AI/text-generation-webui
1
0
Fork 0
mirror of https://github.com/oobabooga/text-generation-webui.git synced 2024-10-01 01:26:03 -04:00
Code Issues Packages Projects Releases Wiki Activity
6ec997f195
text-generation-webui/docs/12 - OpenAI API.md
oobabooga 6ec997f195
Update 12 - OpenAI API.md
2023-11-07 12:36:52 -03:00

18 KiB
Raw Blame History

OpenAI compatible API

The main API for this project is meant to be a drop-in replacement to the OpenAI API, including Chat and Completions endpoints.

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, also add the --public-api flag.
  • To listen on your local network, also add the --listen flag.
  • To change the port, which is 5000 by default, use --api-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 the following variables in 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

For the documentation with all the parameters, consult http://127.0.0.1:5000/docs or the typing.py file.

The official examples in the OpenAI documentation should also work, and the same parameters apply (although the API here has more optional parameters).

Completions

curl http://127.0.0.1:5000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "This is a cake recipe:\n\n1.",
    "max_tokens": 200,
    "temperature": 1,
    "top_p": 0.9,
    "seed": 10
  }'

Chat completions

Works best with instruction-following models. If the "instruction_template" variable is not provided, it will be guessed automatically based on the model name using the regex patterns in models/config.yaml.

curl http://127.0.0.1:5000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": "Hello!"
      }
    ],
    "mode": "instruct",
    "instruction_template": "Alpaca"
  }'

Chat completions with characters

curl http://127.0.0.1:5000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": "Hello! Who are you?"
      }
    ],
    "mode": "chat",
    "character": "Example"
  }'

SSE streaming

curl http://127.0.0.1:5000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [
      {
        "role": "user",
        "content": "Hello!"
      }
    ],
    "mode": "instruct",
    "instruction_template": "Alpaca",
    "stream": true
  }'

Python chat example

import requests

url = "http://127.0.0.1:5000/v1/chat/completions"

headers = {
    "Content-Type": "application/json"
}

history = []

while True:
    user_message = input("> ")
    history.append({"role": "user", "content": user_message})
    data = {
        "mode": "chat",
        "character": "Example",
        "messages": history
    }

    response = requests.post(url, headers=headers, json=data, verify=False)
    assistant_message = response.json()['choices'][0]['message']['content']
    history.append({"role": "assistant", "content": assistant_message})
    print(assistant_message)

Python chat example with streaming

Start the script with python -u to see the output in real time.

import requests
import sseclient  # pip install sseclient-py
import json

url = "http://127.0.0.1:5000/v1/chat/completions"

headers = {
    "Content-Type": "application/json"
}

history = []

while True:
    user_message = input("> ")
    history.append({"role": "user", "content": user_message})
    data = {
        "mode": "instruct",
        "stream": True,
        "messages": history
    }

    stream_response = requests.post(url, headers=headers, json=data, verify=False, stream=True)
    client = sseclient.SSEClient(stream_response)

    for event in client.events():
        payload = json.loads(event.data)
        print(payload['choices'][0]['message']['content'], end='')

    print()

Python completions example with streaming

Start the script with python -u to see the output in real time.

import json
import requests
import sseclient  # pip install sseclient-py

url = "http://127.0.0.1:5000/v1/completions"

headers = {
    "Content-Type": "application/json"
}

data = {
    "prompt": "This is a cake recipe:\n\n1.",
    "max_tokens": 200,
    "temperature": 1,
    "top_p": 0.9,
    "seed": 10,
    "stream": True,
}

stream_response = requests.post(url, headers=headers, json=data, verify=False, stream=True)
client = sseclient.SSEClient(stream_response)

print(data['prompt'], end='')
for event in client.events():
    payload = json.loads(event.data)
    print(payload['choices'][0]['text'], end='')

print()

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