llm_server/database/database.py

@@ -1,7 +1,6 @@
 import json
 import time
 import traceback
-from threading import Thread
 from typing import Union
 
 from llm_server import opts
@@ -10,12 +9,10 @@ from llm_server.database.conn import database
 from llm_server.llm import get_token_count
 
 
-def log_prompt(ip: str, token: str, prompt: str, response: Union[str, None], gen_time: Union[int, float, None], parameters: dict, headers: dict, backend_response_code: int, request_url: str, backend_url: str, response_tokens: int = None, is_error: bool = False):
+def do_db_log(ip: str, token: str, prompt: str, response: Union[str, None], gen_time: Union[int, float, None], parameters: dict, headers: dict, backend_response_code: int, request_url: str, backend_url: str, response_tokens: int = None, is_error: bool = False):
     assert isinstance(prompt, str)
     assert isinstance(backend_url, str)
 
-    def background_task():
-        nonlocal ip, token, prompt, response, gen_time, parameters, headers, backend_response_code, request_url, backend_url, response_tokens, is_error
     # Try not to shove JSON into the database.
     if isinstance(response, dict) and response.get('results'):
         response = response['results'][0]['text']
@@ -27,6 +24,8 @@ def log_prompt(ip: str, token: str, prompt: str, response: Union[str, None], gen
         pass
 
     prompt_tokens = get_token_count(prompt, backend_url)
+    print('starting')
+
     if not is_error:
         if not response_tokens:
             response_tokens = get_token_count(response, backend_url)
@@ -65,11 +64,6 @@ def log_prompt(ip: str, token: str, prompt: str, response: Union[str, None], gen
     finally:
         cursor.close()
 
-    # TODO: use async/await instead of threads
-    thread = Thread(target=background_task)
-    thread.start()
-    thread.join()
-
 
 def is_valid_api_key(api_key):
     cursor = database.cursor()

llm_server/database/log_to_db.py

@@ -0,0 +1,27 @@
+import pickle
+from typing import Union
+
+from redis import Redis
+
+
+def log_to_db(ip: str, token: str, prompt: str, response: Union[str, None], gen_time: Union[int, float, None], parameters: dict, headers: dict, backend_response_code: int, request_url: str, backend_url: str, response_tokens: int = None, is_error: bool = False):
+    r = Redis(host='localhost', port=6379, db=3)
+    data = {
+        'function': 'log_prompt',
+        'args': [],
+        'kwargs': {
+            'ip': ip,
+            'token': token,
+            'prompt': prompt,
+            'response': response,
+            'gen_time': gen_time,
+            'parameters': parameters,
+            'headers': headers,
+            'backend_response_code': backend_response_code,
+            'request_url': request_url,
+            'backend_url': backend_url,
+            'response_tokens': response_tokens,
+            'is_error': is_error
+        }
+    }
+    r.publish('database-logger', pickle.dumps(data))

llm_server/llm/oobabooga/ooba_backend.py

@@ -2,7 +2,7 @@ from flask import jsonify
 
 from llm_server.custom_redis import redis
 from ..llm_backend import LLMBackend
-from ...database.database import log_prompt
+from ...database.database import do_db_log
 from ...helpers import safe_list_get
 from ...routes.helpers.client import format_sillytavern_err
 from ...routes.helpers.http import validate_json
@@ -34,7 +34,7 @@ class OobaboogaBackend(LLMBackend):
             else:
                 error_msg = error_msg.strip('.') + '.'
             backend_response = format_sillytavern_err(error_msg, error_type='error', backend_url=self.backend_url)
-            log_prompt(client_ip, token, prompt, backend_response, None, parameters, headers, response_status_code, request.url, is_error=True)
+            log_to_db(client_ip, token, prompt, backend_response, None, parameters, headers, response_status_code, request.url, is_error=True)
             return jsonify({
                 'code': 500,
                 'msg': error_msg,
@@ -57,13 +57,13 @@ class OobaboogaBackend(LLMBackend):
             if not backend_err:
                 redis.incr('proompts')
 
