Performance issue when upserting 10k+ edges using MERGE

Hi, I am trying to insert/update around 70K edges into the graph using age.
I created the following function:

```
CREATE OR REPLACE FUNCTION create_update_edge(edge_list TEXT)
RETURNS void AS $$
DECLARE
query TEXT;
BEGIN
query := format($q$
SELECT * FROM ag_catalog.cypher(
'test_attribute_instance_graph',
$cypher$
UNWIND %s AS edge
MERGE (a:attribute_instance {name: edge.source_name, value: edge.source_value})-[r:cooccurs_with]-(b:attribute_instance {name: edge.target_name, value: edge.target_value})
SET r.count = edge.count
SET r.first_seen = edge.first_seen
SET r.last_seen = edge.last_seen

$cypher$
) AS (ignored agtype);
$q$, edge_list);

EXECUTE query;

RAISE NOTICE 'Edges merged and properties set.';
END;
$$ LANGUAGE plpgsql;
```

These are some of the points I had to take care of :
1. Function signature of ag_catalog.cyphe is (name, cstring, agtype)) So, I was not directly able to pass JSONB format as a parameter, and had to change the list of edges(each data is mapped to a dict) to a Cypher-compatible map(keys are unquoted) before passing it to the above function.
2. Trying to use MERGE..ON CREATE/ON MATCH SET gave syntax error, so I simply used MERGE SET to do the upsertion of edges.
3. Trying to B-tree indexing was a failed attempt, as again I was coming across a syntax error using the standard CREATE INDEX for B-tree command, so I used GIN Index.

Under this setup and batch unwind (batch size 1000 edges): I got the following results
| Edges Inserted | Time (s) | Time (min) |
|----------------|----------|------------|
| 1,000 | 0.62 | 0.01 |
| 2,000 | 16.35 | 0.27 |
| 3,000 | 22.42 | 0.37 |
| 4,000 | 69.63 | 1.16 |
| 5,000 | 104.75 | 1.75 |
| 6,000 | 177.12 | 2.95 |
| 7,000 | 447.66 | 7.46 |
| 8,000 | 598.38 | 9.97 |
| 9,000 | 913.82 | 15.23 |
| 10,000 | 1320.22 | 22.00 |

Clearly, the performance is pretty bad, so I was wondering if this is a limitation of age or a wrong implementation on my end. If someone could give me insights on my method and any other ways I should try inserting the data/or optimise the current method, it'd be of great help!
Thankyou!!

Start by reproducing the reported benchmark using the create_update_edge PL/pgSQL function, ag_catalog.cypher, UNWIND, MERGE, and batches of 1,000 edges. Compare the current GIN-indexed setup with the reported timings and investigate the documented B-tree and MERGE syntax limitations. Done means identifying whether the slowdown is expected or an implementation limitation and documenting a supported optimization path.

Written by the indexing model from the issue text.