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MIAMLDS I VIRTUAL MOD 11 SESION 12 DR PABLO
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MIAMLDS I VIRTUAL MOD 11 SESION 12 DR PABLO

Introduction and Setup

Class Initiation

  • Students are welcomed to the class, with instructions to log into the platform and navigate to Unit 4.
  • The instructor mentions two examples previously worked on: "primera gente" and "Daina," which will be provided in the workflow and Wordbook.

File Download Instructions

  • Students are instructed to download the last three files from Unit 4, confirming that they can see all five files available for download.
  • A student raises a concern about one of the files being located in the recording section, which is acknowledged by the instructor.

Workflow Overview

Initial Steps

  • The instructor prompts students to open the Wordbook as they begin relating their previous learning experiences to current tasks.
  • A project diagram is shared, emphasizing that students should focus on understanding how it works rather than just completing tasks.

System Definition

  • The system discussed is an "Agenic" content extraction and production tool based on trends, aimed at SEO marketing and market intelligence.
  • It automates processes like identifying current trends, validating them with reliable sources, and generating optimized content automatically.

Market Research Tools

Importance of Google Trends

  • The system addresses issues such as eliminating manual trend research, reducing irrelevant content creation risks, and speeding up idea generation based on verified data.
  • Google Trends is introduced as a vital tool for market research; it helps users understand what topics are trending over time across different regions.

Practical Demonstration

  • The instructor demonstrates how Google Trends can show interest levels in various topics over specific periods using COVID as an example. This includes regional analysis capabilities within Bolivia.
  • Further exploration of other topics (e.g., artificial intelligence agents) reveals varying global interests while highlighting Bolivia's lack of engagement with certain subjects compared to other countries like Costa Rica or Colombia.

Conclusion of Session

Final Thoughts on Market Analysis

  • Emphasis is placed on utilizing tools like Google Trends for analyzing variables relevant to students' research projects or business ideas before launching them into the market. This approach ensures informed decision-making based on real-time data trends rather than assumptions or outdated information.

API Trends and Perplexity Integration

Overview of API Request for Trend Analysis

  • The discussion begins with the need for an API request to Super API to obtain current trends based on configured region and category. This is crucial for targeted data analysis.

Importance of Reliable Sources

  • The speaker emphasizes the use of Perplexity as a tool for finding reliable documents, highlighting its role in sourcing trustworthy information. This is essential for ensuring accuracy in trend analysis.

Evolution of Perplexity

  • Perplexity has evolved from a simple search tool to an AI-integrated resource that provides comprehensive insights by linking various academic databases, enhancing its utility in research.

Validating Information with Google Trends

  • The process involves validating trends through Google Trends while ensuring that the sources are credible via Perplexity, thus avoiding reliance on potentially unreliable blogs or websites. This step is critical for maintaining data integrity.

Objectives of the Workflow

  • The main objective outlined is to autonomously identify relevant trends using real-time data from Bullet Trends, evaluate them based on search volume and strategic potential, and transform these insights into optimized content ideas ready for marketing strategies.

Automation in Market Research

Streamlining Market Studies

  • Automation is proposed as a solution to enhance efficiency in market research, particularly when comparing academic offerings across universities in Bolivia versus international institutions, reducing time spent on manual document review.

System Agent Design Map

  • A detailed design map outlines various agents within the system:
  • Entry Agent: Initiates workflow by receiving activation events and defining initial parameters like region and category.
  • Validation Agent: Ensures quality by filtering out low-volume or incomplete trends from Google Trends data.
  • Evaluation Agent: Analyzes filtered trends using AI models to assess their strategic content potential based on context and audience interest.

Decision-Making Process

  • The decision agent autonomously selects which trends meet criteria for further development into content production, streamlining the workflow significantly.

Execution and Notification Agents

  • Execution Agent: Responsible for generating final content such as articles or blog ideas.
  • Notification Agent: Communicates results back to stakeholders once content has been generated and stored, ensuring transparency throughout the process.

Results and Workflow in Content Generation

Final Results Definitions

  • The positive final result is strategic content generated automatically from real trends, validated with reliable sources, optimized for SEO, and properly stored for review, publication, and reuse in marketing strategies.
  • Negative results include discarded trends that do not meet criteria of volume, relevance, or informational quality to avoid generating irrelevant or unsupported content. This also includes recording cases for future system evaluations.

