fix: Python 3.13 compatibility and import path corrections

Co-authored-by: Claude <noreply@anthropic.com>

Author: jxnl

instructor/processing/multimodal.py

@@ -151,9 +151,19 @@ def from_gs_url(cls, data_uri: str, timeout: int = 30) -> Image:
     def from_raw_base64(cls, data: str) -> Image:
         try:
             decoded = base64.b64decode(data)
-            import imghdr
 
-            img_type = imghdr.what(None, decoded)
+            # Detect image type from file signature (magic bytes)
+            # This replaces imghdr which was removed in Python 3.13
+            img_type = None
+            if decoded.startswith(b'\xff\xd8\xff'):
+                img_type = 'jpeg'
+            elif decoded.startswith(b'\x89PNG\r\n\x1a\n'):
+                img_type = 'png'
+            elif decoded.startswith(b'GIF87a') or decoded.startswith(b'GIF89a'):
+                img_type = 'gif'
+            elif decoded.startswith(b'RIFF') and decoded[8:12] == b'WEBP':
+                img_type = 'webp'
+
             if img_type:
                 media_type = f"image/{img_type}"
                 if media_type in VALID_MIME_TYPES:

instructor/providers/gemini/utils.py

@@ -961,7 +961,7 @@ def handle_vertexai_tools(
 def handle_vertexai_json(
     response_model: type[Any] | None, new_kwargs: dict[str, Any]
 ) -> tuple[type[Any] | None, dict[str, Any]]:
-    from instructor.client_vertexai import vertexai_process_json_response
+    from instructor.providers.vertexai.client import vertexai_process_json_response
 
     """
     Handle Vertex AI JSON mode.

tests/test_response_model_conversion.py

@@ -14,7 +14,11 @@
 
 def get_system_prompt(user_tool_definition, mode):
     if mode == instructor.Mode.ANTHROPIC_JSON:
-        return user_tool_definition["system"]
+        system = user_tool_definition["system"]
+        # Handle both string and list[dict] formats
+        if isinstance(system, list):
+            return "".join(block.get("text", "") for block in system)
+        return system
     elif mode == instructor.Mode.GEMINI_JSON:
         return "\n".join(user_tool_definition["contents"][0]["parts"])
     elif mode == instructor.Mode.VERTEXAI_JSON: