researchowl/README.md

# 🦉 ResearchOwl

**Exhaustive research engine with Telegram interface.**

Recursively discovers, scrapes, and processes sources from across the web,
then generates podcast scripts, blog posts, reports, or social threads using Ollama.

## Architecture

```
Telegram (/research <topic>)
    ↓
ExhaustiveScraper
    ├── DuckDuckGo (8 queries × 5 results)
    ├── Wikipedia + recursive internal links
    ├── Reddit (top posts + top comments)
    ├── YouTube (transcripts)
    ├── PDFs (public documents)
    └── Web scraping (trafilatura)
         ↓ recursive expansion (depth 1-3)
ContentProcessor (Ollama qwen2.5:7b + bge-m3 embeddings)
    ├── Chunking (800 token chunks, 100 overlap)
    ├── Quality scoring (0-10 per chunk)
    ├── Embeddings (cosine similarity RAG)
    └── Deduplication
         ↓
OutputGenerator (Ollama)
    ├── 🎙️ Podcast script (20-30 min)
    ├── 📝 Blog post (1500-2500 words)
    ├── 📊 Research report (structured)
    └── 🐦 Social thread (15-25 tweets)
```

## Telegram Commands

| Command | Description |
|---------|-------------|
| `/research <topic>` | Start exhaustive research |
| `/status` | Check progress |
| `/finish` | Stop early, proceed to generation |
| `/generate podcast\|blog\|report\|thread` | Generate output |
| `/generate short_en` | Vertical Short: shot spec → grounding check → MP4 |
| `/short_spec` | Last shot spec as a JSON file, to hand-edit and re-render |
| `/upload_short` | Upload the rendered Short to YouTube (private, for review) |
| `/sources` | List all sources found |
| `/cancel` | Cancel current research |

## Shorts (`/generate short_en`)

Claude writes a **shot spec** — typed JSON, not prose — which
[shortsmith](https://git.chemavx.xyz/chemavx/shortsmith) renders into a 1080×1920
MP4. The bot sends the video and, in a separate message, a **claims report**.

```
/research JAL 1628 Alaska 1986 …
/generate blog en          → Ghost draft, article URL stored on the output row
/generate short_en         → spec → grounding → render → video + claims report
/upload_short              → uploads to YouTube as PRIVATE, with metadata filled
                             in; publishing stays a human click in Studio
```

Three things make this different from generating text, and each has its own
mitigation:

- **It is a contract, not prose.** The template schemas are fetched live from
  `GET /templates` and never copied here, so a template added to shortsmith is
  available immediately. A spec is validated locally against those schemas
  before anything renders, and the exact error paths
  (`shots.0.radar_sweep.props.sweeeps`) go back to the model verbatim — up to 3
  attempts.
- **It contains figures and quotes.** `grounding.py` extracts every quote,
  figure, date and proper noun and checks it against the exact chunks the model
  was given. No LLM in that path: normalisation plus substring, deterministic
  and free. Whatever is not in the chunks is checked against the worked example
  that travels in the prompt, so a figure lifted from it is reported as a
  **prompt leak**, not as an invention — different diagnosis, different fix.
  Neither ever blocks the render: both are surfaced next to the video and a
  human decides.
- **It becomes a published video.** `/generate short_en` uploads nothing: the
  MP4 lands in Telegram for review and in `/data/shorts/{session_id}.mp4`.
  Getting it onto the channel is a separate, explicit `/upload_short`.

Fallbacks hold throughout: if shortsmith is unreachable, the job errors, or the
spec never validates, the spec JSON comes back as a file. The expensive part is
the generation, not the render.

Full spec of the phase: `docs/shortsmith-phase2-spec.md`.

## YouTube (`/upload_short`)

Uploads `/data/shorts/{session_id}.mp4` to the channel with the title from the
spec, a description carrying the article link and the sources the Short cites on
screen, and tags derived from the topic. The YouTube URL is written back to the
output row, so a second `/upload_short` on the same session refuses unless you
say `/upload_short force`.

**Read this before setting it up.** Videos uploaded through `videos.insert` from
an **unaudited API project** are [restricted to private viewing
mode](https://developers.google.com/youtube/v3/docs/videos/insert). The lock
belongs to the API project, not to the video — you do not unlock it from Studio,
you unlock it by passing Google's compliance audit. So this command does not
publish. It puts the video on the channel with the metadata already filled in
and hands back the Studio link; a person reviews and presses publish. That is
the same shape as `/publish`, which only ever writes Ghost drafts.

