89 lines
2.7 KiB
Markdown
89 lines
2.7 KiB
Markdown
# PocketFlow Research Agent - Tutorial for Dummy
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This project demonstrates a simple yet powerful LLM-powered research agent built with PocketFlow, a minimalist LLM framework in just 100 lines of code!
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> 📝 **Note:** This implementation is based directly on the tutorial post [LLM Agents are simply Graph — Tutorial For Dummies](https://zacharyhuang.substack.com/p/llm-agent-internal-as-a-graph-tutorial). Check it out for a better understanding of the concepts!
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Want to learn more about PocketFlow and building cool LLM agents? Check out:
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- [PocketFlow GitHub](https://github.com/the-pocket/PocketFlow)
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- [PocketFlow Documentation](https://the-pocket.github.io/PocketFlow/)
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## ✨ What Can This Agent Do?
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This friendly little agent can:
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1. 🔎 Search the web for information when it needs more context
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2. 🧠 Decide intelligently when to search and when it has enough info to answer
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3. 📝 Generate helpful, informative responses based on its research
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## 🚀 Getting Started
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### What You'll Need
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- Python 3.8 or newer
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- An OpenAI API key (don't worry, we'll help you set this up!)
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### Easy Installation
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1. Install the packages you need with this simple command:
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```bash
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pip install -r requirements.txt
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```
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## 📂 Project Structure
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Here's what's in each file:
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- [`main.py`](./main.py): The starting point - runs the whole show!
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- [`flow.py`](./flow.py): Connects everything together into a smart agent
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- [`nodes.py`](./nodes.py): The building blocks that make decisions and take actions
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- [`utils.py`](./utils.py): Helper functions for talking to the LLM and searching the web
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## 🏃♂️ Quick Start Guide
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### Step 1: Set Up Your API Key
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First, let's get your OpenAI API key ready:
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```bash
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export OPENAI_API_KEY="your-api-key-here"
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```
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### Step 2: Make Sure Everything Works
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Let's do a quick check to make sure your API key is working properly:
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```bash
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python utils.py
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```
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This will test both the LLM call and web search features. If you see responses, you're good to go!
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### Step 3: Run Your Agent
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Try out the agent with the default question (about Nobel Prize winners):
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```bash
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python main.py
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```
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### Step 4: Ask Your Own Questions
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Got a burning question? Ask anything you want by using the `--` prefix:
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```bash
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python main.py --"What is quantum computing?"
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```
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## 🧩 How It Works
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The magic happens through a simple but powerful graph structure with three main parts:
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```mermaid
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graph TD
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A[DecideAction] -->|"search"| B[SearchWeb]
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A -->|"answer"| C[AnswerQuestion]
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B -->|"decide"| A
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```
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Here's what each part does:
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1. **DecideAction**: The brain that figures out whether to search or answer
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2. **SearchWeb**: The researcher that goes out and finds information
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3. **AnswerQuestion**: The writer that crafts the final answer |