-            log_prompt(client_ip, token, prompt, backend_response, elapsed_time if not backend_err else None, parameters, headers, response_status_code, request.url, response_tokens=response_json_body.get('details', {}).get('generated_tokens'), is_error=backend_err)
+            log_to_db(client_ip, token, prompt, backend_response, elapsed_time if not backend_err else None, parameters, headers, response_status_code, request.url, response_tokens=response_json_body.get('details', {}).get('generated_tokens'), is_error=backend_err)
             return jsonify({
                 **response_json_body
             }), 200
         else:
             backend_response = format_sillytavern_err(f'The backend did not return valid JSON.', error_type='error', backend_url=self.backend_url)
-            log_prompt(client_ip, token, prompt, backend_response, elapsed_time, parameters, headers, response.status_code, request.url, is_error=True)
+            log_to_db(client_ip, token, prompt, backend_response, elapsed_time, parameters, headers, response.status_code, request.url, is_error=True)
             return jsonify({
                 'code': 500,
                 'msg': 'the backend did not return valid JSON',

llm_server/llm/openai/oai_to_vllm.py

@@ -3,17 +3,19 @@ from flask import jsonify
 from llm_server import opts
 
 
-def oai_to_vllm(request_json_body, hashes: bool, mode):
+def oai_to_vllm(request_json_body, stop_hashes: bool, mode):
     if not request_json_body.get('stop'):
         request_json_body['stop'] = []
     if not isinstance(request_json_body['stop'], list):
         # It is a string, so create a list with the existing element.
         request_json_body['stop'] = [request_json_body['stop']]
 
-    if hashes:
-        request_json_body['stop'].extend(['### INSTRUCTION', '### USER', '### ASSISTANT', '### RESPONSE'])
+    if stop_hashes:
         if opts.openai_force_no_hashes:
-            request_json_body['stop'].append('### ')
+            request_json_body['stop'].append('###')
+        else:
+            # TODO: make stopping strings a configurable
+            request_json_body['stop'].extend(['### INSTRUCTION', '### USER', '### ASSISTANT'])
     else:
         request_json_body['stop'].extend(['user:', 'assistant:'])
 
@@ -41,6 +43,11 @@ def format_oai_err(err_msg):
 
 
 def validate_oai(parameters):
+    if parameters.get('messages'):
+        for m in parameters['messages']:
+            if m['role'].lower() not in ['assistant', 'user', 'system']:
+                return format_oai_err('messages role must be assistant, user, or system')
+
     if parameters.get('temperature', 0) > 2:
         return format_oai_err(f"{parameters['temperature']} is greater than the maximum of 2 - 'temperature'")
     if parameters.get('temperature', 0) < 0:

llm_server/llm/openai/transform.py

@@ -96,7 +96,7 @@ def transform_messages_to_prompt(oai_messages):
             elif msg['role'] == 'assistant':
                 prompt += f'### ASSISTANT: {msg["content"]}\n\n'
             else:
-                return False
+                raise Exception(f'Unknown role: {msg["role"]}')
     except Exception as e:
         # TODO: use logging
         traceback.print_exc()

llm_server/llm/vllm/generate.py

@@ -1,24 +1,16 @@
 """
 This file is used by the worker that processes requests.
 """
-import json
-import time
-from uuid import uuid4
 
 import requests
 
-import llm_server
 from llm_server import opts
-from llm_server.custom_redis import redis
 
 
 # TODO: make the VLMM backend return TPS and time elapsed
 # https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/api_server.py
 
 def prepare_json(json_data: dict):
-    # logit_bias is not currently supported
-    # del json_data['logit_bias']
-
     # Convert back to VLLM.
     json_data['max_tokens'] = json_data.pop('max_new_tokens')
     return json_data

llm_server/llm/vllm/vllm_backend.py

@@ -3,7 +3,7 @@ from typing import Tuple
 from flask import jsonify
 from vllm import SamplingParams
 
