Source code for langchain_community.utilities.dalle_image_generator

"""Utility that calls OpenAI's Dall-E Image Generator."""
import logging
import os
from typing import Any, Dict, Mapping, Optional, Tuple, Union

from langchain_core.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain_core.utils import (

from langchain_community.utils.openai import is_openai_v1

logger = logging.getLogger(__name__)

[docs]class DallEAPIWrapper(BaseModel): """Wrapper for OpenAI's DALL-E Image Generator. Usage instructions: 1. `pip install openai` 2. save your OPENAI_API_KEY in an environment variable """ client: Any #: :meta private: async_client: Any = Field(default=None, exclude=True) #: :meta private: model_name: str = Field(default="dall-e-2", alias="model") model_kwargs: Dict[str, Any] = Field(default_factory=dict) openai_api_key: Optional[str] = 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" ) n: int = 1 """Number of images to generate""" size: str = "1024x1024" """Size of image to generate""" separator: str = "\n" """Separator to use when multiple URLs are returned.""" quality: Optional[str] = "standard" """Quality of the image that will be generated""" max_retries: int = 2 """Maximum number of retries to make when generating.""" 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]( for more details. http_client: Union[Any, None] = None """Optional httpx.Client.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @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", {}) for field_name in list(values): if field_name in extra: raise ValueError(f"Found {field_name} supplied twice.") if field_name not in all_required_field_names: logger.warning( f"""WARNING! {field_name} is not default parameter. {field_name} was transferred to model_kwargs. Please confirm that {field_name} is what you intended.""" ) extra[field_name] = values.pop(field_name) invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) if invalid_model_kwargs: raise ValueError( f"Parameters {invalid_model_kwargs} should be specified explicitly. " f"Instead they were passed in as part of `model_kwargs` parameter." ) values["model_kwargs"] = extra return values @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" values["openai_api_key"] = 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") or 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="", ) try: import openai except ImportError: raise ImportError( "Could not import openai python package. " "Please install it with `pip install openai`." ) if is_openai_v1(): client_params = { "api_key": values["openai_api_key"], "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"], "http_client": values["http_client"], } if not values.get("client"): values["client"] = openai.OpenAI(**client_params).images if not values.get("async_client"): values["async_client"] = openai.AsyncOpenAI(**client_params).images elif not values.get("client"): values["client"] = openai.Image else: pass return values
[docs] def run(self, query: str) -> str: """Run query through OpenAI and parse result.""" if is_openai_v1(): response = self.client.generate( prompt=query, n=self.n, size=self.size, model=self.model_name, quality=self.quality, ) image_urls = self.separator.join([item.url for item in]) else: response = self.client.create( prompt=query, n=self.n, size=self.size, model=self.model_name ) image_urls = self.separator.join([item["url"] for item in response["data"]]) return image_urls if image_urls else "No image was generated"