update fastapi
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# PocketFlow FastAPI Background Job
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A minimal example of running PocketFlow workflows as background jobs with real-time progress updates via Server-Sent Events (SSE).
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## Features
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- Start article generation jobs via REST API
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- Real-time granular progress updates via SSE (shows progress for each section)
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- Background processing with FastAPI
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- Simple three-step workflow: Outline → Content → Style
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- Web interface for easy job submission and monitoring
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## Getting Started
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1. Install dependencies:
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```bash
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pip install -r requirements.txt
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```
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2. Set your OpenAI API key:
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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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3. Run the server:
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```bash
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python main.py
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```
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## Usage
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### Web Interface (Recommended)
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1. Open your browser and go to `http://localhost:8000`
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2. Enter an article topic (e.g., "AI Safety", "Climate Change")
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3. Click "Generate Article"
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4. You'll be redirected to a progress page showing real-time updates
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5. The final article will appear when generation is complete
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### API Usage
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#### Start a Job
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```bash
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curl -X POST "http://localhost:8000/start-job" -d "topic=AI Safety" -H "Content-Type: application/x-www-form-urlencoded"
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```
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Response:
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```json
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{"job_id": "123e4567-e89b-12d3-a456-426614174000", "topic": "AI Safety", "status": "started"}
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```
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#### Monitor Progress
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```bash
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curl "http://localhost:8000/progress/123e4567-e89b-12d3-a456-426614174000"
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```
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SSE Stream:
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```
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data: {"step": "outline", "progress": 33, "data": {"sections": ["Introduction", "Challenges", "Solutions"]}}
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data: {"step": "content", "progress": 44, "data": {"section": "Introduction", "completed_sections": 1, "total_sections": 3}}
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data: {"step": "content", "progress": 55, "data": {"section": "Challenges", "completed_sections": 2, "total_sections": 3}}
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data: {"step": "content", "progress": 66, "data": {"section": "Solutions", "completed_sections": 3, "total_sections": 3}}
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data: {"step": "content", "progress": 66, "data": {"draft_length": 1234, "status": "complete"}}
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data: {"step": "complete", "progress": 100, "data": {"final_article": "..."}}
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```
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## Files
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- `main.py` - FastAPI app with background jobs and SSE
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- `flow.py` - PocketFlow workflow definition
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- `nodes.py` - Workflow nodes (Outline, Content, Style)
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- `utils/call_llm.py` - LLM utility function
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- `static/index.html` - Main page for starting jobs
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- `static/progress.html` - Progress monitoring page with real-time updates
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# Design Doc: PocketFlow FastAPI Background Job with SSE Progress
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> Please DON'T remove notes for AI
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## Requirements
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> Notes for AI: Keep it simple and clear.
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> If the requirements are abstract, write concrete user stories
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**User Story**: As a user, I want to submit an article topic via a web API and receive real-time progress updates while the article is being generated in the background, so I can see the workflow progress without blocking the UI.
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**Core Requirements**:
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1. Submit article topic via REST API endpoint
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2. Start background job for article generation workflow
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3. Receive real-time progress updates via Server-Sent Events (SSE)
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4. Get final article result when workflow completes
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5. Handle multiple concurrent requests
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**Technical Requirements**:
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- FastAPI web server with REST endpoints
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- Background task processing using asyncio
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- Server-Sent Events for progress streaming
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- Simple web interface to test the functionality
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## Flow Design
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> Notes for AI:
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> 1. Consider the design patterns of agent, map-reduce, rag, and workflow. Apply them if they fit.
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> 2. Present a concise, high-level description of the workflow.
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### Applicable Design Pattern:
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**Workflow Pattern**: Sequential processing of article generation steps with progress reporting at each stage.
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### Flow High-level Design:
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1. **Generate Outline Node**: Creates a structured outline for the article topic
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2. **Write Content Node**: Writes content for each section in the outline
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3. **Apply Style Node**: Applies conversational styling to the final article
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Each node puts progress updates into an asyncio.Queue for SSE streaming.
