Made changes to Getting started with ALwrity and added lot of details on API keys
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ajaysi
@@ -24,14 +24,14 @@
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# Scrape the website for contact details (email addresses, contact forms, etc.).
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# Use natural language processing (NLP) to understand the type of content on the website and who the contact person might be (webmaster, editor, or guest post manager).
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# Website Content Understanding:
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# Scrape a summary of each website’s content (e.g., their blog topics, categories, and tone) to personalize the email based on the site's focus.
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# Scrape a summary of each website's content (e.g., their blog topics, categories, and tone) to personalize the email based on the site's focus.
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#
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# Personalized Outreach:
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# AI Email Composition:
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# Compose personalized outreach emails based on:
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# The scraped data (website content, topic focus, etc.).
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# The user's input (what kind of guest post or content they want to contribute).
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# Example: “Hi [Webmaster Name], I noticed that your site [Site Name] features high-quality content about [Topic]. I would love to contribute a guest post on [Proposed Topic] in exchange for a backlink.”
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# Example: "Hi [Webmaster Name], I noticed that your site [Site Name] features high-quality content about [Topic]. I would love to contribute a guest post on [Proposed Topic] in exchange for a backlink."
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#
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# Automated Email Sending:
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# Review Emails (Optional HITL):
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@@ -50,7 +50,7 @@
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# Automated Responses:
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# If a website replies positively, AI can respond with predefined follow-up emails (e.g., proposing topics, confirming submission deadlines).
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# Follow-up Reminders:
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# If there’s no reply, the system can send polite follow-up reminders at pre-set intervals.
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# If there's no reply, the system can send polite follow-up reminders at pre-set intervals.
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#
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#Key Features:
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#
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@@ -71,7 +71,7 @@
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#
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# Lead Tracking and Management:
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# Track all emails sent, monitor replies, and keep track of successful backlinks.
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# Log each lead’s status (e.g., emailed, responded, no reply) to manage future interactions.
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# Log each lead's status (e.g., emailed, responded, no reply) to manage future interactions.
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#
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# Multiple Keywords/Queries:
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# Allow users to run the same process for a batch of keywords, automatically generating relevant search queries for each.
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@@ -89,13 +89,13 @@
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# Prioritize high-authority websites to maximize the impact of backlinks.
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#
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# Spam Detection:
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# Use AI to detect and avoid spammy or low-quality websites that might harm the user’s SEO.
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# Use AI to detect and avoid spammy or low-quality websites that might harm the user's SEO.
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#
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# Contact Form Auto-Fill:
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# If the site only offers a contact form (without email), automatically fill and submit the form with AI-generated content.
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#
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# Dynamic Content Suggestions:
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# Suggest guest post topics based on the website’s focus, using NLP to analyze the site's existing content.
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# Suggest guest post topics based on the website's focus, using NLP to analyze the site's existing content.
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#
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# Bulk Email Support:
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# Allow users to bulk-send outreach emails while still personalizing each message for scalability.
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@@ -130,7 +130,7 @@
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import sys
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from googlesearch import search
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# from googlesearch import search # Temporarily disabled for future enhancement
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from loguru import logger
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from lib.ai_web_researcher.firecrawl_web_crawler import scrape_website
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from lib.gpt_providers.text_generation.main_text_generation import llm_text_gen
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@@ -180,48 +180,32 @@ def find_backlink_opportunities(keyword):
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search_queries = generate_search_queries(keyword)
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results = []
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for query in search_queries:
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urls = search_for_urls(query)
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for url in urls:
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website_data = scrape_website(url)
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logger.info(f"Scraped Website content for {url}: {website_data}")
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if website_data:
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contact_info = extract_contact_info(website_data)
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logger.info(f"Contact details found for {url}: {contact_info}")
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# Temporarily disabled Google search functionality
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# for query in search_queries:
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# urls = search_for_urls(query)
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# for url in urls:
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# website_data = scrape_website(url)
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# logger.info(f"Scraped Website content for {url}: {website_data}")
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# if website_data:
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# contact_info = extract_contact_info(website_data)
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# logger.info(f"Contact details found for {url}: {contact_info}")
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# Placeholder return for now
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return []
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# AI-driven insights using website data
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insights_prompt = f"""
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You are an expert in analyzing website content. Below is the content of a website. Please analyze it and provide actionable insights for a personalized guest post outreach:
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Website Content:
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{website_data.get("content_summary", "")}
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1. **Website Focus**: What is the primary topic, audience, and tone?
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2. **Guest Posting Guidelines**: Are there any guest post preferences (content type, length, etc.)?
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3. **Suggested Topics**: Based on the site’s content, what topics might align well?
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4. **Personalization Tips**: How can we make the outreach more tailored to this site?
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"""
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insights = llm_text_gen(insights_prompt)
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detailed_result = {
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"url": url,
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"metadata": {
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"title": website_data.get("metadata", {}).get("title", ""),
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"description": website_data.get("metadata", {}).get("description", ""),
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"keywords": website_data.get("metadata", {}).get("keywords", []),
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},
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"content_summary": website_data.get("content_summary", ""),
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"contact_info": contact_info,
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"insights": insights,
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"backlink_opportunity": {
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"query": query,
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"context": "Guest post opportunity"
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}
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}
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results.append(detailed_result)
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return results
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def search_for_urls(query):
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"""
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Search for URLs using Google search.
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Args:
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query (str): The search query.
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Returns:
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list: List of URLs found.
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"""
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# Temporarily disabled Google search functionality
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# return list(search(query, num_results=10))
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return []
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def compose_personalized_email(website_data, insights, user_proposal):
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"""
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@@ -300,24 +284,6 @@ def send_email(smtp_server, smtp_port, smtp_user, smtp_password, to_email, subje
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logger.error(f"Failed to send email to {to_email}: {e}")
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return False
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def search_for_urls(query):
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"""
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Search for URLs based on a query using Firecrawl.
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Args:
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query (str): The search query.
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Returns:
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list: A list of URLs.
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"""
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# We can use Firecrawl, which also provides AI extraction.
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try:
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google_search_result = search(query, max_results=5)
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print(google_search_result)
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return google_search_result
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except Exception as err:
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logger.error(f"Failed to do GoogleSearch: {err}")
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def extract_contact_info(website_data):
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"""
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Extract contact information from website data.
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