3b766e1aac
- Added Colab badge linking to the demo notebook - Added call-to-action encouraging users to try the demo in Colab - Provides zero-setup cloud environment for testing 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
132 lines
4.4 KiB
Markdown
132 lines
4.4 KiB
Markdown
# Crawl4AI Prospect‑Wizard – step‑by‑step guide
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[](https://colab.research.google.com/drive/10nRCwmfxPjVrRUHyJsYlX7BH5bvPoGpx?usp=sharing)
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A three‑stage demo that goes from **LinkedIn scraping** ➜ **LLM reasoning** ➜ **graph visualisation**.
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**Try it in Google Colab!** Click the badge above to run this demo in a cloud environment with zero setup required.
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```
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prospect‑wizard/
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├─ c4ai_discover.py # Stage 1 – scrape companies + people
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├─ c4ai_insights.py # Stage 2 – embeddings, org‑charts, scores
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├─ graph_view_template.html # Stage 3 – graph viewer (static HTML)
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└─ data/ # output lands here (*.jsonl / *.json)
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```
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---
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## 1 Install & boot a LinkedIn profile (one‑time)
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### 1.1 Install dependencies
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```bash
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pip install crawl4ai litellm sentence-transformers pandas rich
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```
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### 1.2 Create / warm a LinkedIn browser profile
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```bash
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crwl profiles
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```
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1. The interactive shell shows **New profile** – hit **enter**.
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2. Choose a name, e.g. `profile_linkedin_uc`.
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3. A Chromium window opens – log in to LinkedIn, solve whatever CAPTCHA, then close.
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> Remember the **profile name**. All future runs take `--profile-name <your_name>`.
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---
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## 2 Discovery – scrape companies & people
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```bash
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python c4ai_discover.py full \
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--query "health insurance management" \
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--geo 102713980 \ # Malaysia geoUrn
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--title-filters "" \ # or "Product,Engineering"
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--max-companies 10 \ # default set small for workshops
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--max-people 20 \ # \^ same
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--profile-name profile_linkedin_uc \
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--outdir ./data \
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--concurrency 2 \
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--log-level debug
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```
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**Outputs** in `./data/`:
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* `companies.jsonl` – one JSON per company
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* `people.jsonl` – one JSON per employee
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🛠️ **Dry‑run:** `C4AI_DEMO_DEBUG=1 python c4ai_discover.py full --query coffee` uses bundled HTML snippets, no network.
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### Handy geoUrn cheatsheet
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| Location | geoUrn |
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|----------|--------|
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| Singapore | **103644278** |
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| Malaysia | **102713980** |
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| United States | **103644922** |
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| United Kingdom | **102221843** |
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| Australia | **101452733** |
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_See more: <https://www.linkedin.com/search/results/companies/?geoUrn=XXX> – the number after `geoUrn=` is what you need._
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---
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## 3 Insights – embeddings, org‑charts, decision makers
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```bash
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python c4ai_insights.py \
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--in ./data \
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--out ./data \
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--embed-model all-MiniLM-L6-v2 \
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--llm-provider gemini/gemini-2.0-flash \
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--llm-api-key "" \
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--top-k 10 \
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--max-llm-tokens 8024 \
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--llm-temperature 1.0 \
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--workers 4
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```
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Emits next to the Stage‑1 files:
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* `company_graph.json` – inter‑company similarity graph
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* `org_chart_<handle>.json` – one per company
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* `decision_makers.csv` – hand‑picked ‘who to pitch’ list
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Flags reference (straight from `build_arg_parser()`):
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| Flag | Default | Purpose |
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|------|---------|---------|
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| `--in` | `.` | Stage‑1 output dir |
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| `--out` | `.` | Destination dir |
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| `--embed_model` | `all-MiniLM-L6-v2` | Sentence‑Transformer model |
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| `--top_k` | `10` | Neighbours per company in graph |
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| `--openai_model` | `gpt-4.1` | LLM for scoring decision makers |
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| `--max_llm_tokens` | `8024` | Token budget per LLM call |
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| `--llm_temperature` | `1.0` | Creativity knob |
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| `--stub` | off | Skip OpenAI and fabricate tiny charts |
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| `--workers` | `4` | Parallel LLM workers |
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---
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## 4 Visualise – interactive graph
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After Stage 2 completes, simply open the HTML viewer from the project root:
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```bash
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open graph_view_template.html # or Live Server / Python -http
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```
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The page fetches `data/company_graph.json` and the `org_chart_*.json` files automatically; keep the `data/` folder beside the HTML file.
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* Left pane → list of companies (clans).
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* Click a node to load its org‑chart on the right.
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* Chat drawer lets you ask follow‑up questions; context is pulled from `people.jsonl`.
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---
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## 5 Common snags
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| Symptom | Fix |
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|---------|-----|
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| Infinite CAPTCHA | Use a residential proxy: `--proxy http://user:pass@ip:port` |
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| 429 Too Many Requests | Lower `--concurrency`, rotate profile, add delay |
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| Blank graph | Check JSON paths, clear `localStorage` in browser |
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---
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### TL;DR
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`crwl profiles` → `c4ai_discover.py` → `c4ai_insights.py` → open `graph_view_template.html`.
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Live long and `import crawl4ai`.
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