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add openai compatible api (#1475)

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# An OpenedAI API (openai like)
This extension creates an API that works kind of like openai (ie. api.openai.com).
It's incomplete so far but perhaps is functional enough for you.
## Setup & installation
Optional (for flask_cloudflared, embeddings):
```
pip3 install -r requirements.txt
```
### Embeddings (alpha)
Embeddings requires ```sentence-transformers``` installed, but chat and completions will function without it loaded. The embeddings endpoint is currently using the HuggingFace model: ```sentence-transformers/all-mpnet-base-v2``` for embeddings. This produces 768 dimensional embeddings (the same as the text-davinci-002 embeddings), which is different from OpenAI's current default ```text-embedding-ada-002``` model which produces 1536 dimensional embeddings. The model is small-ish and fast-ish. This model and embedding size may change in the future.
| model name | dimensions | input max tokens | speed | size | Avg. performance |
| --- | --- | --- | --- | --- | --- |
| text-embedding-ada-002 | 1536 | 8192| - | - | - |
| text-davinci-002 | 768 | 2046 | - | - | - |
| all-mpnet-base-v2 | 768 | 384 | 2800 | 420M | 63.3 |
| all-MiniLM-L6-v2 | 384 | 256 | 14200 | 80M | 58.8 |
In short, the all-MiniLM-L6-v2 model is 5x faster, 5x smaller ram, 2x smaller storage, and still offers good quality. Stats from (https://www.sbert.net/docs/pretrained_models.html). To change the model from the default you can set the environment variable OPENEDAI_EMBEDDING_MODEL, ex. "OPENEDAI_EMBEDDING_MODEL=all-MiniLM-L6-v2".
Warning: You cannot mix embeddings from different models even if they have the same dimensions. They are not comparable.
### Client Application Setup
Almost everything you use it with will require you to set a dummy OpenAI API key environment variable.
With the [official python openai client](https://github.com/openai/openai-python), you can set the OPENAI_API_BASE environment variable before you import the openai module, like so:
```
OPENAI_API_KEY=dummy
OPENAI_API_BASE=http://127.0.0.1:5001/v1
```
If needed, replace 127.0.0.1 with the IP/port of your server.
If using .env files to save the OPENAI_API_BASE and OPENAI_API_KEY variables, you can ensure compatibility by loading the .env file before loading the openai module, like so in python:
```
from dotenv import load_dotenv
load_dotenv()
import openai
```
With the [official Node.js openai client](https://github.com/openai/openai-node) it is slightly more more complex because the environment variables are not used by default, so small source code changes may be required to use the environment variables, like so:
```
const openai = OpenAI(Configuration({
apiKey: process.env.OPENAI_API_KEY,
basePath: process.env.OPENAI_API_BASE,
}));
```
For apps made with the [chatgpt-api Node.js client library](https://github.com/transitive-bullshit/chatgpt-api):
```
const api = new ChatGPTAPI({
apiKey: process.env.OPENAI_API_KEY,
apiBaseUrl: process.env.OPENAI_API_BASE,
})
```
## Compatibility & not so compatibility
What's working:
| API endpoint | tested with | notes |
| --- | --- | --- |
| /v1/models | openai.Model.list() | returns the currently loaded model_name and some mock compatibility options |
| /v1/models/{id} | openai.Model.get() | returns whatever you ask for, model does nothing yet anyways |
| /v1/text_completion | openai.Completion.create() | the most tested, only supports single string input so far |
| /v1/chat/completions | openai.ChatCompletion.create() | depending on the model, this may add leading linefeeds |
| /v1/embeddings | openai.Embedding.create() | Using Sentence Transformer, dimensions are different and may never be directly comparable to openai embeddings. |
| /v1/moderations | openai.Moderation.create() | does nothing. successfully. |
| /v1/engines/\*/... completions, embeddings, generate | python-openai v0.25 and earlier | Legacy engines endpoints |
The model name setting is ignored in completions, but you may need to adjust the maximum token length to fit the model (ie. set to <2048 tokens instead of 4096, 8k, etc). To mitigate some of this, the max_tokens value is halved until it is less than truncation_length for the model (typically 2k).
Streaming, temperature, top_p, max_tokens, stop, should all work as expected, but not all parameters are mapped correctly.
Some hacky mappings:
| OpenAI | text-generation-webui | note |
| --- | --- | --- |
| frequency_penalty | encoder_repetition_penalty | this seems to operate with a different scale and defaults, I tried to scale it based on range & defaults, but the results are terrible. hardcoded to 1.18 until there is a better way |
| presence_penalty | repetition_penalty | same issues as frequency_penalty, hardcoded to 1.0 |
| best_of | top_k | |
| stop | custom_stopping_strings | this is also stuffed with ['\nsystem:', '\nuser:', '\nhuman:', '\nassistant:', '\n###', ] for good measure. |
| n | 1 | hardcoded, it may be worth implementing this but I'm not sure how yet |
| 1.0 | typical_p | hardcoded |
| 1 | num_beams | hardcoded |
| max_tokens | max_new_tokens | max_tokens is scaled down by powers of 2 until it's smaller than truncation length. |
| logprobs | - | ignored |
defaults are mostly from openai, so are different. I use the openai defaults where I can and try to scale them to the webui defaults with the same intent.
