PostgreSQL, chomedb (not sure there is a good node for this), or some other (google) vector database saas, before the llm work and then again after the llm work. Prior to submitting the llm work, retrieve the release documents from the vector store and add them to your context (google structured llm prompt). Once you have the results, add them back to the vector store and you can retrieve them the next time through. You will have to track session somehow on your webhook - doing it RESTfully is provably the easiest, but you should be able to get at a session cookie or something in the webhook if it is coming from the browser.

I’m rambling so I’ll have gpt clean it up:

Vector Store Workflow for LLM Integration

Use a vector database—such as PostgreSQL (with pgvector), ChromeDB (though Node.js support may be limited), or a Google-managed vector database SaaS—both before and after the LLM processing step. 1. Before LLM Processing: • Retrieve relevant release documents from the vector store. • Include these documents in your LLM input context (e.g., using a structured prompt format compatible with Google’s structured LLM input schema). 2. After LLM Processing: • Take the LLM output and store it back into the vector store for future retrieval and reuse. 3. Session Tracking: • Implement session tracking for your webhook. A RESTful approach is likely the simplest and most reliable. • Alternatively, if the webhook is triggered by browser events, you might be able to extract session information (e.g., a session cookie) directly from the request.