Workflow Initiation Types

  • There are three types of initiation: manual start, flow-based start, and scheduled start. The manual start acts as a switch that triggers the next decision-making process.
  • In the manual initiation mode, an action is executed when the switch is pressed; it’s recommended to keep this mode in test configuration during initial design phases.
  • Flow-based initiation receives valid payloads from other workflows and initiates execution through external orchestration. Scheduled initiation occurs at a configured time using an input agent.

Trigger Mechanisms

  • Triggers can be set up to execute actions based on time intervals; for example, every two days at 2 PM can be configured easily without additional setup once established correctly.
  • The first three blocks of initiation are crucial for testing flows before moving onto more complex configurations; they help ensure everything functions as intended during early stages of development.

Trend Request Process

  • A trend request involves validation agents like Google Trends extractors which respond with data indicating whether trends were successfully retrieved (HTTP response code 200). This connects directly to Google Trends routing via Serapi permissions.
  • The raw dataset obtained will categorize trends by region and type; understanding how to utilize APIs effectively is essential for retrieving relevant data efficiently from platforms like Google Trends through Serapi.com.

API Utilization Insights

  • Users must register with their Gmail accounts on Serapi.com to access the Google Trends API; this registration allows them to download necessary tools similar to previous experiences with other APIs like APIF.
  • Once registered and authenticated via JSON methods provided by Serapi.com, users can leverage these APIs effectively within their systems for trend analysis purposes.

Workflow Overview Understanding the Evaluation Agent

Introduction to Workflow

  • The workflow begins with an evaluation agent that receives input and accesses Google Trends to search for requested data.
  • It displays the last four trends based on a coding function, filtering through extracted data to present a concise list of usable trends.

Data Processing Steps

  • Configuration is set in JavaScript, utilizing a console that extracts information from HTTP requests via Serapi, narrowing down selections to two or four trends.
  • A high-volume filter is applied by the validation agent, which checks if selected trends meet user-defined criteria regarding interest volume.

Decision-Making Process Evaluating Trend Potential

Validation and Selection

  • The validation agent assesses whether the chosen trends fulfill the user's requirements based on their characteristics.
  • The decision agent determines which trend has the highest content potential, ensuring it aligns with user expectations.

Integration with Machine Learning

  • In N8N, machine learning models are employed to select trending products based on rankings derived from previous evaluations.
  • Credentials must be assigned for integration with GPT 4.0 for enhanced automation capabilities in trend selection.

Content Generation Strategy Crafting Viral Blog Topics

Generating Keywords

  • A prompt guides users in selecting high-potential keywords for viral blog topics optimized for SEO, emphasizing relevance and current trends.
  • Users are instructed to generate five to eight related keywords that complement the primary keyword while prioritizing trend type and search volume.

Output Structure Requirements

  • The output must adhere strictly to a JSON format without additional text, detailing selected keywords along with their respective values and links.

Final Evaluation Steps Ensuring Reliability of Selected Trends

Verification Process

  • An automated system evaluates whether selected trends are credible by consulting reliable sources through an API called Perplexity.

Structuring Content Inputs

  • The execution agent queries if the gathered information can be structured as content inputs for further development.

Understanding API Integration in N8N

Mapping and API Connection

  • The discussion begins with the mapping of search functionality within N8N, emphasizing the organization of input lists for effective data handling.
  • A brief mention of a video tutorial on connecting to the Perplexity API is noted, aimed at simplifying user access to these APIs.
  • The speaker highlights the importance of using JSON for body content in API requests, indicating that specific data needs to be extracted from the agent's decision-making process.

Working with JSON and Agents

  • The focus shifts to selecting the correct agent from a list, which will influence how JSON data is structured and utilized in requests.
  • Emphasis is placed on configuring search mapping effectively, where users can define variable names like "reset" for better clarity in their workflows.

Content Production Workflow

  • Transitioning into content production mode, the speaker outlines a two-part blog generation process: part one focuses on key points derived from search mapping.
  • An AI execution agent is introduced that processes messages and drafts content based on predefined parameters set by the user.

Detailed Blog Generation Steps

  • In part two of blog generation, there’s a distinction made between summarizing key points versus completing development; this indicates different stages in content creation.
  • Specific instructions are provided regarding writing style and SEO considerations for creating engaging blog posts that maintain reader interest throughout.

Finalizing Content Structure

  • The first half of the blog should attract readers with compelling openings while ensuring clarity and depth through organized subheadings.
  • For the second half, maintaining coherence with earlier sections is crucial; it should deepen ideas presented previously while preparing readers for engagement post-publication.

This structured approach provides insights into integrating APIs within N8N while also detailing steps necessary for effective content generation.

