fix(main.py): map list input to ollama prompt input format

litellm/exceptions.py

@@ -24,6 +24,7 @@ from openai import (
     PermissionDeniedError,
 )
 import httpx
+from typing import Optional
 
 
 class AuthenticationError(AuthenticationError):  # type: ignore
@@ -50,11 +51,19 @@ class NotFoundError(NotFoundError):  # type: ignore
 
 
 class BadRequestError(BadRequestError):  # type: ignore
-    def __init__(self, message, model, llm_provider, response: httpx.Response):
+    def __init__(
+        self, message, model, llm_provider, response: Optional[httpx.Response] = None
+    ):
         self.status_code = 400
         self.message = message
         self.model = model
         self.llm_provider = llm_provider
+        response = response or httpx.Response(
+            status_code=self.status_code,
+            request=httpx.Request(
+                method="GET", url="https://litellm.ai"
+            ),  # mock request object
+        )
         super().__init__(
             self.message, response=response, body=None
         )  # Call the base class constructor with the parameters it needs

litellm/main.py

@@ -2590,10 +2590,28 @@ def embedding(
                 model_response=EmbeddingResponse(),
             )
         elif custom_llm_provider == "ollama":
+            ollama_input = None
+            if isinstance(input, list) and len(input) > 1:
+                raise litellm.BadRequestError(
+                    message=f"Ollama Embeddings don't support batch embeddings",
+                    model=model,  # type: ignore
+                    llm_provider="ollama",  # type: ignore
+                )
+            if isinstance(input, list) and len(input) == 1:
+                ollama_input = "".join(input[0])
+            elif isinstance(input, str):
+                ollama_input = input
+            else:
+                raise litellm.BadRequestError(
+                    message=f"Invalid input for ollama embeddings. input={input}",
+                    model=model,  # type: ignore
+                    llm_provider="ollama",  # type: ignore
+                )
+
             if aembedding == True:
                 response = ollama.ollama_aembeddings(
                     model=model,
-                    prompt=input,
+                    prompt=ollama_input,
                     encoding=encoding,
                     logging_obj=logging,
                     optional_params=optional_params,