diff --git a/paperqa/llms.py b/paperqa/llms.py
index 444aa46f..34122f12 100644
--- a/paperqa/llms.py
+++ b/paperqa/llms.py
@@ -27,7 +27,14 @@
     get_model_cost_map,
     token_counter,
 )
-from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, model_validator
+from pydantic import (
+    BaseModel,
+    ConfigDict,
+    Field,
+    TypeAdapter,
+    field_validator,
+    model_validator,
+)
 
 from paperqa.prompts import default_system_prompt
 from paperqa.rate_limiter import GLOBAL_LIMITER
@@ -79,7 +86,7 @@ class EmbeddingModes(StrEnum):
 
 class EmbeddingModel(ABC, BaseModel):
     name: str
-    config: dict = Field(
+    config: dict[str, Any] = Field(
         default_factory=dict,
         description="Optional `rate_limit` key, value must be a RateLimitItem or RateLimitItem string for parsing",
     )
@@ -102,7 +109,21 @@ async def embed_documents(self, texts: list[str]) -> list[list[float]]:
 
 
 class LiteLLMEmbeddingModel(EmbeddingModel):
+
     name: str = Field(default="text-embedding-3-small")
+    config: dict[str, Any] = Field(
+        default_factory=dict,  # See below field_validator for injection of kwargs
+        description="Optional `rate_limit` key, value must be a RateLimitItem or RateLimitItem string for parsing",
+    )
+
+    @field_validator("config")
+    @classmethod
+    def set_up_default_config(cls, value: dict[str, Any]) -> dict[str, Any]:
+        if "kwargs" not in value:
+            value["kwargs"] = get_litellm_retrying_config(
+                timeout=120,  # 2-min timeout seemed reasonable
+            )
+        return value
 
     def _truncate_if_large(self, texts: list[str]) -> list[str]:
         """Truncate texts if they are too large by using litellm cost map."""
@@ -489,6 +510,11 @@ async def rate_limited_generator() -> AsyncGenerator[LLMModelOrChild, None]:
     )
 
 
+def get_litellm_retrying_config(timeout: float = 60.0) -> dict[str, Any]:
+    """Get retrying configuration for litellm.acompletion and litellm.aembedding."""
+    return {"num_retries": 3, "timeout": timeout}
+
+
 class LiteLLMModel(LLMModel):
     """A wrapper around the litellm library.
 
@@ -527,10 +553,8 @@ def maybe_set_config_attribute(cls, data: dict[str, Any]) -> dict[str, Any]:
             } | data.get("config", {})
 
         if "router_kwargs" not in data.get("config", {}):
-            data["config"]["router_kwargs"] = {
-                "num_retries": 3,
-                "retry_after": 5,
-                "timeout": 60,
+            data["config"]["router_kwargs"] = get_litellm_retrying_config() | {
+                "retry_after": 5
             }
 
         # we only support one "model name" for now, here we validate
@@ -769,7 +793,6 @@ async def similarity_search(
 
 
 def embedding_model_factory(embedding: str, **kwargs) -> EmbeddingModel:
-
     if embedding.startswith("hybrid"):
         embedding_model_name = "-".join(embedding.split("-")[1:])
         return HybridEmbeddingModel(
@@ -780,5 +803,6 @@ def embedding_model_factory(embedding: str, **kwargs) -> EmbeddingModel:
         )
     if embedding == "sparse":
         return SparseEmbeddingModel(**kwargs)
-
-    return LiteLLMEmbeddingModel(name=embedding, config=kwargs)
+    if kwargs:  # Only override the default config if there are actually kwargs
+        kwargs = {"config": kwargs}
+    return LiteLLMEmbeddingModel(name=embedding, **kwargs)