> For the complete documentation index, see [llms.txt](https://docs.hyper-space.io/hyperspace-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.hyper-space.io/hyperspace-docs/~/changes/uCQNjcW7J3OXknfYJaVa/getting-started/quick-start.md).

# Quick Start

To start using Hyperspace, follow these steps:

### 1. Installing the Hyperspace API Client

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

```python
pip install git+https://github.com/hyper-space-io/hyperspace-py
```

for more information, see [here](https://docs.hyper-space.io/hyperspace-docs/projects/setting-up/installing-the-hyperspace-api-client).

### **2. Creating 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)
```

### **3. Running 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](https://docs.hyper-space.io/hyperspace-docs/projects/setting-up/creating-a-database-schema-configuration-file) section. 

**Create a collection**

Copy the following code snippet to create a collection

```python
hyperspace_client.create_collection('schema.json', 'collection_name')
```

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. 

**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)
```

**Where** –

* <mark style="color:purple;">data\_point</mark> – Represents the document to upload. Each document must have dictionary like structure with a keys according to the database schema configuration file.
* <mark style="color:purple;">BATCH\_SIZE</mark> – Specifies the number of documents in a batch.
* `commit` is required for vector search only

**Build and run a query**

Hyperspace queries are one of the following types of search –

* **Classic Search**
* **Vector Search**
* **Hybrid Search**&#x20;

Classic and hybrid search require a [score function](https://docs.hyper-space.io/hyperspace-docs/projects/score-function-commands) of the following form:

<pre class="language-python"><code class="lang-python"><strong> def score_function (params , doc) :
</strong>    score = 0.0
    if match ('metadata field 1'):
       score = 1.0
       if match ('metadata field 1'):
          score 2.0
 return score
</code></pre>

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

```python
hyperspace_client.set_function(score_function_filename,
                                collection_name=collection_name,
                                function_name='score_function')
```

**To run a hybrid search query –**

define the query schema and run&#x20;

```python
results = hyperspace_client.search({'params': query_body},
                                   size=5,                 
                                   collection_name=collection_name
                                   function_name='score_function')
```

`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&#x20;

**Retrieve Results**

To retrieve results, use the following command

```
results = hyperspace_client.search(vector_query_schema, size=5,   collection_name=collection_name)                 
```

**results** is a dictionary has 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 –

```python
print(results)['similarity']) 
```

\[{'score: 513.7000122070312, 'vector\_id': '78254'},\
&#x20;{'score: 512.5500126784442, 'vector\_id': '23091'},\
&#x20;{'score: 485.5471220787652, 'vector\_id': '85432'}]

&#x20;a more detailed guide is available [here](https://docs.hyper-space.io/hyperspace-docs/projects/setting-up).


---

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