One-time setup, in [console.cloud.google.com](https://console.cloud.google.com):

1. Enable **YouTube Data API v3** on a project.
2. OAuth consent screen → External → **publish it to "In production"**. Leaving
   it in "Testing" makes Google revoke the refresh token after seven days, and
   the bot dies on its own the following Tuesday.
3. Credentials → OAuth client ID → **Desktop app**.
4. `python scripts/youtube_oauth.py --client-id … --client-secret …`, which
   opens a browser, catches the redirect on localhost and prints the refresh
   token.
5. Put `youtube-client-id`, `youtube-client-secret` and `youtube-refresh-token`
   into Infisical (they arrive as `researchowl-secrets-infisical`).

The scope requested is `youtube.upload` only: a leaked token cannot read or
delete anything on the channel — the worst it can do is upload. Quota is not a
concern (1 unit per upload, 100 uploads a day). `YOUTUBE_ENABLED=false` is the
kill switch.

## Local Development

```bash
# 1. Clone and setup
git clone https://git.chemavx.xyz/chemavx/researchowl
cd researchowl

# 2. Create virtualenv
python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt

# 3. Configure
cp .env.example .env
# Edit .env with your values

# 4. Run
python main.py
```

## Deploy to k3s

```bash
# 1. Create namespace and secrets
kubectl create namespace researchowl
kubectl create secret generic researchowl-secrets \
  --from-literal=telegram-bot-token=YOUR_TOKEN \
  --from-literal=telegram-allowed-users=YOUR_USER_ID \
  -n researchowl

# 2. Copy manifests to your k8s-manifests repo
cp k8s/*.yaml /path/to/k8s-manifests/researchowl/

# 3. Apply ArgoCD app
kubectl apply -f k8s/argocd-app.yaml

# 4. Push to Gitea → Gitea Actions builds → ArgoCD deploys
git add . && git commit -m "feat: add researchowl" && git push
```

## Tuning

| Variable | Default | Description |
|----------|---------|-------------|
| `MAX_SOURCES` | 150 | Hard cap on sources |
| `MAX_DEPTH` | 3 | Link recursion depth |
| `QUALITY_THRESHOLD` | 0.4 | Min chunk quality (0-1) |
| `REQUEST_DELAY` | 1.0s | Delay between requests |

**Want more thoroughness?**
- Increase `MAX_SOURCES` to 300+
- Increase `MAX_DEPTH` to 4-5
- Lower `QUALITY_THRESHOLD` to 0.3

**Want faster results?**
- Lower `MAX_SOURCES` to 50
- Set `MAX_DEPTH` to 1-2
- Higher `QUALITY_THRESHOLD` to 0.6

## Bot avatar

The profile picture of `@chemavx_researchowl_bot` is not an opaque binary
checked into the repo: `assets/make_avatar.py` draws it with PIL at 4× and
scales it down, so the emblem can be retouched without hunting for an original.
Telegram crops avatars to a **circle**, so everything that matters lives inside
the inscribed circle; verified legible at 48 px.

```bash
python3 assets/make_avatar.py     # writes assets/avatar.png
```

It is applied **over the API with the token from the secret, no BotFather**.
Watch out for `setMyProfilePhoto`: its `photo` parameter is not the file, it is
an `InputProfilePhoto` object pointing at the attachment. Posting the file on
its own gets you a baffling `photo isn't specified`.

```bash
TOK=$(kubectl get secret researchowl-secrets-infisical -n researchowl \
        -o jsonpath='{.data.telegram-bot-token}' | base64 -d)
curl -s -F 'photo={"type":"static","photo":"attach://av"}' \
     -F "av=@assets/avatar.png" \
     "https://api.telegram.org/bot$TOK/setMyProfilePhoto"
unset TOK
```

## Notes

- Uses **qwen2.5:7b** (scoring) and **bge-m3** (embeddings) on your existing Ollama — zero API cost
- Optionally add `ANTHROPIC_API_KEY` for Claude fallback on generation
- SQLite database stored in `/data/researchowl.db`
- All outputs saved to DB and available via `/outputs`