Make initial version of splitllm

Author: unsecretised

Cargo.lock

@@ -1,4 +1,4 @@
 [workspace]
 resolver = "2"
-members = [ "one-bot","oneAI-backend", "onellm"]
+members = ["oneAI-backend", "onellm", "splitllm"]
 default-run = "oneAI-backend"

Cargo.toml

@@ -10,9 +10,7 @@ pub async fn login(email: String, password: String) -> Option<User> {
     };
 
     match verify_password(password, user.password.as_str()) {
-        Ok(_) => {
-            Some(user)
-        }
+        Ok(_) => Some(user),
         Err(_) => None,
     }
 }
@@ -45,12 +43,8 @@ pub async fn update_bal(email: String, change: i32) -> Option<User> {
         )
         .await
     {
-        Ok(_) => {
-            Some(user)
-        }
-        Err(_) => {
-            None
-        }
+        Ok(_) => Some(user),
+        Err(_) => None,
     }
 }
 

oneAI-backend/src/auth/basicauth.rs

@@ -116,7 +116,7 @@ impl APIInput {
                 deepseek.into()
             }
         };
-        match update_bal(user.email, -(((price * total)/100) as i32)).await {
+        match update_bal(user.email, -(((price * total) / 100) as i32)).await {
             Some(_) => Ok(unified_response),
             None => Err("An Unexpected error occurred".into()),
         }

oneAI-backend/src/requests/requests.rs

@@ -57,9 +57,7 @@ pub async fn verify_email(Json(payload): Json<VerifyInput>) -> Json<FailOrSucc>
         Ok(()) => {
             return Json(FailOrSucc::Successful("Successful".to_string()));
         }
-        Err(e) => {
-            Json(FailOrSucc::Failure(e.to_string()))
-        }
+        Err(e) => Json(FailOrSucc::Failure(e.to_string())),
     }
 }
 
@@ -246,14 +244,10 @@ pub async fn handle_post_website(Json(query): Json<WebInput>) -> impl IntoRespon
                 .unwrap();
 
             match user {
-                Some(_) => {
-                    Json(FailOrSucc::Successful(String::from("Successful operation")))
-                }
-                None => {
-                    Json(FailOrSucc::Failure(String::from(
-                        "Error while trying to create your account",
-                    )))
-                }
+                Some(_) => Json(FailOrSucc::Successful(String::from("Successful operation"))),
+                None => Json(FailOrSucc::Failure(String::from(
+                    "Error while trying to create your account",
+                ))),
             }
         }
 
@@ -291,11 +285,9 @@ pub async fn handle_post_website(Json(query): Json<WebInput>) -> impl IntoRespon
 
             Json(FailOrSucc::User(hidden_user))
         }
-        _ => {
-            Json(FailOrSucc::Failure(
-                "Tried to do Handle API at POST section".to_owned(),
-            ))
-        }
+        _ => Json(FailOrSucc::Failure(
+            "Tried to do Handle API at POST section".to_owned(),
+        )),
     }
 }
 

oneAI-backend/src/server.rs

@@ -0,0 +1,11 @@
+[package]
+name = "splitllm"
+version = "0.1.0"
+edition = "2024"
+
+[dependencies]
+anyhow = "1.0.98"
+onellm = {path = "../onellm"}
+schemars = "1.0.4"
+serde = "1.0.219"
+serde_json = "1.0.140"

splitllm/Cargo.toml

@@ -0,0 +1,57 @@
+use anyhow::Result;
+use onellm::input::{APIInput, Message, Model, ResponseFormat};
+use schemars::{JsonSchema, schema_for};
+
+#[derive(JsonSchema)]
+pub enum Style {
+    Agile,
+    Waterfall,
+}
+
+#[derive(JsonSchema)]
+pub enum Tier {
+    High,
+    Medium,
+    Low,
+}
+
+impl Tier {
+    pub fn tier_from_model(model: Model) -> Tier {
+        let price = model.price();
+        match price {
+            0..=499 => Tier::Low,
+            500..=1999 => Tier::Medium,
+            _ => Tier::High,
+        }
+    }
+}
+
+#[derive(JsonSchema)]
+pub struct PromptSplit {
+    pub style: Style,
+    pub prompts: Vec<String>,
+}
+
+pub async fn split_prompt(input: &str) -> Result<()> {
+    let endpoint = "https://api.openai.com/v1/responses".to_string();
+    let system_message = Message {
+        role: "system".to_string(),
+        content: include_str!("prompt.txt").to_string(),
+    };
+
+    let model = Model::Gpt4o;
+    let prompt = Message {
+        role: "user".to_string(),
+        content: input.to_string(),
+    };
+    let messages = vec![prompt, system_message];
+    let mut query = APIInput::new(endpoint, model, messages, 1000);
+
+    let schema = schema_for!(PromptSplit);
+
+    query.response_format = Some(ResponseFormat {
+        r#type: serde_json::to_string_pretty(&schema).unwrap(),
+    });
+
+    Ok(())
+}

splitllm/src/lib.rs

@@ -0,0 +1,32 @@
+You are a task-splitting assistant.
+
+Your job is to take natural language prompts that describe high-level goals (e.g. “Make me a blog website”) and return a structured, machine-readable JSON object that breaks the goal into a sequence of actionable tasks.
+
+Your output **must always be valid JSON**, with the following schema:
+
+{
+  "title": string,                     // A concise title summarizing the user prompt
+  "original_prompt": string,          // The original user prompt verbatim
+  "tasks": [                          // An array of subtasks derived from the main prompt
+    {
+      "name": string,                 // Short name of the task
+      "description": string,          // What this task involves and its purpose
+      "recommended_models": string[]  // Suggested AI models or tools to use for this task
+    }
+  ],
+  "output_format": string             // "json", "markdown", or "html" — based on the user's expected output type if known
+}
+
+Guidelines:
+- Always return ONLY valid JSON. Do not include markdown formatting, explanations, or commentary outside the JSON.
+- Ensure that tasks are broken down logically, in the order they should be executed.
+- Be concise but clear — avoid vague task names like “Do the thing.”
+- Only include relevant models (e.g., GPT-4o for text, DALL·E 3 for images, Code Llama for code).
+- If the original prompt is ambiguous, make educated assumptions to split the task effectively.
+
+Examples:
+User prompt: "Make me a blog website" → Generate tasks like defining requirements, planning structure, building layout.
+
+User prompt: "Generate an AI image of a castle on Mars" → Split into prompt crafting, JSON structuring, image generation.
+
+You are a reliable system that always responds with accurate, complete JSON that can be parsed and executed by downstream tools.