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```mermaid
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flowchart LR
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outline[Generate Outline] --> content[Write Content]
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content --> styling[Apply Style]
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```
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## Utility Functions
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> Notes for AI:
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> 1. Understand the utility function definition thoroughly by reviewing the doc.
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> 2. Include only the necessary utility functions, based on nodes in the flow.
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1. **Call LLM** (`utils/call_llm.py`)
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- *Input*: prompt (str)
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- *Output*: response (str)
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- Used by all workflow nodes for LLM tasks
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## Node Design
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### Shared Store
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> Notes for AI: Try to minimize data redundancy
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The shared store structure is organized as follows:
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```python
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shared = {
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"topic": "user-provided-topic",
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"sse_queue": asyncio.Queue(), # For sending SSE updates
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"sections": ["section1", "section2", "section3"],
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"draft": "combined-section-content",
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"final_article": "styled-final-article"
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}
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```
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### Node Steps
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> Notes for AI: Carefully decide whether to use Batch/Async Node/Flow.
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1. **Generate Outline Node**
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- *Purpose*: Create a structured outline with 3 main sections using YAML output
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- *Type*: Regular Node (synchronous LLM call)
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- *Steps*:
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- *prep*: Read "topic" from shared store
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- *exec*: Call LLM to generate YAML outline, parse and validate structure
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- *post*: Write "sections" to shared store, put progress update in sse_queue
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2. **Write Content Node**
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- *Purpose*: Generate concise content for each outline section
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- *Type*: BatchNode (processes each section independently)
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- *Steps*:
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- *prep*: Read "sections" from shared store (returns list of sections)
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- *exec*: For one section, call LLM to write 100-word content
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- *post*: Combine all section content into "draft", put progress update in sse_queue
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3. **Apply Style Node**
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- *Purpose*: Apply conversational, engaging style to the combined content
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- *Type*: Regular Node (single LLM call for styling)
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- *Steps*:
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- *prep*: Read "draft" from shared store
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- *exec*: Call LLM to rewrite in conversational style
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- *post*: Write "final_article" to shared store, put completion update in sse_queue
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from pocketflow import Flow
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from nodes import GenerateOutline, WriteContent, ApplyStyle
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def create_article_flow():
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"""
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Create and configure the article writing workflow
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"""
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# Create node instances
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outline_node = GenerateOutline()
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content_node = WriteContent()
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style_node = ApplyStyle()
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# Connect nodes in sequence
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outline_node >> content_node >> style_node
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# Create flow starting with outline node
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article_flow = Flow(start=outline_node)
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return article_flow
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import asyncio
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import json
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import uuid
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from fastapi import FastAPI, BackgroundTasks, Form
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from fastapi.responses import StreamingResponse
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from fastapi.staticfiles import StaticFiles
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from fastapi.responses import FileResponse
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from flow import create_article_flow
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app = FastAPI()
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# Mount static files
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app.mount("/static", StaticFiles(directory="static"), name="static")
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# Store active jobs and their SSE queues
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active_jobs = {}