### Applications
Everything needs OPENAI_API_KEY=dummy set.
| Compatibility | Application/Library | url | notes / setting |
| --- | --- | --- | --- |
| ✅❌ | openai-python | https://github.com/openai/openai-python | only the endpoints from above are working. OPENAI_API_BASE=http://127.0.0.1:5001/v1 |
| ✅❌ | openai-node | https://github.com/openai/openai-node | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above) |
| ✅❌ | chatgpt-api | https://github.com/transitive-bullshit/chatgpt-api | only the endpoints from above are working. environment variables don't work by default, but can be configured (see above) |
| ✅ | shell_gpt | https://github.com/TheR1D/shell_gpt | OPENAI_API_HOST=http://127.0.0.1:5001 |
| ✅ | gpt-shell | https://github.com/jla/gpt-shell | OPENAI_API_BASE=http://127.0.0.1:5001/v1 |
| ✅ | gpt-discord-bot | https://github.com/openai/gpt-discord-bot | OPENAI_API_BASE=http://127.0.0.1:5001/v1 |
| ✅❌ | langchain | https://github.com/hwchase17/langchain | OPENAI_API_BASE=http://127.0.0.1:5001/v1 even with a good 30B-4bit model the result is poor so far. It assumes zero shot python/json coding. Some model tailored prompt formatting improves results greatly. |
| ✅❌ | Auto-GPT | https://github.com/Significant-Gravitas/Auto-GPT | OPENAI_API_BASE=http://127.0.0.1:5001/v1 Same issues as langchain. Also assumes a 4k+ context |
| ✅❌ | babyagi | https://github.com/yoheinakajima/babyagi | OPENAI_API_BASE=http://127.0.0.1:5001/v1 |
## Future plans
* better error handling
* model changing, esp. something for swapping loras or embedding models
* consider switching to FastAPI + starlette for SSE (openai SSE seems non-standard)
* do something about rate limiting or locking requests for completions, most systems will only be able handle a single request at a time before OOM
* the whole api, images (stable diffusion), audio (whisper), fine-tunes (training), edits, files, etc.

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flask_cloudflared==0.0.12
sentence-transformers

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import json, time, os
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
from threading import Thread
from modules import shared
from modules.text_generation import encode, generate_reply
params = {
'port': int(os.environ('OPENEDAI_PORT')) if 'OPENEDAI_PORT' in os.environ else 5001,
}
debug = True if 'OPENEDAI_DEBUG' in os.environ else False
# Optional, install the module and download the model to enable
# v1/embeddings
try:
from sentence_transformers import SentenceTransformer
except ImportError:
pass
st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
embedding_model = None
standard_stopping_strings = ['\nsystem:', '\nuser:', '\nhuman:', '\nassistant:', '\n###', ]
# little helper to get defaults if arg is present but None and should be the same type as default.
def default(dic, key, default):
val = dic.get(key, default)
if type(val) != type(default):
# maybe it's just something like 1 instead of 1.0
try:
v = type(default)(val)
if type(val)(v) == val: # if it's the same value passed in, it's ok.
return v
except:
pass
val = default
return val
def clamp(value, minvalue, maxvalue):
return max(minvalue, min(value, maxvalue))
class Handler(BaseHTTPRequestHandler):
def do_GET(self):
if self.path.startswith('/v1/models'):
self.send_response(200)
self.send_header('Content-Type', 'application/json')
self.end_headers()
# TODO: list all models and allow model changes via API? Lora's?
# This API should list capabilities, limits and pricing...
models = [{
"id": shared.model_name, # The real chat/completions model
"object": "model",
"owned_by": "user",
"permission": []
}, {
"id": st_model, # The real sentence transformer embeddings model
"object": "model",
"owned_by": "user",
"permission": []
}, { # these are expected by so much, so include some here as a dummy
"id": "gpt-3.5-turbo", # /v1/chat/completions
"object": "model",
"owned_by": "user",
"permission": []
}, {
"id": "text-curie-001", # /v1/completions, 2k context
"object": "model",
"owned_by": "user",
"permission": []
}, {
"id": "text-davinci-002", # /v1/embeddings text-embedding-ada-002:1536, text-davinci-002:768
"object": "model",
"owned_by": "user",
"permission": []
}]
response = ''
if self.path == '/v1/models':
response = json.dumps({
"object": "list",
"data": models,
})
else:
the_model_name = self.path[len('/v1/models/'):]
response = json.dumps({
"id": the_model_name,
"object": "model",
"owned_by": "user",
"permission": []
})
self.wfile.write(response.encode('utf-8'))
else:
self.send_error(404)
def do_POST(self):
content_length = int(self.headers['Content-Length'])
body = json.loads(self.rfile.read(content_length).decode('utf-8'))
if debug: print(self.headers) # did you know... python-openai sends your linux kernel & python version?
