wehub-resource-sync
535 lines
19 KiB
Plaintext
535 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Content Planning and Publishing Crew"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook demonstrates how to create an AI crew for planning and publishing content using CrewAI Flows.\n",
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"The crew will take a link to blog post, download content as markdown using firecrawl, analyze it and generate a twitter thread and schedule it on Typefully."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Initialization and Setup\n",
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"Initial imports for the CrewAI Flow and Crew and setting up the environment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Importing necessary libraries\n",
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"import getpass\n",
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"import os\n",
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"import datetime\n",
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"import uuid\n",
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"import yaml\n",
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"import json\n",
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"import subprocess\n",
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"from pathlib import Path\n",
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"import pydantic\n",
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"from pydantic import BaseModel\n",
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"from typing import Optional\n",
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"\n",
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"# Firecrawl SDK\n",
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"from firecrawl import FirecrawlApp\n",
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"\n",
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"# Typefully scheduler\n",
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"import scheduler\n",
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"\n",
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"# Importing Crew related components\n",
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"from crewai import Agent, Task, Crew, LLM\n",
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"\n",
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"# Importing CrewAI Flow related components\n",
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"from crewai.flow.flow import Flow, listen, start, router, or_\n",
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"\n",
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"from dotenv import load_dotenv\n",
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"load_dotenv()\n",
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"# Apply a patch to allow nested asyncio loops in Jupyter\n",
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"import nest_asyncio\n",
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"nest_asyncio.apply()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup LLM\n",
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"\n",
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"Make sure you have ollama installed and running on your machine"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = LLM(\n",
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" model=\"ollama/llama3.2\",\n",
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" base_url=\"http://localhost:11434\"\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Blog Post URL"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"blog_post_url = \"https://blog.dailydoseofds.com/p/5-chunking-strategies-for-rag\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Plan for our Flow\n",
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"\n",
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"1. Scrape the blog post\n",
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"2. Decode where to post using a router\n",
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"3. Kickoff the right **[Crew of Agents]** to prepare a draft ready to publish\n",
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"4. Publish it using typefully"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<img src=\"content_writing_flow.png\" width=\"1000\" height=\"750\"/>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"execution_count": 22,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from IPython.display import HTML\n",
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"HTML('<img src=\"content_writing_flow.png\" width=\"1000\" height=\"750\"/>')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Twitter Thread Planning Crew\n",
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"\n",
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"This structure will be used to capture the output of the planning crew which will be used to create the twitter thread and schedule it on Typefully."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"class Tweet(BaseModel):\n",
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" \"\"\"Represents an individual tweet in a thread\"\"\"\n",
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" content: str\n",
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" is_hook: bool = False # Identifies if this is the opening/hook tweet\n",
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" media_urls: Optional[list[str]] = [] # Optional media attachments (images, code snippets)\n",
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"\n",
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"class Thread(BaseModel):\n",
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" \"\"\"Represents a Twitter thread\"\"\"\n",
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" topic: str # Main topic/subject of the thread\n",
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" tweets: list[Tweet] # List of tweets in the thread"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [],
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"source": [
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"from crewai_tools import (\n",
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" DirectoryReadTool,\n",
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" FileReadTool,\n",
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")\n",
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"\n",
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"# Load agent and task configurations from YAML files\n",
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"with open('config/planner_agents.yaml', 'r') as f:\n",
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" agents_config = yaml.safe_load(f)\n",
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"\n",
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"with open('config/planner_tasks.yaml', 'r') as f:\n",
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" tasks_config = yaml.safe_load(f)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 32,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2024-12-19 14:18:07,847 - 7988681536 - __init__.py-__init__:537 - WARNING: Overriding of current TracerProvider is not allowed\n"
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]
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}
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],
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"source": [
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"draft_analyzer = Agent(config=agents_config['draft_analyzer'], tools=[\n",
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" DirectoryReadTool(),\n",
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" FileReadTool()\n",
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"], llm=llm)\n",
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"twitter_thread_planner = Agent(config=agents_config['twitter_thread_planner'], tools=[\n",
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" DirectoryReadTool(),\n",
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" FileReadTool()\n",
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"], llm=llm)\n",
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"\n",
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"analyze_draft = Task(\n",
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" config=tasks_config['analyze_draft'],\n",
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" agent=draft_analyzer\n",
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")\n",
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"create_twitter_thread_plan = Task(\n",
