mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-12-25 22:08:53 +01:00
797 lines
33 KiB
Python
797 lines
33 KiB
Python
import base64
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import json
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import os
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import time
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import requests
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import yaml
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from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
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from threading import Thread
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from modules.utils import get_available_models
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import numpy as np
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from modules import shared
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from modules.text_generation import encode, generate_reply
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params = {
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'port': int(os.environ.get('OPENEDAI_PORT')) if 'OPENEDAI_PORT' in os.environ else 5001,
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}
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debug = True if 'OPENEDAI_DEBUG' in os.environ else False
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# Slightly different defaults for OpenAI's API
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default_req_params = {
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'max_new_tokens': 200,
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'temperature': 1.0,
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'top_p': 1.0,
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'top_k': 1,
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'repetition_penalty': 1.18,
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'encoder_repetition_penalty': 1.0,
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'suffix': None,
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'stream': False,
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'echo': False,
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'seed': -1,
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# 'n' : default(body, 'n', 1), # 'n' doesn't have a direct map
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'truncation_length': 2048,
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'add_bos_token': True,
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'do_sample': True,
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'typical_p': 1.0,
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'epsilon_cutoff': 0, # In units of 1e-4
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'eta_cutoff': 0, # In units of 1e-4
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'tfs': 1.0,
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'top_a': 0.0,
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'min_length': 0,
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'no_repeat_ngram_size': 0,
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'num_beams': 1,
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'penalty_alpha': 0.0,
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'length_penalty': 1,
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'early_stopping': False,
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'mirostat_mode': 0,
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'mirostat_tau': 5,
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'mirostat_eta': 0.1,
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'ban_eos_token': False,
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'skip_special_tokens': True,
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'custom_stopping_strings': [],
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}
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# Optional, install the module and download the model to enable
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# v1/embeddings
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try:
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from sentence_transformers import SentenceTransformer
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except ImportError:
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pass
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st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
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embedding_model = None
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standard_stopping_strings = ['\nsystem:', '\nuser:', '\nhuman:', '\nassistant:', '\n###', ]
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# little helper to get defaults if arg is present but None and should be the same type as default.
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def default(dic, key, default):
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val = dic.get(key, default)
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if type(val) != type(default):
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# maybe it's just something like 1 instead of 1.0
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try:
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v = type(default)(val)
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if type(val)(v) == val: # if it's the same value passed in, it's ok.
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return v
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except:
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pass
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val = default
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return val
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def clamp(value, minvalue, maxvalue):
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return max(minvalue, min(value, maxvalue))
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def deduce_template():
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# Alpaca is verbose so a good default prompt
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default_template = (
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"Below is an instruction that describes a task, paired with an input that provides further context. "
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"Write a response that appropriately completes the request.\n\n"
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"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n"
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)
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# Use the special instruction/input/response template for anything trained like Alpaca
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if shared.settings['instruction_template'] in ['Alpaca', 'Alpaca-Input']:
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return default_template
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try:
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instruct = yaml.safe_load(open(f"characters/instruction-following/{shared.settings['instruction_template']}.yaml", 'r'))
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template = instruct['turn_template']
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template = template\
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.replace('<|user|>', instruct.get('user', ''))\
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.replace('<|bot|>', instruct.get('bot', ''))\
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.replace('<|user-message|>', '{instruction}\n{input}')
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return instruct.get('context', '') + template[:template.find('<|bot-message|>')].rstrip(' ')
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except:
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return default_template
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def float_list_to_base64(float_list):
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# Convert the list to a float32 array that the OpenAPI client expects
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float_array = np.array(float_list, dtype="float32")
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# Get raw bytes
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bytes_array = float_array.tobytes()
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# Encode bytes into base64
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encoded_bytes = base64.b64encode(bytes_array)
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# Turn raw base64 encoded bytes into ASCII
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ascii_string = encoded_bytes.decode('ascii')
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return ascii_string
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class Handler(BaseHTTPRequestHandler):
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def send_access_control_headers(self):
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self.send_header("Access-Control-Allow-Origin", "*")
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self.send_header("Access-Control-Allow-Credentials", "true")
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self.send_header(
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"Access-Control-Allow-Methods",
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"GET,HEAD,OPTIONS,POST,PUT"
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)
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self.send_header(
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"Access-Control-Allow-Headers",
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"Origin, Accept, X-Requested-With, Content-Type, "
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"Access-Control-Request-Method, Access-Control-Request-Headers, "
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"Authorization"
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)
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def do_OPTIONS(self):
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self.send_response(200)
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self.send_access_control_headers()
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self.send_header('Content-Type', 'application/json')
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self.end_headers()
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self.wfile.write("OK".encode('utf-8'))
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def do_GET(self):
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if self.path.startswith('/v1/models'):
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self.send_response(200)
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self.send_access_control_headers()
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self.send_header('Content-Type', 'application/json')
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self.end_headers()
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# TODO: list all models and allow model changes via API? Lora's?
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# This API should list capabilities, limits and pricing...
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models = [{
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"id": shared.model_name, # The real chat/completions model
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"object": "model",
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"owned_by": "user",
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"permission": []
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}, {
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"id": st_model, # The real sentence transformer embeddings model
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"object": "model",
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"owned_by": "user",
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"permission": []
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}, { # these are expected by so much, so include some here as a dummy
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"id": "gpt-3.5-turbo", # /v1/chat/completions
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"object": "model",
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"owned_by": "user",
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"permission": []
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}, {
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"id": "text-curie-001", # /v1/completions, 2k context
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"object": "model",
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"owned_by": "user",
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"permission": []
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}, {
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"id": "text-davinci-002", # /v1/embeddings text-embedding-ada-002:1536, text-davinci-002:768
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"object": "model",
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"owned_by": "user",
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"permission": []
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}]
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models.extend([{ "id": id, "object": "model", "owned_by": "user", "permission": [] } for id in get_available_models() ])
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response = ''
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if self.path == '/v1/models':
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response = json.dumps({
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"object": "list",
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"data": models,
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})
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else:
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the_model_name = self.path[len('/v1/models/'):]
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response = json.dumps({
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"id": the_model_name,
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"object": "model",
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"owned_by": "user",
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"permission": []
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})
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self.wfile.write(response.encode('utf-8'))
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elif '/billing/usage' in self.path:
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# Ex. /v1/dashboard/billing/usage?start_date=2023-05-01&end_date=2023-05-31
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self.send_response(200)
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self.send_access_control_headers()
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self.send_header('Content-Type', 'application/json')
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self.end_headers()
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response = json.dumps({
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"total_usage": 0,
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})
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self.wfile.write(response.encode('utf-8'))
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else:
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self.send_error(404)
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def do_POST(self):
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if debug:
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print(self.headers) # did you know... python-openai sends your linux kernel & python version?
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content_length = int(self.headers['Content-Length'])
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body = json.loads(self.rfile.read(content_length).decode('utf-8'))
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if debug:
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print(body)
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if '/completions' in self.path or '/generate' in self.path:
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is_legacy = '/generate' in self.path
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is_chat = 'chat' in self.path
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resp_list = 'data' if is_legacy else 'choices'
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# XXX model is ignored for now
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# model = body.get('model', shared.model_name) # ignored, use existing for now
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model = shared.model_name
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created_time = int(time.time())
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cmpl_id = "chatcmpl-%d" % (created_time) if is_chat else "conv-%d" % (created_time)
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# Try to use openai defaults or map them to something with the same intent
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stopping_strings = default(shared.settings, 'custom_stopping_strings', [])
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if 'stop' in body:
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if isinstance(body['stop'], str):
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stopping_strings = [body['stop']]
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elif isinstance(body['stop'], list):
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stopping_strings = body['stop']
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truncation_length = default(shared.settings, 'truncation_length', 2048)
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truncation_length = clamp(default(body, 'truncation_length', truncation_length), 1, truncation_length)
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default_max_tokens = truncation_length if is_chat else 16 # completions default, chat default is 'inf' so we need to cap it.