if debug: print(body)
if '/completions' in self.path or '/generate' in self.path:
is_legacy = '/generate' in self.path
is_chat = 'chat' in self.path
resp_list = 'data' if is_legacy else 'choices'
# XXX model is ignored for now
#model = body.get('model', shared.model_name) # ignored, use existing for now
model = shared.model_name
created_time = int(time.time())
cmpl_id = "conv-%d" % (created_time)
# Try to use openai defaults or map them to something with the same intent
stopping_strings = default(shared.settings, 'custom_stopping_strings', [])
if 'stop' in body:
if isinstance(body['stop'], str):
stopping_strings = [body['stop']]
elif isinstance(body['stop'], list):
stopping_strings = body['stop']
truncation_length = default(shared.settings, 'truncation_length', 2048)
truncation_length = clamp(default(body, 'truncation_length', truncation_length), 1, truncation_length)
default_max_tokens = truncation_length if is_chat else 16 # completions default, chat default is 'inf' so we need to cap it., the default for chat is "inf"
max_tokens_str = 'length' if is_legacy else 'max_tokens'
max_tokens = default(body, max_tokens_str, default(shared.settings, 'max_new_tokens', default_max_tokens))
# hard scale this, assuming the given max is for GPT3/4, perhaps inspect the requested model and lookup the context max
while truncation_length <= max_tokens:
max_tokens = max_tokens // 2
req_params = {
'max_new_tokens': max_tokens,
'temperature': default(body, 'temperature', 1.0),
'top_p': default(body, 'top_p', 1.0),
'top_k': default(body, 'best_of', 1),
### XXX not sure about this one, seems to be the right mapping, but the range is different (-2..2.0) vs 0..2
# 0 is default in openai, but 1.0 is default in other places. Maybe it's scaled? scale it.
'repetition_penalty': 1.18, # (default(body, 'presence_penalty', 0) + 2.0 ) / 2.0, # 0 the real default, 1.2 is the model default, but 1.18 works better.
### XXX not sure about this one either, same questions. (-2..2.0), 0 is default not 1.0, scale it.
'encoder_repetition_penalty': 1.0, #(default(body, 'frequency_penalty', 0) + 2.0) / 2.0,
'suffix': body.get('suffix', None),
'stream': default(body, 'stream', False),
'echo': default(body, 'echo', False),
#####################################################
'seed': shared.settings.get('seed', -1),
#int(body.get('n', 1)) # perhaps this should be num_beams or chat_generation_attempts? 'n' doesn't have a direct map
# unofficial, but it needs to get set anyways.
'truncation_length': truncation_length,
# no more args.
'add_bos_token': shared.settings.get('add_bos_token', True),
'do_sample': True,
'typical_p': 1.0,
'min_length': 0,
'no_repeat_ngram_size': 0,
'num_beams': 1,
'penalty_alpha': 0.0,
'length_penalty': 1,
'early_stopping': False,
'ban_eos_token': False,
'skip_special_tokens': True,
}
# fixup absolute 0.0's
for par in ['temperature', 'repetition_penalty', 'encoder_repetition_penalty']:
req_params[par] = clamp(req_params[par], 0.001, 1.999)
self.send_response(200)
if req_params['stream']:
self.send_header('Content-Type', 'text/event-stream')
self.send_header('Cache-Control', 'no-cache')
#self.send_header('Connection', 'keep-alive')
else:
self.send_header('Content-Type', 'application/json')
self.end_headers()
token_count = 0
completion_token_count = 0
prompt = ''
stream_object_type = ''
object_type = ''
if is_chat:
stream_object_type = 'chat.completions.chunk'
object_type = 'chat.completions'
messages = body['messages']
system_msg = '' # You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible. Knowledge cutoff: {knowledge_cutoff} Current date: {current_date}
if 'prompt' in body: # Maybe they sent both? This is not documented in the API, but some clients seem to do this.
system_msg = body['prompt']
chat_msgs = []
for m in messages:
role = m['role']
content = m['content']
#name = m.get('name', 'user')
if role == 'system':
system_msg += content
else:
chat_msgs.extend([f"\n{role}: {content.strip()}"]) ### Strip content? linefeed?