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def run_article_workflow(job_id: str, topic: str):
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"""Run the article workflow in background"""
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try:
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# Create shared store with SSE queue
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sse_queue = asyncio.Queue()
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shared = {
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"topic": topic,
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"sse_queue": sse_queue,
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"sections": [],
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"draft": "",
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"final_article": ""
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}
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# Store the queue for SSE access
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active_jobs[job_id] = sse_queue
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# Run the workflow
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flow = create_article_flow()
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flow.run(shared)
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except Exception as e:
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# Send error message
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error_msg = {"step": "error", "progress": 0, "data": {"error": str(e)}}
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if job_id in active_jobs:
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active_jobs[job_id].put_nowait(error_msg)
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@app.post("/start-job")
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async def start_job(background_tasks: BackgroundTasks, topic: str = Form(...)):
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"""Start a new article generation job"""
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job_id = str(uuid.uuid4())
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# Start background task
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background_tasks.add_task(run_article_workflow, job_id, topic)
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return {"job_id": job_id, "topic": topic, "status": "started"}
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@app.get("/progress/{job_id}")
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async def get_progress(job_id: str):
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"""Stream progress updates via SSE"""
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async def event_stream():
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if job_id not in active_jobs:
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yield f"data: {json.dumps({'error': 'Job not found'})}\n\n"
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return
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sse_queue = active_jobs[job_id]
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try:
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while True:
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# Wait for next progress update
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try:
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# Use asyncio.wait_for to avoid blocking forever
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progress_msg = await asyncio.wait_for(sse_queue.get(), timeout=1.0)
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yield f"data: {json.dumps(progress_msg)}\n\n"
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# If job is complete, clean up and exit
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if progress_msg.get("step") == "complete":
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del active_jobs[job_id]
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break
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except asyncio.TimeoutError:
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# Send heartbeat to keep connection alive
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yield f"data: {json.dumps({'heartbeat': True})}\n\n"
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except Exception as e:
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yield f"data: {json.dumps({'error': str(e)})}\n\n"
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return StreamingResponse(
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event_stream(),
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media_type="text/plain",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"Content-Type": "text/event-stream"
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}
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)
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@app.get("/")
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async def get_index():
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"""Serve the main page"""
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return FileResponse("static/index.html")
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@app.get("/progress.html")
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async def get_progress_page():
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"""Serve the progress page"""
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return FileResponse("static/progress.html")
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000)
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import yaml
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from pocketflow import Node, BatchNode
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from utils.call_llm import call_llm
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class GenerateOutline(Node):
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def prep(self, shared):
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return shared["topic"]
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def exec(self, topic):
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prompt = f"""
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Create a simple outline for an article about {topic}.
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Include at most 3 main sections (no subsections).
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Output the sections in YAML format as shown below:
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```yaml
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sections:
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- First section title
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- Second section title
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- Third section title
