workflows.md

Workflow Examples

Workflow 1: Building a Contextual Data Knowledge Store from Scratch

graph LR
    A[Context Planner] --> B{Interview Agent};
    B --> C[Context Extractor];
    C --> D[Vector Database];

1. Start with the Context Planner to identify key areas for context development (e.g., career aspirations, skills, personal interests).
2. Use the General Interviewer or Gap-Filler Interviewer to conduct interviews and generate context snippets for each area.
3. Employ the Context Extractor to refine and format the interview data into structured context snippets.
4. Store the context snippets in a Vector Database.

Workflow 2: Enhancing Existing AI Agents with Contextual Data

1. Use the Context Extractor to extract relevant information from existing documents and data sources (e.g., resumes, social media profiles, meeting notes).
2. Store the extracted context snippets in a Vector Database.
3. Connect the Vector Database to your AI agents, enabling them to access and utilize the contextual data for more informed and personalized interactions.