-from llm_server.database.database import log_prompt
+from llm_server.database.log_to_db import log_to_db
 from llm_server.llm.llm_backend import LLMBackend
 
 
@@ -18,7 +18,7 @@ class VLLMBackend(LLMBackend):
             # Failsafe
             backend_response = ''
 
-        log_prompt(ip=client_ip, token=token, prompt=prompt, response=backend_response, gen_time=elapsed_time, parameters=parameters, headers=headers, backend_response_code=response_status_code, request_url=request.url,
+        log_to_db(ip=client_ip, token=token, prompt=prompt, response=backend_response, gen_time=elapsed_time, parameters=parameters, headers=headers, backend_response_code=response_status_code, request_url=request.url,
                   response_tokens=response_json_body.get('details', {}).get('generated_tokens'), backend_url=self.backend_url)
 
         return jsonify({'results': [{'text': backend_response}]}), 200
@@ -29,14 +29,15 @@ class VLLMBackend(LLMBackend):
             top_k = parameters.get('top_k', self._default_params['top_k'])
             if top_k <= 0:
                 top_k = -1
+
             sampling_params = SamplingParams(
                 temperature=parameters.get('temperature', self._default_params['temperature']),
                 top_p=parameters.get('top_p', self._default_params['top_p']),
                 top_k=top_k,
                 use_beam_search=True if parameters.get('num_beams', 0) > 1 else False,
-                stop=list(set(parameters.get('stopping_strings', self._default_params['stop']) or parameters.get('stop', self._default_params['stop']))),
+                stop=list(set(parameters.get('stopping_strings') or parameters.get('stop', self._default_params['stop']))),
                 ignore_eos=parameters.get('ban_eos_token', False),
-                max_tokens=parameters.get('max_new_tokens', self._default_params['max_tokens']) or parameters.get('max_tokens', self._default_params['max_tokens']),
+                max_tokens=parameters.get('max_new_tokens') or parameters.get('max_tokens', self._default_params['max_tokens']),
                 presence_penalty=parameters.get('presence_penalty', self._default_params['presence_penalty']),
                 frequency_penalty=parameters.get('frequency_penalty', self._default_params['frequency_penalty'])
             )

llm_server/routes/ooba_request_handler.py

@@ -4,7 +4,8 @@ import flask
 from flask import jsonify, request
 
 from llm_server import opts
-from llm_server.database.database import log_prompt
+from llm_server.database.database import do_db_log
+from llm_server.database.log_to_db import log_to_db
 from llm_server.routes.helpers.client import format_sillytavern_err
 from llm_server.routes.request_handler import RequestHandler
 
@@ -40,7 +41,7 @@ class OobaRequestHandler(RequestHandler):
         msg = 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.'
         backend_response = self.handle_error(msg)
         if do_log:
-            log_prompt(self.client_ip, self.token, self.request_json_body.get('prompt', ''), backend_response[0].data.decode('utf-8'), None, self.parameters, dict(self.request.headers), 429, self.request.url, self.backend_url, is_error=True)
+            log_to_db(self.client_ip, self.token, self.request_json_body.get('prompt', ''), backend_response[0].data.decode('utf-8'), None, self.parameters, dict(self.request.headers), 429, self.request.url, self.backend_url, is_error=True)
         return backend_response[0], 429
 
     def handle_error(self, error_msg: str, error_type: str = 'error') -> Tuple[flask.Response, int]:

llm_server/routes/openai/chat_completions.py

@@ -3,6 +3,7 @@ import time
 import traceback
 
 from flask import Response, jsonify, request
+from redis import Redis
 
 from llm_server.custom_redis import redis
 from . import openai_bp
@@ -10,7 +11,7 @@ from ..helpers.http import validate_json
 from ..openai_request_handler import OpenAIRequestHandler
 from ..queue import decr_active_workers, decrement_ip_count, priority_queue
 from ... import opts
-from ...database.database import log_prompt
+from ...database.log_to_db import log_to_db
 from ...llm.generator import generator
 from ...llm.openai.oai_to_vllm import oai_to_vllm, validate_oai
 from ...llm.openai.transform import generate_oai_string, transform_messages_to_prompt, trim_messages_to_fit
@@ -18,6 +19,7 @@ from ...llm.openai.transform import generate_oai_string, transform_messages_to_p
 