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" config=tasks_config['create_twitter_thread_plan'],\n",
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" agent=twitter_thread_planner,\n",
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" output_pydantic=Thread\n",
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")\n",
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"\n",
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"planning_crew = Crew(\n",
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" agents=[draft_analyzer, twitter_thread_planner],\n",
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" tasks=[analyze_draft, create_twitter_thread_plan],\n",
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" verbose=False\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# LinkedIn Post Planning Crew"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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"class LinkedInPost(BaseModel):\n",
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" \"\"\"Represents a LinkedIn post\"\"\"\n",
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" content: str\n",
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" media_url: str # Main image url for the post"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"2024-12-19 14:25:39,453 - 7988681536 - __init__.py-__init__:537 - WARNING: Overriding of current TracerProvider is not allowed\n"
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]
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}
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],
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"source": [
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"linkedin_post_planner = Agent(config=agents_config['linkedin_post_planner'], llm=llm)\n",
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"\n",
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"create_linkedin_post_plan = Task(\n",
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" config=tasks_config['create_linkedin_post_plan'],\n",
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" agent=linkedin_post_planner,\n",
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" output_pydantic=LinkedInPost\n",
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")\n",
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"\n",
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"linkedin_planning_crew = Crew(\n",
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" agents=[draft_analyzer, linkedin_post_planner],\n",
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" tasks=[analyze_draft, create_linkedin_post_plan],\n",
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" verbose=False\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Create Content Planning Flow\n",
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"\n",
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"A Flow to create the content planning for twitter and linkedin using separate crews for twitter and linkedin"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<img src=\"content_writing_flow.png\" width=\"1000\" height=\"750\"/>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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]
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},
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"execution_count": 37,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from IPython.display import HTML\n",
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"HTML('<img src=\"content_writing_flow.png\" width=\"1000\" height=\"750\"/>')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"from crewai.flow.flow import Flow, listen, start, router, or_\n",
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"\n",
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"class ContentPlanningState(BaseModel):\n",
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" \"\"\"\n",
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" State for the content planning flow\n",
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" \"\"\"\n",
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" blog_post_url: str = blog_post_url\n",
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" draft_path: Path = \"workdir/\"\n",
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" post_type: str = \"twitter\"\n",
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" path_to_example_threads: str = \"workdir/example_threads.txt\"\n",
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"\n",
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"class CreateContentPlanningFlow(Flow[ContentPlanningState]):\n",
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" # Scrape the blog post \n",
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" # No need for AI Agents on this step, so we just use regular Python code\n",
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" @start()\n",
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" def scrape_blog_post(self):\n",
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" print(f\"# fetching draft from: {self.state.blog_post_url}\")\n",
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" app = FirecrawlApp(api_key=os.getenv(\"FIRECRAWL_API_KEY\"))\n",
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" scrape_result = app.scrape_url(self.state.blog_post_url, params={'formats': ['markdown', 'html']})\n",
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" try:\n",
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" title = scrape_result['metadata']['title']\n",
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" except Exception as e:\n",
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" title = str(uuid.uuid4())\n",
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" self.state.draft_path = f'workdir/{title}.md'\n",
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" with open(self.state.draft_path, 'w') as f:\n",
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" f.write(scrape_result['markdown'])\n",
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" return self.state\n",
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"\n",
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" @router(scrape_blog_post)\n",
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" def select_platform(self):\n",
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" if self.state.post_type == \"twitter\":\n",
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" return \"twitter\"\n",
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" elif self.state.post_type == \"linkedin\":\n",
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" return \"linkedin\"\n",
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"\n",
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" @listen(\"twitter\")\n",
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" def twitter_draft(self):\n",
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" print(f\"# Planning content for: {self.state.draft_path}\")\n",
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" result = planning_crew.kickoff(inputs={'draft_path': self.state.draft_path, 'path_to_example_threads': self.state.path_to_example_threads})\n",
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" print(f\"# Planned content for {self.state.draft_path}:\")\n",
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" for tweet in result.pydantic.tweets:\n",
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" print(f\" - {tweet.content}\")\n",
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" return result\n",
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" \n",
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" @listen(\"linkedin\")\n",
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" def linkedin_draft(self):\n",
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" print(f\"# Planning content for: {self.state.draft_path}\")\n",
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" result = linkedin_planning_crew.kickoff(inputs={'draft_path': self.state.draft_path})\n",
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" print(f\"# Planned content for {self.state.draft_path}:\")\n",
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" print(f\" - {result.pydantic.content}\")\n",
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" return result\n",
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"\n",
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" @listen(or_(twitter_draft, linkedin_draft))\n",
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" def save_plan(self, plan):\n",
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" with open(f'thread/{self.state.draft_path.split(\"/\")[-1]}_{self.state.post_type}.json', 'w') as f:\n",
