Make /v1/embeddings functional, add request/response types

docs/12 - OpenAI API.md

@@ -211,7 +211,7 @@ The following environment variables can be used (they take precendence over ever
 | `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_MODEL` | Embedding model (if applicable) |          sentence-transformers/all-mpnet-base-v2                  |
 | `OPENEDAI_EMBEDDING_DEVICE` | Embedding device (if applicable) |           cuda                 |
 
 #### Persistent settings with `settings.yaml`
@@ -220,7 +220,7 @@ You can also set the following variables in your `settings.yaml` file:
 
 ```
 openai-embedding_device: cuda
-openai-embedding_model: all-mpnet-base-v2
+openai-embedding_model: "sentence-transformers/all-mpnet-base-v2"
 openai-sd_webui_url: http://127.0.0.1:7861
 openai-debug: 1
 ```

extensions/openai/cache_embedding_model.py

@@ -1,11 +1,11 @@
 #!/usr/bin/env python3
 # preload the embedding model, useful for Docker images to prevent re-download on config change
 # Dockerfile:
-# ENV OPENEDAI_EMBEDDING_MODEL=all-mpnet-base-v2 # Optional
+# ENV OPENEDAI_EMBEDDING_MODEL="sentence-transformers/all-mpnet-base-v2"  # Optional
 # RUN python3 cache_embedded_model.py
 import os
 
 import sentence_transformers
 
-st_model = os.environ.get("OPENEDAI_EMBEDDING_MODEL", "all-mpnet-base-v2")
+st_model = os.environ.get("OPENEDAI_EMBEDDING_MODEL", "sentence-transformers/all-mpnet-base-v2")
 model = sentence_transformers.SentenceTransformer(st_model)

extensions/openai/embeddings.py

@@ -3,8 +3,7 @@ import os
 import numpy as np
 from extensions.openai.errors import ServiceUnavailableError
 from extensions.openai.utils import debug_msg, float_list_to_base64
-from modules import shared
+from modules.logging_colors import logger
-from transformers import AutoModel
 
 embeddings_params_initialized = False
 
@@ -16,38 +15,44 @@ 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
+
+        global st_model, embeddings_model, embeddings_device
+
         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):
+    try:
+        from sentence_transformers import SentenceTransformer
+    except ModuleNotFoundError:
+        logger.error("The sentence_transformers module has not been found. Please install it manually with pip install -U sentence-transformers.")
+        raise ModuleNotFoundError
+
     initialize_embedding_params()
     global embeddings_device, embeddings_model
     try:
         print(f"Try embedding model: {model} on {embeddings_device}")
-        trust = shared.args.trust_remote_code
+        embeddings_model = SentenceTransformer(model, device=embeddings_device)
-        if embeddings_device == 'cpu':
+        print(f"Loaded embedding model: {model}")
-            embeddings_model = AutoModel.from_pretrained(model, trust_remote_code=trust).to("cpu", dtype=float)
-        else: #use the auto mode
-            embeddings_model = AutoModel.from_pretrained(model, trust_remote_code=trust)
-        print(f"\nLoaded embedding model: {model} on {embeddings_model.device}")
     except Exception as e:
         embeddings_model = None
         raise ServiceUnavailableError(f"Error: Failed to load embedding model: {model}", internal_message=repr(e))
 
 
-def get_embeddings_model() -> AutoModel:
+def get_embeddings_model():
     initialize_embedding_params()
     global embeddings_model, st_model
     if st_model and not embeddings_model:
         load_embedding_model(st_model)  # lazy load the model
+
     return embeddings_model
 
 
@@ -66,9 +71,7 @@ def get_embeddings(input: list) -> np.ndarray:
 
 
 def embeddings(input: list, encoding_format: str) -> dict:
-
     embeddings = get_embeddings(input)
-
     if encoding_format == "base64":
         data = [{"object": "embedding", "embedding": float_list_to_base64(emb), "index": n} for n, emb in enumerate(embeddings)]
     else:
@@ -85,5 +88,4 @@ def embeddings(input: list, encoding_format: str) -> dict:
     }
 
     debug_msg(f"Embeddings return size: {len(embeddings[0])}, number: {len(embeddings)}")
-
     return response

extensions/openai/models.py

@@ -56,7 +56,7 @@ def _load_model(data):
 
     shared.model, shared.tokenizer = load_model(model_name)
     shared.model_name = model_name
-    
+
     # Update shared.settings with custom generation defaults
     if settings:
         for k in settings:

extensions/openai/script.py

@@ -31,6 +31,8 @@ from .typing import (
     CompletionResponse,
     DecodeRequest,
     DecodeResponse,
+    EmbeddingsRequest,
+    EmbeddingsResponse,
     EncodeRequest,
     EncodeResponse,
     LoadModelRequest,
@@ -41,7 +43,7 @@ from .typing import (
 
 params = {
     'embedding_device': 'cpu',
-    'embedding_model': 'all-mpnet-base-v2',
+    'embedding_model': 'sentence-transformers/all-mpnet-base-v2',
     'sd_webui_url': '',
     'debug': 0
 }
@@ -196,19 +198,16 @@ async def handle_image_generation(request: Request):
     return JSONResponse(response)
 
 
-@app.post("/v1/embeddings")
+@app.post("/v1/embeddings", response_model=EmbeddingsResponse)
-async def handle_embeddings(request: Request):
+async def handle_embeddings(request: Request, request_data: EmbeddingsRequest):
-    body = await request.json()
+    input = request_data.input
-    encoding_format = body.get("encoding_format", "")
-
-    input = body.get('input', body.get('text', ''))
     if not input:
         raise HTTPException(status_code=400, detail="Missing required argument input")
 
     if type(input) is str:
         input = [input]
 
-    response = OAIembeddings.embeddings(input, encoding_format)
+    response = OAIembeddings.embeddings(input, request_data.encoding_format)
     return JSONResponse(response)
 
 

extensions/openai/typing.py

@@ -154,6 +154,19 @@ class LoadModelRequest(BaseModel):
     settings: dict | None = None
 
 
+class EmbeddingsRequest(BaseModel):
+    input: str | List[str]
+    model: str | None = Field(default=None, description="Unused parameter. To change the model, set the OPENEDAI_EMBEDDING_MODEL and OPENEDAI_EMBEDDING_DEVICE environment variables before starting the server.")
+    encoding_format: str = Field(default="float", description="Can be float or base64.")
+    user: str | None = Field(default=None, description="Unused parameter.")
+
+
+class EmbeddingsResponse(BaseModel):
+    index: int
+    embedding: List[float]
+    object: str = "embedding"
+
+
 def to_json(obj):
     return json.dumps(obj.__dict__, indent=4)