 # TODO: add rate-limit headers?
 
+
 @openai_bp.route('/chat/completions', methods=['POST'])
 def openai_chat_completions():
     request_valid_json, request_json_body = validate_json(request)
@@ -36,12 +38,20 @@ def openai_chat_completions():
                 return 'Internal server error', 500
         else:
             if not opts.enable_streaming:
-                return 'DISABLED', 401
+                return
+
+            handler.parameters, _ = handler.get_parameters()
+            handler.request_json_body = {
+                'messages': handler.request_json_body['messages'],
+                'model': handler.request_json_body['model'],
+                **handler.parameters
+            }
 
             invalid_oai_err_msg = validate_oai(handler.request_json_body)
             if invalid_oai_err_msg:
                 return invalid_oai_err_msg
-            handler.request_json_body = oai_to_vllm(handler.request_json_body, hashes=False, mode=handler.cluster_backend_info['mode'])
+
+            handler.request_json_body = oai_to_vllm(handler.request_json_body, stop_hashes=True, mode=handler.cluster_backend_info['mode'])
 
             if opts.openai_silent_trim:
                 handler.prompt = transform_messages_to_prompt(trim_messages_to_fit(handler.request.json['messages'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url))
@@ -64,7 +74,7 @@ def openai_chat_completions():
                 # Add a dummy event to the queue and wait for it to reach a worker
                 event = priority_queue.put((None, handler.client_ip, handler.token, None, handler.backend_url), handler.token_priority, handler.selected_model)
                 if not event:
-                    log_prompt(
+                    log_to_db(
                         handler.client_ip,
                         handler.token,
                         handler.prompt,
@@ -82,7 +92,6 @@ def openai_chat_completions():
                 _, _, _ = event.wait()
 
                 try:
-                    response = generator(msg_to_backend, handler.backend_url)
                     r_headers = dict(request.headers)
                     r_url = request.url
                     model = redis.get('running_model', 'ERROR', dtype=str) if opts.openai_expose_our_model else request_json_body.get('model')
@@ -90,6 +99,7 @@ def openai_chat_completions():
 
                     def generate():
                         try:
+                            response = generator(msg_to_backend, handler.backend_url)
                             generated_text = ''
                             partial_response = b''
                             for chunk in response.iter_content(chunk_size=1):
@@ -125,8 +135,7 @@ def openai_chat_completions():
                             yield 'data: [DONE]\n\n'
                             end_time = time.time()
                             elapsed_time = end_time - start_time
-
-                            log_prompt(
+                            log_to_db(
                                 handler.client_ip,
                                 handler.token,
                                 handler.prompt,

llm_server/routes/openai/completions.py

@@ -10,7 +10,8 @@ from ..helpers.http import validate_json
 from ..ooba_request_handler import OobaRequestHandler
 from ..queue import decr_active_workers, decrement_ip_count, priority_queue
 from ... import opts
-from ...database.database import log_prompt
+from ...database.database import do_db_log
+from ...database.log_to_db import log_to_db
 from ...llm import get_token_count
 from ...llm.generator import generator
 from ...llm.openai.oai_to_vllm import oai_to_vllm, validate_oai
@@ -34,7 +35,7 @@ def openai_completions():
         invalid_oai_err_msg = validate_oai(handler.request_json_body)
         if invalid_oai_err_msg:
             return invalid_oai_err_msg
-        handler.request_json_body = oai_to_vllm(handler.request_json_body, hashes=False, mode=handler.cluster_backend_info['mode'])
+        handler.request_json_body = oai_to_vllm(handler.request_json_body, stop_hashes=False, mode=handler.cluster_backend_info['mode'])
 
         if opts.openai_silent_trim:
             handler.request_json_body['prompt'] = trim_string_to_fit(request_json_body['prompt'], handler.cluster_backend_info['model_config']['max_position_embeddings'], handler.backend_url)
@@ -102,7 +103,7 @@ def openai_completions():
                 # Add a dummy event to the queue and wait for it to reach a worker
                 event = priority_queue.put((None, handler.client_ip, handler.token, None, handler.backend_url), handler.token_priority, handler.selected_model)
                 if not event:
-                    log_prompt(
+                    log_to_db(
                         handler.client_ip,
                         handler.token,
                         handler.prompt,
@@ -164,7 +165,7 @@ def openai_completions():
                             end_time = time.time()
                             elapsed_time = end_time - start_time
 
-                            log_prompt(
+                            log_to_db(
                                 handler.client_ip,
                                 handler.token,
                                 handler.prompt,

llm_server/routes/openai_request_handler.py

@@ -11,7 +11,8 @@ from flask import Response, jsonify, make_response
 from llm_server import opts
 from llm_server.cluster.backend import get_model_choices
 from llm_server.custom_redis import redis
-from llm_server.database.database import is_api_key_moderated, log_prompt
+from llm_server.database.database import is_api_key_moderated, do_db_log
+from llm_server.database.log_to_db import log_to_db
 from llm_server.llm import get_token_count
 from llm_server.llm.openai.oai_to_vllm import oai_to_vllm, validate_oai
 from llm_server.llm.openai.transform import ANTI_CONTINUATION_RE, ANTI_RESPONSE_RE, generate_oai_string, transform_messages_to_prompt, trim_messages_to_fit
@@ -58,7 +59,7 @@ class OpenAIRequestHandler(RequestHandler):
                 traceback.print_exc()
 
         # TODO: support Ooba
-        self.parameters = oai_to_vllm(self.parameters, hashes=True, mode=self.cluster_backend_info['mode'])
+        self.parameters = oai_to_vllm(self.parameters, stop_hashes=('instruct' not in self.request_json_body['model'].lower()), mode=self.cluster_backend_info['mode'])
 
         llm_request = {**self.parameters, 'prompt': self.prompt}
         (success, _, _, _), (backend_response, backend_response_status_code) = self.generate_response(llm_request)
@@ -88,7 +89,7 @@ class OpenAIRequestHandler(RequestHandler):
         response.headers['x-ratelimit-reset-requests'] = f"{w}s"
 
         if do_log:
-            log_prompt(self.client_ip, self.token, self.request_json_body.get('prompt', ''), response.data.decode('utf-8'), None, self.parameters, dict(self.request.headers), 429, self.request.url, self.backend_url, is_error=True)
+            log_to_db(self.client_ip, self.token, self.request_json_body.get('prompt', ''), response.data.decode('utf-8'), None, self.parameters, dict(self.request.headers), 429, self.request.url, self.backend_url, is_error=True)
 
         return response, 429
 
@@ -146,6 +147,6 @@ class OpenAIRequestHandler(RequestHandler):
         invalid_oai_err_msg = validate_oai(self.request_json_body)
         if invalid_oai_err_msg:
             return False, invalid_oai_err_msg
-        self.request_json_body = oai_to_vllm(self.request_json_body, hashes=('instruct' not in self.request_json_body['model'].lower()), mode=self.cluster_backend_info['mode'])
+        self.request_json_body = oai_to_vllm(self.request_json_body, stop_hashes=('instruct' not in self.request_json_body['model'].lower()), mode=self.cluster_backend_info['mode'])
         # If the parameters were invalid, let the superclass deal with it.
         return super().validate_request(prompt, do_log)

