LLM Templates - Created startup technical ideas LLM chat app

agenta-cli/agenta/sdk/agenta_decorator.py

@@ -12,10 +12,9 @@
 import agenta
 from fastapi import Body, FastAPI, UploadFile
 from fastapi.middleware.cors import CORSMiddleware
-from fastapi.openapi.models import Server
 from fastapi.responses import JSONResponse
 
-from .context import get_contexts, save_context
+from .context import save_context
 from .router import router as router
 from .types import (
     Context,
@@ -345,18 +344,15 @@ def find_in_schema(schema: dict, param_name: str, xparam: str):
         f"Body_{func_name}_{endpoint}_post"
     ]["properties"]
     for param_name, param_val in params.items():
-        # print(param_name, param_val)
-        if isinstance(param_val, MultipleChoiceParam):
+        if (
+            isinstance(param_val, inspect.Parameter)
+            and param_val.annotation is MultipleChoiceParam
+        ):
             subschema = find_in_schema(schema_to_override, param_name, "choice")
-            default = str(param_val)
-            param_choices = param_val.choices
-            choices = (
-                [default] + param_choices
-                if param_val not in param_choices
-                else param_choices
-            )
-            subschema["enum"] = choices
-            subschema["default"] = default if default in param_choices else choices[0]
+            default = param_val.default.choices[0]
+            param_choices = param_val.default.choices
+            subschema["enum"] = param_choices
+            subschema["default"] = default
         if isinstance(param_val, FloatParam):
             subschema = find_in_schema(schema_to_override, param_name, "float")
             subschema["minimum"] = param_val.minval

agenta-cli/agenta/sdk/types.py

@@ -67,24 +67,23 @@ def __modify_schema__(cls, field_schema):
         )
 
 
-class MultipleChoiceParam(str):
-    def __new__(cls, default: str = None, choices: List[str] = None):
-        if type(default) is list:
-            raise ValueError(
-                "The order of the parameters for MultipleChoiceParam is wrong! It's MultipleChoiceParam(default, choices) and not the opposite"
-            )
-        if default is None and choices:
-            # if a default value is not provided,
-            # uset the first value in the choices list
-            default = choices[0]
-
-        if default is None and not choices:
-            # raise error if no default value or choices is provided
-            raise ValueError("You must provide either a default value or choices")
+class ChoiceMixin:
+    """
+    Provides a mixin for managing a list of choices.
+    It is used as a base class for the MultipleChoiceParam class to \
+        provide the functionality for handling the choices attribute.
+    """
 
-        instance = super().__new__(cls, default)
-        instance.choices = choices
-        instance.default = default
+    def __init__(self, choices: List[str] = None):
+        if not choices:
+            raise ValueError("You must provide either a list of choices")
+        self.choices = choices
+
+
+class MultipleChoiceParam(str, ChoiceMixin):
+    def __new__(cls, choices: List[str] = None):
+        instance = super().__new__(cls, choices)
+        instance.default = choices[0]
         return instance
 
     @classmethod
@@ -106,7 +105,7 @@ class Message(BaseModel):
 class MessagesInput(list):
     """Messages Input for Chat-completion.
 
-    Parameters:
+    Args:
         messages (List[Dict[str, str]]): The list of messages inputs.
         Required. Each message should be a dictionary with "role" and "content" keys.
 

agenta-cli/agenta/templates/startup_technical_ideas/.env.example

@@ -0,0 +1,2 @@
+# Rename this file to .env
+OPENAI_API_KEY=sk-xxxxxxxxxxxxxxxxxxxxxxxx

agenta-cli/agenta/templates/startup_technical_ideas/README.md

@@ -0,0 +1,9 @@
+# Using this template
+
+Please make sure to create a `.env` file with your OpenAI API key before running the app.
+OPENAI_API_KEY=sk-xxxxxxx
+
+You can find your keys here:
+https://platform.openai.com/account/api-keys
+
+Go back to the [Getting started tutorial](https://docs.agenta.ai/getting-started) to continue

agenta-cli/agenta/templates/startup_technical_ideas/app.py

@@ -0,0 +1,40 @@
+import agenta as ag
+from agenta import FloatParam, MessagesInput, MultipleChoiceParam
+from openai import OpenAI
+
+client = OpenAI()
+
+SYSTEM_PROMPT = "You have expertise in offering technical ideas to startups."
+CHAT_LLM_GPT = [
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-4",
+]
+
+ag.init(app_name="technical_ideas", base_name="app")
+ag.config.default(
+    temperature=FloatParam(0.2),
+)
+
+
+@ag.entrypoint
+def chat(
+    model: MultipleChoiceParam = MultipleChoiceParam(CHAT_LLM_GPT),
+    inputs: MessagesInput = MessagesInput(
+        [{"role": "system", "content": SYSTEM_PROMPT}]
+    ),
+) -> str:
+    messages = [
+        {
+            "role": message["role"],
+            "content": message["content"],
+        }
+        for message in inputs
+    ]
+    chat_completion = client.chat.completions.create(
+        model=model, messages=messages, temperature=ag.config.temperature
+    )
+    return chat_completion.choices[0].message.content

agenta-cli/agenta/templates/startup_technical_ideas/template.toml

@@ -0,0 +1 @@
+short_desc="Simple chat app that offers technical ideas to startups"

examples/startup_technical_ideas/app.py

@@ -0,0 +1,40 @@
+import agenta as ag
+from agenta import FloatParam, MessagesInput, MultipleChoiceParam
+from openai import OpenAI
+
+client = OpenAI()
+
+SYSTEM_PROMPT = "You have expertise in offering technical ideas to startups."
+CHAT_LLM_GPT = [
+    "gpt-3.5-turbo",
+    "gpt-3.5-turbo-16k",
+    "gpt-3.5-turbo-0301",
+    "gpt-3.5-turbo-0613",
+    "gpt-3.5-turbo-16k-0613",
+    "gpt-4",
+]
+
+ag.init(app_name="technical_ideas", base_name="app")
+ag.config.default(
+    temperature=FloatParam(0.2),
+)
+
+
+@ag.entrypoint
+def chat(
+    model: MultipleChoiceParam = MultipleChoiceParam(CHAT_LLM_GPT),
+    inputs: MessagesInput = MessagesInput(
+        [{"role": "system", "content": SYSTEM_PROMPT}]
+    ),
+) -> str:
+    messages = [
+        {
+            "role": message["role"],
+            "content": message["content"],
+        }
+        for message in inputs
+    ]
+    chat_completion = client.chat.completions.create(
+        model=model, messages=messages, temperature=ag.config.temperature
+    )
+    return chat_completion.choices[0].message.content

examples/startup_technical_ideas/requirements.txt

@@ -0,0 +1,2 @@
+agenta
+openai