# REST API

## How to Run Vector Search in the Deep Lake Tensor Database using the REST API

{% hint style="danger" %}
The REST API is currently in Alpha, and the syntax may change without announcement.
{% endhint %}

To use the REST API, Deep Lake data must be stored in the [Managed Tensor Database](/v3.8.19/performance-features/managed-database.md) by specifying the `deeplake_path = hub://org_id/dataset_name` and `runtime = {"tensor_db": True}`. [Full details on path and storage management are available here](/v3.8.19/storage-and-credentials/storage-options.md).

### Performing Vector Search Using the REST API

Let's query this Vector Store stored in the Managed Tensor Database using the [REST API](/v3.8.19/performance-features/managed-database/rest-api.md). The steps are:

1. Define the authentication tokens and search terms
2. Embed the search search term using OpenAI
3. Reformat the embedding to an `embedding_search` string that can be passed to the REST API request.
4. Create the query string using [Deep Lake TQL](/v3.8.19/performance-features/querying-datasets/query-syntax.md). The `dataset_path` and `embedding_search` are a part of the query string.  
5. Submit the request and print the response data data

{% tabs %}
{% tab title="Python" %}

```python
import requests
import openai
import os

# Tokens should be set in environmental variables.
ACTIVELOOP_TOKEN = os.environ['ACTIVELOOP_TOKEN']
DATASET_PATH = 'hub://activeloop/twitter-algorithm'
ENDPOINT_URL = 'https://app.activeloop.ai/api/query/v1'
SEARCH_TERM = 'What do the trust and safety models do?'
# os.environ['OPENAI_API_KEY'] OPEN AI TOKEN should also exist in env variables

# The headers contains the user token
headers = {
    "Authorization": f"Bearer {ACTIVELOOP_TOKEN}",
}

# Embed the search term
embedding = openai.Embedding.create(input=SEARCH_TERM, model="text-embedding-ada-002")["data"][0]["embedding"]

# Format the embedding array or list as a string, so it can be passed in the REST API request.
embedding_string = ",".join([str(item) for item in embedding])

# Create the query using TQL
query = f"select * from (select text, cosine_similarity(embedding, ARRAY[{embedding_string}]) as score from \"{dataset_path}\") order by score desc limit 5"
          
# Submit the request                              
response = requests.post(ENDPOINT_URL, json={"query": query}, headers=headers)

data = response.json()

print(data)
```

{% endtab %}

{% tab title="Node.js" %}

```javascript
const axios = require('axios');

OPENAI_API_KEY = process.env.OPENAI_API_KEY;
ACTIVELOOP_TOKEN = process.env.ACTIVELOOP_TOKEN;

const QUERY = 'What do the trust and safety models do?';
const DATASET_PATH = 'hub://activeloop/twitter-algorithm';
const ENDPOINT_URL = 'https://app.activeloop.ai/api/query/v1';

// Function to get the embeddings of a text from Open AI API
async function getEmbedding(text) {
  const response = await axios.post('https://api.openai.com/v1/embeddings', {
    input: text,
    model: "text-embedding-ada-002"
  }, {
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${OPENAI_API_KEY}`
    }
  });

  return response.data;
}

// Function to search the dataset using the given query on Activeloop
async function searchDataset(query) {
  const response = await axios.post(${ENDPOINT_URL}, {
    query: query,
  }, {
    headers: {
      'Content-Type': 'application/json',
      'Authorization': `Bearer ${ACTIVELOOP_TOKEN}`
    }
  });

  return response.data;
}

// Main function to search for similar texts in the dataset based on the query_term
async function searchSimilarTexts(query, dataset_path) {
  // Get the embedding of the query_term
  const embedding = await getEmbedding(query);
  const embedding_search = embedding.data[0].embedding.join(',');

  // Construct the search query
  const TQL = `SELECT * FROM (
                    SELECT text, l2_norm(embedding - ARRAY[${embedding_search}]) AS score 
                    from "${dataset_path}"
                  ) ORDER BY score DESC LIMIT 5`;

  // Search the dataset using the constructed query
  const response = await searchDataset(TQL);

  // Log the search results
  console.log(response);
}

searchSimilarTexts(QUERY, DATASET_PATH)
```

{% endtab %}
{% endtabs %}

Congrats! You performed a vector search using the Deep Lake Managed Database! 🎉


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs-v3.activeloop.ai/v3.8.19/example-code/tutorials/vector-store/vector-search-options/rest-api.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.