text-generation-webui/docs/12 - OpenAI API.md

OpenAI compatible API

This project includes an API compatible with multiple OpenAI endpoints, including Chat and Completions.

If you did not use the one-click installers, you may need to install the requirements first:

pip install -r extensions/openai/requirements.txt

Starting the API

Add --extensions openai to your command-line flags.

• To create a public Cloudflare URL, add the --public-api flag.
• To listen on your local network, add the --listen flag.
• To change the port, which is 5000 by default, use --port 1234 (change 1234 to your desired port number).
• To use SSL, add --ssl-keyfile key.pem --ssl-certfile cert.pem. Note that it doesn't work with --public-api.

Environment variables

The following environment variables can be used (they take precendence over everything else):

Variable Name	Description	Example Value
OPENEDAI_PORT	Port number	5000
OPENEDAI_CERT_PATH	SSL certificate file path	cert.pem
OPENEDAI_KEY_PATH	SSL key file path	key.pem
OPENEDAI_DEBUG	Enable debugging (set to 1)	1
SD_WEBUI_URL	WebUI URL (used by endpoint)	http://127.0.0.1:7861
OPENEDAI_EMBEDDING_MODEL	Embedding model (if applicable)	all-mpnet-base-v2
OPENEDAI_EMBEDDING_DEVICE	Embedding device (if applicable)	cuda

Persistent settings with settings.yaml

You can also set default values by adding these lines to your settings.yaml file:

openai-embedding_device: cuda
openai-embedding_model: all-mpnet-base-v2
openai-sd_webui_url: http://127.0.0.1:7861
openai-debug: 1

Examples

Client Application Setup

You can usually force an application that uses the OpenAI API to connect to the local API by using the following environment variables:

OPENAI_API_HOST=http://127.0.0.1:5000

or

OPENAI_API_KEY=sk-111111111111111111111111111111111111111111111111
OPENAI_API_BASE=http://127.0.0.1:500/v1

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 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, 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
});

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.

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()	Removed, use /v1/chat/completions instead
/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.*	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

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