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max_tokens_str = 'length' if is_legacy else 'max_tokens'
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max_tokens = default(body, max_tokens_str, default(shared.settings, 'max_new_tokens', default_max_tokens))
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# if the user assumes OpenAI, the max_tokens is way too large - try to ignore it unless it's small enough
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req_params = default_req_params.copy()
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req_params['max_new_tokens'] = max_tokens
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req_params['truncation_length'] = truncation_length
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req_params['temperature'] = clamp(default(body, 'temperature', default_req_params['temperature']), 0.001, 1.999) # fixup absolute 0.0
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req_params['top_p'] = clamp(default(body, 'top_p', default_req_params['top_p']), 0.001, 1.0)
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req_params['top_k'] = default(body, 'best_of', default_req_params['top_k'])
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req_params['suffix'] = default(body, 'suffix', default_req_params['suffix'])
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req_params['stream'] = default(body, 'stream', default_req_params['stream'])
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req_params['echo'] = default(body, 'echo', default_req_params['echo'])
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req_params['seed'] = shared.settings.get('seed', default_req_params['seed'])
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req_params['add_bos_token'] = shared.settings.get('add_bos_token', default_req_params['add_bos_token'])
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self.send_response(200)
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self.send_access_control_headers()
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if req_params['stream']:
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self.send_header('Content-Type', 'text/event-stream')
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self.send_header('Cache-Control', 'no-cache')
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# self.send_header('Connection', 'keep-alive')
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else:
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self.send_header('Content-Type', 'application/json')
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self.end_headers()
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token_count = 0
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completion_token_count = 0
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prompt = ''
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stream_object_type = ''
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object_type = ''
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if is_chat:
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# Chat Completions
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stream_object_type = 'chat.completions.chunk'
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object_type = 'chat.completions'
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messages = body['messages']
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role_formats = {
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'user': 'user: {message}\n',
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'assistant': 'assistant: {message}\n',
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'system': '{message}',
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'context': 'You are a helpful assistant. Answer as concisely as possible.',
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'prompt': 'assistant:',
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}
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# Instruct models can be much better
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try:
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instruct = yaml.safe_load(open(f"characters/instruction-following/{shared.settings['instruction_template']}.yaml", 'r'))
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template = instruct['turn_template']
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system_message_template = "{message}"
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system_message_default = instruct['context']
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bot_start = template.find('<|bot|>') # So far, 100% of instruction templates have this token
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user_message_template = template[:bot_start].replace('<|user-message|>', '{message}').replace('<|user|>', instruct['user'])
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bot_message_template = template[bot_start:].replace('<|bot-message|>', '{message}').replace('<|bot|>', instruct['bot'])
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bot_prompt = bot_message_template[:bot_message_template.find('{message}')].rstrip(' ')
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role_formats = {
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'user': user_message_template,
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'assistant': bot_message_template,
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'system': system_message_template,
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'context': system_message_default,
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'prompt': bot_prompt,
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}
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if debug:
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print(f"Loaded instruction role format: {shared.settings['instruction_template']}")
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except:
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if debug:
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print("Loaded default role format.")
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system_msgs = []
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chat_msgs = []
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# You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. Knowledge cutoff: {knowledge_cutoff} Current date: {current_date}
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context_msg = role_formats['system'].format(message=role_formats['context']) if role_formats['context'] else ''
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if context_msg:
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system_msgs.extend([context_msg])
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# Maybe they sent both? This is not documented in the API, but some clients seem to do this.
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if 'prompt' in body:
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prompt_msg = role_formats['system'].format(message=body['prompt'])
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system_msgs.extend([prompt_msg])
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for m in messages:
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role = m['role']
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content = m['content']
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msg = role_formats[role].format(message=content)
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if role == 'system':
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system_msgs.extend([msg])
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else:
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chat_msgs.extend([msg])
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# can't really truncate the system messages
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system_msg = '\n'.join(system_msgs)
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if system_msg and system_msg[-1] != '\n':
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system_msg = system_msg + '\n'
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system_token_count = len(encode(system_msg)[0])
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remaining_tokens = req_params['truncation_length'] - system_token_count
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chat_msg = ''
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while chat_msgs:
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new_msg = chat_msgs.pop()
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new_size = len(encode(new_msg)[0])
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if new_size <= remaining_tokens:
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chat_msg = new_msg + chat_msg
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remaining_tokens -= new_size
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else:
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print(f"Warning: too many messages for context size, dropping {len(chat_msgs) + 1} oldest message(s).")
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break
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prompt = system_msg + chat_msg + role_formats['prompt']
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token_count = len(encode(prompt)[0])
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else:
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# Text Completions
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stream_object_type = 'text_completion.chunk'
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object_type = 'text_completion'
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# ... encoded as a string, array of strings, array of tokens, or array of token arrays.
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if is_legacy:
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prompt = body['context'] # Older engines.generate API
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else:
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prompt = body['prompt'] # XXX this can be different types
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if isinstance(prompt, list):
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prompt = ''.join(prompt) # XXX this is wrong... need to split out to multiple calls?
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token_count = len(encode(prompt)[0])
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if token_count >= req_params['truncation_length']:
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new_len = int(len(prompt) * shared.settings['truncation_length'] / token_count)
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prompt = prompt[-new_len:]
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new_token_count = len(encode(prompt)[0])
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print(f"Warning: truncating prompt to {new_len} characters, was {token_count} tokens. Now: {new_token_count} tokens.")
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token_count = new_token_count
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if req_params['truncation_length'] - token_count < req_params['max_new_tokens']:
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print(f"Warning: Ignoring max_new_tokens ({req_params['max_new_tokens']}), too large for the remaining context. Remaining tokens: {req_params['truncation_length'] - token_count}")
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req_params['max_new_tokens'] = req_params['truncation_length'] - token_count
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print(f"Warning: Set max_new_tokens = {req_params['max_new_tokens']}")
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# pass with some expected stop strings.
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# some strange cases of "##| Instruction: " sneaking through.
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stopping_strings += standard_stopping_strings
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req_params['custom_stopping_strings'] = stopping_strings
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if req_params['stream']:
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shared.args.chat = True
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# begin streaming
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chunk = {
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"id": cmpl_id,
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"object": stream_object_type,
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"created": created_time,
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"model": shared.model_name,
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resp_list: [{
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"index": 0,
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"finish_reason": None,
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}],
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}
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if stream_object_type == 'text_completion.chunk':
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chunk[resp_list][0]["text"] = ""
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else:
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# So yeah... do both methods? delta and messages.
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chunk[resp_list][0]["message"] = {'role': 'assistant', 'content': ''}
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chunk[resp_list][0]["delta"] = {'role': 'assistant', 'content': ''}
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data_chunk = 'data: ' + json.dumps(chunk) + '\r\n\r\n'
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chunk_size = hex(len(data_chunk))[2:] + '\r\n'
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response = chunk_size + data_chunk
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self.wfile.write(response.encode('utf-8'))
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# generate reply #######################################
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if debug:
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print({'prompt': prompt, 'req_params': req_params, 'stopping_strings': stopping_strings})
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generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings, is_chat=False)
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answer = ''
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seen_content = ''
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longest_stop_len = max([len(x) for x in stopping_strings])
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for a in generator:
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answer = a
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stop_string_found = False
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len_seen = len(seen_content)
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search_start = max(len_seen - longest_stop_len, 0)
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for string in stopping_strings:
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idx = answer.find(string, search_start)
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if idx != -1:
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answer = answer[:idx] # clip it.
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stop_string_found = True
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if stop_string_found:
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break
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# If something like "\nYo" is generated just before "\nYou:"
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# is completed, buffer and generate more, don't send it
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buffer_and_continue = False
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for string in stopping_strings:
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for j in range(len(string) - 1, 0, -1):
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if answer[-j:] == string[:j]:
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buffer_and_continue = True
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break
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else:
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continue
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break
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if buffer_and_continue:
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continue
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if req_params['stream']:
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# Streaming
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new_content = answer[len_seen:]
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if not new_content or chr(0xfffd) in new_content: # partial unicode character, don't send it yet.
