133 lines
5.5 KiB
Python
133 lines
5.5 KiB
Python
import re
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import time
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from uuid import uuid4
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import tiktoken
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from flask import jsonify
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from llm_server import opts
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from llm_server.database import log_prompt
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from llm_server.routes.helpers.client import format_sillytavern_err
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from llm_server.routes.helpers.http import validate_json
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from llm_server.routes.queue import priority_queue
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from llm_server.routes.request_handler import RequestHandler
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tokenizer = tiktoken.get_encoding("cl100k_base")
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class OpenAIRequestHandler(RequestHandler):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.prompt = None
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def handle_request(self):
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if self.used:
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raise Exception
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request_valid_json, self.request_json_body = validate_json(self.request)
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self.prompt = self.transform_messages_to_prompt()
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if not request_valid_json or not self.prompt:
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return jsonify({'code': 400, 'msg': 'Invalid JSON'}), 400
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params_valid, request_valid = self.validate_request()
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if not request_valid[0] or not params_valid[0]:
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error_messages = [msg for valid, msg in [request_valid, params_valid] if not valid and msg]
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combined_error_message = ', '.join(error_messages)
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err = format_sillytavern_err(f'Validation Error: {combined_error_message}.', 'error')
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log_prompt(self.client_ip, self.token, self.request_json_body.get('prompt', ''), err, 0, self.parameters, dict(self.request.headers), 0, self.request.url, is_error=True)
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# TODO: add a method to LLMBackend to return a formatted response string, since we have both Ooba and OpenAI response types
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return jsonify({
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'code': 400,
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'msg': 'parameter validation error',
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'results': [{'text': err}]
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}), 200
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# Reconstruct the request JSON with the validated parameters and prompt.
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self.parameters['stop'].extend(['\n### INSTRUCTION', '\n### USER', '\n### ASSISTANT', '\n### RESPONSE'])
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llm_request = {**self.parameters, 'prompt': self.prompt}
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if not self.is_client_ratelimited():
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event = priority_queue.put((llm_request, self.client_ip, self.token, self.parameters), self.priority)
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else:
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event = None
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if not event:
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return self.handle_ratelimited()
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event.wait()
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success, backend_response, error_msg = event.data
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end_time = time.time()
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elapsed_time = end_time - self.start_time
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self.used = True
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response, response_status_code = self.backend.handle_response(success=success, request=self.request, response=backend_response, error_msg=error_msg, client_ip=self.client_ip, token=self.token, prompt=self.prompt, elapsed_time=elapsed_time, parameters=self.parameters, headers=dict(self.request.headers))
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return build_openai_response(self.prompt, response.json['results'][0]['text']), 200
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def handle_ratelimited(self):
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backend_response = format_sillytavern_err(f'Ratelimited: you are only allowed to have {opts.simultaneous_requests_per_ip} simultaneous requests at a time. Please complete your other requests before sending another.', 'error')
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log_prompt(ip=self.client_ip, token=self.token, prompt=self.request_json_body.get('prompt', ''), response=backend_response, gen_time=None, parameters=self.parameters, headers=dict(self.request.headers), backend_response_code=429, request_url=self.request.url, is_error=True)
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return build_openai_response(self.prompt, backend_response), 200
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def transform_messages_to_prompt(self):
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try:
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prompt = f'### INSTRUCTION: {opts.openai_system_prompt}'
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for msg in self.request.json['messages']:
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if not msg.get('content') or not msg.get('role'):
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return False
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if msg['role'] == 'system':
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prompt += f'### INSTRUCTION: {msg["content"]}\n\n'
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elif msg['role'] == 'user':
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prompt += f'### USER: {msg["content"]}\n\n'
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elif msg['role'] == 'assistant':
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prompt += f'### ASSISTANT: {msg["content"]}\n\n'
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else:
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return False
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except:
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return False
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prompt = prompt.strip(' ').strip('\n').strip('\n\n') # TODO: this is really lazy
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prompt += '\n\n### RESPONSE: '
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return prompt
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def build_openai_response(prompt, response):
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# Seperate the user's prompt from the context
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x = prompt.split('### USER:')
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if len(x) > 1:
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prompt = re.sub(r'\n$', '', x[-1].strip(' '))
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# Make sure the bot doesn't put any other instructions in its response
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y = response.split('\n### ')
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if len(x) > 1:
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response = re.sub(r'\n$', '', y[0].strip(' '))
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prompt_tokens = len(tokenizer.encode(prompt))
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response_tokens = len(tokenizer.encode(response))
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return jsonify({
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"id": f"chatcmpl-{uuid4()}",
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"object": "chat.completion",
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"created": int(time.time()),
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"model": opts.running_model,
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"choices": [{
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"index": 0,
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"message": {
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"role": "assistant",
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"content": response,
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},
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"finish_reason": "stop"
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}],
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"usage": {
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"prompt_tokens": prompt_tokens,
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"completion_tokens": response_tokens,
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"total_tokens": prompt_tokens + response_tokens
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}
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})
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# def transform_prompt_to_text(prompt: list):
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# text = ''
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# for item in prompt:
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# text += item['content'] + '\n'
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# return text.strip('\n')
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