| from smolagents import ( |
| CodeAgent, |
| DuckDuckGoSearchTool, |
| InferenceClientModel, |
| load_tool, |
| tool |
| ) |
|
|
| import datetime |
| import pytz |
| import yaml |
|
|
| from tools.final_answer import FinalAnswerTool |
| from Gradio_UI import GradioUI |
|
|
|
|
| |
| |
| |
| @tool |
| def get_current_time_in_timezone(timezone: str) -> str: |
| """ |
| Gets the current local date and time for a timezone. |
| |
| Args: |
| timezone: Valid timezone such as America/New_York, |
| Europe/London, or Asia/Kolkata. |
| """ |
|
|
| try: |
| tz = pytz.timezone(timezone) |
|
|
| local_time = datetime.datetime.now(tz).strftime( |
| "%Y-%m-%d %H:%M:%S" |
| ) |
|
|
| return f"The current local time in {timezone} is {local_time}" |
|
|
| except Exception as e: |
| return f"Could not get time for {timezone}: {str(e)}" |
|
|
|
|
| |
| |
| |
| @tool |
| def analyze_ai_technology(topic: str) -> str: |
| """ |
| Gives a quick engineering impact analysis for common AI technologies. |
| |
| Args: |
| topic: AI technology or concept such as MCP, RAG, |
| LangGraph, vector database, agents, or embeddings. |
| """ |
|
|
| technologies = { |
| "mcp": { |
| "name": "Model Context Protocol (MCP)", |
| "impact": 90, |
| "description": |
| "A standard protocol for connecting AI applications " |
| "to tools, APIs, services, and external data.", |
| "use_case": |
| "Let an AI agent access GitHub, databases, files, " |
| "Slack, APIs, and enterprise systems." |
| }, |
|
|
| "rag": { |
| "name": "Retrieval-Augmented Generation (RAG)", |
| "impact": 88, |
| "description": |
| "Retrieves relevant information before asking an LLM " |
| "to generate an answer.", |
| "use_case": |
| "Enterprise search, documentation assistants, " |
| "knowledge bots, and support systems." |
| }, |
|
|
| "langgraph": { |
| "name": "LangGraph", |
| "impact": 82, |
| "description": |
| "Framework for building stateful and multi-step " |
| "LLM workflows and agents.", |
| "use_case": |
| "Agent orchestration, approval workflows, " |
| "multi-agent systems, and long-running AI tasks." |
| }, |
|
|
| "vector database": { |
| "name": "Vector Database", |
| "impact": 80, |
| "description": |
| "Stores embeddings and performs semantic similarity search.", |
| "use_case": |
| "RAG, recommendation systems, semantic search, " |
| "and AI memory." |
| }, |
|
|
| "embeddings": { |
| "name": "Embeddings", |
| "impact": 78, |
| "description": |
| "Numeric representations of text, images, or other data " |
| "that capture semantic meaning.", |
| "use_case": |
| "Similarity search, clustering, recommendations, and RAG." |
| }, |
|
|
| "agents": { |
| "name": "AI Agents", |
| "impact": 92, |
| "description": |
| "LLM-powered systems that can reason about a task " |
| "and decide which tools or actions to execute.", |
| "use_case": |
| "Coding assistants, research agents, workflow automation, " |
| "customer support, and enterprise automation." |
| } |
| } |
|
|
| key = topic.lower().strip() |
|
|
| |
| aliases = { |
| "agent": "agents", |
| "ai agent": "agents", |
| "ai agents": "agents", |
| "model context protocol": "mcp", |
| "retrieval augmented generation": "rag", |
| "vector db": "vector database", |
| "vectors": "vector database", |
| "embedding": "embeddings" |
| } |
|
|
| key = aliases.get(key, key) |
|
|
| if key not in technologies: |
| return ( |
| f"I don't have a predefined analysis for '{topic}'. " |
| "Use web search to research it instead." |
| ) |
|
|
| item = technologies[key] |
|
|
| return f""" |
| Technology: {item['name']} |
| Estimated engineering impact: {item['impact']}% |
| |
| What it is: |
| {item['description']} |
| |
| Typical use case: |
| {item['use_case']} |
| """ |
|
|
|
|
| |
| |
| |
| @tool |
| def recommend_ai_learning_topic(current_skill: str) -> str: |
| """ |
| Suggests an AI engineering topic to learn based on a developer's skill. |
| |
| Args: |
| current_skill: Developer skill such as React, Node.js, |
| Python, backend, cloud, or fullstack. |
| """ |
|
|
| skill = current_skill.lower() |
|
|
| if "react" in skill or "frontend" in skill: |
| return ( |
| "Recommended next topic: AI application development.\n" |
| "Learn LLM APIs, streaming responses, tool calling, " |
| "AI SDKs, and agent UIs." |
| ) |
|
|
| if "node" in skill or "backend" in skill: |
| return ( |
| "Recommended next topic: Agent orchestration.\n" |
| "Learn tool calling, MCP, RAG, LangGraph, queues, " |
| "and asynchronous AI workflows." |
| ) |
|
|
| if "python" in skill: |
| return ( |
| "Recommended next topic: AI agent frameworks.\n" |
| "Explore smolagents, LangGraph, PydanticAI, " |
| "RAG pipelines, and evaluation." |
| ) |
|
|
| if "cloud" in skill or "aws" in skill: |
| return ( |
| "Recommended next topic: AI platform engineering.\n" |
| "Learn model gateways, vector databases, observability, " |
| "guardrails, GPU inference, and MCP servers." |
| ) |
|
|
| if "fullstack" in skill: |
| return ( |
| "Recommended path:\n" |
| "1. LLM APIs\n" |
| "2. Structured outputs\n" |
| "3. Tool calling\n" |
| "4. RAG\n" |
| "5. MCP\n" |
| "6. LangGraph\n" |
| "7. Agent evaluation and observability" |
| ) |
|
|
| return ( |
| "Start with LLM APIs, prompt engineering, structured outputs, " |
| "tool calling, RAG, and then AI agents." |
| ) |
|
|
|
|
| |
| |
| |
| final_answer = FinalAnswerTool() |
|
|
|
|
| |
| |
| |
| model = InferenceClientModel( |
| max_tokens=2096, |
| temperature=0.5, |
| model_id="Qwen/Qwen2.5-Coder-32B-Instruct", |
| custom_role_conversions=None, |
| ) |
|
|
|
|
| |
| |
| |
| image_generation_tool = load_tool( |
| "agents-course/text-to-image", |
| trust_remote_code=True |
| ) |
|
|
|
|
| |
| |
| |
| with open("prompts.yaml", "r") as stream: |
| prompt_templates = yaml.safe_load(stream) |
|
|
|
|
| |
| |
| |
| agent = CodeAgent( |
| model=model, |
|
|
| tools=[ |
| |
| DuckDuckGoSearchTool(), |
|
|
| |
| get_current_time_in_timezone, |
| analyze_ai_technology, |
| recommend_ai_learning_topic, |
|
|
| |
| image_generation_tool, |
|
|
| |
| final_answer, |
| ], |
|
|
| max_steps=8, |
| verbosity_level=1, |
| grammar=None, |
| planning_interval=None, |
| name="AI Developer Assistant", |
|
|
| description=( |
| "An AI engineering assistant that can search the web, " |
| "analyze AI technologies, recommend learning topics, " |
| "check world times, and generate images." |
| ), |
|
|
| prompt_templates=prompt_templates |
| ) |
|
|
|
|
| |
| |
| |
| GradioUI(agent).launch() |
