Create openai/oai_extractor.py

openai/oai_extractor.py

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