✨ Add support for vision in LLM engines

src/engines/LLMEngine/AnthropicLLMEngine.py

@@ -26,7 +26,8 @@ class AnthropicLLMEngine(BaseLLMEngine):
     def generate(
         self,
         system_prompt: str,
-        chat_prompt: str,
+        chat_prompt: str = "",
+        messages: list = [],
         max_tokens: int = 1024,
         temperature: float = 1.0,
         json_mode: bool = False,
@@ -36,9 +37,11 @@ class AnthropicLLMEngine(BaseLLMEngine):
     ) -> str | dict:
         tries = 0
         while tries < 2:
-            messages = [
-                {"role": "user", "content": chat_prompt},
-            ]
+            if chat_prompt:
+                messages = [
+                    {"role": "user", "content": chat_prompt},
+                    *messages,
+                ]
             if json_mode:
                 # anthropic does not officially support JSON mode, but we can bias the output towards a JSON-like format
                 messages.append({"role": "assistant", "content": "{"})
@@ -47,7 +50,7 @@ class AnthropicLLMEngine(BaseLLMEngine):
                 messages=messages,
                 model=self.model,
                 top_p=top_p,
-                temperature=temperature,
+                temperature=temperature if temperature <= 1.0 else 1.0,
                 system=system_prompt,
             )
 
@@ -93,3 +96,11 @@ class AnthropicLLMEngine(BaseLLMEngine):
             return gr.update(value=api_key)
 
         save.click(save_api_key, inputs=[api_key_input])
+
+    @property
+    def supports_vision(self) -> bool:
+        return (
+            True
+            if self.model in ["claude-3-opus-20240229", "claude-3-sonnet-20240229"]
+            else False
+        )

src/engines/LLMEngine/BaseLLMEngine.py

@@ -4,11 +4,14 @@ from ..BaseEngine import BaseEngine
 
 
 class BaseLLMEngine(BaseEngine):
+    supports_vision = False
+
     @abstractmethod
     def generate(
         self,
         system_prompt: str,
-        chat_prompt: str,
+        chat_prompt: str = "",
+        messages: list[dict] = [],
         max_tokens: int = 512,
         temperature: float = 1.0,
         json_mode: bool = False,

src/engines/LLMEngine/OpenaiLLMEngine.py

@@ -8,8 +8,8 @@ from .BaseLLMEngine import BaseLLMEngine
 
 OPENAI_POSSIBLE_MODELS = [  # Theese shall be the openai models supporting force_json
     "gpt-3.5-turbo-0125",
-    "gpt-4-turbo-preview",
     "gpt-4-turbo",
+    "gpt-4o"
 ]
 
 
@@ -29,7 +29,8 @@ class OpenaiLLMEngine(BaseLLMEngine):
     def generate(
         self,
         system_prompt: str,
-        chat_prompt: str,
+        chat_prompt: str = "",
+        messages: list = [],
         max_tokens: int = 512,
         temperature: float = 1.0,
         json_mode: bool = False,
@@ -40,19 +41,36 @@ class OpenaiLLMEngine(BaseLLMEngine):
         logging.info(
             f"Generating with OpenAI model {self.model} and system prompt: \n{system_prompt} and chat prompt: \n{chat_prompt[0:100]}..."
         )
+        if chat_prompt:
+            messages = [
+                {"role": "user", "content": chat_prompt},
+                *messages,
+            ]
+        for i, message in enumerate(messages):
+            if type(message["content"]) is list:
+                for i, content in enumerate(message["content"]):
+                    if content["type"] == "image":
+                        message["content"][i] = {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:{content['source']['media_type']};base64,{content['source']['data']}",
+                            },
+                        }
+                messages[i] = message
+
         response = self.client.chat.completions.create(
             model=self.model,
             messages=[
                 {"role": "system", "content": system_prompt},
-                {"role": "user", "content": chat_prompt},
+                *messages,
             ],
-            max_tokens=int(max_tokens) if max_tokens else openai._types.NOT_GIVEN,
+            max_tokens=int(max_tokens) if max_tokens else openai.NOT_GIVEN,
             temperature=temperature,
             top_p=top_p,
             frequency_penalty=frequency_penalty,
             presence_penalty=presence_penalty,
             response_format=(
-                {"type": "json_object"} if json_mode else openai._types.NOT_GIVEN
+                {"type": "json_object"} if json_mode else openai.NOT_GIVEN
             ),
         )
         return (
@@ -88,3 +106,7 @@ class OpenaiLLMEngine(BaseLLMEngine):
             return gr.update(value=api_key)
 
         save.click(save_api_key, inputs=[api_key_input])
+
+    @property
+    def supports_vision(self) -> bool:
+        return True if self.model in ["gpt-4-turbo-preview", "gpt-4-turbo", "gpt-4o"] else False