added new route

routes/study.py

@@ -6,7 +6,8 @@ from pydantic import BaseModel
 
 router = APIRouter(prefix="/api/study")
 
-# Existing Request Models
+# --- Existing Request Models ---
+
 class NeighborhoodRequest(BaseModel):
     word: str
     n_neighbors: int = 20
@@ -29,7 +30,7 @@ class SemanticFieldRequest(BaseModel):
     words: List[str]
     n_neighbors: int = 5
 
-# New Request Models for additional study operations
+# --- New Request Models for additional study operations ---
 
 class PhraseRequest(BaseModel):
     words: List[str]
@@ -58,6 +59,10 @@ class CombineRequest(BaseModel):
     positive: List[WeightedWord]
     negative: List[WeightedWord] = []
 
+# --- New Request Model for Similar By Vector Endpoint ---
+class SimilarByVectorRequest(BaseModel):
+    vector: List[float]
+    n: int = 10
 
 def init_router(study_service: StudyService):
     router = APIRouter(prefix="/api/study")
@@ -123,8 +128,6 @@ def init_router(study_service: StudyService):
             logger.error(f"Error retrieving visualization data: {str(e)}")
             raise HTTPException(status_code=500, detail="Internal server error")
 
-    # New Endpoints
-
     @router.post("/phrase")
     async def get_phrase_vector(request: PhraseRequest):
         """
@@ -201,5 +204,20 @@ def init_router(study_service: StudyService):
         except Exception as e:
             logger.error(f"Error combining word vectors: {str(e)}")
             raise HTTPException(status_code=500, detail="Internal server error")
+    
+    # --- New Endpoint: Similar By Vector ---
+    @router.post("/similar-by-vector")
+    async def similar_by_vector_endpoint(request: SimilarByVectorRequest):
+        """
+        Given a vector (list of floats) and a number n, return the n words most similar to that vector.
+        """
+        try:
+            import numpy as np
+            vector = np.array(request.vector)
+            similar = await study_service.word_service.get_similar_by_vector(vector, n=request.n)
+            return {"similar_words": similar}
+        except Exception as e:
+            logger.error(f"Error computing similar words by vector: {str(e)}")
+            raise HTTPException(status_code=500, detail="Internal server error")
 
     return router