Edge n-gram suggestions and 'starts with' keyword in Elasticsearch - elasticsearch

Question

I'm trying to build a food search engine on Elasticsearch that should meet following use cases -
If the user searches for 'coff' then it should return all the documents with phrase 'coffee' in their name and the priority should be for food items that have 'coffee' at the starting of their name.
If the user searches for 'green tea' then it should give priority to the documents that have both the phrases 'green tea' instead of splitting 'green' and 'tea'
If the phrase does not exist in the 'name' then it should also search in the alias field.
To manage the first case, I've used the edge n-grams analyzer.
Mapping -
{
"settings": {
"index": {
"analysis": {
"filter": {},
"analyzer": {
"analyzer_keyword": {
"tokenizer": "standard",
"filter": "lowercase"
},
"edge_ngram_analyzer": {
"filter": [
"lowercase"
],
"tokenizer": "edge_ngram_tokenizer"
}
},
"tokenizer": {
"edge_ngram_tokenizer": {
"type": "edge_ngram",
"min_gram": 2,
"max_gram": 5,
"token_chars": [
"letter"
]
}
}
}
}
},
"mappings": {
"doc": {
"properties": {
"alias": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"name": {
"type": "text",
"search_analyzer": "analyzer_keyword",
"analyzer": "edge_ngram_analyzer",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
}
}
}
This is the search query that I'm using but it's not exactly returning the relevant search results
{
"query": {
"multi_match": {
"query": "coffee",
"fields": ["name^2", "alias"]
}
}
}
There are over 1500 food items with 'coffee' in their name but the above query is only returning 2
{
"took": 745,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 8.657346,
"hits": [
{
"_index": "food-master",
"_type": "doc",
"_id": "a9uzinABb4g7LgmgoI1I",
"_score": 8.657346,
"_source": {
"id": 17463,
"name": "Rotiboy, coffee bun",
"alias": [
"Mexican Coffee Bun (Rotiboy)",
"Mexican coffee bun"
],
}
},
{
"_index": "food-master",
"_type": "doc",
"_id": "TNuzinABb4g7LgmgoFVI",
"_score": 7.0164866,
"_source": {
"id": 1344,
"name": "Coffee with sugar",
"alias": [
"Heart Friendly",
"Coffee With Sugar",
"Coffee With Milk and Sugar",
"Gluten Free",
"Hypertension Friendly"
],
}
}
]
}
}
In the mapping, if I remove the analyzer_keyword then it returns relevant results but the documents that start with 'coffee' are not prioritized
{
"took": 5,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 1323,
"max_score": 57.561867,
"hits": [
{
"_index": "food-master-new",
"_type": "doc",
"_id": "nduzinABb4g7LgmgoINI",
"_score": 57.561867,
"_source": {
"name": "Egg Coffee",
"alias": [],
"id": 12609
}
},
{
"_index": "food-master-new",
"_type": "doc",
"_id": "dNuzinABb4g7LgmgoFVI",
"_score": 55.811295,
"_source": {
"name": "Coffee (Black)",
"alias": [
"Weight Loss",
"Diabetes Friendly",
"Gluten Free",
"Lactose Free",
"Heart Friendly",
"Hypertension Friendly"
],
"id": 1341
}
},
{
"_index": "food-master-new",
"_type": "doc",
"_id": "NduzinABb4g7LgmgoHxI",
"_score": 54.303185,
"_source": {
"name": "Brewed Coffee",
"alias": [
"StarBucks"
],
"id": 15679
}
},
{
"_index": "food-master-new",
"_type": "doc",
"_id": "ltuzinABb4g7LgmgoJJI",
"_score": 54.303185,
"_source": {
"name": "Coffee - Masala",
"alias": [],
"id": 11329
}
},
{
"_index": "food-master-new",
"_type": "doc",
"_id": "oduzinABb4g7LgmgoGpI",
"_score": 53.171227,
"_source": {
"name": "Coffee, German",
"alias": [],
"id": 12257
}
},
{
"_index": "food-master-new",
"_type": "doc",
"_id": "YNuzinABb4g7LgmgoFRI",
"_score": 52.929176,
"_source": {
"name": "Soy Milk Coffee",
"alias": [
"Gluten Free",
"Lactose Free",
"Weight Loss",
"Diabetes Friendly",
"Heart Friendly",
"Hypertension Friendly"
],
"id": 978
}
},
{
"_index": "food-master-new",
"_type": "doc",
"_id": "8duzinABb4g7LgmgoFRI",
"_score": 52.068523,
"_source": {
"name": "Cold Coffee (Soy Milk)",
"alias": [
"Soy Milk"
],
"id": 1097
}
},
{
"_index": "food-master-new",
"_type": "doc",
"_id": "tNuzinABb4g7LgmgoF9I",
"_score": 50.956154,
"_source": {
"name": "Coffee Frappe",
"alias": [],
"id": 3142
}
},
{
"_index": "food-master-new",
"_type": "doc",
"_id": "ZduzinABb4g7LgmgoF5I",
"_score": 49.810112,
"_source": {
"name": "Big Apple Coffee",
"alias": [],
"id": 3130
}
},
{
"_index": "food-master-new",
"_type": "doc",
"_id": "eduzinABb4g7LgmgoHtI",
"_score": 49.62197,
"_source": {
"name": "Mexican Coffee",
"alias": [],
"id": 13604
}
}
]
}
}
If I change the tokenizer to 'keyword' from 'standard' then I face the same problem and it also splits phrases into individual words - 'green tea' to 'green' and 'tea'
Any suggestions on what I might be getting wrong with respect to analyzers? I've tried all possible combinations but meeting all 3 scenarios with high accuracy is getting a little difficult.