Draft answers to support questions from your own documents

from clickruntime import on_event, credentials, notify, ai, http

SUPABASE = "https://yourproject.supabase.co"

@on_event
def handle(event):
    q = event.json["question"]
    key = credentials.secret("SUPABASE_KEY")
    h = {"apikey": key, "Authorization": f"Bearer {key}"}
    emb = http.post("https://api.openai.com/v1/embeddings", headers={"Authorization": f"Bearer {credentials.secret('OPENAI_API_KEY')}"},
                    json={"model": "text-embedding-3-small", "input": q}).json()["data"][0]["embedding"]
    chunks = http.post(f"{SUPABASE}/rest/v1/rpc/match_documents", headers=h, json={"query_embedding": emb, "match_count": 5}).json()
    if not chunks:
        notify.email_me("Support question with no sources", q)
        return {"sources": 0}
    context = "\n\n".join(f"[{i}] {c['content'][:1500]}" for i, c in enumerate(chunks, 1))
    answer = ai.generate(f"Answer using only these passages and cite [n]:\n{context}\n\nQuestion: {q}", max_output_tokens=500).text
    gtoken = credentials.connection("google", "work")
    links = []
    for c in chunks:
        meta = http.get(f"https://www.googleapis.com/drive/v3/files/{c['file_id']}", params={"fields": "name,webViewLink"},
                        headers={"Authorization": f"Bearer {gtoken}"}).json()
        links.append(f"{meta.get('name')}: {meta.get('webViewLink')}")
    notify.email_me(f"Draft answer: {q[:80]}", f"Q: {q}\n\n{answer}\n\nSources:\n" + "\n".join(links))
    return {"sources": len(chunks)}