Automation & agent integration

Two complete, copy-paste-able automations, followed by guidance for agent builders. Both use only the standard library / requests so they drop into any environment.

Ask your knowledge base from a script

import requests

BASE = "https://dashboard.insightai.pro/api/v1"   # or your own instance
MODEL = "llama3.2"

session = requests.Session()

def signin(email: str, password: str) -> None:
    r = session.post(f"{BASE}/signin", json={"email": email, "password": password})
    r.raise_for_status()   # session now carries the auth cookie

def ask(question: str, chat_id: str | None = None, timeout: int = 300) -> tuple[str, str]:
    """Ask a question; returns (chat_id, answer). Reuse chat_id for follow-ups."""
    if chat_id is None:
        r = session.post(f"{BASE}/chats",
                         json={"model_name": MODEL, "message": question},
                         timeout=timeout)
        r.raise_for_status()
        chat = r.json()
        return chat["id"], chat["messages"][-1]["content"]
    r = session.post(f"{BASE}/chats/{chat_id}/followup",
                     json={"model_name": MODEL, "message": question},
                     timeout=timeout)
    r.raise_for_status()
    return chat_id, r.json()[-1]["content"]

signin("you@example.com", "YourPass123")
chat_id, answer = ask("What does our expense policy say about pre-approval?")
print(answer)
_, followup = ask("What's the receipt deadline?", chat_id)
print(followup)

Notes that matter in practice:

• Generous timeouts. Responses arrive only when fully generated.
• Handle 401 by re-signing-in — tokens last 24 hours.
• Answers are grounded in whatever your pipelines have indexed; there is no per-request collection selector, and no structured citation field — the answer is plain text/Markdown.

Scheduled re-indexing

Content on a file share drifts; a nightly re-run keeps the knowledge base current. Pair this with cron:

import time, requests

BASE = "https://dashboard.insightai.pro/api/v1"
PIPELINE = "<pipeline_id>"

session = requests.Session()
session.post(f"{BASE}/signin",
             json={"email": "svc@example.com", "password": "…"}).raise_for_status()

r = session.post(f"{BASE}/data_pipelines/{PIPELINE}/ingest")
if r.status_code == 409:
    raise SystemExit("a run is already in progress")
r.raise_for_status()

while True:
    s = session.get(f"{BASE}/data_pipelines/{PIPELINE}/ingest/status").json()
    if s["status"] == "completed":
        print(f"done: {s['entities_processed']} files indexed")
        break
    if s["status"] == "failed":
        raise SystemExit(f"failed: {s['error_message']}")
    time.sleep(10)

Remember each run rebuilds the collection from scratch — schedule runs when a briefly incomplete index is acceptable, and don’t overlap pipelines that share a collection (they shouldn’t share one at all).

Wiring Insight AI into an AI agent

The chat endpoint makes Insight AI a natural tool for an agent: a “company knowledge” function the agent can call when a task needs internal context. The pattern:

1. Expose one function to your agent, e.g. search_company_knowledge(question: str) -> str, implemented exactly like ask() above with a fresh chat per call (stateless) or a persistent chat_id per agent session (conversational memory on the Insight side).
2. Describe it honestly in the tool description: “Answers questions using the organization’s indexed documents (policies, reports, shared drives). Returns prose, not citations.” Agents make better call decisions when the description matches reality.
3. Pick a capable model for the model_name — the quality of tool results is bounded by the model answering them.
4. Treat answers as evidence, not ground truth. Retrieval is best-effort similarity search; have the agent verify critical figures against the source documents where it matters.

For heavier integrations (ingest-on-demand, provisioning, model management), everything in the resource map is available — the dashboard has no private APIs.