Q&A over docs

Chat with your own document: long context (wk13) + grounding (wk9) + a moderation check (wk16). Paste a document, put your question on a `Q:` line, and get an answer grounded in the text.

README Try it Source

Showcase — Q&A over docs

Chat with your own document: long context (wk13) + grounding (wk9) + a moderation check (wk16). Paste a document, put your question on a Q: line, and get an answer grounded in the text.

Run

bash bootstrap-secrets.sh              # reads ../../../../.env, writes secrets/
docker compose up --build              # default: PROVIDER=openai

Open http://localhost:3000. Gemini: PROVIDER=gemini docker compose up --build.

What's where

backend/ai_openai.py / backend/ai_gemini.py — parse doc + question, moderate, grounded answer.
frontend/app/page.tsx — document + question box, grounded answer.

Stop

docker compose down

Run locally

Download the project as a ZIP and run it with Docker. Brings up a FastAPI backend + Next.js frontend on localhost:3000.

Download qa-over-docs.zip

unzip qa-over-docs.zip
cd qa-over-docs
bash bootstrap-secrets.sh   # one-time: pulls API keys into ./secrets
docker compose up --build   # default provider: openai
# or:  PROVIDER=gemini docker compose up --build

Type some input, pick a provider, and run the same code shown in Source against the live API. Sign-in required.

Input

Provider

Output

The same modules the Run button hits. The whole project (frontend, Dockerfile, compose) is in the ZIP under README.

`backend/ai_openai.py`

"""Showcase 3 (OpenAI): grounded Q&A over your own document.

Paste a document and a question (on a 'Q:' line) and get an answer grounded in
the text, with the question moderated first. Long context (week 13) plus
grounding (week 9) plus a safety check (week 16) — the everyday "chat with a
document" feature, built from parts you now know cold.
"""
from openai import OpenAI

_client = OpenAI()

_MODEL = "gpt-5.4-nano"


def run(text: str) -> str:
    body = text.strip()
    if "Q:" in body:
        idx = body.rfind("Q:")
        document, question = body[:idx].strip(), body[idx + 2:].strip()
    else:
        document, question = body, "Summarize this document."
    if not document:
        return "Paste a document, then put your question on a line starting with 'Q:'."
    if _client.moderations.create(model="omni-moderation-latest", input=question).results[0].flagged:
        return "[question blocked by moderation]"
    response = _client.responses.create(
        model=_MODEL,
        instructions=f"Answer the question using ONLY this document. Quote the relevant part. If it "
                     f"isn't covered, say so.\n\nDOCUMENT:\n{document}",
        input=[{"role": "user", "content": question}],
    )
    return response.output_text

`backend/ai_gemini.py`

"""Showcase 3 (Gemini): grounded Q&A over your own document.

Same long-context + grounding + moderation flow, on Gemini.
"""
import os

from google import genai
from google.genai import types

_client = genai.Client(api_key=os.environ["GEMINI_API_KEY"])

_MODEL = "gemini-3.1-flash-lite"


def _flagged(text: str) -> bool:
    r = _client.models.generate_content(
        model=_MODEL, contents=[types.Content(role="user", parts=[types.Part(text=text)])],
        config=types.GenerateContentConfig(system_instruction=(
            "Reply 'flag' if this text is hateful, harassing, sexual, violent, or dangerous; else 'ok'. One word.")),
    )
    return "flag" in (r.text or "").lower()


def run(text: str) -> str:
    body = text.strip()
    if "Q:" in body:
        idx = body.rfind("Q:")
        document, question = body[:idx].strip(), body[idx + 2:].strip()
    else:
        document, question = body, "Summarize this document."
    if not document:
        return "Paste a document, then put your question on a line starting with 'Q:'."
    if _flagged(question):
        return "[question blocked by moderation]"
    response = _client.models.generate_content(
        model=_MODEL,
        contents=[types.Content(role="user", parts=[types.Part(text=question)])],
        config=types.GenerateContentConfig(
            system_instruction=f"Answer the question using ONLY this document. Quote the relevant part. "
                               f"If it isn't covered, say so.\n\nDOCUMENT:\n{document}",
        ),
    )
    return response.text or ""