Parser conditional compilation

Cargo.toml

@@ -1,7 +1,9 @@
 [workspace]
 members = [
     "crates/neatcoder",
-    "crates/parser"
+    "crates/parser",
+    "crates/oai",
+    "crates/wasmer"
 ]
 exclude = [
     "examples/projects/*/jobs/codebase",

README.md

@@ -62,3 +62,13 @@ Install VS extension:
 <https://marketplace.visualstudio.com/items?itemName=amodio.tsl-problem-matcher>
 
 Copy `tasks.json` and `launch.json` from vscode to .vscode folder
+
+
+
+## To compile oai crate
+
+As default native:
+`cargo build`
+
+As a Wasm Lib:
+`cargo build --target wasm32-unknown-unknown --features wasm --no-default-features`

bin/create_interface.py

@@ -27,7 +27,7 @@ def process_file(input_filename, output_filename):
             # Check for methods by looking for lines with parentheses and ignore them
             if "(" in stripped_line or ")" in stripped_line:
                 continue
-            elif stripped_line.endswith(";"):
+            elif stripped_line.endswith(";"): ## Can either be a method or readonly field
                 # If it's a readonly field, remove the readonly keyword
                 # Ensure indentation when adding to output
                 output_lines.append("    " + stripped_line.replace("readonly ", ""))

crates/neatcoder/Cargo.toml

@@ -11,6 +11,8 @@ crate-type = ["cdylib"]
 
 [dependencies]
 parser = { path = "../parser" }
+oai = { path = "../oai", default-features = false, features = ["wasm"] }
+wasmer = { path = "../wasmer" }
 
 anyhow = "1.0"
 bytes = "1.4.0"

crates/neatcoder/src/endpoints/get_chat_title.rs

@@ -1,10 +1,12 @@
 use anyhow::{anyhow, Result};
 use js_sys::Function;
-
-use crate::openai::{
-    msg::{GptRole, OpenAIMsg},
-    params::{OpenAIModels, OpenAIParams},
-    request::chat_raw,
+use oai::models::{
+    chat::{
+        params::wasm::ChatParamsWasm as ChatParams, request::wasm::chat_raw,
+    },
+    message::wasm::GptMessageWasm as GptMessage,
+    model::GptModel,
+    role::Role as GptRole,
 };
 
 pub async fn get_chat_title(
@@ -13,16 +15,13 @@ pub async fn get_chat_title(
 ) -> Result<String> {
     let mut prompts = Vec::new();
 
-    prompts.push(OpenAIMsg {
-        role: GptRole::System,
-        content: String::from(
-            "
+    prompts.push(GptMessage::new(GptRole::System, String::from(
+        "
 - Context: Briefly describe the key topics or themes of the chat.
 - Title Specifications: The title should be concise, and not exceed 6 words. It should reflect the tone of the chat (e.g., professional, casual, informative, provocative, etc.).
 - Output: Provide a title that encapsulates the main focus of the chat.
-            ",
-        ),
-    });
+        ",
+    )));
 
     let main_prompt = format!(
         "
@@ -33,15 +32,11 @@ The title of the prompt is:",
         msg
     );
 
-    prompts.push(OpenAIMsg {
-        role: GptRole::User,
-        content: main_prompt,
-    });
+    prompts.push(GptMessage::new(GptRole::User, main_prompt));
 
-    let prompts = prompts.iter().map(|x| x).collect::<Vec<&OpenAIMsg>>();
+    let prompts = prompts.iter().map(|x| x).collect::<Vec<&GptMessage>>();
 
-    let ai_params =
-        OpenAIParams::empty(OpenAIModels::Gpt35Turbo).max_tokens(15);
+    let ai_params = ChatParams::empty(GptModel::Gpt35Turbo).max_tokens(15);
 
     let chat =
         chat_raw(request_callback, &ai_params, &prompts, &[], &[]).await?;

crates/neatcoder/src/endpoints/scaffold_project.rs

@@ -1,5 +1,9 @@
 use anyhow::{anyhow, Result};
 use js_sys::{Function, JsString};
+use oai::models::{
+    chat::params::wasm::ChatParamsWasm as ChatParams,
+    message::wasm::GptMessageWasm as GptMessage, role::Role as GptRole,
+};
 use serde::{Deserialize, Serialize};
 use serde_json::{Map, Value};
 use std::{
@@ -12,10 +16,6 @@ use wasm_bindgen::prelude::wasm_bindgen;
 use crate::{
     consts::{CONFIG_EXTENSIONS, CONFIG_FILES},
     models::app_data::language::Language,
-    openai::{
-        msg::{GptRole, OpenAIMsg},
-        params::OpenAIParams,
-    },
     utils::write_json,
 };
 
@@ -41,18 +41,16 @@ impl ScaffoldParams {
 
 pub async fn scaffold_project(
     language: &Language,
-    ai_params: &OpenAIParams,
+    ai_params: &ChatParams,
     client_params: &ScaffoldParams,
     request_callback: &Function,
 ) -> Result<(Value, Files)> {
     let mut prompts = Vec::new();
 
