Update README

README.md

@@ -2,17 +2,15 @@
 
 `gpt-json` is a wrapper around GPT that allows for declarative definition of expected output format. Set up a schema, write a prompt, and get results back as beautiful typehinted objects.
 
-Specifically this library:
-
-- Utilizes Pydantic schema definitions for type casting and validations
-- Adds typehinting for both the API and the output schema
-- Allows GPT to respond with both single-objects and lists of objects
-- Includes some lightweight transformations of the output to remove superfluous context and fix broken json
-- Includes retry logic for the most common API failures
-- Formats the JSON schema as a flexible prompt that can be added into any message
-- Supports templating of prompts to allow for dynamic content
-- Validate typehinted function calls in the new GPT models, to better support agent creation
-- Lightweight dependencies: only OpenAI, pydantic, and backoff
+This library introduces the following features:
+
+- 🏗️ Pydantic schema definitions for type casting and validations
+- 🧵 Templating of prompts to allow for dynamic content
+- 🔎 Supports Vision API, Function Calling, and standard chat prompts
+- 🚕 Lightweight transformations of the output to fix broken json
+- ♻️ Retry logic for the most common API failures
+- 📋 Predict single-objects and lists of objects
+- ✈️ Lightweight dependencies: only OpenAI, pydantic, and backoff
 
 ## Getting Started
 

gpt_json/gpt.py

@@ -60,6 +60,9 @@
 handler.setFormatter(formatter)
 logger.addHandler(handler)
 
+# https://github.com/openai/openai-python/issues/1306
+ChatCompletionChunk.model_rebuild()
+
 
 def handle_backoff(details):
     logger.warning(
@@ -261,11 +264,11 @@ async def run(
             except (ValueError, ValidationError):
                 raise InvalidFunctionParameters(function_name, function_args_string)
 
-        raw_response = GPTMessage.model_validate_json(response_message.model_dump_json())
+        raw_response = GPTMessage.model_validate(response_message.model_dump())
         raw_response.allow_templating = False
 
         extracted_json, fixed_payload = self.extract_json(
-            response_message, self.extract_type
+            raw_response, self.extract_type
         )
 
         # Cast to schema model
@@ -372,12 +375,12 @@ async def stream(
                 yield partial_response
                 previous_partial = partial_response
 
-    def extract_json(self, response_message, extract_type: ResponseType):
+    def extract_json(self, response_message: GPTMessage, extract_type: ResponseType):
         """
         Assumes one main block of results, either list of dictionary
         """
 
-        full_response = response_message.content
+        full_response = self.get_content_text(response_message.get_content_payloads())
         if not full_response:
             return None, None