Search In OpenSearch
WIP
Work In Progress
Keyword Search
Match
Use the match query for full-text search on a specific document field. If you run a match query on a text field, the match query analyzes the provided search string and returns documents that match any of the string’s terms.
python
q = 'parquet'
query = {
"size": 2,
"query": {
"match": {
"question": q
}
}
}
response = client.search(
body = query,
index = 'zoomcamp-db'
)
documents = response.get('hits').get('hits')
documentsMultimatch
A multi-match operation functions similarly to the match operation. You can use a multi_match query to search multiple fields.
The ^ “boosts” certain fields. Boosts are multipliers that weigh matches in one field more heavily than matches in other fields. In the following example, a match for “parquet” in the question field influences _score four times as much as a match in the answer field:
python
q = 'parquet'
query = {
'size': 5,
'query': {
'multi_match': {
'query': q,
'fields': ['question^4', 'answer']
}
}
}
response = client.search(
body = query,
index = 'zoomcamp-db'
)
documents = response.get('hits').get('hits')
documentsVector Search
Create
python
document_body_vector = {
"settings": {
"index": {
"knn": True,
"knn.algo_param.ef_search": 100
}
},
"mappings": {
"properties": {
"embedding": {
"type": "knn_vector",
"dimension": 1536, # embedding model size
"method": {
"name": "hnsw",
"space_type": "l2",
"engine": "nmslib",
"parameters": {
"ef_construction": 128,
"m": 24
}
}
}
}
}
}
try:
response = client.indices.create(
index='INDEX_NAME',
body=document_body_vector
)
print(f"Created index: {response}")
except Exception as e:
print(f"Error creating index: {e}")Insert
Example:
python
_embeddings = {
"cat": embeddings.embed_query("cat"),
"tiger": embeddings.embed_query("tiger"),
"dog": embeddings.embed_query("dog"),
"train": embeddings.embed_query("train"),
"airplane": embeddings.embed_query("airplane")
}
documents = [
{"name": "cat", "embedding": _embeddings["cat"]},
{"name": "tiger", "embedding": _embeddings["tiger"]},
{"name": "dog", "embedding": _embeddings["dog"]},
{"name": "train", "embedding": _embeddings["train"]},
{"name": "airplane", "embedding": _embeddings["airplane"]}
]
try:
for doc in documents:
response = client.index(
index='zoomcamp-db-vector',
body=doc,
refresh=True
)
print(f"Indexed document ID: {response['_id']}")
except Exception as e:
print(f"Error indexing document: {e}")Query
python
query_vector = documents[0].get('embedding') # your vector to use in similarity search
# k-NN search request
knn_search_query = {
"size": 5,
"query": {
"knn": {
"embedding": {
"vector": query_vector,
"k": 3
}
}
}
}
try:
response = client.search(
index='zoomcamp-db-vector',
body=knn_search_query
)
for hit in response['hits']['hits']:
print(f"Document ID: {hit['_id']}, Score: {hit['_score']}, Name: {hit['_source']['name']}")
except Exception as e:
print(f"Error during k-NN search: {e}")