diff --git a/router.py b/router.py
index d7a4cbd..51e0137 100644
--- a/router.py
+++ b/router.py
@@ -302,7 +302,25 @@ class rechunk:
                                     "response": chunk.choices[0].text
             }
         return rechunk
+    
+    def openai_embeddings2ollama(chunk: dict):
+        rechunk = {"embedding": chunk.data[0].embedding}
+        return rechunk
 
+    def openai_embed2ollama(chunk: dict, model: str):
+        rechunk = { "model": model,
+                                    "created_at": iso8601_ns(),
+                                    "done": None,
+                                    "done_reason": None,
+                                    "total_duration": None,
+                                    "load_duration": None,
+                                    "prompt_eval_count": None,
+                                    "prompt_eval_duration": None,
+                                    "eval_count": None,
+                                    "eval_duration": None,
+                                    "embeddings": [chunk.data[0].embedding]
+            }
+        return rechunk
 # ------------------------------------------------------------------
 # SSE Helpser
 # ------------------------------------------------------------------
@@ -639,13 +657,21 @@ async def embedding_proxy(request: Request):
     # 2. Endpoint logic
     endpoint = await choose_endpoint(model)
     await increment_usage(endpoint, model)
-    client = ollama.AsyncClient(host=endpoint)
+    is_openai_endpoint = "/v1" in endpoint
+    if is_openai_endpoint:
+        client = openai.AsyncOpenAI(base_url=endpoint, api_key=config.api_keys[endpoint])
+    else:
+        client = ollama.AsyncClient(host=endpoint)
 
     # 3. Async generator that streams embedding data and decrements the counter
     async def stream_embedding_response():
         try:
             # The chat method returns a generator of dicts (or GenerateResponse)
-            async_gen = await client.embeddings(model=model, prompt=prompt, options=options, keep_alive=keep_alive)
+            if is_openai_endpoint:
+                async_gen = await client.embeddings.create(input=[prompt], model=model)
+                async_gen = rechunk.openai_embeddings2ollama(async_gen)
+            else:
+                async_gen = await client.embeddings(model=model, prompt=prompt, options=options, keep_alive=keep_alive)
             if hasattr(async_gen, "model_dump_json"):
                 json_line = async_gen.model_dump_json()
             else:
@@ -676,7 +702,7 @@ async def embed_proxy(request: Request):
         payload = json.loads(body_bytes.decode("utf-8"))
 
         model = payload.get("model")
-        input = payload.get("input")
+        _input = payload.get("input")
         truncate = payload.get("truncate")
         options = payload.get("options")
         keep_alive = payload.get("keep_alive")
@@ -685,7 +711,7 @@ async def embed_proxy(request: Request):
             raise HTTPException(
                 status_code=400, detail="Missing required field 'model'"
             )
-        if not input:
+        if not _input:
             raise HTTPException(
                 status_code=400, detail="Missing required field 'input'"
             )
@@ -695,13 +721,21 @@ async def embed_proxy(request: Request):
     # 2. Endpoint logic
     endpoint = await choose_endpoint(model)
     await increment_usage(endpoint, model)
-    client = ollama.AsyncClient(host=endpoint)
+    is_openai_endpoint = "/v1" in endpoint
+    if is_openai_endpoint:
+        client = openai.AsyncOpenAI(base_url=endpoint, api_key=config.api_keys[endpoint])
+    else:
+        client = ollama.AsyncClient(host=endpoint)
 
     # 3. Async generator that streams embed data and decrements the counter
     async def stream_embedding_response():
         try:
             # The chat method returns a generator of dicts (or GenerateResponse)
-            async_gen = await client.embed(model=model, input=input, truncate=truncate, options=options, keep_alive=keep_alive)
+            if is_openai_endpoint:
+                async_gen = await client.embeddings.create(input=[_input], model=model)
+                async_gen = rechunk.openai_embed2ollama(async_gen, model)
+            else:
+                async_gen = await client.embed(model=model, input=_input, truncate=truncate, options=options, keep_alive=keep_alive)
             if hasattr(async_gen, "model_dump_json"):
                 json_line = async_gen.model_dump_json()
             else: