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).

Setup & installation

Install the requirements:

pip3 install -r requirements.txt

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

Make sure you enable it in server launch parameters, it should include:

--extensions openai

You can also use the --listen argument to make the server available on the networ, and/or the --share argument to enable a public Cloudflare endpoint.

To enable the basic image generation support (txt2img) set the environment variable SD_WEBUI_URL to point to your Stable Diffusion API (Automatic1111).

For example:

SD_WEBUI_URL=http://127.0.0.1:7861

Quick start

1. Install the requirements.txt (pip)
2. Enable the openeai module (--extensions openai), restart the server.
3. Configure the openai client

Most openai application can be configured to connect the API if you set the following environment variables:

# Sample .env file:
OPENAI_API_KEY=sk-111111111111111111111111111111111111111111111111
OPENAI_API_BASE=http://0.0.0.0:5001/v1

If needed, replace 0.0.0.0 with the IP/port of your server.

Models

This has been successfully tested with Alpaca, Koala, Vicuna, WizardLM and their variants, (ex. gpt4-x-alpaca, GPT4all-snoozy, stable-vicuna, wizard-vicuna, etc.) and many others. Models that have been trained for Instruction Following work best. If you test with other models please let me know how it goes. Less than satisfying results (so far) from: RWKV-4-Raven, llama, mpt-7b-instruct/chat.

For best results across all API endpoints, a model like vicuna-13b-v1.3-GPTQ, stable-vicuna-13B-GPTQ or airoboros-13B-gpt4-1.3-GPTQ is a good start.

For good results with the Completions API endpoint, in addition to the above models, you can also try using a base model like falcon-7b or Llama.

For good results with the ChatCompletions or Edits API endpoints you can use almost any model trained for instruction following. Be sure that the proper instruction template is detected and loaded or the results will not be good.

For the proper instruction format to be detected you need to have a matching model entry in your models/config.yaml file. Be sure to keep this file up to date. A matching instruction template file in the characters/instruction-following/ folder will loaded and applied to format messages correctly for the model - this is critical for good results.

For example, the Wizard-Vicuna family of models are trained with the Vicuna 1.1 format. In the models/config.yaml file there is this matching entry:

.*wizard.*vicuna:
  mode: 'instruct'
  instruction_template: 'Vicuna-v1.1'

This refers to characters/instruction-following/Vicuna-v1.1.yaml, which looks like this:

user: "USER:"
bot: "ASSISTANT:"
turn_template: "<|user|> <|user-message|>\n<|bot|> <|bot-message|></s>\n"
context: "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions.\n\n"

For most common models this is already setup, but if you are using a new or uncommon model you may need add a matching entry to the models/config.yaml and possibly create your own instruction-following template and for best results.

If you see this in your logs, it probably means that the correct format could not be loaded:

Warning: Loaded default instruction-following template for model.

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, set the OPENAI_API_BASE environment variables:

# Sample .env file:
OPENAI_API_KEY=sk-111111111111111111111111111111111111111111111111
OPENAI_API_BASE=http://0.0.0.0:5001/v1

If needed, replace 0.0.0.0 with the IP/port of your server.

If using .env files to save the OPENAI_API_BASE and OPENAI_API_KEY variables, make sure the .env file is loaded before the openai module is imported:

from dotenv import load_dotenv
load_dotenv() # make sure the environment variables are set before import
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,
})

API Documentation & Examples

The OpenAI API is well documented, you can view the documentation here: https://platform.openai.com/docs/api-reference

Examples of how to use the Completions API in Python can be found here: https://platform.openai.com/examples Not all of them will work with all models unfortunately, See the notes on Models for how to get the best results.

Here is a simple python example.

import os
os.environ['OPENAI_API_KEY']="sk-111111111111111111111111111111111111111111111111"
os.environ['OPENAI_API_BASE']="http://0.0.0.0:5001/v1"
import openai

response = openai.ChatCompletion.create(
  model="x",
  messages = [{ 'role': 'system', 'content': "Answer in a consistent style." },
    {'role': 'user', 'content': "Teach me about patience."},
    {'role': 'assistant', 'content': "The river that carves the deepest valley flows from a modest spring; the grandest symphony originates from a single note; the most intricate tapestry begins with a solitary thread."},
    {'role': 'user', 'content': "Teach me about the ocean."},
  ]
)
text = response['choices'][0]['message']['content']
print(text)

Compatibility & not so compatibility

API endpoint	tested with	notes
/v1/chat/completions	openai.ChatCompletion.create()	Use it with instruction following models
/v1/embeddings	openai.Embedding.create()	Using SentenceTransformer embeddings
/v1/images/generations	openai.Image.create()	Bare bones, no model configuration, response_format='b64_json' only.
/v1/moderations	openai.Moderation.create()	Basic initial support via embeddings
/v1/models	openai.Model.list()	Lists models, Currently loaded model first, plus some compatibility options
/v1/models/{id}	openai.Model.get()	returns whatever you ask for
/v1/edits	openai.Edit.create()	Deprecated by openai, good with instruction following models
/v1/text_completion	openai.Completion.create()	Legacy endpoint, variable quality based on the model
/v1/completions	openai api completions.create	Legacy endpoint (v0.25)
/v1/engines/*/embeddings	python-openai v0.25	Legacy endpoint
/v1/engines/*/generate	openai engines.generate	Legacy endpoint
/v1/engines	openai engines.list	Legacy Lists models
/v1/engines/{model_name}	openai engines.get -i {model_name}	You can use this legacy endpoint to load models via the api or command line
/v1/images/edits	openai.Image.create_edit()	not yet supported
/v1/images/variations	openai.Image.create_variation()	not yet supported
/v1/audio/*	openai.Audio.*	not yet supported
/v1/files*	openai.Files.*	not yet supported
/v1/fine-tunes*	openai.FineTune.*	not yet supported
/v1/search	openai.search, engines.search	not yet supported

Because of the differences in OpenAI model context sizes (2k, 4k, 8k, 16k, etc,) you may need to adjust the max_tokens to fit into the context of the model you choose.

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
model	-	Ignored, the model is not changed
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	default is 1 (top_k is 20 for chat, which doesn't support best_of)
n	1	variations are not supported yet.
1	num_beams	hardcoded to 1
1.0	typical_p	hardcoded to 1.0
logprobs & logit_bias	-	experimental, llama only, transformers-kin only (ExLlama_HF ok), can also use llama tokens if 'model' is not an openai model or will convert from tiktoken for the openai model specified in 'model'
messages.name	-	not supported yet
suffix	-	not supported yet
user	-	not supported yet
functions/function_call	-	function calls are not supported yet

Applications

Almost everything needs the OPENAI_API_KEY and OPENAI_API_BASE environment variable set, but there are some exceptions.

Compatibility	Application/Library	Website	Notes
✅❌	openai-python (v0.25+)	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, Images also work
✅	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
✅	OpenAI for Notepad++	https://github.com/Krazal/nppopenai	api_url=http://127.0.0.1:5001 in the config file, or environment variables
✅	vscode-openai	https://marketplace.visualstudio.com/items?itemName=AndrewButson.vscode-openai	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
❌	guidance	https://github.com/microsoft/guidance	logit_bias and logprobs not yet supported

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)

Bugs? Feedback? Comments? Pull requests?

To enable debugging and get copious output you can set the OPENEDAI_DEBUG=1 environment variable.

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