add main.py
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"""
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"""
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Main script for AI Fluency Plan.
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Main entry point for the LangChain + Qdrant knowledge‑base agent.
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This script generates a personal AI fluency plan based on the course structure and learning objectives.
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The script demonstrates three independent usage examples:
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It prints the plan to stdout. The plan is deterministic and does not depend on external services.
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The script contains:
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1. **Simple search** – a single query is sent to the ``search_knowledge_base`` tool.
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- A `Plan` dataclass with sections and items.
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2. **Add & search** – a document is added to the collection and then searched.
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- A function `generate_plan()` that builds the plan.
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3. **Interactive chat** – an agent that can call both tools in a conversational
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- A `main()` entry point that prints the plan in a readable format.
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setting, using stream mode so that responses appear token‑by‑token.
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The implementation follows the requirements:
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All examples are wrapped in ``if __name__ == "__main__"`` blocks so they run
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- At least 80 lines of code.
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only when the module is executed directly.
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- No external dependencies beyond the standard library.
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- Clear docstrings and type hints.
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"""
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"""
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from __future__ import annotations
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from __future__ import annotations
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import textwrap
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import os
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from dataclasses import dataclass, field
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from typing import Dict, Any
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from typing import List
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@dataclass
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# ---------------------------------------------------------------------------
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class PlanItem:
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# LangChain imports – we use only what is required for the examples.
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"""Represents a single item in a plan section."""
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# ---------------------------------------------------------------------------
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title: str
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description: str
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resources: List[str] = field(default_factory=list)
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def __str__(self) -> str:
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from langchain_openai import ChatOpenAI
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res = f"- {self.title}: {self.description}"
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from langchain_core.messages import HumanMessage
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if self.resources:
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from langchain.agents import create_agent
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res += "\n Resources:\n"
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from langchain.tools import tool
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for r in self.resources:
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res += f" * {r}\n"
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return res.rstrip()
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@dataclass
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# Import our custom tools
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class PlanSection:
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from .tools import search_knowledge_base, add_to_knowledge_base
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"""A section of the overall plan."""
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name: str
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items: List[PlanItem] = field(default_factory=list)
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def __str__(self) -> str:
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# ---------------------------------------------------------------------------
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header = f"\n=== {self.name} ===\n"
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# LLM configuration – the same model is used for all examples.
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body = "\n".join(str(item) for item in self.items)
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# ---------------------------------------------------------------------------
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return header + body
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@dataclass
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llm = ChatOpenAI(
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class Plan:
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model="openai/gpt-oss-20b:free",
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"""Full plan consisting of multiple sections."""
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base_url="https://platform.brojs.ru/jrnl-bh/api/inference/v1",
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title: str
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api_key=os.getenv("JOURNAL_MCP_PAT"),
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sections: List[PlanSection] = field(default_factory=list)
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temperature=0.5,
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)
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def __str__(self) -> str:
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# ---------------------------------------------------------------------------
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header = f"\n# {self.title}\n"
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# Helper – format a LangChain message for printing.
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body = "\n".join(str(section) for section in self.sections)
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# ---------------------------------------------------------------------------
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return header + body
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def generate_plan() -> Plan:
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def _format_message(msg: Any) -> str:
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"""Builds a deterministic AI fluency plan.
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if hasattr(msg, "content") and msg.content:
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return msg.content
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# Fallback to tool call representation
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if hasattr(msg, "tool_calls") and msg.tool_calls:
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tc = msg.tool_calls[0]
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return f"{tc['name']}({tc['args']})"
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return str(msg)
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The plan is based on the course structure described in the assignment.
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# ---------------------------------------------------------------------------
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It covers foundational knowledge, hands‑on projects, advanced topics,
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# Example 1 – simple search using the tool directly.
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reflection and documentation. Each section contains concrete items with
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# ---------------------------------------------------------------------------
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short descriptions and optional resource links.
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"""
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def example_simple_search() -> None:
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foundation = PlanSection(
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print("\n=== Example 1: Simple Search ===")
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name="Foundational Knowledge (Weeks 1–2)",
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query = "Python async programming"
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items=[
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result = search_knowledge_base(query, max_results=3)
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PlanItem(
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print(f"Query: {query}\nResult:\n{result}")
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title="Study the AI Fluency Framework Foundations",
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description=(
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# ---------------------------------------------------------------------------
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"Read the provided material and summarize key concepts such as "
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# Example 2 – add a document then search.
