added the endpoint

main.py

@@ -1,27 +1,67 @@
+# from fastapi import FastAPI
+# from pydantic import BaseModel
+# from typing import List, Tuple
+# import numpy as np
+# from sentence_transformers import SentenceTransformer
+
+# # Load the pre-trained model
+# model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
+
+# # Define request model
+# class MessageRequest(BaseModel):
+#     messages: List[str]
+
+# # Define response model
+# class EmbeddingResponse(BaseModel):
+#     dimensions: int  # Only return embedding size
+#     numeric_values: List[List[float]]
+
+# # Initialize FastAPI app
+# app = FastAPI()
+
+# @app.get("/")
+# def home ():
+#     return {"Message":"Welcome to homepage, kindly proceed by giving /docs in the URL" }
+
+# @app.post("/embed", response_model=EmbeddingResponse)
+# def embed(request: MessageRequest):
+#     new_embeddings = model.encode(request.messages, convert_to_tensor=True)
+#     return EmbeddingResponse(
+#         dimensions=new_embeddings.shape[1],  # Return only the embedding dimension
+#         numeric_values=new_embeddings.tolist()
+#     )
+
 from fastapi import FastAPI
 from pydantic import BaseModel
-from typing import List, Tuple
+from typing import List
 import numpy as np
 from sentence_transformers import SentenceTransformer
 
 # Load the pre-trained model
 model = SentenceTransformer('Alibaba-NLP/gte-base-en-v1.5', trust_remote_code=True)
 
-# Define request model
+# Define request models
 class MessageRequest(BaseModel):
     messages: List[str]
 
-# Define response model
+class CosineSimilarityRequest(BaseModel):
+    text1: str
+    text2: str
+
+# Define response models
 class EmbeddingResponse(BaseModel):
     dimensions: int  # Only return embedding size
     numeric_values: List[List[float]]
 
+class CosineSimilarityResponse(BaseModel):
+    similarity: float
+
 # Initialize FastAPI app
 app = FastAPI()
 
 @app.get("/")
-def home ():
-    return {"Message":"Welcome to homepage, kindly proceed by giving /docs in the URL" }
+def home():
+    return {"Message": "Welcome to homepage, kindly proceed by giving /docs in the URL"}
 
 @app.post("/embed", response_model=EmbeddingResponse)
 def embed(request: MessageRequest):
@@ -30,3 +70,9 @@ def embed(request: MessageRequest):
         dimensions=new_embeddings.shape[1],  # Return only the embedding dimension
         numeric_values=new_embeddings.tolist()
     )
+
+@app.post("/cosine_similarity", response_model=CosineSimilarityResponse)
+def cosine_similarity(request: CosineSimilarityRequest):
+    embeddings = model.encode([request.text1, request.text2], convert_to_tensor=True)
+    cos_sim = np.dot(embeddings[0], embeddings[1]) / (np.linalg.norm(embeddings[0]) * np.linalg.norm(embeddings[1]))
+    return CosineSimilarityResponse(similarity=cos_sim)