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" json.dump(plan.pydantic.model_dump(), f, indent=2)\n",
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"\n",
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" @listen(or_(twitter_draft, linkedin_draft))\n",
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" def publish(self, plan):\n",
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" print(f\"# Publishing thread for: {self.state.draft_path}\")\n",
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" ## Schedule for 1 hour from now \n",
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" response = scheduler.schedule(\n",
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" thread_model=plan,\n",
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" post_type=self.state.post_type\n",
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" )\n",
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" print(f\"# Thread scheduled for: {self.state.draft_path}\")\n",
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" print(f\"Here's the link to scheduled draft: {response['share_url']}\")\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Implementing helper methods to plot and execute the flow in a Jupyter notebook"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 43,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Plot saved as crewai_flow.html\n"
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]
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}
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],
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"source": [
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"# Plot the flow\n",
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"flow = CreateContentPlanningFlow()\n",
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"flow.plot()\n",
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"\n",
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"# Display the flow visualization using IFrame\n",
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"from IPython.display import IFrame\n",
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"\n",
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"# Display the flow visualization\n",
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"# IFrame(src='./crewai_flow.html', width='100%', height=400)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"ContentPlanningState(blog_post_url='https://blog.dailydoseofds.com/p/5-chunking-strategies-for-rag', draft_path='workdir/', post_type='twitter', path_to_example_threads='workdir/example_threads.txt')"
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]
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},
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"execution_count": 44,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"post_type = \"twitter\"\n",
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"flow = CreateContentPlanningFlow()\n",
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"flow.state.post_type = post_type\n",
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"flow.state"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"# fetching draft from: https://blog.dailydoseofds.com/p/5-chunking-strategies-for-rag\n",
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"# Planning content for: workdir/5 Chunking Strategies For RAG - by Avi Chawla.md\n",
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"# Planned content for workdir/5 Chunking Strategies For RAG - by Avi Chawla.md:\n",
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" - 5 Chunking Strategies For RAG\n",
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" - What is Retrieval-Augmented Generation (RAG)? 🤔 It's a technique that boosts language models with information retrieval, refining the interaction with large data. Chunking plays a crucial role here!\n",
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" - 1️⃣ Fixed-size Chunking: This method splits text into uniform segments. While straightforward, it risks losing contextual meaning. Use with care! 🔍\n",
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" - 2️⃣ Semantic Chunking: Here, we create chunks based on meaningful units (like sentences). This ensures better coherence and relevance in information retrieval. 🤝\n",
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" - 3️⃣ Recursive Chunking: Breaks text using natural language boundaries, like paragraphs, and refines chunks that are too large. Preserve flow while keeping it efficient! 📏\n",
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" - 4️⃣ Document Structure-based Chunking: Leverage document headings and sections for chunk boundaries. This maintains logical structure and aids retrieval accuracy! 📚\n",
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" - 5️⃣ LLM-based Chunking: Use large language models to generate semantically-rich chunks. This is powerful but comes with high computational costs. ⚡️\n",
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" - Key Takeaway: Each chunking strategy has its own strengths and weaknesses. Select the right method based on your model and content to optimize performance! 🚀\n",
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" - If you're diving into RAG, embrace chunking. Explore these strategies and elevate your AI's ability to communicate knowledge effectively! Join the discussion! 💬\n",
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"# Publishing thread for: workdir/5 Chunking Strategies For RAG - by Avi Chawla.md\n",
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"######## Thread JSON: {'topic': '5 Chunking Strategies For RAG', 'tweets': [{'content': '5 Chunking Strategies For RAG', 'is_hook': True, 'media_urls': []}, {'content': \"What is Retrieval-Augmented Generation (RAG)? 🤔 It's a technique that boosts language models with information retrieval, refining the interaction with large data. Chunking plays a crucial role here!\", 'is_hook': False, 'media_urls': ['https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_lossy/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6878b8fa-5e74-45a1-9a89-5aab92889126_2366x990.gif']}, {'content': '1️⃣ Fixed-size Chunking: This method splits text into uniform segments. While straightforward, it risks losing contextual meaning. Use with care! 🔍', 'is_hook': False, 'media_urls': ['https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F98c422a0-f0e2-457c-a256-4476a56a601f_943x232.png']}, {'content': '2️⃣ Semantic Chunking: Here, we create chunks based on meaningful units (like sentences). This ensures better coherence and relevance in information retrieval. 🤝', 'is_hook': False, 'media_urls': ['https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fa6ad83a6-2879-4c77-9e49-393f16577aef_1066x288.gif']}, {'content': '3️⃣ Recursive Chunking: Breaks text using natural language boundaries, like paragraphs, and refines chunks that are too large. Preserve flow while keeping it efficient! 📏', 'is_hook': False, 'media_urls': ['https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Ff4009caa-34fc-48d6-8102-3d0f6f2c1386_1066x316.gif']}, {'content': '4️⃣ Document Structure-based Chunking: Leverage document headings and sections for chunk boundaries. This maintains logical structure and aids retrieval accuracy! 📚', 'is_hook': False, 'media_urls': ['https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe8febecd-ee68-42ff-ab06-41a0a3a43cd3_1102x306.gif']}, {'content': '5️⃣ LLM-based Chunking: Use large language models to generate semantically-rich chunks. This is powerful but comes with high computational costs. ⚡️', 'is_hook': False, 'media_urls': ['https://substackcdn.com/image/fetch/w_1456,c_limit,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F4d1b6d60-8956-4030-8525-d899ee61a9d5_1140x198.gif']}, {'content': 'Key Takeaway: Each chunking strategy has its own strengths and weaknesses. Select the right method based on your model and content to optimize performance! 🚀', 'is_hook': False, 'media_urls': []}, {'content': \"If you're diving into RAG, embrace chunking. Explore these strategies and elevate your AI's ability to communicate knowledge effectively! Join the discussion! 💬\", 'is_hook': False, 'media_urls': []}]}\n",
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"Thread scheduled successfully!\n",
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"# Thread scheduled for: workdir/5 Chunking Strategies For RAG - by Avi Chawla.md\n",
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"Here's the link to scheduled draft: https://typefully.com/t/hXG9c1d\n"
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