. If an error occurs during model output parsing it will be raised. If True then both the raw model response (a BaseMessage) and the parsed model response will be returned. If an error occurs during output parsing it will be caught and returned as well. The final output is always a dict with keys "raw", "parsed", and "parsing_error". Returns: A Runnable that takes any ChatModel input and returns as output: If include_raw is True then a dict with keys: raw: BaseMessage parsed: Optional[_DictOrPydantic] parsing_error: Optional[BaseException] If include_raw is False then just _DictOrPydantic is returned, where _DictOrPydantic depends on the schema: If schema is a Pydantic class then _DictOrPydantic is the Pydantic class. If schema is a dict then _DictOrPydantic is a dict. Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False): .. code-block:: python from langchain_openai import ChatOpenAI from langchain_core.pydantic_v1 import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) structured_llm = llm.with_structured_output(AnswerWithJustification) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> AnswerWithJustification( # answer='They weigh the same', # justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.' # ) Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True): .. code-block:: python from langchain_openai import ChatOpenAI from langchain_core.pydantic_v1 import BaseModel class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) structured_llm = llm.with_structured_output( AnswerWithJustification, include_raw=True ) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}), # 'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'), # 'parsing_error': None # } Example: Function-calling, dict schema (method="function_calling", include_raw=False): .. code-block:: python from langchain_openai import ChatOpenAI from langchain_core.pydantic_v1 import BaseModel from langchain_core.utils.function_calling import convert_to_openai_tool class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str dict_schema = convert_to_openai_tool(AnswerWithJustification) llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) structured_llm = llm.with_structured_output(dict_schema) structured_llm.invoke( "What weighs more a pound of bricks or a pound of feathers" ) # -> { # 'answer': 'They weigh the same', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.' # } Example: JSON mode, Pydantic schema (method="json_mode", include_raw=True): .. code-block:: from langchain_openai import ChatOpenAI from langchain_core.pydantic_v1 import BaseModel class AnswerWithJustification(BaseModel): answer: str justification: str llm = ChatOpenAI(model="gpt-3.5-turbo-0125", temperature=0) structured_llm = llm.with_structured_output( AnswerWithJustification, method="json_mode", include_raw=True ) structured_llm.invoke( "Answer the following question. " "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n" "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'), # 'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'), # 'parsing_error': None # } Example: JSON mode, no schema (schema=None, method="json_mode", include_raw=True): .. code-block:: from langchain_openai import ChatOpenAI structured_llm = llm.with_structured_output(method="json_mode", include_raw=True) structured_llm.invoke( "Answer the following question. " "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n" "What's heavier a pound of bricks or a pound of feathers?" ) # -> { # 'raw': AIMessage(content='{\n "answer": "They are both the same weight.",\n "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'), # 'parsed': { # 'answer': 'They are both the same weight.', # 'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.' # }, # 'parsing_error': None # } """ # noqa: E501 if kwargs: raise ValueError(f"Received unsupported arguments {kwargs}") is_pydantic_schema = _is_pydantic_class(schema) if method == "function_calling": if schema is None: raise ValueError( "schema must be specified when method is 'function_calling'. " "Received None." ) llm = self.bind_tools([schema], tool_choice=True, parallel_tool_calls=False) if is_pydantic_schema: output_parser: OutputParserLike = PydanticToolsParser( tools=[schema], first_tool_only=True ) else: key_name = convert_to_openai_tool(schema)["function"]["name"] output_parser = JsonOutputKeyToolsParser( key_name=key_name, first_tool_only=True ) elif method == "json_mode": llm = self.bind(response_format={"type": "json_object"}) output_parser = ( PydanticOutputParser(pydantic_object=schema) if is_pydantic_schema else JsonOutputParser() ) else: raise ValueError( f"Unrecognized method argument. Expected one of 'function_calling' or " f"'json_mode'. Received: '{method}'" ) if include_raw: parser_assign = RunnablePassthrough.assign( parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None ) parser_none = RunnablePassthrough.assign(parsed=lambda _: None) parser_with_fallback = parser_assign.with_fallbacks( [parser_none], exception_key="parsing_error" ) return RunnableMap(raw=llm) | parser_with_fallback else: return llm | output_parser class ChatOpenAI(BaseChatOpenAI): """OpenAI chat model integration. Setup: Install ``langchain-openai`` and set environment variable ``OPENAI_API_KEY``. .. code-block:: bash pip install -U langchain-openai export OPENAI_API_KEY="your-api-key" Key init args — completion params: model: str Name of OpenAI model to use. temperature: float Sampling temperature. max_tokens: Optional[int] Max number of tokens to generate. logprobs: Optional[bool] Whether to return logprobs. stream_options: Dict Configure streaming outputs, like whether to return token usage when streaming (``{"include_usage": True}``). Key init args — client params: timeout: Union[float, Tuple[float, float], Any, None] Timeout for requests. max_retries: int Max number of retries. api_key: Optional[str] OpenAI API key. If not passed in will be read from env var OPENAI_API_KEY. base_url: Optional[str] Base URL for API requests. Only specify if using a proxy or service emulator. organization: Optional[str] OpenAI organization ID. If not passed in will be read from env var OPENAI_ORG_ID. See full list of supported init args and their descriptions in the params section. Instantiate: .. code-block:: python from langchain_openai import ChatOpenAI llm = ChatOpenAI( model="gpt-4o", temperature=0, max_tokens=None, timeout=None, max_retries=2, # api_key="...", # base_url="...", # organization="...", # other params... ) **NOTE**: Any param which is not explicitly supported will be passed directly to the ``openai.OpenAI.chat.completions.create(...)`` API every time to the model is invoked. For example: .. code-block:: python from langchain_openai import ChatOpenAI import openai ChatOpenAI(..., frequency_penalty=0.2).invoke(...) # results in underlying API call of: openai.OpenAI(..).chat.completions.create(..., frequency_penalty=0.2) # which is also equivalent to: ChatOpenAI(...).invoke(..., frequency_penalty=0.2) Invoke: .. code-block:: python messages = [ ( "system", "You are a helpful translator. Translate the user sentence to French.", ), ("human", "I love programming."), ] llm.invoke(messages) .. code-block:: python AIMessage( content="J'adore la programmation.", response_metadata={ "token_usage": { "completion_tokens": 5, "prompt_tokens": 31, "total_tokens": 36, }, "model_name": "gpt-4o", "system_fingerprint": "fp_43dfabdef1", "finish_reason": "stop", "logprobs": None, }, id="run-012cffe2-5d3d-424d-83b5-51c6d4a593d1-0", usage_metadata={"input_tokens": 31, "output_tokens": 5, "total_tokens": 36}, ) Stream: .. code-block:: python for chunk in llm.stream(messages): print(chunk) .. code-block:: python AIMessageChunk(content="", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0") AIMessageChunk(content="J", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0") AIMessageChunk(content="'adore", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0") AIMessageChunk(content=" la", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0") AIMessageChunk( content=" programmation", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0" ) AIMessageChunk(content=".", id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0") AIMessageChunk( content="", response_metadata={"finish_reason": "stop"}, id="run-9e1517e3-12bf-48f2-bb1b-2e824f7cd7b0", ) .. code-block:: python stream = llm.stream(messages) full = next(stream) for chunk in stream: full += chunk full .. code-block:: python AIMessageChunk( content="J'adore la programmation.", response_metadata={"finish_reason": "stop"}, id="run-bf917526-7f58-4683-84f7-36a6b671d140", ) Async: .. code-block:: python await llm.ainvoke(messages) # stream: # async for chunk in (await llm.astream(messages)) # batch: # await llm.abatch([messages]) .. code-block:: python AIMessage( content="J'adore la programmation.", response_metadata={ "token_usage": { "completion_tokens": 5, "prompt_tokens": 31, "total_tokens": 36, }, "model_name": "gpt-4o", "system_fingerprint": "fp_43dfabdef1", "finish_reason": "stop", "logprobs": None, }, id="run-012cffe2-5d3d-424d-83b5-51c6d4a593d1-0", usage_metadata={"input_tokens": 31, "output_tokens": 5, "total_tokens": 36}, ) Tool calling: .. code-block:: python from langchain_core.pydantic_v1 import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field( ..., description="The city and state, e.g. San Francisco, CA" ) class GetPopulation(BaseModel): '''Get the current population