text-generation-webui/extensions/openai

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

It listens on tcp port 5001 by default. You can use the OPENEDAI_PORT environment variable to change this.

To enable the bare bones image generation (txt2img) set: SD_WEBUI_URL to point to your Stable Diffusion API (Automatic1111).

Example:

SD_WEBUI_URL=http://127.0.0.1:7861

Make sure you enable it in server launch parameters. Just make sure they include:

--extensions openai

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, you can set the OPENAI_API_BASE environment variable before you import the openai module, like so:

OPENAI_API_KEY=sk-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 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:

const api = new ChatGPTAPI({
  apiKey: process.env.OPENAI_API_KEY,
  apiBaseUrl: process.env.OPENAI_API_BASE,
})

Compatibility & not so compatibility

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/edits	openai.Edit.create()	Assumes an instruction following model, but may work with others
/v1/images/generations	openai.Image.create()	Bare bones, no model configuration, response_format='b64_json' only.
/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
/v1/images/edits	openai.Image.create_edit()	not supported
/v1/images/variations	openai.Image.create_variation()	not supported
/v1/audio/*	openai.Audio.*	not supported
/v1/files*	openai.Files.*	not supported
/v1/fine-tunes*	openai.FineTune.*	not supported

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.

Models

This has been successfully tested with Koala, Alpaca, gpt4-x-alpaca, GPT4all-snoozy, wizard-vicuna, stable-vicuna and Vicuna 1.1 - ie. Instruction Following models. If you test with other models please let me know how it goes. Less than satisfying results (so far): RWKV-4-Raven, llama, mpt-7b-instruct/chat

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)
✅	anse	https://github.com/anse-app/anse	API Key & URL configurable in UI
✅	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

Bugs? Feedback? Comments? Pull requests?

Are all appreciated, please @matatonic and I'll try to get back to you as soon as possible.