system_token_count = len(encode(system_msg)[0])
remaining_tokens = req_params['truncation_length'] - req_params['max_new_tokens'] - system_token_count
chat_msg = ''
while chat_msgs:
new_msg = chat_msgs.pop()
new_size = len(encode(new_msg)[0])
if new_size <= remaining_tokens:
chat_msg = new_msg + chat_msg
remaining_tokens -= new_size
else:
# TODO: clip a message to fit?
# ie. user: ...<clipped message>
break
if len(chat_msgs) > 0:
print(f"truncating chat messages, dropping {len(chat_msgs)} messages.")
if system_msg:
prompt = 'system: ' + system_msg + '\n' + chat_msg + '\nassistant: '
else:
prompt = chat_msg + '\nassistant: '
token_count = len(encode(prompt)[0])
# pass with some expected stop strings.
# some strange cases of "##| Instruction: " sneaking through.
stopping_strings += standard_stopping_strings
req_params['custom_stopping_strings'] = stopping_strings
else:
stream_object_type = 'text_completion.chunk'
object_type = 'text_completion'
# ... encoded as a string, array of strings, array of tokens, or array of token arrays.
if is_legacy:
prompt = body['context'] # Older engines.generate API
else:
prompt = body['prompt'] # XXX this can be different types
if isinstance(prompt, list):
prompt = ''.join(prompt) # XXX this is wrong... need to split out to multiple calls?
token_count = len(encode(prompt)[0])
if token_count >= req_params['truncation_length']:
new_len = int(len(prompt) * (float(shared.settings['truncation_length']) - req_params['max_new_tokens']) / token_count)
prompt = prompt[-new_len:]
print(f"truncating prompt to {new_len} characters, was {token_count} tokens. Now: {len(encode(prompt)[0])} tokens.")
# pass with some expected stop strings.
# some strange cases of "##| Instruction: " sneaking through.
stopping_strings += standard_stopping_strings
req_params['custom_stopping_strings'] = stopping_strings
shared.args.no_stream = not req_params['stream']
if not shared.args.no_stream:
shared.args.chat = True
# begin streaming
chunk = {
"id": cmpl_id,
"object": stream_object_type,
"created": created_time,
"model": shared.model_name,
resp_list: [{
"index": 0,
"finish_reason": None,
}],
}
if stream_object_type == 'text_completion.chunk':
chunk[resp_list][0]["text"] = ""
else:
# This is coming back as "system" to the openapi cli, not sure why.
# So yeah... do both methods? delta and messages.
chunk[resp_list][0]["message"] = {'role': 'assistant', 'content': ''}
chunk[resp_list][0]["delta"] = {'role': 'assistant', 'content': ''}
#{ "role": "assistant" }
response = 'data: ' + json.dumps(chunk) + '\n'
self.wfile.write(response.encode('utf-8'))
# generate reply #######################################
if debug: print ({'prompt': prompt, 'req_params': req_params, 'stopping_strings': stopping_strings})
generator = generate_reply(prompt, req_params, stopping_strings=stopping_strings)
answer = ''
seen_content = ''
longest_stop_len = max([ len(x) for x in stopping_strings ])
for a in generator:
if isinstance(a, str):
answer = a
else:
answer = a[0]
stop_string_found = False
len_seen = len(seen_content)
search_start = max(len_seen - longest_stop_len, 0)
for string in stopping_strings:
idx = answer.find(string, search_start)
if idx != -1:
answer = answer[:idx] # clip it.
stop_string_found = True
if stop_string_found:
break
# If something like "\nYo" is generated just before "\nYou:"
# is completed, buffer and generate more, don't send it
buffer_and_continue = False
for string in stopping_strings:
for j in range(len(string) - 1, 0, -1):
if answer[-j:] == string[:j]:
buffer_and_continue = True
break
else:
continue
break
if buffer_and_continue:
continue
if not shared.args.no_stream:
# Streaming
new_content = answer[len_seen:]
if not new_content or chr(0xfffd) in new_content: # partial unicode character, don't send it yet.
continue
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,
}],
}
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 }
response = 'data: ' + json.dumps(chunk) + '\n'
self.wfile.write(response.encode('utf-8'))
completion_token_count += len(encode(new_content)[0])
if not shared.args.no_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'] = {}
response = 'data: ' + json.dumps(chunk) + '\ndata: [DONE]\n'
self.wfile.write(response.encode('utf-8'))
###### Finished if streaming.
if debug: print({'response': answer})
return
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 '/embeddings' in self.path and embedding_model != None:
self.send_response(200)
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()
data = [ {"object": "embedding", "embedding": 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_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_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 {public_url}/')
except ImportError:
print('You should install flask_cloudflared manually')
else:
print(f'Starting OpenAI compatible api at http://{server_addr[0]}:{server_addr[1]}/')
server.serve_forever()
def setup():
Thread(target=run_server, daemon=True).start()