Content Creation Workflow Overview

Basic Requirements and Configuration

  • The initial setup includes basic requirements for style, emphasizing the need to write in neutral Spanish.
  • A two-part configuration is established: one part stores data while the second complements it by closing the text.

Text Cleaning Process

  • The text cleaner focuses on unifying content and normalizing formats to eliminate duplicates.
  • The execution agent manages notifications and learning processes, ensuring that results are logged appropriately.

Content Generation Agents

  • An execution agent creates documents based on a message model, saving them with positive or negative outcomes depending on trend relevance.
  • If trends lack sufficient sources, they are recorded as discarded for future learning.

Content Structuring Guidelines

Title and Meta Description Generation

  • Users are tasked with generating five attractive title options in Spanish, each limited to 60 characters including the main keyword.
  • Short URL options must be created without accents or special characters, also incorporating the main keyword.

Summary and Meta Description Rules

  • Three meta description options should be crafted between 150–160 characters, highlighting benefits without adding value statements.
  • Summaries must consist of three clear sentences explaining the blog's content while adhering to specific formatting rules.

System Logic and Learning Mechanisms

System Architecture Overview

  • The system's core functions involve decision-making processes driven by an autonomous agent named Leo that oversees content production.

Learning from Trends

  • The reasoning process analyzes context trends through nodes that assess information relevance and structure content accordingly.

Adaptability and Feedback Loop

  • Adaptability occurs dynamically based on detected trends; criteria can adjust according to search volume and content focus.

Workflow Execution Steps

Content Production Flowchart

  • The workflow begins with data extraction from Google Trends via an evaluation agent assessing available trends for potential content creation.

System Workflow and Evaluation Process

Overview of the System's Functionality

  • The system begins with a search volume filter, transitioning to an agent for validation, followed by a decision-making agent that assesses four trends for viability.
  • If trends are deemed viable, an evaluation agent checks their reliability using Perplexity API for mapping searches before executing segmentation through an execution agent.
  • In cases where trends are not viable, the process is halted, leading to a negative result which prompts reevaluation based on new user inputs.

Learning and Feedback Mechanism

  • The system incorporates a learning agent that continuously adapts based on instructions received from users, enhancing its decision-making capabilities over time.
  • User feedback is solicited regarding the ease of use and functionality of the system as part of ongoing improvements.

Meeting Assistant Template

Introduction to Meeting Assistant

  • A meeting assistant template is introduced as part of project work aimed at streamlining meeting preparations through automation.
  • Users will receive guidance on connecting with Perplexity API instead of OpenAI to avoid previous issues encountered.

Functionality and Features

  • The assistant automates meeting notifications by sending reminders with relevant information extracted from LinkedIn profiles and recent correspondence.
  • It operates on a scheduled trigger every hour to check upcoming meetings within the next hour, ensuring timely notifications.

Data Extraction Process

  • The assistant extracts attendee data from calendar invitations using AI tools to summarize necessary information without requiring extensive coding knowledge.
  • An extractor node analyzes invitation details to gather contact information efficiently, facilitating better preparation for meetings.

Conclusion and Next Steps

  • Users are encouraged to utilize this structured approach in their workflows while adapting settings according to their specific needs.

How to Configure and Execute a Workflow with LLMs

Setting Up the Workflow

  • The workflow is pre-configured, requiring only execution to extract necessary information. Users must select an appropriate model for the task.
  • Once executed, the system accesses the calendar to gather data and send it directly to a listing without additional configuration.

Handling LinkedIn Data

  • Email retrieval and LinkedIn data actions are complex; thus, they are divided into workflow executions for easier development and maintenance.
  • Two verification calls are made within the sub-workflow, which are then merged into a single call at the end.

Combining Inputs

  • Gmail and LinkedIn data will be integrated into the workflow using a combination node that merges two types of input efficiently.
  • After combining inputs, data is stored for further processing in the final node of the workflow.

Generating Notifications

  • A notification generation node prepares details about upcoming meetings based on extracted information from emails and LinkedIn summaries.
  • Users can choose how to deliver notifications (e.g., WhatsApp or email), ensuring messages remain concise due to platform limitations.

Practical Applications of Notifications

  • WhatsApp is highlighted as an effective messaging tool compatible with N8N workflows; users can switch channels based on preference.
  • Emphasis on adapting workflows according to user familiarity with tools; initial document preparation is crucial before implementation.