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continue
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|
|
|
seen_content = answer
|
|
chunk = {
|
|
"id": cmpl_id,
|
|
"object": stream_object_type,
|
|
"created": created_time,
|
|
"model": shared.model_name,
|
|
resp_list: [{
|
|
"index": 0,
|
|
"finish_reason": None,
|
|
}],
|
|
}
|
|
|
|
# strip extra leading space off new generated content
|
|
if len_seen == 0 and new_content[0] == ' ':
|
|
new_content = new_content[1:]
|
|
|
|
if stream_object_type == 'text_completion.chunk':
|
|
chunk[resp_list][0]['text'] = new_content
|
|
else:
|
|
# So yeah... do both methods? delta and messages.
|
|
chunk[resp_list][0]['message'] = {'content': new_content}
|
|
chunk[resp_list][0]['delta'] = {'content': new_content}
|
|
data_chunk = 'data: ' + json.dumps(chunk) + '\r\n\r\n'
|
|
chunk_size = hex(len(data_chunk))[2:] + '\r\n'
|
|
response = chunk_size + data_chunk
|
|
self.wfile.write(response.encode('utf-8'))
|
|
completion_token_count += len(encode(new_content)[0])
|
|
|
|
if req_params['stream']:
|
|
chunk = {
|
|
"id": cmpl_id,
|
|
"object": stream_object_type,
|
|
"created": created_time,
|
|
"model": model, # TODO: add Lora info?
|
|
resp_list: [{
|
|
"index": 0,
|
|
"finish_reason": "stop",
|
|
}],
|
|
"usage": {
|
|
"prompt_tokens": token_count,
|
|
"completion_tokens": completion_token_count,
|
|
"total_tokens": token_count + completion_token_count
|
|
}
|
|
}
|
|
if stream_object_type == 'text_completion.chunk':
|
|
chunk[resp_list][0]['text'] = ''
|
|
else:
|
|
# So yeah... do both methods? delta and messages.
|
|
chunk[resp_list][0]['message'] = {'content': ''}
|
|
chunk[resp_list][0]['delta'] = {'content': ''}
|
|
|
|
data_chunk = 'data: ' + json.dumps(chunk) + '\r\n\r\n'
|
|
chunk_size = hex(len(data_chunk))[2:] + '\r\n'
|
|
done = 'data: [DONE]\r\n\r\n'
|
|
response = chunk_size + data_chunk + done
|
|
self.wfile.write(response.encode('utf-8'))
|
|
# Finished if streaming.
|
|
if debug:
|
|
if answer and answer[0] == ' ':
|
|
answer = answer[1:]
|
|
print({'answer': answer}, chunk)
|
|
return
|
|
|
|
# strip extra leading space off new generated content
|
|
if answer and answer[0] == ' ':
|
|
answer = answer[1:]
|
|
|
|
if debug:
|
|
print({'response': answer})
|
|
|
|
completion_token_count = len(encode(answer)[0])
|
|
stop_reason = "stop"
|
|
if token_count + completion_token_count >= req_params['truncation_length']:
|
|
stop_reason = "length"
|
|
|
|
resp = {
|
|
"id": cmpl_id,
|
|
"object": object_type,
|
|
"created": created_time,
|
|
"model": model, # TODO: add Lora info?
|
|
resp_list: [{
|
|
"index": 0,
|
|
"finish_reason": stop_reason,
|
|
}],
|
|
"usage": {
|
|
"prompt_tokens": token_count,
|
|
"completion_tokens": completion_token_count,
|
|
"total_tokens": token_count + completion_token_count
|
|
}
|
|
}
|
|
|
|
if is_chat:
|
|
resp[resp_list][0]["message"] = {"role": "assistant", "content": answer}
|
|
else:
|
|
resp[resp_list][0]["text"] = answer
|
|
|
|
response = json.dumps(resp)
|
|
self.wfile.write(response.encode('utf-8'))
|
|
elif '/edits' in self.path:
|
|
self.send_response(200)
|
|
self.send_access_control_headers()
|
|
self.send_header('Content-Type', 'application/json')
|
|
self.end_headers()
|
|
|
|
created_time = int(time.time())