in a given location''' location: str = Field( ..., description="The city and state, e.g. San Francisco, CA" ) llm_with_tools = llm.bind_tools([GetWeather, GetPopulation]) ai_msg = llm_with_tools.invoke( "Which city is hotter today and which is bigger: LA or NY?" ) ai_msg.tool_calls .. code-block:: python [ { "name": "GetWeather", "args": {"location": "Los Angeles, CA"}, "id": "call_6XswGD5Pqk8Tt5atYr7tfenU", }, { "name": "GetWeather", "args": {"location": "New York, NY"}, "id": "call_ZVL15vA8Y7kXqOy3dtmQgeCi", }, { "name": "GetPopulation", "args": {"location": "Los Angeles, CA"}, "id": "call_49CFW8zqC9W7mh7hbMLSIrXw", }, { "name": "GetPopulation", "args": {"location": "New York, NY"}, "id": "call_6ghfKxV264jEfe1mRIkS3PE7", }, ] Note that ``openai >= 1.32`` supports a ``parallel_tool_calls`` parameter that defaults to ``True``. This parameter can be set to ``False`` to disable parallel tool calls: .. code-block:: python ai_msg = llm_with_tools.invoke( "What is the weather in LA and NY?", parallel_tool_calls=False ) ai_msg.tool_calls .. code-block:: python [ { "name": "GetWeather", "args": {"location": "Los Angeles, CA"}, "id": "call_4OoY0ZR99iEvC7fevsH8Uhtz", } ] Like other runtime parameters, ``parallel_tool_calls`` can be bound to a model using ``llm.bind(parallel_tool_calls=False)`` or during instantiation by setting ``model_kwargs``. See ``ChatOpenAI.bind_tools()`` method for more. Structured output: .. code-block:: python from typing import Optional from langchain_core.pydantic_v1 import BaseModel, Field class Joke(BaseModel): '''Joke to tell user.''' setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10") structured_llm = llm.with_structured_output(Joke) structured_llm.invoke("Tell me a joke about cats") .. code-block:: python Joke( setup="Why was the cat sitting on the computer?", punchline="To keep an eye on the mouse!", rating=None, ) See ``ChatOpenAI.with_structured_output()`` for more. JSON mode: .. code-block:: python json_llm = llm.bind(response_format={"type": "json_object"}) ai_msg = json_llm.invoke( "Return a JSON object with key 'random_ints' and a value of 10 random ints in [0-99]" ) ai_msg.content .. code-block:: python '\\n{\\n "random_ints": [23, 87, 45, 12, 78, 34, 56, 90, 11, 67]\\n}' Image input: .. code-block:: python import base64 import httpx from langchain_core.messages import HumanMessage image_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Gfp-wisconsin-madison-the-nature-boardwalk.jpg/2560px-Gfp-wisconsin-madison-the-nature-boardwalk.jpg" image_data = base64.b64encode(httpx.get(image_url).content).decode("utf-8") message = HumanMessage( content=[ {"type": "text", "text": "describe the weather in this image"}, { "type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}, }, ] ) ai_msg = llm.invoke([message]) ai_msg.content .. code-block:: python "The weather in the image appears to be clear and pleasant. The sky is mostly blue with scattered, light clouds, suggesting a sunny day with minimal cloud cover. There is no indication of rain or strong winds, and the overall scene looks bright and calm. The lush green grass and clear visibility further indicate good weather conditions." Token usage: .. code-block:: python ai_msg = llm.invoke(messages) ai_msg.usage_metadata .. code-block:: python {"input_tokens": 28, "output_tokens": 5, "total_tokens": 33} When streaming, set the ``stream_usage`` kwarg: .. code-block:: python stream = llm.stream(messages, stream_usage=True) full = next(stream) for chunk in stream: full += chunk full.usage_metadata .. code-block:: python {"input_tokens": 28, "output_tokens": 5, "total_tokens": 33} Alternatively, setting ``stream_usage`` when instantiating the model can be useful when incorporating ``ChatOpenAI`` into LCEL chains-- or when using methods like ``.with_structured_output``, which generate chains under the hood. .. code-block:: python llm = ChatOpenAI(model="gpt-4o", stream_usage=True) structured_llm = llm.with_structured_output(...) Logprobs: .. code-block:: python logprobs_llm = llm.bind(logprobs=True) ai_msg = logprobs_llm.invoke(messages) ai_msg.response_metadata["logprobs"] .. code-block:: python { "content": [ { "token": "J", "bytes": [74], "logprob": -4.9617593e-06, "top_logprobs": [], }, { "token": "'adore", "bytes": [39, 97, 100, 111, 114, 101], "logprob": -0.25202933, "top_logprobs": [], }, { "token": " la", "bytes": [32, 108, 97], "logprob": -0.20141791, "top_logprobs": [], }, { "token": " programmation", "bytes": [ 32, 112, 114, 111, 103, 114, 97, 109, 109, 97, 116, 105, 111, 110, ], "logprob": -1.9361265e-07, "top_logprobs": [], }, { "token": ".", "bytes": [46], "logprob": -1.2233183e-05, "top_logprobs": [], }, ] } Response metadata .. code-block:: python