Rerank search results with cross-encoder when using an inference server

If an inference server is being used, we can expect the cross encoder
to be running fast enough to rerank search results by default

src/khoj/processor/embeddings.py

@@ -93,12 +93,11 @@ def __init__(
         self.inference_endpoint = cross_encoder_inference_endpoint
         self.api_key = cross_encoder_inference_endpoint_api_key
 
+    def is_inference_endpoint_enabled(self) -> bool:
+        return self.api_key is not None and self.inference_endpoint is not None
+
     def predict(self, query, hits: List[SearchResponse], key: str = "compiled"):
-        if (
-            self.api_key is not None
-            and self.inference_endpoint is not None
-            and "huggingface" in self.inference_endpoint
-        ):
+        if self.is_inference_endpoint_enabled() and "huggingface" in self.inference_endpoint:
             target_url = f"{self.inference_endpoint}"
             payload = {"inputs": {"query": query, "passages": [hit.additional[key] for hit in hits]}}
             headers = {"Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json"}

src/khoj/routers/api.py

@@ -146,8 +146,12 @@ async def search(
                     # Collate results
                     results += text_search.collate_results(hits, dedupe=dedupe)
 
-                    # Rerank results if explicitly requested or if device has GPU
-                    rerank = r or state.device.type != "cpu"
+                    # Rerank results if explicitly requested, if device has GPU or if an inference server is being used
+                    rerank = (
+                        r
+                        or state.device.type != "cpu"
+                        or state.cross_encoder_model[search_model.name].is_inference_endpoint_enabled()
+                    )
 
                     # Sort results across all content types and take top results
                     results = text_search.rerank_and_sort_results(