Add helper inputs

Author: okaris

src/inferencesh/sdk.py

@@ -5,8 +5,9 @@
 import urllib.request
 import urllib.parse
 import tempfile
-
+from pydantic import Field
 from typing import Any, Dict, List
+
 import inspect
 import ast
 import textwrap
@@ -214,4 +215,70 @@ def exists(self) -> bool:
 
     def refresh_metadata(self) -> None:
         """Refresh all metadata from the file."""
-        self._populate_metadata() 
+        self._populate_metadata() 
+
+
+class ContextMessage(BaseModel):
+    role: str = Field(
+        description="The role of the message",
+        enum=["user", "assistant", "system"]
+    )
+    text: str = Field(
+        description="The text content of the message"
+    )
+
+class ContextMessageWithImage(ContextMessage):
+    image: Optional[File] = Field(
+        description="The image url of the message",
+        default=None
+    )
+
+class LLMInput(BaseAppInput):
+    system_prompt: str = Field(
+        description="The system prompt to use for the model",
+        default="You are a helpful assistant that can answer questions and help with tasks.",
+        examples=[
+            "You are a helpful assistant that can answer questions and help with tasks.",
+            "You are a certified medical professional who can provide accurate health information.",
+            "You are a certified financial advisor who can give sound investment guidance.",
+            "You are a certified cybersecurity expert who can explain security best practices.",
+            "You are a certified environmental scientist who can discuss climate and sustainability.",
+        ]
+    )
+    context: list[ContextMessage] = Field(
+        description="The context to use for the model",
+        examples=[
+            [
+                {"role": "user", "content": [{"type": "text", "text": "What is the capital of France?"}]}, 
+                {"role": "assistant", "content": [{"type": "text", "text": "The capital of France is Paris."}]}
+            ],
+            [
+                {"role": "user", "content": [{"type": "text", "text": "What is the weather like today?"}]}, 
+                {"role": "assistant", "content": [{"type": "text", "text": "I apologize, but I don't have access to real-time weather information. You would need to check a weather service or app to get current weather conditions for your location."}]}
+            ],
+            [
+                {"role": "user", "content": [{"type": "text", "text": "Can you help me write a poem about spring?"}]}, 
+                {"role": "assistant", "content": [{"type": "text", "text": "Here's a short poem about spring:\n\nGreen buds awakening,\nSoft rain gently falling down,\nNew life springs anew.\n\nWarm sun breaks through clouds,\nBirds return with joyful song,\nNature's sweet rebirth."}]}
+            ],
+            [
+                {"role": "user", "content": [{"type": "text", "text": "Explain quantum computing in simple terms"}]}, 
+                {"role": "assistant", "content": [{"type": "text", "text": "Quantum computing is like having a super-powerful calculator that can solve many problems at once instead of one at a time. While regular computers use bits (0s and 1s), quantum computers use quantum bits or \"qubits\" that can be both 0 and 1 at the same time - kind of like being in two places at once! This allows them to process huge amounts of information much faster than regular computers for certain types of problems."}]}
+            ]
+        ],
+        default=[]
+    )
+    text: str = Field(
+        description="The user prompt to use for the model",
+        examples=[
+            "What is the capital of France?",
+            "What is the weather like today?",
+            "Can you help me write a poem about spring?",
+            "Explain quantum computing in simple terms"
+        ],
+    )
+
+class LLMInputWithImage(LLMInput):
+    image: Optional[File] = Field(
+        description="The image to use for the model",
+        default=None
+    )