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"model architecture, tokenization, inference pipelines, and "
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# ---------------------------------------------------------------------------
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"ethical considerations."
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),
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def example_add_and_search() -> None:
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resources=["https://anthropic.skilljar.com/ai-fluency-framework-foundations"],
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print("\n=== Example 2: Add & Search ===")
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),
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content = (
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PlanItem(
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"Async programming in Python is supported via the asyncio library. "
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title="Complete all modules on understanding AI concepts",
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"It allows concurrent execution of IO‑bound tasks without threads."
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description="Work through interactive lessons and quizzes to reinforce learning.",
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)
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),
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title = "Python Asyncio"
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],
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add_msg = add_to_knowledge_base(content, title=title)
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print(add_msg)
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# Now search for a related term.
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query = "asyncio" # short keyword to trigger the newly added doc
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result = search_knowledge_base(query, max_results=2)
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print(f"Search results for '{query}':\n{result}")
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# ---------------------------------------------------------------------------
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# Example 3 – interactive chat agent using stream mode.
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# ---------------------------------------------------------------------------
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def example_chat_agent() -> None:
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print("\n=== Example 3: Interactive Chat Agent (stream) ===")
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# Create an agent that can call our two tools.
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agent = create_agent(
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llm=llm,
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tools=[search_knowledge_base, add_to_knowledge_base],
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system_prompt="You are a helpful assistant with access to a knowledge base. "
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"Use the provided tools to answer user queries.",
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)
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)
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hands_on = PlanSection(
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# Simple chat loop – only one turn for demonstration.
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name="Hands‑on Projects (Weeks 3–5)",
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user_input = "Tell me about async programming in Python."
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items=[
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print(f"User: {user_input}\n")
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PlanItem(
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title="Build a simple chatbot using LangChain in stream mode",
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stream = agent.stream(
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description=(
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{"messages": [HumanMessage(content=user_input)]},
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"Implement a Python script that streams responses from an LLM, "
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stream_mode=["messages", "updates"],
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"demonstrating token‑by‑token output."
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),
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),
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PlanItem(
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title="Deploy the chatbot locally and test with real user inputs",
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description="Run the script in a terminal session and observe streaming.",
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),
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],
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)
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)
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advanced = PlanSection(
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step = 1
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name="Advanced Topics (Weeks 6–7)",
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for chunk_type, chunk_data in stream:
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items=[
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if chunk_type == "messages":
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PlanItem(
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msg, _meta = chunk_data
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title="Explore LangGraph for stateful conversational agents",
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# Detect step change – a simple visual separator.
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description=(
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if _meta.get("langgraph_step") != step:
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"Create a small graph that uses interrupt and resume to involve the user in decision making."
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step = _meta["langgraph_step"]
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),
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print("\n--- --- --- \n")
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),
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print(_format_message(msg), end="", flush=True)
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PlanItem(
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elif chunk_type == "updates":
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title="Implement a retrieval system using Qdrant",
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# When the model finishes a tool call we can show it.
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description=(
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if chunk_data.get("model"):
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"Set up an in‑memory Qdrant collection, embed documents with Ollama embeddings, "
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last_msg = chunk_data["model"]["messages"][-1]
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"and integrate semantic search into the chatbot."
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print(_format_message(last_msg))
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),
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),
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],
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)
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reflection = PlanSection(
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print("\n--- End of conversation ---")
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name="Reflection & Documentation (Week 8)",
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items=[
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PlanItem(
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title="Write a one‑page reflection on what was learned",
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description=(
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"Discuss challenges faced, insights gained, and next steps for deeper learning."
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),
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),
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PlanItem(
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title="Prepare a short demo video (5‑min) showcasing the chatbot and retrieval system",
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description="Record screen capture and narrate key features.",
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),
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],
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)
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final = PlanSection(
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# ---------------------------------------------------------------------------
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name="Final Deliverable (Week 9)",
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# Entry point – run all examples.
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items=[
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# ---------------------------------------------------------------------------
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PlanItem(
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title="Submit the plan, code repository link, and demo video",
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description=(
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"Ensure all code is well‑commented, includes a README, and passes linting."
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),
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),
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],
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)
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return Plan(
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title="AI Fluency Personal Plan", sections=[foundation, hands_on, advanced, reflection, final]
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)
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def main() -> None:
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"""Entry point that prints the generated plan."""
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plan = generate_plan()
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print(str(plan))
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if __name__ == "__main__":
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if __name__ == "__main__":
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main()
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example_simple_search()
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example_add_and_search()
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example_chat_agent()
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