Additional Resources and Tools

  • Participants will receive supplementary materials, including Wordbooks for continued learning about WhatsApp integration in workflows.
  • Pao, an assistant designed for sales registration via WhatsApp triggers, processes incoming messages through multiple branches for analysis.

Utilizing AI in Message Processing

  • Pao extracts messages from WhatsApp triggers and sends them through four processing branches utilizing AI for summarization or transcription.
  • Wikipedia is incorporated as a resource within Pao's framework to provide comprehensive answers by leveraging its extensive database.

WhatsApp Chatbot Integration and Functionality

Overview of the Chatbot's Data Sources

  • The chatbot utilizes Wikipedia as a data source to clarify user queries, especially when users make typographical errors in their requests.
  • It is emphasized that the response message is sent back to the user via WhatsApp, highlighting the importance of activating the workflow for proper functionality.

Workflow Activation and Requirements

  • Users must activate the workflow to utilize the WhatsApp chatbot; it is not available in a free version, necessitating payment for service.
  • If using a self-hosted server, it’s crucial to ensure connectivity with WhatsApp for seamless operation.

Message Handling Capabilities

  • The system can handle four types of messages: audio, video, images, and text. This categorization allows for effective processing based on message type.
  • Audio messages are transcribed using HTTP requests which retrieve data from WhatsApp and store it accordingly.

Processing Video and Image Messages

  • For video messages, a multimodal model like Google Gemini is recommended due to its proven effectiveness in handling such tasks.
  • Image messages follow similar configurations as video processing; both require specific models capable of interpreting multimedia content.

Text Message Management

  • Text messages are treated as plain text requiring minimal transformation; summarization tools are employed to enhance comprehension.
  • All processed information is stored systematically, allowing an AI agent to generate coherent responses based on accumulated data.

AI Agent Functionality and User Interaction

  • The AI agent manages various interactions including customer support and appointment scheduling by utilizing stored information effectively.
  • The system anticipates high volumes of inquiries (e.g., during course launches), necessitating robust memory management strategies due to potential data overload.

Final Demonstration and User Engagement

  • A demonstration concludes with sending a test message through WhatsApp; users can also send images directly through this node.
  • Participants are encouraged to ask questions about the chatbot functionalities or workflows they have prepared for their projects.

Course Structure Clarification

  • There are no final projects required; instead, participants will focus on completing assigned tasks related to their workflows.

Group Work and Deliverables

Group Dynamics and Task Structure

  • The task can be completed in groups, maintaining the same group configurations as before (groups of four or two), while individuals without groups may work alone.
  • Deliverables include a document similar to what has been shared and a "Jon" of the Workflow, both submitted in WinRAR format.
  • Participants must label their submissions with the names of all group members involved in the first instance.

Clarifications on Group Composition

  • Groups should remain consistent from the first week; changes are not encouraged unless necessary for personal preference.
  • The platform remains open until February 22nd for edits and uploads, allowing flexibility for participants to adjust their contributions.

Technical Queries and Issues

Credential Management

  • Participants are advised against sharing credentials within workflows to maintain security; specific provider details must be communicated for proper execution.

Troubleshooting Import Issues

  • A participant raised concerns about missing JSON code during an HTTP request import into N8N, questioning whether it was due to an error during upload or incompleteness of the example provided.

Updates on Vulnerabilities

Security Recommendations

  • A vulnerability rated 9.5 out of 10 was discovered recently in N8N, necessitating updates to avoid potential server control issues.
  • This vulnerability was identified on February 6th, with patches likely released shortly thereafter; participants are urged to update their systems promptly.

Deadlines and Activity Scheduling

Submission Deadlines

  • All activities need to be submitted by specified deadlines: Activity One closed on February 8th, while Activity Two is due by February 15th.

Future Activities Planning

  • Activities Three and Four will have a deadline set for February 20th, considering upcoming carnival festivities that may affect continuity in programming.

Discussion on Activity Deadlines

Setting the Deadline for Activities

  • The speaker discusses the timeline for completing certain tasks, suggesting a deadline of Friday, the 20th. They express concern about overlapping with another instructor's schedule.
  • A participant suggests extending the deadline to the 22nd but acknowledges that this could lead to conflicts with other activities. They propose keeping it as a maximum limit.
  • The group agrees on a final deadline of Saturday, the 21st, and confirms that any questions or concerns should be communicated through their WhatsApp group.

Communication and Resources

  • The speaker mentions sending out messages via WhatsApp regarding any updates or resources related to sales and templates for participants to work on.
  • Emphasis is placed on maintaining open communication within the group for clarity and support throughout the activity period.