extensions/openai: load extension settings via settings.yaml (#3953)

extensions/openai/README.md

@@ -44,6 +44,18 @@ OPENAI_API_BASE=http://0.0.0.0:5001/v1
 
 If needed, replace 0.0.0.0 with the IP/port of your server.
 
+
+## Settings
+
+To adjust your default settings, you can add the following to your `settings.yaml` file.
+
+```
+openai-port: 5002
+openai-embedding_device: cuda
+openai-sd_webui_url: http://127.0.0.1:7861
+openai-debug: 1
+```
+
 ### 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.

extensions/openai/cache_embedding_model.py

@@ -6,6 +6,7 @@
 import os
 
 import sentence_transformers
+from extensions.openai.script import params
 
-st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
+st_model = os.environ.get("OPENEDAI_EMBEDDING_MODEL", params.get('embedding_model', 'all-mpnet-base-v2'))
 model = sentence_transformers.SentenceTransformer(st_model)

extensions/openai/embeddings.py

@@ -5,15 +5,25 @@ from extensions.openai.errors import ServiceUnavailableError
 from extensions.openai.utils import debug_msg, float_list_to_base64
 from sentence_transformers import SentenceTransformer
 
-st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
-embeddings_model = None
-# OPENEDAI_EMBEDDING_DEVICE: auto (best or cpu), cpu, cuda, ipu, xpu, mkldnn, opengl, opencl, ideep, hip, ve, fpga, ort, xla, lazy, vulkan, mps, meta, hpu, mtia, privateuseone
-embeddings_device = os.environ.get("OPENEDAI_EMBEDDING_DEVICE", "cpu")
-if embeddings_device.lower() == 'auto':
-    embeddings_device = None
+embeddings_params_initialized = False
+# using 'lazy loading' to avoid circular import
+# so this function will be executed only once
+def initialize_embedding_params():
+    global embeddings_params_initialized
+    if not embeddings_params_initialized:
+        global st_model, embeddings_model, embeddings_device
+        from extensions.openai.script import params
+        st_model = os.environ.get("OPENEDAI_EMBEDDING_MODEL", params.get('embedding_model', 'all-mpnet-base-v2'))
+        embeddings_model = None
+        # OPENEDAI_EMBEDDING_DEVICE: auto (best or cpu), cpu, cuda, ipu, xpu, mkldnn, opengl, opencl, ideep, hip, ve, fpga, ort, xla, lazy, vulkan, mps, meta, hpu, mtia, privateuseone
+        embeddings_device = os.environ.get("OPENEDAI_EMBEDDING_DEVICE", params.get('embedding_device', 'cpu'))
+        if embeddings_device.lower() == 'auto':
+            embeddings_device = None
+        embeddings_params_initialized = True
 
 
 def load_embedding_model(model: str) -> SentenceTransformer:
+    initialize_embedding_params()
     global embeddings_device, embeddings_model
     try:
         embeddings_model = 'loading...'  # flag
@@ -29,6 +39,7 @@ def load_embedding_model(model: str) -> SentenceTransformer:
 
 
 def get_embeddings_model() -> SentenceTransformer:
+    initialize_embedding_params()
     global embeddings_model, st_model
     if st_model and not embeddings_model:
         embeddings_model = load_embedding_model(st_model)  # lazy load the model
@@ -36,6 +47,7 @@ def get_embeddings_model() -> SentenceTransformer:
 
 
 def get_embeddings_model_name() -> str:
+    initialize_embedding_params()
     global st_model
     return st_model
 

extensions/openai/images.py

@@ -49,9 +49,9 @@ def generations(prompt: str, size: str, response_format: str, n: int):
         'created': int(time.time()),
         'data': []
     }
-
+    from extensions.openai.script import params
     # TODO: support SD_WEBUI_AUTH username:password pair.
-    sd_url = f"{os.environ['SD_WEBUI_URL']}/sdapi/v1/txt2img"
+    sd_url = f"{os.environ.get('SD_WEBUI_URL', params.get('sd_webui_url', ''))}/sdapi/v1/txt2img"
 
     response = requests.post(url=sd_url, json=payload)
     r = response.json()

extensions/openai/script.py

@@ -25,10 +25,16 @@ import speech_recognition as sr
 from pydub import AudioSegment
 
 params = {
-    'port': int(os.environ.get('OPENEDAI_PORT')) if 'OPENEDAI_PORT' in os.environ else 5001,
+    # default params
+    'port': 5001,
+    'embedding_device': 'cpu',
+    'embedding_model': 'all-mpnet-base-v2',
+    
+    # optional params
+    'sd_webui_url': '',
+    'debug': 0
 }
 
-
 class Handler(BaseHTTPRequestHandler):
     def send_access_control_headers(self):
         self.send_header("Access-Control-Allow-Origin", "*")
@@ -251,7 +257,7 @@ class Handler(BaseHTTPRequestHandler):
             self.return_json(response)
 
         elif '/images/generations' in self.path:
-            if 'SD_WEBUI_URL' not in os.environ:
+            if not os.environ.get('SD_WEBUI_URL', params.get('sd_webui_url', '')):
                 raise ServiceUnavailableError("Stable Diffusion not available. SD_WEBUI_URL not set.")
 
             prompt = body['prompt']
@@ -313,12 +319,13 @@ class Handler(BaseHTTPRequestHandler):
 
 
 def run_server():
-    server_addr = ('0.0.0.0' if shared.args.listen else '127.0.0.1', params['port'])
+    port = int(os.environ.get('OPENEDAI_PORT', params.get('port', 5001)))
+    server_addr = ('0.0.0.0' if shared.args.listen else '127.0.0.1', port)
     server = ThreadingHTTPServer(server_addr, Handler)
     if shared.args.share:
         try:
             from flask_cloudflared import _run_cloudflared
-            public_url = _run_cloudflared(params['port'], params['port'] + 1)
+            public_url = _run_cloudflared(port, port + 1)
             print(f'OpenAI compatible API ready at: OPENAI_API_BASE={public_url}/v1')
         except ImportError:
             print('You should install flask_cloudflared manually')

extensions/openai/utils.py

@@ -3,7 +3,6 @@ import os
 
 import numpy as np
 
-
 def float_list_to_base64(float_array: np.ndarray) -> str:
     # Convert the list to a float32 array that the OpenAPI client expects
     # float_array = np.array(float_list, dtype="float32")
@@ -26,5 +25,6 @@ def end_line(s):
 
 
 def debug_msg(*args, **kwargs):
-    if 'OPENEDAI_DEBUG' in os.environ:
+    from extensions.openai.script import params
+    if os.environ.get("OPENEDAI_DEBUG", params.get('debug', 0)):
         print(*args, **kwargs)