Fix prompt template bugs: build template ignored and runtime override not wired (#173)

* Fix prompt template bugs in build and search

Bug 1: Build template ignored in new format
- Updated compute_embeddings_openai() to read build_prompt_template or prompt_template
- Updated compute_embeddings_ollama() with same fix
- Maintains backward compatibility with old single-template format

Bug 2: Runtime override not wired up
- Wired CLI search to pass provider_options to searcher.search()
- Enables runtime template override during search via --embedding-prompt-template

All 42 prompt template tests passing.

Fixes #155

* Fix: Prevent embedding server from applying templates during search

- Filter out all prompt templates (build_prompt_template, query_prompt_template, prompt_template) from provider_options when launching embedding server during search
- Templates are already applied in compute_query_embedding() before server call
- Prevents double-templating and ensures runtime override works correctly

This fixes the issue where --embedding-prompt-template during search was ignored because the server was applying build_prompt_template instead.

* Format code with ruff

packages/leann-core/src/leann/searcher_base.py

@@ -71,6 +71,15 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
             or "mips"
         )
 
+        # Filter out ALL prompt templates from provider_options during search
+        # Templates are applied in compute_query_embedding (line 109-110) BEFORE server call
+        # The server should never apply templates during search to avoid double-templating
+        search_provider_options = {
+            k: v
+            for k, v in self.embedding_options.items()
+            if k not in ("build_prompt_template", "query_prompt_template", "prompt_template")
+        }
+
         server_started, actual_port = self.embedding_server_manager.start_server(
             port=port,
             model_name=self.embedding_model,
@@ -78,7 +87,7 @@ class BaseSearcher(LeannBackendSearcherInterface, ABC):
             passages_file=passages_source_file,
             distance_metric=distance_metric,
             enable_warmup=kwargs.get("enable_warmup", False),
-            provider_options=self.embedding_options,
+            provider_options=search_provider_options,
         )
         if not server_started:
             raise RuntimeError(f"Failed to start embedding server on port {actual_port}")