llm_server/routes/request_handler.py

@@ -7,7 +7,8 @@ from flask import Response, request
 from llm_server import opts
 from llm_server.cluster.cluster_config import cluster_config, get_a_cluster_backend
 from llm_server.custom_redis import redis
-from llm_server.database.database import get_token_ratelimit, log_prompt
+from llm_server.database.database import get_token_ratelimit, do_db_log
+from llm_server.database.log_to_db import log_to_db
 from llm_server.helpers import auto_set_base_client_api
 from llm_server.llm.oobabooga.ooba_backend import OobaboogaBackend
 from llm_server.llm.vllm.vllm_backend import VLLMBackend
@@ -41,9 +42,11 @@ class RequestHandler:
         if not self.cluster_backend_info.get('mode'):
             print('keyerror: mode -', selected_model, self.backend_url, self.cluster_backend_info)
         if not self.cluster_backend_info.get('model'):
-            print('keyerror: mode -', selected_model, self.backend_url, self.cluster_backend_info)
+            print('keyerror: model -', selected_model, self.backend_url, self.cluster_backend_info)
+        if not self.cluster_backend_info.get('model_config'):
+            print('keyerror: model_config -', selected_model, self.backend_url, self.cluster_backend_info)
 
-        if not self.cluster_backend_info.get('mode') or not self.cluster_backend_info.get('model'):
+        if not self.cluster_backend_info.get('mode') or not self.cluster_backend_info.get('model') or not self.cluster_backend_info.get('model_config'):
             self.offline = True
         else:
             self.offline = False
@@ -74,8 +77,6 @@ class RequestHandler:
             return self.request.remote_addr
 
     def get_parameters(self):
-        if self.request_json_body.get('max_tokens'):
-            self.request_json_body['max_new_tokens'] = self.request_json_body.pop('max_tokens')
         parameters, parameters_invalid_msg = self.backend.get_parameters(self.request_json_body)
         return parameters, parameters_invalid_msg
 
@@ -117,7 +118,7 @@ class RequestHandler:
             backend_response = self.handle_error(combined_error_message, 'Validation Error')
 
             if do_log:
-                log_prompt(self.client_ip, self.token, self.request_json_body.get('prompt', ''), backend_response[0].data.decode('utf-8'), 0, self.parameters, dict(self.request.headers), 0, self.request.url, self.backend_url, is_error=True)
+                log_to_db(self.client_ip, self.token, self.request_json_body.get('prompt', ''), backend_response[0].data.decode('utf-8'), 0, self.parameters, dict(self.request.headers), 0, self.request.url, self.backend_url, is_error=True)
             return False, backend_response
         return True, (None, 0)
 
@@ -160,7 +161,7 @@ class RequestHandler:
             else:
                 error_msg = error_msg.strip('.') + '.'
             backend_response = self.handle_error(error_msg)
-            log_prompt(ip=self.client_ip,
+            log_to_db(ip=self.client_ip,
                       token=self.token,
                       prompt=prompt,
                       response=backend_response[0].data.decode('utf-8'),
@@ -190,7 +191,7 @@ class RequestHandler:
         if return_json_err:
             error_msg = 'The backend did not return valid JSON.'
             backend_response = self.handle_error(error_msg)
-            log_prompt(self.client_ip, self.token, prompt, backend_response[0].data.decode('utf-8'), elapsed_time, self.parameters, dict(self.request.headers), response_status_code, self.request.url, self.backend_url, is_error=True)
+            log_to_db(self.client_ip, self.token, prompt, backend_response[0].data.decode('utf-8'), elapsed_time, self.parameters, dict(self.request.headers), response_status_code, self.request.url, self.backend_url, is_error=True)
             return (False, None, None, 0), backend_response
 
         # ===============================================

llm_server/routes/v1/generate_stream.py

@@ -9,7 +9,8 @@ from ..helpers.http import require_api_key, validate_json
 from ..ooba_request_handler import OobaRequestHandler
 from ..queue import decr_active_workers, decrement_ip_count, priority_queue
 from ... import opts
-from ...database.database import log_prompt
+from ...database.database import do_db_log
+from ...database.log_to_db import log_to_db
 from ...llm.generator import generator
 from ...sock import sock
 
