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Create oai/oai_extractor.py
Browse files- oai/oai_extractor.py +78 -0
oai/oai_extractor.py
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from typing import List, Union, Optional
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from indexify_extractor_sdk import Content, Extractor, Feature
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from pydantic import BaseModel, Field
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import os
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import base64
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from openai import OpenAI
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from pdf2image import convert_from_path
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import tempfile
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import mimetypes
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class OAIExtractorConfig(BaseModel):
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model_name: Optional[str] = Field(default='gpt-3.5-turbo')
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key: Optional[str] = Field(default=None)
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prompt: str = Field(default='You are a helpful assistant.')
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query: Optional[str] = Field(default=None)
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class OAIExtractor(Extractor):
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name = "tensorlake/openai"
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description = "An extractor that let's you use LLMs from OpenAI."
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system_dependencies = []
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input_mime_types = ["text/plain", "application/pdf", "image/jpeg", "image/png"]
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def __init__(self):
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super(OAIExtractor, self).__init__()
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def extract(self, content: Content, params: OAIExtractorConfig) -> List[Union[Feature, Content]]:
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contents = []
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model_name = params.model_name
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key = params.key
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prompt = params.prompt
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query = params.query
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if content.content_type in ["application/pdf"]:
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with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
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temp_file.write(content.data)
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file_path = temp_file.name
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images = convert_from_path(file_path)
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image_files = []
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for i in range(len(images)):
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with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as temp_image_file:
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images[i].save(temp_image_file.name, 'JPEG')
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image_files.append(temp_image_file.name)
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elif content.content_type in ["image/jpeg", "image/png"]:
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image_files = []
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suffix = mimetypes.guess_extension(content.content_type)
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with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as temp_image_file:
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temp_image_file.write(content.data)
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file_path = temp_image_file.name
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image_files.append(file_path)
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else:
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text = content.data.decode("utf-8")
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if query is None:
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query = text
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file_path = None
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def encode_image(image_path):
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode('utf-8')
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if ('OPENAI_API_KEY' not in os.environ) and (key is None):
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response_content = "The OPENAI_API_KEY environment variable is not present."
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else:
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if ('OPENAI_API_KEY' in os.environ) and (key is None):
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client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
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else:
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client = OpenAI(api_key=key)
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if file_path:
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encoded_images = [encode_image(image_path) for image_path in image_files]
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messages_content = [ { "role": "user", "content": [ { "type": "text", "text": prompt, } ] + [ { "type": "image_url", "image_url": { "url": f"data:image/jpeg;base64,{encoded_image}", }, } for encoded_image in encoded_images ], } ]
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else:
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messages_content = [ {"role": "system", "content": prompt}, {"role": "user", "content": query} ]
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response = client.chat.completions.create( model=model_name, messages=messages_content )
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response_content = response.choices[0].message.content
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contents.append(Content.from_text(response_content))
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return contents
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