Ensure memory-efficient high-level document symbol cache #944

* Introduce SymbolBody representation (compact), from which the
    body can be reconstructed, rendering body computation lazy
  * Introduce SymbolBodyFactory for the creation of such instances,
    ensuring that instances created from the same factory share
    the same file content, allowing the in-memory and on-disk caches
    to have a significantly smaller memory footprint
  * Change type of "body" key in UnifiedSymbolRepresentation to use
    SymbolBody instead of str
  * Adjust all usages of the "body" key

test/solidlsp/dart/test_dart_basic.py

@@ -212,7 +212,8 @@ class TestDartLanguageServer:
             assert containing_symbol["name"] == "add"
             assert containing_symbol["kind"] == SymbolKind.Method
             if "body" in containing_symbol:
-                assert "add" in containing_symbol["body"] or "final result" in containing_symbol["body"]
+                body = containing_symbol["body"].get_text()
+                assert "add" in body or "final result" in body
 
     @pytest.mark.parametrize("language_server", [Language.DART], indirect=True)
     def test_request_containing_symbol_class(self, language_server: SolidLanguageServer) -> None: