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extensions/openai: Fixes for: embeddings, tokens, better errors. +Docs update, +Images, +logit_bias/logprobs, +more. (#3122)

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extensions/openai/README.md
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@ -1,17 +1,15 @@
# 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):
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.
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:
@ -21,13 +19,30 @@ Make sure you enable it in server launch parameters, it should include:
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](https://github.com/AUTOMATIC1111/stable-diffusion-webui)).
To enable the basic image generation support (txt2img) set the environment variable ```SD_WEBUI_URL``` to point to your Stable Diffusion API ([Automatic1111](https://github.com/AUTOMATIC1111/stable-diffusion-webui)).
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:
```shell
# 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.
@ -36,7 +51,7 @@ For best results across all API endpoints, a model like [vicuna-13b-v1.3-GPTQ](h
For good results with the [Completions](https://platform.openai.com/docs/api-reference/completions) API endpoint, in addition to the above models, you can also try using a base model like [falcon-7b](https://huggingface.co/tiiuae/falcon-7b) or Llama.
For good results with the [ChatCompletions](https://platform.openai.com/docs/api-reference/chat) or [Edits](https://platform.openai.com/docs/api-reference/edits) API endpoints you can use almost any model trained for instruction following - within the limits of the model. Be sure that the proper instruction template is detected and loaded or the results will not be good.
For good results with the [ChatCompletions](https://platform.openai.com/docs/api-reference/chat) or [Edits](https://platform.openai.com/docs/api-reference/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.
@ -76,7 +91,7 @@ Embeddings requires ```sentence-transformers``` installed, but chat and completi
| 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".
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.
@ -85,26 +100,27 @@ Warning: You cannot mix embeddings from different models even if they have the s
Almost everything you use it with will require you to set a dummy OpenAI API key environment variable.
With the [official python openai client](https://github.com/openai/openai-python), you can set the OPENAI_API_BASE environment variable before you import the openai module, like so:
With the [official python openai client](https://github.com/openai/openai-python), set the ```OPENAI_API_BASE``` environment variables:
```
```shell
# Sample .env file:
OPENAI_API_KEY=sk-111111111111111111111111111111111111111111111111
OPENAI_API_BASE=http://127.0.0.1:5001/v1
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 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, you can ensure compatibility by loading the .env file before loading the openai module, like so in python:
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:
```
```python
from dotenv import load_dotenv
load_dotenv()
load_dotenv() # make sure the environment variables are set before import
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,
@ -113,7 +129,7 @@ const openai = OpenAI(Configuration({
For apps made with the [chatgpt-api Node.js client library](https://github.com/transitive-bullshit/chatgpt-api):
```
```js
const api = new ChatGPTAPI({
apiKey: process.env.OPENAI_API_KEY,
apiBaseUrl: process.env.OPENAI_API_BASE,
@ -127,39 +143,43 @@ The OpenAI API is well documented, you can view the documentation here: https://
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 of how you can use the Edit endpoint as a translator.
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.Edit.create(
response = openai.ChatCompletion.create(
model="x",
instruction="Translate this into French",
input="Our mission is to ensure that artificial general intelligence benefits all of humanity.",
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."},
]
)
print(response['choices'][0]['text'])
# Sample Output:
# Notre mission est de garantir que l'intelligence artificielle généralisée profite à tous les membres de l'humanité.
text = response['choices'][0]['message']['content']
print(text)
```
## Compatibility & not so compatibility
| API endpoint | tested with | notes |
| --- | --- | --- |
| /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, model does nothing yet anyways |
| /v1/text_completion | openai.Completion.create() | the most tested, only supports single string input so far, variable quality based on the model |
| /v1/chat/completions | openai.ChatCompletion.create() | Quality depends a lot on the model |
| /v1/edits | openai.Edit.create() | Works the best of all, perfect for instruction following models |
| /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/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/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, doesn't support array input, 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 |
| /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 |
@ -167,7 +187,7 @@ print(response['choices'][0]['text'])
| /v1/fine-tunes\* | openai.FineTune.\* | not yet supported |
| /v1/search | openai.search, engines.search | not yet 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).
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.
@ -175,41 +195,29 @@ 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 |
| stop | custom_stopping_strings | this is also stuffed with ['\n###', "\n{user prompt}", "{user prompt}" ] for good measure. |
| 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 |
| max_tokens | max_new_tokens | For Text Completions max_tokens is set smaller than the truncation_length minus the prompt length. This can cause no input to be generated if the prompt is too large. For ChatCompletions, the older chat messages may be dropped to fit the max_new_tokens requested |
| logprobs | - | not supported yet |
| logit_bias | - | not supported yet |
| 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 |
| user | - | not supported yet |
| functions/function_call | - | function calls are not supported yet |
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.
### Applications
Almost everything needs the OPENAI_API_KEY environment variable set, for example:
```
OPENAI_API_KEY=sk-111111111111111111111111111111111111111111111111
```
Some apps are picky about key format, but 'dummy' or 'sk-dummy' also work in most cases.
Most application will work if you also set:
```
OPENAI_API_BASE=http://127.0.0.1:5001/v1
```
but there are some exceptions.
Almost everything needs the ```OPENAI_API_KEY``` and ```OPENAI_API_BASE``` environment variable set, but there are some exceptions.
| Compatibility | Application/Library | url | notes / setting |
| 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 |
| ✅ | 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 |
@ -221,11 +229,12 @@ but there are some exceptions.
| ❌ | 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.
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.
Are all appreciated, please @matatonic and I'll try to get back to you as soon as possible.