| { |
| "Universal LLM Initialization": { |
| "prefix": "ud-init", |
| "body": [ |
| "from universal_developer import UniversalLLM", |
| "", |
| "llm = UniversalLLM(", |
| " provider=\"${1|anthropic,openai,qwen,gemini,ollama|}\",", |
| " api_key=\"${2:your_api_key}\"", |
| ")" |
| ], |
| "description": "Initialize a Universal Developer LLM instance" |
| }, |
| "Thinking Mode Generator": { |
| "prefix": "ud-think", |
| "body": [ |
| "response = llm.generate(", |
| " ${1:system_prompt=\"${2:You are a helpful assistant.}\",}", |
| " prompt=\"/think ${3:What are the implications of ${4:technology} on ${5:domain}?}\"", |
| ")" |
| ], |
| "description": "Generate response using thinking mode" |
| }, |
| "Fast Mode Generator": { |
| "prefix": "ud-fast", |
| "body": [ |
| "response = llm.generate(", |
| " ${1:system_prompt=\"${2:You are a helpful assistant.}\",}", |
| " prompt=\"/fast ${3:${4:Summarize} ${5:this information}}\"", |
| ")" |
| ], |
| "description": "Generate concise response using fast mode" |
| }, |
| "Loop Mode Generator": { |
| "prefix": "ud-loop", |
| "body": [ |
| "response = llm.generate(", |
| " ${1:system_prompt=\"${2:You are a helpful assistant.}\",}", |
| " prompt=\"/loop --iterations=${3:3} ${4:Improve this ${5:text}: ${6:content}}\"", |
| ")" |
| ], |
| "description": "Generate iteratively refined response using loop mode" |
| }, |
| "Reflection Mode Generator": { |
| "prefix": "ud-reflect", |
| "body": [ |
| "response = llm.generate(", |
| " ${1:system_prompt=\"${2:You are a helpful assistant.}\",}", |
| " prompt=\"/reflect ${3:${4:Analyze} the ${5:implications} of ${6:topic}}\"", |
| ")" |
| ], |
| "description": "Generate self-reflective response using reflection mode" |
| }, |
| "Fork Mode Generator": { |
| "prefix": "ud-fork", |
| "body": [ |
| "response = llm.generate(", |
| " ${1:system_prompt=\"${2:You are a helpful assistant.}\",}", |
| " prompt=\"/fork --count=${3:2} ${4:Generate different ${5:approaches} to ${6:problem}}\"", |
| ")" |
| ], |
| "description": "Generate multiple alternative responses using fork mode" |
| }, |
| "Chain Commands": { |
| "prefix": "ud-chain", |
| "body": [ |
| "response = llm.generate(", |
| " ${1:system_prompt=\"${2:You are a helpful assistant.}\",}", |
| " prompt=\"/${3|think,loop,reflect,fork|} /${4|think,loop,reflect,fork|} ${5:Prompt text}\"", |
| ")" |
| ], |
| "description": "Generate response using chained symbolic commands" |
| }, |
| "Custom Command Registration": { |
| "prefix": "ud-custom", |
| "body": [ |
| "def transform_custom_command(prompt, options):", |
| " \"\"\"Custom command transformation function\"\"\"", |
| " system_prompt = options.get('system_prompt', '') + \"\"\"", |
| "${1:Custom system prompt instructions}", |
| "\"\"\"", |
| " ", |
| " return {", |
| " \"system_prompt\": system_prompt,", |
| " \"user_prompt\": prompt,", |
| " \"model_parameters\": {", |
| " \"${2:temperature}\": ${3:0.7}", |
| " }", |
| " }", |
| "", |
| "llm.register_command(", |
| " \"${4:command_name}\",", |
| " description=\"${5:Command description}\",", |
| " parameters=[", |
| " {", |
| " \"name\": \"${6:param_name}\",", |
| " \"description\": \"${7:Parameter description}\",", |
| " \"required\": ${8:False},", |
| " \"default\": ${9:\"default_value\"}", |
| " }", |
| " ],", |
| " transform=transform_custom_command", |
| ")" |
| ], |
| "description": "Register a custom symbolic command" |
| }, |
| "Flask API Integration": { |
| "prefix": "ud-flask", |
| "body": [ |
| "from flask import Flask, request, jsonify", |
| "from universal_developer import UniversalLLM", |
| "import os", |
| "", |
| "app = Flask(__name__)", |
| "", |
| "llm = UniversalLLM(", |
| " provider=\"${1|anthropic,openai,qwen,gemini,ollama|}\",", |
| " api_key=os.environ.get(\"${2:${1/(anthropic|openai|qwen|gemini)/${1:/upcase}_API_KEY/}}\")", |
| ")", |
| "", |
| "@app.route('/api/generate', methods=['POST'])", |
| "def generate():", |
| " data = request.json", |
| " prompt = data.get('prompt')", |
| " system_prompt = data.get('system_prompt', '')", |
| " ", |
| " # Get command from query param or default to /think", |
| " command = request.args.get('command', 'think')", |
| " ", |
| " try:", |
| " response = llm.generate(", |
| " system_prompt=system_prompt,", |
| " prompt=f\"/{command} {prompt}\"", |
| " )", |
| " return jsonify({'response': response})", |
| " except Exception as e:", |
| " return jsonify({'error': str(e)}), 500", |
| "", |
| "if __name__ == '__main__':", |
| " app.run(debug=True)" |
| ], |
| "description": "Flask API integration with Universal Developer" |
| }, |
| "FastAPI Integration": { |
| "prefix": "ud-fastapi", |
| "body": [ |
| "from fastapi import FastAPI, HTTPException, Query", |
| "from pydantic import BaseModel", |
| "from typing import Optional", |
| "from universal_developer import UniversalLLM", |
| "import os", |
| "", |
| "app = FastAPI()", |
| "", |
| "llm = UniversalLLM(", |
| " provider=\"${1|anthropic,openai,qwen,gemini,ollama|}\",", |
| " api_key=os.environ.get(\"${2:${1/(anthropic|openai|qwen|gemini)/${1:/upcase}_API_KEY/}}\")", |
| ")", |
| "", |
| "class GenerateRequest(BaseModel):", |
| " prompt: str", |
| " system_prompt: Optional[str] = \"\"", |
| "", |
| "@app.post(\"/api/generate\")", |
| "async def generate(", |
| " request: GenerateRequest,", |
| " command: str = Query(\"think\", description=\"Symbolic command to use\")", |
| "):", |
| " try:", |
| " response = llm.generate(", |
| " system_prompt=request.system_prompt,", |
| " prompt=f\"/{command} {request.prompt}\"", |
| " )", |
| " return {\"response\": response}", |
| " except Exception as e:", |
| " raise HTTPException(status_code=500, detail=str(e))" |
| ], |
| "description": "FastAPI integration with Universal Developer" |
| }, |
| "Streamlit Integration": { |
| "prefix": "ud-streamlit", |
| "body": [ |
| "import streamlit as st", |
| "from universal_developer import UniversalLLM", |
| "import os", |
| "", |
| "# Initialize LLM", |
| "@st.cache_resource", |
| "def get_llm():", |
| " return UniversalLLM(", |
| " provider=\"${1|anthropic,openai,qwen,gemini,ollama|}\",", |
| " api_key=os.environ.get(\"${2:${1/(anthropic|openai|qwen|gemini)/${1:/upcase}_API_KEY/}}\")", |
| " )", |
| "", |
| "llm = get_llm()", |
| "", |
| "st.title(\"Universal Developer Demo\")", |
| "", |
| "# Command selection", |
| "command = st.selectbox(", |
| " \"Select symbolic command\",", |
| " [\"think\", \"fast\", \"loop\", \"reflect\", \"fork\", \"collapse\"]", |
| ")", |
| "", |
| "# Command parameters", |
| "if command == \"loop\":", |
| " iterations = st.slider(\"Iterations\", 1, 5, 3)", |
| " command_str = f\"/loop --iterations={iterations}\"", |
| "elif command == \"fork\":", |
| " count = st.slider(\"Alternative count\", 2, 5, 2)", |
| " command_str = f\"/fork --count={count}\"", |
| "else:", |
| " command_str = f\"/{command}\"", |
| "", |
| "# User input", |
| "prompt = st.text_area(\"Enter your prompt\", \"\")", |
| "", |
| "if st.button(\"Generate\") and prompt:", |
| " with st.spinner(\"Generating response...\"):", |
| " response = llm.generate(", |
| " prompt=f\"{command_str} {prompt}\"", |
| " )", |
| " st.markdown(response)" |
| ], |
| "description": "Streamlit integration with Universal Developer" |
| } |
| } |
|
|