-    prompts.push(OpenAIMsg {
-        role: GptRole::System,
-        content: format!(
+    prompts.push(GptMessage::new(GptRole::System,format!(
             "You are a software engineer who is specialised in building software in {}.", language.name()
         ),
-    });
+    ));
 
     // TODO: We should add the Database and API interfaces in previous messages, and add the name of the files here in order to index them
     let main_prompt = format!("
@@ -63,12 +61,9 @@ Based on the information provided write the project's folder structure, starting
 
 Answer in JSON format (Do not forget to start with ```json). For each file provide a brief description included in the json", language.name(), client_params.specs);
 
-    prompts.push(OpenAIMsg {
-        role: GptRole::User,
-        content: main_prompt,
-    });
+    prompts.push(GptMessage::new(GptRole::User, main_prompt));
 
-    let prompts = prompts.iter().map(|x| x).collect::<Vec<&OpenAIMsg>>();
+    let prompts = prompts.iter().map(|x| x).collect::<Vec<&GptMessage>>();
 
     let (_, mut scaffold_json) =
         write_json(&ai_params, &prompts, request_callback).await?;

crates/neatcoder/src/endpoints/stream_code.rs

@@ -1,18 +1,20 @@
 use anyhow::{anyhow, Result};
 use js_sys::JsString;
+use oai::models::{
+    chat::{
+        params::wasm::ChatParamsWasm as ChatParams, request::request_stream,
+    },
+    message::{
+        wasm::GptMessageWasm as GptMessage, GptMessage as GptMessageInner,
+    },
+    role::Role as GptRole,
+};
 use serde::{Deserialize, Serialize};
-use std::collections::BTreeMap;
+use std::{collections::BTreeMap, ops::Deref};
 use wasm_bindgen::prelude::wasm_bindgen;
+use wasmer::log;
 
-use crate::{
-    models::app_data::{interfaces::AsContext, AppData},
-    openai::{
-        msg::{GptRole, OpenAIMsg},
-        params::OpenAIParams,
-        request::request_stream,
-    },
-    utils::log,
-};
+use crate::models::app_data::{interfaces::AsContext, AppData};
 
 #[wasm_bindgen]
 #[derive(Debug, Deserialize, Serialize, Clone)]
@@ -40,7 +42,7 @@ impl CodeGenParams {
 
 pub fn stream_code(
     app_state: &AppData,
-    ai_params: &OpenAIParams,
+    ai_params: &ChatParams,
     task_params: &CodeGenParams,
     codebase: BTreeMap<String, String>,
 ) -> Result<String> {
@@ -72,35 +74,28 @@ pub fn stream_code(
         anyhow!("It seems that the the field `specs` is missing..")
     })?;
 
-    prompts.push(OpenAIMsg {
-        role: GptRole::System,
-        content: format!(
+    prompts.push(GptMessage::new(
+        GptRole::System,
+        format!(
             "You are a software engineer who is specialised in {}.",
             language.name()
         ),
-    });
+    ));
 
-    prompts.push(OpenAIMsg {
-        role: GptRole::User,
-        content: String::from(project_description),
-    });
+    prompts.push(GptMessage::new(
+        GptRole::User,
+        String::from(project_description),
+    ));
 
     for file in codebase.keys() {
         let code = codebase
             .get(file)
             .ok_or_else(|| anyhow!("Unable to find fild {:?}", file))?;
 
-        prompts.push(OpenAIMsg {
-            role: GptRole::User,
-            content: code.clone(),
-        });
+        prompts.push(GptMessage::new(GptRole::User, code.clone()));
     }
 
-    prompts.push(OpenAIMsg {
-        role: GptRole::User,
-        // Needs to be optimized
-        content: project_scaffold.to_string(),
-    });
+    prompts.push(GptMessage::new(GptRole::User, project_scaffold.to_string()));
 
     let mut main_prompt = format!(
         "
@@ -132,14 +127,16 @@ pub fn stream_code(
         ));
     }
 
-    prompts.push(OpenAIMsg {
-        role: GptRole::User,
-        content: main_prompt,
-    });
+    prompts.push(GptMessage::new(GptRole::User, main_prompt));
+
+    // Assuming prompts is a Vec<&GptMessageWasm>
+    let msgs: Vec<&GptMessageInner> =
+        prompts.iter().map(|m_wasm| (*m_wasm).deref()).collect();
 
-    let prompts = prompts.iter().map(|x| x).collect::<Vec<&OpenAIMsg>>();
+    let prompts_slice: &[&GptMessageInner] = &msgs;
 
-    let request_body = request_stream(ai_params, &prompts, &[], &[])?;
+    let request_body =
+        request_stream(ai_params.deref(), &prompts_slice, &[], &[])?;
 
     Ok(request_body)
 }