임베딩 벡터 반환

api/embedding.py

@@ -0,0 +1,14 @@
+from fastapi import APIRouter
+from service.embedding import get_embedding_vector
+from pydantic import BaseModel
+
+router = APIRouter(tags=["Embedding"])
+
+
+class EmbeddingRequest(BaseModel):
+    text: str
+
+
+@router.post("/embedding")
+async def get_embedding(request: EmbeddingRequest):
+    return get_embedding_vector(request.text)

main.py

@@ -1,8 +1,9 @@
 from fastapi import FastAPI
-from api import health, icebreaking, summary
+from api import health, icebreaking, summary, embedding
 
 app = FastAPI(root_path="/ai")
 
 app.include_router(health.router)
 app.include_router(icebreaking.router)
 app.include_router(summary.router)
+app.include_router(embedding.router)

requirements.txt

@@ -3,4 +3,5 @@ uvicorn
 pydantic
 pydantic-settings
 requests
-openai==1.82.0
+openai==1.82.0
+sentence-transformers

service/embedding.py

@@ -0,0 +1,8 @@
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("sentence-transformers/paraphrase-MiniLM-L6-v2")  # 384차원
+
+
+def get_embedding_vector(request_text):
+    embedding = model.encode(request_text).tolist()
+    return {"embedding": embedding}