ai_msg = llm.invoke(messages) ai_msg.response_metadata .. code-block:: python { "token_usage": { "completion_tokens": 5, "prompt_tokens": 28, "total_tokens": 33, }, "model_name": "gpt-4o", "system_fingerprint": "fp_319be4768e", "finish_reason": "stop", "logprobs": None, } """ # noqa: E501 stream_usage: bool = False """Whether to include usage metadata in streaming output. If True, additional message chunks will be generated during the stream including usage metadata. """ @property def lc_secrets(self) -> Dict[str, str]: return {"openai_api_key": "OPENAI_API_KEY"} @classmethod def get_lc_namespace(cls) -> List[str]: """Get the namespace of the langchain object.""" return ["langchain", "chat_models", "openai"] @property def lc_attributes(self) -> Dict[str, Any]: attributes: Dict[str, Any] = {} if self.openai_organization: attributes["openai_organization"] = self.openai_organization if self.openai_api_base: attributes["openai_api_base"] = self.openai_api_base if self.openai_proxy: attributes["openai_proxy"] = self.openai_proxy return attributes @classmethod def is_lc_serializable(cls) -> bool: """Return whether this model can be serialized by Langchain.""" return True def _should_stream_usage( self, stream_usage: Optional[bool] = None, **kwargs: Any ) -> bool: """Determine whether to include usage metadata in streaming output. For backwards compatibility, we check for `stream_options` passed explicitly to kwargs or in the model_kwargs and override self.stream_usage. """ stream_usage_sources = [ # order of preference stream_usage, kwargs.get("stream_options", {}).get("include_usage"), self.model_kwargs.get("stream_options", {}).get("include_usage"), self.stream_usage, ] for source in stream_usage_sources: if isinstance(source, bool): return source return self.stream_usage def _stream( self, *args: Any, stream_usage: Optional[bool] = None, **kwargs: Any ) -> Iterator[ChatGenerationChunk]: """Set default stream_options.""" stream_usage = self._should_stream_usage(stream_usage, **kwargs) kwargs["stream_options"] = {"include_usage": stream_usage} return super()._stream(*args, **kwargs) async def _astream( self, *args: Any, stream_usage: Optional[bool] = None, **kwargs: Any ) -> AsyncIterator[ChatGenerationChunk]: """Set default stream_options.""" stream_usage = self._should_stream_usage(stream_usage, **kwargs) kwargs["stream_options"] = {"include_usage": stream_usage} async for chunk in super()._astream(*args, **kwargs): yield chunk def _is_pydantic_class(obj: Any) -> bool: return isinstance(obj, type) and issubclass(obj, BaseModel) def _lc_tool_call_to_openai_tool_call(tool_call: ToolCall) -> dict: return { "type": "function", "id": tool_call["id"], "function": { "name": tool_call["name"], "arguments": json.dumps(tool_call["args"]), }, } def _lc_invalid_tool_call_to_openai_tool_call( invalid_tool_call: InvalidToolCall, ) -> dict: return { "type": "function", "id": invalid_tool_call["id"], "function": { "name": invalid_tool_call["name"], "arguments": invalid_tool_call["args"], }, } def _url_to_size(image_source: str) -> Optional[Tuple[int, int]]: try: from PIL import Image # type: ignore[import] except ImportError: logger.info( "Unable to count image tokens. To count image tokens please install " "`pip install -U pillow httpx`." ) return None if _is_url(image_source): try: import httpx except ImportError: logger.info( "Unable to count image tokens. To count image tokens please install " "`pip install -U httpx`." ) return None response = httpx.get(image_source) response.raise_for_status() width, height = Image.open(BytesIO(response.content)).size return width, height elif _is_b64(image_source): _, encoded = image_source.split(",", 1) data = base64.b64decode(encoded) width, height = Image.open(BytesIO(data)).size return width, height else: return None def _count_image_tokens(width: int, height: int) -> int: # Reference: https://platform.openai.com/docs/guides/vision/calculating-costs width, height = _resize(width, height) h = ceil(height / 512) w = ceil(width / 512) return (170 * h * w) + 85 def _is_url(s: str) -> bool: try: result = urlparse(s) return all([result.scheme, result.netloc]) except Exception as e: logger.debug(f"Unable to parse URL: {e}") return False def _is_b64(s: str) -> bool: return s.startswith("data:image") def _resize(width: int, height: int) -> Tuple[int, int]: # larger side must be <= 2048 if width > 2048 or height > 2048: if width > height: height = (height * 2048) // width width = 2048 else: width = (width * 2048) // height height = 2048 # smaller side must be <= 768 if width > 768 and height > 768: if width > height: width = (width * 768) // height height = 768 else: height = (width * 768) // height width = 768 return width, height