README.md

metadata

title: Intelligent Documentation Generator Agent
colorFrom: blue
colorTo: purple
sdk: gradio
python_version: 3.11
sdk_version: 5.49.1
app_file: app.py
short_description: Generate documentation and chat with code
tags:
  - documentation
  - code-assistant
  - large-language-model
  - fire-works-ai
models:
  - accounts/fireworks/models/glm-4p6
pinned: false

🧠 Intelligent Documentation Generator Agent

Built with GLM-4.6 on Fireworks AI and Gradio

---

📘 Overview

The Intelligent Documentation Generator Agent automatically generates structured, multi-layer documentation and provides a chat interface to explore Python codebases. This version is powered by GLM-4.6 via the Fireworks AI Inference API and implemented in Gradio, offering a lightweight, interactive browser-based UI.

Capabilities:

• Analyze Python files from uploads, pasted code, or GitHub links
• Generate consistent, well-structured documentation (overview, API breakdown, usage examples)
• Chat directly with your code to understand logic, dependencies, and optimization opportunities

---

⚙️ Architecture

User (Upload / Paste / GitHub)
          │
          ▼
   Gradio UI (Tabs)
 ┌─────────────────────┐
 │  Documentation Tab  │──► Fireworks GLM-4.6 → Markdown Docs
 │  Chat Tab            │──► Fireworks GLM-4.6 → Q&A Responses
 └─────────────────────┘

---

🧩 Core Features

📄 Documentation Generator

• Input via:

  • Pasted Python code
  • Uploaded .py file
  • GitHub file link (supports automatic conversion to raw URL)

• Produces:

  • Overview and purpose
  • Key functions/classes with signatures
  • Dependencies and relationships
  • Example usage and improvement suggestions

• Outputs documentation in Markdown

💬 Code Chatbot

• Conversational Q&A with the analyzed code
• References exact functions and dependencies
• Maintains interactive chat history using Gradio’s Chatbot component
• Uses the same GLM-4.6 model context for accurate answers

---

🧱 Tech Stack

Layer	Technology
Model	GLM-4.6 on Fireworks AI
UI Framework	Gradio
Language	Python 3.9+
HTTP Requests	requests
Deployment	Localhost / Containerized environments

---

🚀 Installation

1. Clone the Repository

git clone https://github.com/<your-username>/intelligent-doc-agent.git
cd intelligent-doc-agent

2. Install Dependencies

pip install gradio requests

3. Configure Fireworks API Key

Set your API key as an environment variable:

export FIREWORKS_API_KEY="your_fireworks_api_key"

Alternatively, enter your API key directly in the UI when prompted.

4. Run the Application

python app_gradio.py

Then visit http://127.0.0.1:7860 in your browser.

---

💡 Usage Guide

🧠 Generate Documentation

1. Open the 📄 Generate Documentation tab.

2. Choose an input mode:

   • Paste code into the text area
   • Upload a .py file
   • Enter a GitHub file link (e.g., https://github.com/.../file.py)

3. Click 🚀 Generate Documentation to process your file.

4. View formatted Markdown output instantly.

💬 Chat with Code

1. Switch to the 💬 Chat with Code tab.
2. Ask questions about your code (e.g., “What does this function do?” or “How can I improve performance?”).
3. The model responds contextually, referencing the uploaded file.

---

🧠 Model Integration Example

payload = {
  "model": "accounts/fireworks/models/glm-4p6",
  "max_tokens": 4096,
  "temperature": 0.6,
  "messages": messages
}
response = requests.post(
  "https://api.fireworks.ai/inference/v1/chat/completions",
  headers={"Authorization": f"Bearer {FIREWORKS_API_KEY}"},
  data=json.dumps(payload)
)
print(response.json()["choices"][0]["message"]["content"])

---

📦 Project Structure

.
├── app.py          # Main Gradio interface
├── README.md              # Project documentation
└── requirements.txt       # Dependencies (gradio, requests)

---

🔮 Future Enhancements

• Multi-file repository analysis with hierarchical context summarization
• Semantic vector store (Chroma / Pinecone) for persistent knowledge retrieval
• Multi-agent orchestration using LangGraph or MCP protocols
• Continuous documentation updates via Git hooks or CI/CD pipelines

---

🧾 Example

Input

def calculate_mean(numbers):
    return sum(numbers) / len(numbers)

Output

### Function: calculate_mean
Computes the arithmetic mean of a numeric list.

**Parameters:**
- numbers (list): Sequence of numbers to average.

**Returns:**
- float: Mean of the list.

**Usage Example:**
>>> calculate_mean([1, 2, 3, 4])
2.5

Chat Example

“How can I modify this to avoid division by zero errors?”

---

🔐 Best Practices

• Use raw GitHub links (https://raw.githubusercontent.com/...) for accurate file fetches.
• Limit input size (~4k tokens) for optimal latency and context accuracy.
• Keep your API key private — never commit it in source files.

---

🧭 License

Released under