API: Move OpenAI-compatible API from extensions/openai to modules/api

modules/api/embeddings.py

@@ -0,0 +1,96 @@
+import os
+
+import numpy as np
+from transformers import AutoModel
+
+from .errors import ServiceUnavailableError
+from .utils import debug_msg, float_list_to_base64
+from modules.logging_colors import logger
+
+embeddings_params_initialized = False
+
+
+def initialize_embedding_params():
+    '''
+    using 'lazy loading' to avoid circular import
+    so this function will be executed only once
+    '''
+    global embeddings_params_initialized
+    if not embeddings_params_initialized:
+        global st_model, embeddings_model, embeddings_device
+
+        st_model = os.environ.get("OPENEDAI_EMBEDDING_MODEL", 'sentence-transformers/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 = 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}")
+        if 'jina-embeddings' in model:
+            embeddings_model = AutoModel.from_pretrained(model, trust_remote_code=True)  # trust_remote_code is needed to use the encode method
+            embeddings_model = embeddings_model.to(embeddings_device)
+        else:
+            embeddings_model = SentenceTransformer(model, device=embeddings_device)
+
+        print(f"Loaded embedding model: {model}")
+    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():
+    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
+
+
+def get_embeddings_model_name() -> str:
+    initialize_embedding_params()
+    global st_model
+    return st_model
+
+
+def get_embeddings(input: list) -> np.ndarray:
+    model = get_embeddings_model()
+    debug_msg(f"embedding model : {model}")
+    embedding = model.encode(input, convert_to_numpy=True, normalize_embeddings=True, convert_to_tensor=False)
+    debug_msg(f"embedding result : {embedding}")  # might be too long even for debug, use at you own will
+    return embedding
+
+
+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:
+        data = [{"object": "embedding", "embedding": emb.tolist(), "index": n} for n, emb in enumerate(embeddings)]
+
+    response = {
+        "object": "list",
+        "data": data,
+        "model": st_model,  # return the real model
+        "usage": {
+            "prompt_tokens": 0,
+            "total_tokens": 0,
+        }
+    }
+
+    debug_msg(f"Embeddings return size: {len(embeddings[0])}, number: {len(embeddings)}")
+    return response