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288 lines
11 KiB
Python
288 lines
11 KiB
Python
import json
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from application.core.settings import settings
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from application.llm.base import BaseLLM
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class OpenAILLM(BaseLLM):
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def __init__(self, api_key=None, user_api_key=None, *args, **kwargs):
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from openai import OpenAI
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super().__init__(*args, **kwargs)
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if settings.OPENAI_BASE_URL:
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self.client = OpenAI(api_key=api_key, base_url=settings.OPENAI_BASE_URL)
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else:
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self.client = OpenAI(api_key=api_key)
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self.api_key = api_key
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self.user_api_key = user_api_key
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def _clean_messages_openai(self, messages):
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cleaned_messages = []
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for message in messages:
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role = message.get("role")
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content = message.get("content")
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if role == "model":
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role = "assistant"
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if role and content is not None:
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if isinstance(content, str):
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cleaned_messages.append({"role": role, "content": content})
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elif isinstance(content, list):
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for item in content:
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if "text" in item:
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cleaned_messages.append(
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{"role": role, "content": item["text"]}
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)
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elif isinstance(item, dict):
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content_parts = []
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if "text" in item:
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content_parts.append({"type": "text", "text": item["text"]})
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elif "type" in item and item["type"] == "text" and "text" in item:
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content_parts.append(item)
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elif "type" in item and item["type"] == "file" and "file" in item:
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content_parts.append(item)
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cleaned_messages.append({"role": role, "content": content_parts})
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elif "function_call" in item:
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tool_call = {
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"id": item["function_call"]["call_id"],
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"type": "function",
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"function": {
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"name": item["function_call"]["name"],
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"arguments": json.dumps(
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item["function_call"]["args"]
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),
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},
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}
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cleaned_messages.append(
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{
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"role": "assistant",
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"content": None,
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"tool_calls": [tool_call],
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}
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)
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elif "function_response" in item:
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cleaned_messages.append(
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{
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"role": "tool",
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"tool_call_id": item["function_response"][
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"call_id"
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],
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"content": json.dumps(
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item["function_response"]["response"]["result"]
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),
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}
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)
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else:
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raise ValueError(
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f"Unexpected content dictionary format: {item}"
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)
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else:
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raise ValueError(f"Unexpected content type: {type(content)}")
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return cleaned_messages
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def _raw_gen(
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self,
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baseself,
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model,
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messages,
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stream=False,
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tools=None,
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engine=settings.AZURE_DEPLOYMENT_NAME,
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**kwargs,
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):
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messages = self._clean_messages_openai(messages)
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if tools:
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response = self.client.chat.completions.create(
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model=model,
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messages=messages,
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stream=stream,
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tools=tools,
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**kwargs,
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)
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return response.choices[0]
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else:
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response = self.client.chat.completions.create(
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model=model, messages=messages, stream=stream, **kwargs
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)
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return response.choices[0].message.content
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def _raw_gen_stream(
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self,
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baseself,
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model,
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messages,
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stream=True,
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tools=None,
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engine=settings.AZURE_DEPLOYMENT_NAME,
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**kwargs,
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):
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messages = self._clean_messages_openai(messages)
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if tools:
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response = self.client.chat.completions.create(
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model=model,
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messages=messages,
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stream=stream,
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tools=tools,
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**kwargs,
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)
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else:
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response = self.client.chat.completions.create(
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model=model, messages=messages, stream=stream, **kwargs
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)
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for line in response:
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if len(line.choices) > 0 and line.choices[0].delta.content is not None and len(line.choices[0].delta.content) > 0:
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yield line.choices[0].delta.content
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elif len(line.choices) > 0:
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yield line.choices[0]
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def _supports_tools(self):
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return True
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def prepare_messages_with_attachments(self, messages, attachments=None):
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"""
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Process attachments using OpenAI's file API for more efficient handling.
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Args:
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messages (list): List of message dictionaries.
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attachments (list): List of attachment dictionaries with content and metadata.
