Build AI Business Solutions with Workflow Automation

graph TD %%{init: {'theme': 'mc','layout': 'elk'}}%% TextInput-dvpjv[<div><img src="/_astro/type.Dy26vmDy.svg" style="height: 20px !important;width: 20px !important"/></div>Carrera] style TextInput-dvpjv stroke:#a170ff TextInput-s2vgd[<div><img src="/_astro/type.Dy26vmDy.svg" style="height: 20px !important;width: 20px !important"/></div>Certificados] style TextInput-s2vgd stroke:#a170ff TextInput-oz3io[<div><img src="/_astro/type.Dy26vmDy.svg" style="height: 20px !important;width: 20px !important"/></div>Habilidades] style TextInput-oz3io stroke:#a170ff TextInput-6r3sb[<div><img src="/_astro/type.Dy26vmDy.svg" style="height: 20px !important;width: 20px !important"/></div>Experiencia] style TextInput-6r3sb stroke:#a170ff TextInput-ep0wz[<div><img src="/_astro/type.Dy26vmDy.svg" style="height: 20px !important;width: 20px !important"/></div>Educación] style TextInput-ep0wz stroke:#a170ff ChatOutput-zlpn1[<div><img src="/_astro/messages-square.BaSDmT6g.svg" style="height: 20px !important;width: 20px !important"/></div>Chat Output] style ChatOutput-zlpn1 stroke:#a170ff OpenAIEmbeddings-5myel[<div><img src="/_astro/openAI.BhmuxEs3.svg" style="height: 20px !important;width: 20px !important"/></div>OpenAI Embeddings] style OpenAIEmbeddings-5myel stroke:#a170ff GDriveFilesComponent-3xa20[<div><img src="/_astro/google_drive.wKmDsV2c.svg" style="height: 20px !important;width: 20px !important"/></div>Obtener CV] style GDriveFilesComponent-3xa20 stroke:#a170ff Prompt-3a8sl[<div><img src="/_astro/square-terminal.BMOXc-nZ.svg" style="height: 20px !important;width: 20px !important"/></div>Requisitos] style Prompt-3a8sl stroke:#a170ff OpenAIEmbeddings-6p7a2[<div><img src="/_astro/openAI.BhmuxEs3.svg" style="height: 20px !important;width: 20px !important"/></div>OpenAI Embeddings2] style OpenAIEmbeddings-6p7a2 stroke:#a170ff LanguageRecursiveTextSplitter-rweqg[Language Recursive Text Splitter] style LanguageRecursiveTextSplitter-rweqg stroke:#a170ff AstraDB-un98t[Astra DB] style AstraDB-un98t stroke:#a170ff AstraDB-un1f3[Astra DB2] style AstraDB-un1f3 stroke:#a170ff LanggraphRAGAgent-cgjw7[RAG Agent] style LanggraphRAGAgent-cgjw7 stroke:#a170ff OpenAIModel-448zp[<div><img src="/_astro/openAI.BhmuxEs3.svg" style="height: 20px !important;width: 20px !important"/></div>OpenAI] style OpenAIModel-448zp stroke:#a170ff TextInput-dvpjv -.- Prompt-3a8sl linkStyle 0 stroke:#a170ff TextInput-s2vgd -.- Prompt-3a8sl linkStyle 1 stroke:#a170ff TextInput-ep0wz -.- Prompt-3a8sl linkStyle 2 stroke:#a170ff TextInput-6r3sb -.- Prompt-3a8sl linkStyle 3 stroke:#a170ff TextInput-oz3io -.- Prompt-3a8sl linkStyle 4 stroke:#a170ff GDriveFilesComponent-3xa20 -.- LanguageRecursiveTextSplitter-rweqg linkStyle 5 stroke:#a170ff LanguageRecursiveTextSplitter-rweqg -.- AstraDB-un98t linkStyle 6 stroke:#a170ff OpenAIEmbeddings-5myel -.- AstraDB-un98t linkStyle 7 stroke:#a170ff LanggraphRAGAgent-cgjw7 -.- ChatOutput-zlpn1 linkStyle 8 stroke:#a170ff OpenAIEmbeddings-6p7a2 -.- AstraDB-un1f3 linkStyle 9 stroke:#a170ff AstraDB-un1f3 -.- LanggraphRAGAgent-cgjw7 linkStyle 10 stroke:#a170ff Prompt-3a8sl -.- LanggraphRAGAgent-cgjw7 linkStyle 11 stroke:#a170ff OpenAIModel-448zp -.- LanggraphRAGAgent-cgjw7 linkStyle 12 stroke:#a170ff

CV Match

🧩 Overview

This workflow automates the process of matching candidate CVs against a set of specific job requirements. It retrieves CVs from a designated source, processes them into a searchable knowledge base, and uses a Retrieval-Augmented Generation (RAG) agent to intelligently find and present the most suitable candidates. This streamlines the initial screening phase of recruitment by providing a ranked, AI-powered analysis of candidate fit.

⚙️ Main Features

Ingests CVs from a Google Drive folder and processes them for vector-based search.
Creates a structured prompt from user-defined job requirements such as education, skills, and experience.
Employs a RAG agent to search the CV database and identify the best-matching candidates.
Presents the agent's findings in a clear, conversational chat output.

🔄 Workflow Steps

Component Name	Role in the Workflow	Key Inputs	Key Outputs
Text Input (Carrera)	Defines the required degree or field of study.	User-defined degree (e.g., "Computer Science Engineer")	Degree requirement text
Text Input (Certificados)	Defines the required certifications.	User-defined certifications (e.g., "Engineer")	Certification requirement text
Text Input (Educación)	Defines the required level of education.	User-defined education level (e.g., "University")	Education requirement text
Text Input (Experiencia)	Defines the required years of experience.	User-defined experience (e.g., "1 year")	Experience requirement text
Text Input (Habilidades)	Defines the required technical skills.	User-defined skills (e.g., "Python and C#")	Skills requirement text
Prompt (Requisitos)	Compiles all individual requirements into a single, structured prompt for the agent.	All requirement texts (Degree, Certifications, Education, Experience, Skills)	A consolidated prompt message with the job requirements
Google Drive Files Component	Retrieves CV documents from a specified Google Drive folder.	Target folder path	Raw CV document data
Language Recursive Text Splitter	Splits the retrieved CV documents into smaller, manageable chunks for processing.	Raw CV document data	Chunked text data
OpenAI Embeddings	Generates vector embeddings for the text chunks, enabling semantic search.	Text chunk data	Numerical vector embeddings
Astra DB	Stores the vector embeddings and associated CV text in a vector database for efficient retrieval.	Text chunks and their corresponding vector embeddings	Configured vector store and retriever
OpenAI Model	Provides the core language model that powers the reasoning and response generation of the RAG agent.	Model configuration (e.g., temperature, API settings)	A configured language model instance
RAG Agent	The intelligent agent that receives the job requirements, queries the CV database (via the retriever), and generates a response identifying the best matches.	Job requirements prompt, configured language model, CV database retriever	A response message with the matching CV results
Chat Output	Displays the final response from the RAG agent in a user-friendly chat interface.	Agent's response message	Formatted chat message for the user

🧠 Notes

The workflow requires valid API credentials for OpenAI (for the model and embeddings) and Google Drive to function.
It also depends on a configured Astra DB instance for vector storage and retrieval.
The quality of matches depends on the clarity of the input requirements and the comprehensiveness of the CVs in the source folder.
The RAG agent is configured with checks for answer quality and hallucination to improve response reliability.