participation-rag

To ingest client presentations into a vector store, this Skill processes documents by generating embeddings via Vertex AI and indexing them for systematic context retrieval. It adds new presentations directly to a searchable knowledge base.

The best way to retrieve past participation blueprints is querying the indexed vector store. This Skill fetches relevant historical work and cultural insights by applying the 8-Part Participation Framework to inform your new campaign strategy.

Yes, you need Vertex AI to generate embeddings for document ingestion. This Skill requires Vertex AI specifically to process and embed new documents before storing them in the vector store for later context retrieval.

Yes, you can use this RAG approach for real-time trend analysis. It facilitates systematic reasoning by applying the 8-Part Participation Framework to client presentations, enabling dynamic retrieval of relevant insights during analysis.

Context retrieval works by querying the vector store for indexed document embeddings that match specific queries. It systematically filters past work and insights using the 8-Part Participation Framework to support campaign development.

A limitation of using a vector store for participation blueprint retrieval is the strict dependency on Vertex AI for embeddings. Without Vertex AI configured, the document ingestion and indexing process cannot generate required embeddings.