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```"""
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response = call_llm(prompt)
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yaml_str = response.split("```yaml")[1].split("```")[0].strip()
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structured_result = yaml.safe_load(yaml_str)
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return structured_result
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def post(self, shared, prep_res, exec_res):
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sections = exec_res["sections"]
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shared["sections"] = sections
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# Send progress update via SSE queue
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progress_msg = {"step": "outline", "progress": 33, "data": {"sections": sections}}
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shared["sse_queue"].put_nowait(progress_msg)
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return "default"
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class WriteContent(BatchNode):
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def prep(self, shared):
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# Store sections and sse_queue for use in exec
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self.sections = shared.get("sections", [])
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self.sse_queue = shared["sse_queue"]
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return self.sections
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def exec(self, section):
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prompt = f"""
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Write a short paragraph (MAXIMUM 100 WORDS) about this section:
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{section}
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Requirements:
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- Explain the idea in simple, easy-to-understand terms
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- Use everyday language, avoiding jargon
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- Keep it very concise (no more than 100 words)
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- Include one brief example or analogy
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"""
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content = call_llm(prompt)
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# Send progress update for this section
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current_section_index = self.sections.index(section) if section in self.sections else 0
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total_sections = len(self.sections)
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# Progress from 33% (after outline) to 66% (before styling)
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# Each section contributes (66-33)/total_sections = 33/total_sections percent
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section_progress = 33 + ((current_section_index + 1) * 33 // total_sections)
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progress_msg = {
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"step": "content",
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"progress": section_progress,
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"data": {
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"section": section,
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"completed_sections": current_section_index + 1,
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"total_sections": total_sections
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}
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}
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self.sse_queue.put_nowait(progress_msg)
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return f"## {section}\n\n{content}\n"
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def post(self, shared, prep_res, exec_res_list):
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draft = "\n".join(exec_res_list)
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shared["draft"] = draft
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return "default"
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class ApplyStyle(Node):
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def prep(self, shared):
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return shared["draft"]
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def exec(self, draft):
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prompt = f"""
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Rewrite the following draft in a conversational, engaging style:
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{draft}
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Make it:
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- Conversational and warm in tone
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- Include rhetorical questions that engage the reader
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- Add analogies and metaphors where appropriate
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- Include a strong opening and conclusion
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"""
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return call_llm(prompt)
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def post(self, shared, prep_res, exec_res):
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shared["final_article"] = exec_res
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# Send completion update via SSE queue
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progress_msg = {"step": "complete", "progress": 100, "data": {"final_article": exec_res}}
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shared["sse_queue"].put_nowait(progress_msg)
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return "default"
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fastapi
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uvicorn
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openai
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pyyaml
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python-multipart
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>PocketFlow Article Generator</title>
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<style>
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body {
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font-family: Arial, sans-serif;
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max-width: 600px;
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margin: 50px auto;
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padding: 20px;
