## 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. * It is 100% offline and private. * It doesn't create any logs. * It doesn't connect to OpenAI. * It doesn't use the openai-python library. ### Starting the API Add `--api` 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 `--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`. * To use an API key for authentication, add `--api-key yourkey`. ### Examples For the documentation with all the endpoints, parameters and their types, 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 }' ``` #### Logits ```shell curl -k http://127.0.0.1:5000/v1/internal/logits \ -H "Content-Type: application/json" \ -d '{ "prompt": "Who is best, Asuka or Rei? Answer:", "use_samplers": false }' ``` #### Logits after sampling parameters ```shell curl -k http://127.0.0.1:5000/v1/internal/logits \ -H "Content-Type: application/json" \ -d '{ "prompt": "Who is best, Asuka or Rei? Answer:", "use_samplers": true, "top_k": 3 }' ``` #### List models ```shell curl -k http://127.0.0.1:5000/v1/internal/model/list \ -H "Content-Type: application/json" ``` #### Load model ```shell curl -k http://127.0.0.1:5000/v1/internal/model/load \ -H "Content-Type: application/json" \ -d '{ "model_name": "model_name", "args": { "load_in_4bit": true, "n_gpu_layers": 12 }, "settings": { "instruction_template": "Alpaca" } }' ``` #### 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) ``` #### Python chat example with streaming Start the script with `python -u` to see the output in real time. ```python 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) assistant_message = '' for event in client.events(): payload = json.loads(event.data) chunk = payload['choices'][0]['message']['content'] assistant_message += chunk print(chunk, end='') print() history.append({"role": "assistant", "content": assistant_message}) ``` #### Python completions example with streaming Start the script with `python -u` to see the output in real time. ```python 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() ``` ### Environment variables The following environment variables can be used (they take precedence 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) | sentence-transformers/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: "sentence-transformers/all-mpnet-base-v2" openai-sd_webui_url: http://127.0.0.1:7861 openai-debug: 1 ``` ### Third-party 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://127.0.0.1:5000/v1 ``` With the [official python openai client](https://github.com/openai/openai-python), the address can be set like this: ```python import openai openai.api_key = "..." openai.api_base = "http://127.0.0.1:5000/v1" openai.api_version = "2023-05-15" ``` 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() # 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 }) ); ``` 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 }); ``` ### 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. ### Compatibility & not so compatibility Note: the table below may be obsolete. | 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. Note: the table below may be obsolete. | 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 |