@@ -34,6 +35,7 @@ def stream_with_model(ws, model_name=None):
 
 
 def do_stream(ws, model_name):
+    try:
         def send_err_and_quit(quitting_err_msg):
             ws.send(json.dumps({
                 'event': 'text_stream',
@@ -44,7 +46,7 @@ def do_stream(ws, model_name):
                 'event': 'stream_end',
                 'message_num': 1
             }))
-        log_prompt(ip=handler.client_ip,
+            log_to_db(ip=handler.client_ip,
                        token=handler.token,
                        prompt=input_prompt,
                        response=quitting_err_msg,
@@ -65,7 +67,6 @@ def do_stream(ws, model_name):
         r_url = request.url
         message_num = 0
 
-    try:
         while ws.connected:
             message = ws.receive()
             request_valid_json, request_json_body = validate_json(message)
@@ -197,7 +198,7 @@ def do_stream(ws, model_name):
                         pass
                     end_time = time.time()
                     elapsed_time = end_time - start_time
-                    log_prompt(ip=handler.client_ip,
+                    log_to_db(ip=handler.client_ip,
                                token=handler.token,
                                prompt=input_prompt,
                                response=generated_text,

llm_server/workers/logger.py

@@ -0,0 +1,28 @@
+import pickle
+
+import redis
+
+from llm_server.database.database import do_db_log
+
+
+def db_logger():
+    """
+    We don't want the logging operation to be blocking, so we will use a background worker
+    to do the logging.
+    :return:
+    """
+
+    r = redis.Redis(host='localhost', port=6379, db=3)
+    p = r.pubsub()
+    p.subscribe('database-logger')
+
+    for message in p.listen():
+        if message['type'] == 'message':
+            data = pickle.loads(message['data'])
+            function_name = data['function']
+            args = data['args']
+            kwargs = data['kwargs']
+
+            if function_name == 'log_prompt':
+                do_db_log(*args, **kwargs)
+                print('finished log')

llm_server/workers/threader.py

@@ -2,11 +2,11 @@ import time
 from threading import Thread
 
 from llm_server import opts
-from llm_server.cluster.cluster_config import cluster_config
 from llm_server.cluster.stores import redis_running_models
 from llm_server.cluster.worker import cluster_worker
 from llm_server.routes.v1.generate_stats import generate_stats
 from llm_server.workers.inferencer import start_workers
+from llm_server.workers.logger import db_logger
 from llm_server.workers.mainer import main_background_thread
 from llm_server.workers.moderator import start_moderation_workers
 from llm_server.workers.printer import console_printer
@@ -49,3 +49,8 @@ def start_background():
     t.daemon = True
     t.start()
     print('Started the cluster worker.')
+
+    t = Thread(target=db_logger)
+    t.daemon = True
+    t.start()
+    print('Started background logger')

other/gradio_chat.py

@@ -0,0 +1,33 @@
+import warnings
+
+import gradio as gr
+import openai
+
+warnings.filterwarnings("ignore")
+
+openai.api_key = 'null'
+openai.api_base = 'http://localhost:5000/api/openai/v1'
+
+
+def stream_response(prompt, history):
+    messages = []
+    for x in history:
+        messages.append({'role': 'user', 'content': x[0]})
+        messages.append({'role': 'assistant', 'content': x[1]})
+    messages.append({'role': 'user', 'content': prompt})
+
+    response = openai.ChatCompletion.create(
+        model='0',
+        messages=messages,
+        temperature=0,
+        max_tokens=300,
+        stream=True
+    )
+
+    message = ''
+    for chunk in response:
+        message += chunk['choices'][0]['delta']['content']
+        yield message
+
+
+gr.ChatInterface(stream_response, examples=["hello", "hola", "merhaba"], title="Chatbot Demo", analytics_enabled=False, cache_examples=False, css='#component-0{height:100%!important}').queue().launch()