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Returns:
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list: Messages formatted with file references for OpenAI API.
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"""
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if not attachments:
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return messages
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prepared_messages = messages.copy()
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# Find the user message to attach file_id to the last one
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user_message_index = None
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for i in range(len(prepared_messages) - 1, -1, -1):
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if prepared_messages[i].get("role") == "user":
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user_message_index = i
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break
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if user_message_index is None:
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user_message = {"role": "user", "content": []}
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prepared_messages.append(user_message)
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user_message_index = len(prepared_messages) - 1
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if isinstance(prepared_messages[user_message_index].get("content"), str):
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text_content = prepared_messages[user_message_index]["content"]
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prepared_messages[user_message_index]["content"] = [
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{"type": "text", "text": text_content}
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]
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elif not isinstance(prepared_messages[user_message_index].get("content"), list):
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prepared_messages[user_message_index]["content"] = []
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for attachment in attachments:
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# Upload the file to OpenAI
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try:
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file_id = self._upload_file_to_openai(attachment)
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prepared_messages[user_message_index]["content"].append({
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"type": "file",
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"file": {"file_id": file_id}
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})
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except Exception as e:
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import logging
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logging.error(f"Error uploading attachment to OpenAI: {e}")
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if 'content' in attachment:
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prepared_messages[user_message_index]["content"].append({
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"type": "text",
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"text": f"File content:\n\n{attachment['content']}"
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})
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return prepared_messages
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def _upload_file_to_openai(self, attachment):
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"""
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Upload a file to OpenAI and return the file_id.
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Args:
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attachment (dict): Attachment dictionary with path and metadata.
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Expected keys:
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- path: Path to the file
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- id: Optional MongoDB ID for caching
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Returns:
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str: OpenAI file_id for the uploaded file.
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"""
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import os
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import mimetypes
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# Check if we already have the file_id cached
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if 'openai_file_id' in attachment:
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return attachment['openai_file_id']
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file_path = attachment.get('path')
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if not file_path:
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raise ValueError("No file path provided in attachment")
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# Make path absolute if it's relative
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if not os.path.isabs(file_path):
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current_dir = os.path.dirname(
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os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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)
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file_path = os.path.join(current_dir,"application", file_path)
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if not os.path.exists(file_path):
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raise FileNotFoundError(f"File not found: {file_path}")
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mime_type = attachment.get('mime_type')
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if not mime_type:
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mime_type = mimetypes.guess_type(file_path)[0] or 'application/octet-stream'
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supported_mime_types = ['application/pdf', 'image/png', 'image/jpeg', 'image/gif']
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if mime_type not in supported_mime_types:
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import logging
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logging.warning(f"MIME type {mime_type} not supported by OpenAI for file uploads. Falling back to text.")
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raise ValueError(f"Unsupported MIME type: {mime_type}")
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try:
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with open(file_path, 'rb') as file:
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response = self.client.files.create(
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file=file,
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purpose="assistants"
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)
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file_id = response.id
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from application.core.mongo_db import MongoDB
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mongo = MongoDB.get_client()
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db = mongo["docsgpt"]
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attachments_collection = db["attachments"]
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if '_id' in attachment:
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attachments_collection.update_one(
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{"_id": attachment['_id']},
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{"$set": {"openai_file_id": file_id}}
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)
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return file_id
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except Exception as e:
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import logging
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logging.error(f"Error uploading file to OpenAI: {e}")
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raise
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class AzureOpenAILLM(OpenAILLM):
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def __init__(
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self, api_key, user_api_key, *args, **kwargs
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):
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super().__init__(api_key)
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self.api_base = (settings.OPENAI_API_BASE,)
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self.api_version = (settings.OPENAI_API_VERSION,)
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self.deployment_name = (settings.AZURE_DEPLOYMENT_NAME,)
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from openai import AzureOpenAI
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self.client = AzureOpenAI(
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api_key=api_key,
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api_version=settings.OPENAI_API_VERSION,
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azure_endpoint=settings.OPENAI_API_BASE
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)
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