|
|
|
|
# Using Alpaca format, this may work with other models too.
|
|
instruction = body['instruction']
|
|
input = body.get('input', '')
|
|
|
|
instruction_template = deduce_template()
|
|
edit_task = instruction_template.format(instruction=instruction, input=input)
|
|
|
|
truncation_length = default(shared.settings, 'truncation_length', 2048)
|
|
token_count = len(encode(edit_task)[0])
|
|
max_tokens = truncation_length - token_count
|
|
|
|
req_params = default_req_params.copy()
|
|
|
|
req_params['max_new_tokens'] = max_tokens
|
|
req_params['truncation_length'] = truncation_length
|
|
req_params['temperature'] = clamp(default(body, 'temperature', default_req_params['temperature']), 0.001, 1.999) # fixup absolute 0.0
|
|
req_params['top_p'] = clamp(default(body, 'top_p', default_req_params['top_p']), 0.001, 1.0)
|
|
req_params['seed'] = shared.settings.get('seed', default_req_params['seed'])
|
|
req_params['add_bos_token'] = shared.settings.get('add_bos_token', default_req_params['add_bos_token'])
|
|
|
|
if debug:
|
|
print({'edit_template': edit_task, 'req_params': req_params, 'token_count': token_count})
|
|
|
|
generator = generate_reply(edit_task, req_params, stopping_strings=standard_stopping_strings, is_chat=False)
|
|
|
|
answer = ''
|
|
for a in generator:
|
|
answer = a
|
|
|
|
# some reply's have an extra leading space to fit the instruction template, just clip it off from the reply.
|
|
if edit_task[-1] != '\n' and answer and answer[0] == ' ':
|
|
answer = answer[1:]
|
|
|
|
completion_token_count = len(encode(answer)[0])
|
|
|
|
resp = {
|
|
"object": "edit",
|
|
"created": created_time,
|
|
"choices": [{
|
|
"text": answer,
|
|
"index": 0,
|
|
}],
|
|
"usage": {
|
|
"prompt_tokens": token_count,
|
|
"completion_tokens": completion_token_count,
|
|
"total_tokens": token_count + completion_token_count
|
|
}
|
|
}
|
|
|
|
if debug:
|
|
print({'answer': answer, 'completion_token_count': completion_token_count})
|
|
|
|
response = json.dumps(resp)
|
|
self.wfile.write(response.encode('utf-8'))
|
|
elif '/images/generations' in self.path and 'SD_WEBUI_URL' in os.environ:
|
|
# Stable Diffusion callout wrapper for txt2img
|
|
# Low effort implementation for compatibility. With only "prompt" being passed and assuming DALL-E
|
|
# the results will be limited and likely poor. SD has hundreds of models and dozens of settings.
|
|
# If you want high quality tailored results you should just use the Stable Diffusion API directly.
|
|
# it's too general an API to try and shape the result with specific tags like "masterpiece", etc,
|
|
# Will probably work best with the stock SD models.
|
|
# SD configuration is beyond the scope of this API.
|
|
# At this point I will not add the edits and variations endpoints (ie. img2img) because they
|
|
# require changing the form data handling to accept multipart form data, also to properly support
|
|
# url return types will require file management and a web serving files... Perhaps later!
|
|
|
|
self.send_response(200)
|
|
self.send_access_control_headers()
|
|
self.send_header('Content-Type', 'application/json')
|
|
self.end_headers()
|
|
|
|
width, height = [ int(x) for x in default(body, 'size', '1024x1024').split('x') ] # ignore the restrictions on size
|
|
response_format = default(body, 'response_format', 'url') # or b64_json
|
|
|
|
payload = {
|
|
'prompt': body['prompt'], # ignore prompt limit of 1000 characters
|
|
'width': width,
|
|
'height': height,
|
|
'batch_size': default(body, 'n', 1) # ignore the batch limits of max 10
|
|
}
|
|
|
|
resp = {
|
|
'created': int(time.time()),
|
|
'data': []
|
|
}
|
|
|
|
# TODO: support SD_WEBUI_AUTH username:password pair.
|
|
sd_url = f"{os.environ['SD_WEBUI_URL']}/sdapi/v1/txt2img"
|
|
|
|
response = requests.post(url=sd_url, json=payload)
|
|
r = response.json()
|
|
# r['parameters']...
|
|
for b64_json in r['images']:
|
|
if response_format == 'b64_json':
|
|
resp['data'].extend([{'b64_json': b64_json}])
|
|
else:
|
|
resp['data'].extend([{'url': f'data:image/png;base64,{b64_json}'}]) # yeah it's lazy. requests.get() will not work with this
|
|
|
|
response = json.dumps(resp)
|
|
self.wfile.write(response.encode('utf-8'))
|
|
elif '/embeddings' in self.path and embedding_model is not None:
|
|
self.send_response(200)
|
|
self.send_access_control_headers()
|
|
self.send_header('Content-Type', 'application/json')
|
|
self.end_headers()
|
|
|
|
input = body['input'] if 'input' in body else body['text']
|
|
if type(input) is str:
|
|
input = [input]
|
|
|
|
embeddings = embedding_model.encode(input).tolist()
|
|
|
|
def enc_emb(emb):
