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

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## OpenAI compatible API
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The main API for this project is meant to be a drop-in replacement to the OpenAI API, including Chat and Completions endpoints.
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If you did not use the one-click installers, you may need to install the requirements first:
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```
pip install -r extensions/openai/requirements.txt
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```
### Starting the API
Add `--extensions openai` to your command-line flags.
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* 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 `--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`
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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
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For the documentation with all the parameters, consult `http://127.0.0.1:5000/docs` or the [typing.py](https://github.com/oobabooga/text-generation-webui/blob/main/extensions/openai/typing.py) file.
The official examples in the [OpenAI documentation](https://platform.openai.com/docs/api-reference) should also work, and the same parameters apply (although the API here has more optional parameters).
#### Completions
```shell
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`.
```shell
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
```shell
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
```shell
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
```python
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)
```
### 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:
```shell
OPENAI_API_HOST=http://127.0.0.1:5000
```
or
```shell
OPENAI_API_KEY=sk-111111111111111111111111111111111111111111111111
OPENAI_API_BASE=http://127.0.0.1:500/v1
```
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With the [official python openai client](https://github.com/openai/openai-python), set the `OPENAI_API_BASE` environment variables:
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```shell
# Sample .env file:
OPENAI_API_KEY=sk-111111111111111111111111111111111111111111111111
OPENAI_API_BASE=http://0.0.0.0:5001/v1
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```
If needed, replace 127.0.0.1 with the IP/port of your server.
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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:
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```python
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from dotenv import load_dotenv
load_dotenv() # make sure the environment variables are set before import
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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:
```js
const openai = OpenAI(
Configuration({
apiKey: process.env.OPENAI_API_KEY,
basePath: process.env.OPENAI_API_BASE
})
);
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```
For apps made with the [chatgpt-api Node.js client library](https://github.com/transitive-bullshit/chatgpt-api):
```js
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const api = new ChatGPTAPI({
apiKey: process.env.OPENAI_API_KEY,
apiBaseUrl: process.env.OPENAI_API_BASE
});
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```
### Embeddings (alpha)
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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.
```python
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
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| 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 |
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#### Applications
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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 |