To build a resume review automation through the Vector Shift platform, follow these steps:

1. Create a Sub-Pipeline for Reviewing a Single Resume:

   • Start with an input node to accept a single resume file.
   • Add another input node to accept the criteria for evaluating the resume.
   • Use an LLM (Large Language Model) node to evaluate the resume against the provided criteria and generate a justification.
   • Extract the number of criteria met and the total number of criteria to calculate the criteria score.
   • Extract the full name and LinkedIn profile from the resume text.
   • Connect the data to a Google Sheets node to populate the results.

2. Create a Master Pipeline to Automate the Process:

   • Add an input node to accept a list of resume files.
   • Add another input node to accept the criteria for evaluating the resumes.
   • Use a "Duplicate Item" node to create a list of criteria matching the list of resumes.
   • Connect the resume list and criteria list to the sub-pipeline created in step 1.
   • This master pipeline will now be able to process a batch of resumes and their corresponding criteria.

3. Deploy and Run the Automation:

   • Deploy the changes to the Vector Shift platform.
   • Run the master pipeline by providing the list of resumes and the criteria.
   • The automation will process each resume, evaluate it against the criteria, and populate the results in a Google Sheet.

By following this step-by-step guide, you can build a resume review automation that can efficiently process a large number of resumes and provide a structured output for further review and decision-making.

To automate the resume review process, we will leverage the Vector Shift platform. The key steps are:

1. Create a Sub-Pipeline for Reviewing a Single Resume:

   • Set up an input node to accept a single resume file.
   • Add an input node to specify the review criteria.
   • Use an LLM (Large Language Model) node to evaluate the resume against the criteria and generate a justification.
   • Extract the number of criteria met, the total number of criteria, and the applicant's full name and LinkedIn profile.
   • Output the data to a Google Sheet, including the criteria score and justification.

2. Build a Master Pipeline to Process Multiple Resumes:

   • Create an input node to accept a list of resume files.
   • Add an input node to specify the review criteria.
   • Use a "Duplicate Item" node to create a list of criteria matching the list of resumes.
   • Call the sub-pipeline created in step 1 and pass the resume list and criteria list as inputs.
   • This master pipeline will automate the review of multiple resumes, populating the Google Sheet with the results.

By leveraging the Vector Shift platform, you can efficiently review a large number of resumes, automatically extracting relevant information and scoring them against your specified criteria. This streamlines the resume screening process, allowing you to focus on the most promising candidates.

To automate the process of reviewing resumes and identifying candidates that meet specific criteria, we will build a pipeline on the Vector Shift platform. This pipeline will take in a batch of resumes and a set of criteria, then evaluate each resume against the criteria and populate the results in a Google Sheet.

The key steps are:

1. Resume Sub-Pipeline: This pipeline takes in a single resume and a set of criteria, then uses an LLM (Large Language Model) to evaluate how well the resume meets the criteria. It extracts the full name, LinkedIn profile, criteria score, and justification, and writes this data to a Google Sheet.

2. Resume Master Pipeline: This pipeline takes in a list of resumes and a list of criteria, then calls the Resume Sub-Pipeline for each resume-criteria pair, automating the evaluation process.

The Resume Sub-Pipeline includes the following nodes:

• Resume Input: Accepts a single resume file.
• Criteria Input: Accepts the criteria to evaluate the resume against.
• LLM Node: Evaluates the resume against the criteria and generates a justification.
• Extract Data Nodes: Extract the full name, LinkedIn profile, criteria score, and justification.
• Google Sheets Node: Writes the extracted data to a Google Sheet.

The Resume Master Pipeline includes the following nodes:

• Resumes Input: Accepts a list of resume files.
• Criteria Input: Accepts a list of criteria.
• List Operation Node: Duplicates the criteria list to match the length of the resumes list.
• Resume Sub-Pipeline Node: Calls the Resume Sub-Pipeline for each resume-criteria pair.

By using this automated process, businesses can efficiently review large batches of resumes and identify the most qualified candidates based on their specific criteria.

To efficiently filter through a large volume of resumes, it is crucial to extract the key details that align with the specified criteria. This section outlines the process of extracting the full name, LinkedIn profile, criteria score, and justification for each resume, which can then be populated into a Google Sheet for easy review and sorting.

The pipeline first takes in two input nodes: the resume file and the criteria to be evaluated. An LLM (Large Language Model) node is then used to evaluate the resume against the provided criteria, generating a justification for the score.

Next, an "Extract Data" node is used to determine the number of criteria met and the total number of criteria, which is then formatted into a readable "criteria score" value. Another "Extract Data" node is used to pull the full name and LinkedIn profile from the resume text.

Finally, all the extracted data is populated into a Google Sheets node, creating a new row for each candidate. This allows for easy filtering and review of the resumes based on the criteria score and other relevant details.

By automating this process, businesses can efficiently sort through large volumes of resumes, focusing their manual review efforts on the candidates most likely to meet the desired criteria.

To populate the resume data into Google Sheets, we'll create a Google Sheets node in the pipeline. This node will take the relevant information extracted from the resume, such as the full name, LinkedIn profile, criteria score, and justification, and insert them into the corresponding columns of a Google Sheet.

The steps are as follows:

1. Drag out a Google Sheets node and configure it to connect to the desired Google Sheet and worksheet.
2. Map the output fields from the previous nodes to the corresponding columns in the Google Sheet:
   • Full Name: Extracted from the resume text using an Extract Data node.
   • LinkedIn Profile: Also extracted from the resume text using an Extract Data node.
   • Criteria Score: Calculated by dividing the number of successful criteria matches by the total number of criteria.
   • Justification: Provided by the OpenAI node, which evaluated the resume against the specified criteria.
3. The Google Sheets node will then automatically insert a new row in the sheet for each processed resume, populating the data in the appropriate columns.

This allows you to easily review the results of the resume screening process and identify the candidates that best match the specified criteria.