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background-color: #f5f5f5;
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}
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.container {
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background: white;
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padding: 30px;
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border-radius: 10px;
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box-shadow: 0 2px 10px rgba(0,0,0,0.1);
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}
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h1 {
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color: #333;
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text-align: center;
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margin-bottom: 30px;
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}
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.form-group {
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margin-bottom: 20px;
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}
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label {
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display: block;
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margin-bottom: 5px;
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font-weight: bold;
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color: #555;
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}
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input[type="text"] {
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width: 100%;
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padding: 12px;
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border: 2px solid #ddd;
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border-radius: 5px;
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font-size: 16px;
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box-sizing: border-box;
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}
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input[type="text"]:focus {
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border-color: #4CAF50;
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outline: none;
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}
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button {
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background-color: #4CAF50;
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color: white;
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padding: 12px 30px;
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border: none;
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border-radius: 5px;
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cursor: pointer;
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font-size: 16px;
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width: 100%;
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}
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button:hover {
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background-color: #45a049;
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}
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button:disabled {
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background-color: #cccccc;
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cursor: not-allowed;
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}
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.loading {
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text-align: center;
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color: #666;
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margin-top: 20px;
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}
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</style>
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</head>
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<body>
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<div class="container">
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<h1>🚀 Article Generator</h1>
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<form id="jobForm">
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<div class="form-group">
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<label for="topic">Article Topic:</label>
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<input type="text" id="topic" name="topic" placeholder="e.g., AI Safety, Climate Change, Space Exploration" required>
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</div>
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<button type="submit" id="submitBtn">Generate Article</button>
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</form>
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<div id="loading" class="loading" style="display: none;">
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Starting your article generation...
|
||||
</div>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
document.getElementById('jobForm').addEventListener('submit', async function(e) {
|
||||
e.preventDefault();
|
||||
|
||||
const topic = document.getElementById('topic').value.trim();
|
||||
if (!topic) return;
|
||||
|
||||
// Show loading state
|
||||
document.getElementById('submitBtn').disabled = true;
|
||||
document.getElementById('loading').style.display = 'block';
|
||||
|
||||
try {
|
||||
const response = await fetch('/start-job', {
|
||||
method: 'POST',
|
||||
headers: {
|
||||
'Content-Type': 'application/x-www-form-urlencoded',
|
||||
},
|
||||
body: `topic=${encodeURIComponent(topic)}`
|
||||
});
|
||||
|
||||
const result = await response.json();
|
||||
|
||||
if (result.job_id) {
|
||||
// Redirect to progress page
|
||||
window.location.href = `/progress.html?job_id=${result.job_id}&topic=${encodeURIComponent(topic)}`;
|
||||
} else {
|
||||
alert('Failed to start job');
|
||||
document.getElementById('submitBtn').disabled = false;
|
||||
document.getElementById('loading').style.display = 'none';
|
||||
}
|
||||
} catch (error) {
|
||||
alert('Error starting job: ' + error.message);
|
||||
document.getElementById('submitBtn').disabled = false;
|
||||
document.getElementById('loading').style.display = 'none';
|
||||
}
|
||||
});
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -0,0 +1,223 @@
|
|||
<!DOCTYPE html>
|
||||
<html lang="en">
|
||||
<head>
|
||||
<meta charset="UTF-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
<title>Article Generation Progress</title>
|
||||
<style>
|
||||
body {
|
||||
font-family: Arial, sans-serif;
|
||||
max-width: 800px;
|
||||
margin: 20px auto;
|
||||
padding: 20px;
|
||||
background-color: #f5f5f5;
|
||||
}
|
||||
.container {
|
||||
background: white;
|
||||
padding: 30px;
|
||||
border-radius: 10px;
|
||||
box-shadow: 0 2px 10px rgba(0,0,0,0.1);
|
||||
}
|
||||
