Quick Start

This guide explains how to set up the Hyperspace database in minutes.

To start using Hyperspace, follow these steps:

1. Install the Hyperspace API Client

Run the following shell command in your code or your data terminal –

d host address, use the following code to connect to the database through the Hyperspace API.

Python

pip install hyperspace-py

JavaScript

for more information, see here.

2. Create a local instance of the Hyperspace client

Once you receive credentials and host address, use the following code to connect to the database through the Hyperspace API.

Python

hyperspace_client = hyperspace.HyperspaceClientApi(host=host_address,
                                                      username=username,
                                                      password=password)

Java

JavaScript

3. Run Hyperspace queries

Create a schema file

The schema files outline the data structure, index and metric types, and similar configurations. More info can be found in the configuration file section.

Create a collection

Copy the following code snippet to create a collection

Python

collection_name = 'new_collection'
hyperspace_client.create_collection('schema.json', collection_name)

Java

JavaScript

Where –

• 'schema.json' – Specifies the path to the configuration file that you created locally on your machine.

• collection_name' – Specifies the name of the collection to be created in the Hyperspace database.

Alternatively, you can define the database config schema as a local python object

Python

schema = {
        "configuration": {
            "name": {
                "type": "keyword"
            },
            "id": {
                "type": "keyword",
                "id": True,
            }
        }
    }
hyperspace_client.create_collection(schema, 'collection_name')

Java

JavaScript

Where –

• schema – Specifies the python dictionary that outlines the configuration schema.

• 'collection_name' – Specifies the name of the collection to be created in the Hyperspace database.

Upload Data

Data can be uploaded in batches. Copy the following code snippet to upload data

Python

batch_size = 250
batch = []

for i, data_point in enumerate(documents):
   batch.append(data_point)
   if (i+1) % batch_size == 0:
      response = hyperspace_client.add_batch(batch, collection_name)
      batch.clear()
      
if batch:
  response = hyperspace_client.add_batch(batch, collection_name)
  
hyperspace_client.commit(collection_name)

Java

JavaScript

Where –

• data_point – Represents the document to upload. Each document must have dictionary like structure with a keys according to the database schema configuration file.

• batch_size – Specifies the number of documents in a batch.

• commit is required for vector search only

Build and run a query (Python only)

Hyperspace queries can be of one of the following types –

• Lexical Search

• Vector Search

• Hybrid Search

Lexical search can be performed in DSL syntax, or as using a score function of the following form:

 def score_function (params , doc) :
     score = 0.0
     if match ('metadata field 1'):
       score = 1.0
       if match ('metadata field 1'):
          score 2.0
 return score

To set a hybrid or lexical search query –

Specify that this score function file is to be used for the Search, as follows –

Python

hyperspace_client.set_function(score_function_name,
                                collection_name=collection_name,
                                function_name='score_function')

Java

JavaScript

To run a hybrid or lexical search query –

define the query schema and run

Python

params= {
         "name": "John"
        }
results = hyperspace_client.search(params,
                                   size=10,                 
                                   collection_name=collection_name
                                   function_name='score_function')

Java

JavaScript

query_body is the query in DSL syntax. query_body must have a similar structure to the database documents, according to the query schema config file. If query_body includes fields of type

To run a lexical search query in DSL syntax–

define the query schema and run

Python

results = hyperspace_client.dsl_search({'params': query_body},
                                   size=10,                 
                                   collection_name=collection_name)

Java

JavaScript

query_body is the query in DSL syntax.

results is a dictionary with two keys – {'similarity': {}, 'took_ms': ..}

• took_ms – is a float value that specifies how long the query took to run, such as 8.73ms

• similarity – Returns a list. Each element of the list represents a matching document. For each document, it specifies the score and the vector_id that you can use to retrieve the document from the Collection.

Here is an example of what results might look like if they were printed on the screen –

print(results['similarity']) 

[{'score: 513.7000122070312, 'vector_id': '78254'}, {'score: 512.5500126784442, 'vector_id': '23091'}, {'score: 485.5471220787652, 'vector_id': '85432'}]

You can retrieve additional document fields in the query, using the "fields" keyword.

To run a lexical search query in DSL syntax–

define the query schema and run

Python

query = {
    "query": {
        "bool": {
            "must": [
                {"term": {"name": "John"}}
            ]
        }
    }
}
results = hyperspace_client.search({'params': query_body},
                                   size=10,                 
                                   collection_name=collection_name
                                   function_name='score_function',
                                   fields = ["title", "date"])

Java

JavaScript

query_body is the query in DSL syntax.

In this scenario, each entry in results['similarity'] includes a key named "fields", that includes the fields "title" and "date" per retrieved document.

a more detailed guide is available here.