Matchmaking Tools

Download participant data

In lu.ma, go to your event page and click "Manage". Click "Guests" and click the download button to get the CSV file. Put this in the root directory of this repository as "data.csv"

If you have your own CSV, format it as follows:

Names	Responses
Freeman Jiang	I wish to explore the world
Rajan Agarwal	I want to build AGI
Hudhayfa Nazoordeen	I want to grow more vegetables hydroponically

In generate_embeddings.py, set the COLUMN_INDEX to be the column that your responses are in. For example, In the above csv the index would be 1.

Running the script

Install dependencies

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Generate embeddings

python generate_embeddings.py
python build_graph.py # to create the graph based on the distance threshold
python attendees.py # to create initial cache

You should now successfully have a graphData.json file.

Generating summarized data (optional)

You now have the option to summarize each response. For example,

i want to learn how to sculpt/pottery! creating something from a block of clay would be incredible but would take a lot of time/lessons.

would become

Would learn how to sculpt and create pottery from clay.

We recommend this for events where you can search for people, using the match site. If you are just using the graph, this is not necessary.

python summarize_graph_data.py # (OPTIONAL) to summarize responses with LLM

then, uncomment the line in graph/index.js:

// Use the below line if you want to use the summarized graph instead
//const json = require("../summarizedGraphData.json");

If you choose to summarize the data with an LLM, a summarizedGraphData.json file will be present too. If you do the AI summarization, change ai_summarization in attendees.py to True. Then, run attendees again to recache.

python attendees.py # create attendees.json for searching

Developing the Graph

In the graph/ directory

cd graph
npm i
npm run dev

Next, concurrently run Tailwind in a new terminal:

npm run tailwind

Finally, open graph/index.html in a browser.

To serve:

npm start

Developing the Matchmaking Site

Now in the match/ directory

cd match
npm i
npm run dev

Then open localhost:3000 in a browser.

To serve:

npm start

Deployment

We suggest you deploy using the Vercel CLI, so you do not release personal information in the CSV. You can individually deploy match and graph with the same compiled dataset. You can check out their documentation: Vercel CLI Docs.