|
|
# If base64 is specified, encode. Otherwise, do nothing.
|
|
if body.get("encoding_format", "") == "base64":
|
|
return float_list_to_base64(emb)
|
|
else:
|
|
return emb
|
|
data = [{"object": "embedding", "embedding": enc_emb(emb), "index": n} for n, emb in enumerate(embeddings)]
|
|
|
|
response = json.dumps({
|
|
"object": "list",
|
|
"data": data,
|
|
"model": st_model, # return the real model
|
|
"usage": {
|
|
"prompt_tokens": 0,
|
|
"total_tokens": 0,
|
|
}
|
|
})
|
|
|
|
if debug:
|
|
print(f"Embeddings return size: {len(embeddings[0])}, number: {len(embeddings)}")
|
|
self.wfile.write(response.encode('utf-8'))
|
|
elif '/moderations' in self.path:
|
|
# for now do nothing, just don't error.
|
|
self.send_response(200)
|
|
self.send_access_control_headers()
|
|
self.send_header('Content-Type', 'application/json')
|
|
self.end_headers()
|
|
|
|
response = json.dumps({
|
|
"id": "modr-5MWoLO",
|
|
"model": "text-moderation-001",
|
|
"results": [{
|
|
"categories": {
|
|
"hate": False,
|
|
"hate/threatening": False,
|
|
"self-harm": False,
|
|
"sexual": False,
|
|
"sexual/minors": False,
|
|
"violence": False,
|
|
"violence/graphic": False
|
|
},
|
|
"category_scores": {
|
|
"hate": 0.0,
|
|
"hate/threatening": 0.0,
|
|
"self-harm": 0.0,
|
|
"sexual": 0.0,
|
|
"sexual/minors": 0.0,
|
|
"violence": 0.0,
|
|
"violence/graphic": 0.0
|
|
},
|
|
"flagged": False
|
|
}]
|
|
})
|
|
self.wfile.write(response.encode('utf-8'))
|
|
|
|
elif self.path == '/api/v1/token-count':
|
|
# NOT STANDARD. lifted from the api extension, but it's still very useful to calculate tokenized length client side.
|
|
self.send_response(200)
|
|
self.send_access_control_headers()
|
|
self.send_header('Content-Type', 'application/json')
|
|
self.end_headers()
|
|
|
|
tokens = encode(body['prompt'])[0]
|
|
response = json.dumps({
|
|
'results': [{
|
|
'tokens': len(tokens)
|
|
}]
|
|
})
|
|
self.wfile.write(response.encode('utf-8'))
|
|
else:
|
|
print(self.path, self.headers)
|
|
self.send_error(404)
|
|
|
|
|
|
def run_server():
|
|
global embedding_model
|
|
try:
|
|
embedding_model = SentenceTransformer(st_model)
|
|
print(f"\nLoaded embedding model: {st_model}, max sequence length: {embedding_model.max_seq_length}")
|
|
except:
|
|
print(f"\nFailed to load embedding model: {st_model}")
|
|
pass
|
|
|
|
server_addr = ('0.0.0.0' if shared.args.listen else '127.0.0.1', params['port'])
|
|
server = ThreadingHTTPServer(server_addr, Handler)
|
|
if shared.args.share:
|
|
try:
|
|
from flask_cloudflared import _run_cloudflared
|
|
public_url = _run_cloudflared(params['port'], params['port'] + 1)
|
|
print(f'Starting OpenAI compatible api at\nOPENAI_API_BASE={public_url}/v1')
|
|
except ImportError:
|
|
print('You should install flask_cloudflared manually')
|
|
else:
|
|
print(f'Starting OpenAI compatible api:\nOPENAI_API_BASE=http://{server_addr[0]}:{server_addr[1]}/v1')
|
|
|
|
server.serve_forever()
|
|
|
|
|
|
def setup():
|
|
Thread(target=run_server, daemon=True).start()
|