h1 {
|
||||
color: #333;
|
||||
text-align: center;
|
||||
margin-bottom: 30px;
|
||||
}
|
||||
.topic {
|
||||
text-align: center;
|
||||
color: #666;
|
||||
margin-bottom: 30px;
|
||||
font-style: italic;
|
||||
}
|
||||
.progress-container {
|
||||
margin-bottom: 30px;
|
||||
}
|
||||
.progress-bar {
|
||||
width: 100%;
|
||||
height: 20px;
|
||||
background-color: #f0f0f0;
|
||||
border-radius: 10px;
|
||||
overflow: hidden;
|
||||
}
|
||||
.progress-fill {
|
||||
height: 100%;
|
||||
background-color: #4CAF50;
|
||||
width: 0%;
|
||||
transition: width 0.3s ease;
|
||||
}
|
||||
.progress-text {
|
||||
text-align: center;
|
||||
margin-top: 10px;
|
||||
font-weight: bold;
|
||||
color: #333;
|
||||
}
|
||||
.step-info {
|
||||
background-color: #f8f9fa;
|
||||
padding: 15px;
|
||||
border-radius: 5px;
|
||||
margin-bottom: 20px;
|
||||
border-left: 4px solid #4CAF50;
|
||||
}
|
||||
.article-result {
|
||||
background-color: #f8f9fa;
|
||||
padding: 20px;
|
||||
border-radius: 5px;
|
||||
margin-top: 20px;
|
||||
white-space: pre-wrap;
|
||||
line-height: 1.6;
|
||||
}
|
||||
.error {
|
||||
background-color: #ffebee;
|
||||
color: #c62828;
|
||||
padding: 15px;
|
||||
border-radius: 5px;
|
||||
border-left: 4px solid #f44336;
|
||||
}
|
||||
.back-button {
|
||||
background-color: #2196F3;
|
||||
color: white;
|
||||
padding: 10px 20px;
|
||||
border: none;
|
||||
border-radius: 5px;
|
||||
cursor: pointer;
|
||||
text-decoration: none;
|
||||
display: inline-block;
|
||||
margin-top: 20px;
|
||||
}
|
||||
.back-button:hover {
|
||||
background-color: #1976D2;
|
||||
}
|
||||
.loading-dots {
|
||||
display: inline-block;
|
||||
}
|
||||
.loading-dots:after {
|
||||
content: '';
|
||||
animation: dots 1.5s steps(5, end) infinite;
|
||||
}
|
||||
@keyframes dots {
|
||||
0%, 20% { content: ''; }
|
||||
40% { content: '.'; }
|
||||
60% { content: '..'; }
|
||||
80%, 100% { content: '...'; }
|
||||
}
|
||||
</style>
|
||||
</head>
|
||||
<body>
|
||||
<div class="container">
|
||||
<h1>📝 Generating Your Article</h1>
|
||||
<div class="topic" id="topicDisplay"></div>
|
||||
|
||||
<div class="progress-container">
|
||||
<div class="progress-bar">
|
||||
<div class="progress-fill" id="progressFill"></div>
|
||||
</div>
|
||||
<div class="progress-text" id="progressText">Starting<span class="loading-dots"></span></div>
|
||||
</div>
|
||||
|
||||
<div id="stepInfo" class="step-info" style="display: none;"></div>
|
||||
<div id="errorInfo" class="error" style="display: none;"></div>
|
||||
<div id="articleResult" class="article-result" style="display: none;"></div>
|
||||
|
||||
<a href="/" class="back-button">← Generate Another Article</a>
|
||||
</div>
|
||||
|
||||
<script>
|
||||
// Get job_id and topic from URL parameters
|
||||
const urlParams = new URLSearchParams(window.location.search);
|
||||
const jobId = urlParams.get('job_id');
|
||||
const topic = urlParams.get('topic');
|
||||
|
||||
if (!jobId) {
|
||||
document.getElementById('errorInfo').style.display = 'block';
|
||||
document.getElementById('errorInfo').textContent = 'No job ID provided';
|
||||
} else {
|
||||
document.getElementById('topicDisplay').textContent = `Topic: ${topic || 'Unknown'}`;
|
||||
startProgressMonitoring(jobId);
|
||||
}
|
||||
|
||||
function startProgressMonitoring(jobId) {
|
||||
const eventSource = new EventSource(`/progress/${jobId}`);
|
||||
|
||||
eventSource.onmessage = function(event) {
|
||||
try {
|
||||
const data = JSON.parse(event.data);
|
||||
|
||||
if (data.error) {
|
||||
showError(data.error);
|
||||
eventSource.close();
|
||||
return;
|
||||
}
|
||||
|
||||
if (data.heartbeat) {
|
||||
return; // Ignore heartbeat messages
|
||||
}
|
||||
|
||||
updateProgress(data);
|
||||
|
||||
if (data.step === 'complete') {
|
||||
showFinalResult(data.data.final_article);
|
||||
eventSource.close();
|
||||
}
|
||||
|
||||
} catch (error) {
|
||||
console.error('Error parsing SSE data:', error);
|
||||
}
|
||||
};
|
||||
|
||||
eventSource.onerror = function(event) {
|
||||
console.error('SSE connection error:', event);
|
||||
showError('Connection lost. Please refresh the page.');
|
||||
eventSource.close();
|
||||
};
|
||||
}
|
||||
|
||||
function updateProgress(data) {
|
||||
const progressFill = document.getElementById('progressFill');
|
||||
const progressText = document.getElementById('progressText');
|
||||
const stepInfo = document.getElementById('stepInfo');
|
||||
|
||||
// Update progress bar
|
||||
progressFill.style.width = data.progress + '%';
|
||||
|
||||
// Update progress text and step info
|
||||
switch (data.step) {
|
||||
case 'outline':
|
||||
progressText.textContent = 'Creating outline... (33%)';
|
||||
stepInfo.style.display = 'block';
|
||||
stepInfo.innerHTML = `<strong>Step 1:</strong> Generated outline with sections: ${data.data.sections.join(', ')}`;
|
||||
break;
|
||||
case 'content':
|
||||
if (data.data.section) {
|
||||
// Individual section progress
|
||||
progressText.textContent = `Writing content... (${data.progress}%)`;
|
||||
stepInfo.innerHTML = `<strong>Step 2:</strong> Completed section "${data.data.section}" (${data.data.completed_sections}/${data.data.total_sections})`;
|
||||
} else {
|
||||
// Final content completion
|
||||
progressText.textContent = 'Writing content... (66%)';
|
||||
stepInfo.innerHTML = `<strong>Step 2:</strong> Generated ${data.data.draft_length} characters of content`;
|
||||
}
|
||||
break;
|
||||
case 'complete':
|
||||
progressText.textContent = 'Complete! (100%)';
|
||||
stepInfo.innerHTML = `<strong>Step 3:</strong> Applied conversational styling - Article ready!`;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
function showFinalResult(article) {
|
||||
const resultDiv = document.getElementById('articleResult');
|
||||
resultDiv.style.display = 'block';
|
||||
resultDiv.textContent = article;
|
||||
}
|
||||
|
||||
function showError(errorMessage) {
|
||||
const errorDiv = document.getElementById('errorInfo');
|
||||
errorDiv.style.display = 'block';
|
||||
errorDiv.textContent = `Error: ${errorMessage}`;
|
||||
|
||||
const progressText = document.getElementById('progressText');
|
||||
progressText.textContent = 'Failed';
|
||||
}
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
|
|
@ -0,0 +1 @@
|
|||
# Utils package for FastAPI Background Job Interface
|
||||
|
|
@ -0,0 +1,13 @@
|
|||
import os
|
||||
from openai import OpenAI
|
||||
|
||||
def call_llm(prompt):
|
||||
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY", "your-api-key"))
|
||||
r = client.chat.completions.create(
|
||||
model="gpt-4o",
|
||||
messages=[{"role": "user", "content": prompt}]
|
||||
)
|
||||
return r.choices[0].message.content
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(call_llm("Tell me a short joke"))
|
||||
|
|
@ -2,6 +2,12 @@
|
|||
|
||||
Real-time chat interface with streaming LLM responses using PocketFlow, FastAPI, and WebSocket.
|
||||
|
||||
<p align="center">
|
||||
<img
|
||||
src="./assets/banner.png" width="800"
|
||||
/>
|
||||
</p>
|
||||
|
||||
## Features
|
||||
|
||||
- **Real-time Streaming**: See AI responses typed out in real-time as the LLM generates them
|
||||
|
|
|
|||
Binary file not shown.
|
After Width: | Height: | Size: 668 KiB |
|
|
@ -55,7 +55,7 @@ Here's what each node does:
|
|||
- [`main.py`](./main.py): Main entry point for running the article workflow
|
||||
- [`flow.py`](./flow.py): Defines the flow that connects the nodes
|
||||
- [`nodes.py`](./nodes.py): Contains the node classes for each step in the workflow
|
||||
- [`utils.py`](./utils.py): Utility functions including the LLM wrapper
|
||||
- [`utils/call_llm.py`](./utils/call_llm.py): LLM utility function
|
||||
- [`requirements.txt`](./requirements.txt): Lists the required dependencies
|
||||
|
||||
## Example Output
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
import re
|
||||
from pocketflow import Node, BatchNode
|
||||
from utils import call_llm
|
||||
from utils.call_llm import call_llm
|
||||
import yaml
|
||||
|
||||
class GenerateOutline(Node):
|
||||
|
|
@ -49,16 +49,13 @@ sections:
|
|||
|
||||
return "default"
|
||||
|
||||
class WriteSimpleContent(Node):
|
||||
class WriteSimpleContent(BatchNode):
|
||||
def prep(self, shared):
|
||||
# Get the list of sections to process
|
||||
return shared.get("sections", [])
|
||||
# Get the list of sections to process and store for progress tracking
|
||||
self.sections = shared.get("sections", [])
|
||||
return self.sections
|
||||
|
||||
def exec(self, sections):
|
||||
all_sections_content = []
|
||||
section_contents = {}
|
||||
|
||||
for section in sections:
|
||||
def exec(self, section):
|
||||
prompt = f"""
|
||||
Write a short paragraph (MAXIMUM 100 WORDS) about this section:
|
||||
|
||||
|
|
@ -71,13 +68,24 @@ Requirements:
|
|||
- Include one brief example or analogy
|
||||
"""
|
||||
content = call_llm(prompt)
|
||||
|
||||
# Show progress for this section
|
||||
current_section_index = self.sections.index(section) if section in self.sections else 0
|
||||
total_sections = len(self.sections)
|
||||
print(f"✓ Completed section {current_section_index + 1}/{total_sections}: {section}")
|
||||
|
||||
return section, content
|
||||
|
||||
def post(self, shared, prep_res, exec_res_list):
|
||||
# exec_res_list contains [(section, content), (section, content), ...]
|
||||
section_contents = {}
|
||||
all_sections_content = []
|
||||
|
||||
for section, content in exec_res_list:
|
||||
section_contents[section] = content
|
||||
all_sections_content.append(f"## {section}\n\n{content}\n")
|
||||
|
||||
return sections, section_contents, "\n".join(all_sections_content)
|
||||
|
||||
def post(self, shared, prep_res, exec_res):
|
||||
sections, section_contents, draft = exec_res
|
||||
draft = "\n".join(all_sections_content)
|
||||
|
||||
# Store the section contents and draft
|
||||
shared["section_contents"] = section_contents
|
||||
|
|
|
|||
Loading…
Reference in New Issue