A Complete Guide For Fast And Simple Auto complete Search API – Author Talking

One simple and fast approach to implementing an auto complete search API is to use a trie data structure. A trie is a tree-like data structure where each node represents a letter or a sequence of letters in a word. The root node represents an empty string, and each leaf node represents a complete word.

To build a trie for autocomplete, you can start by inserting all the words in your dataset into the trie. Then, for each query prefix, you can traverse the trie starting from the root and following the path corresponding to the prefix. Once you reach the last node of the prefix, you can retrieve all the words that have this prefix by traversing the subtree rooted at the last node and collecting all the leaf nodes.

One option for a fast and simple autocomplete search API is to use Elasticsearch. Elasticsearch is an open-source, distributed search and analytics engine designed for fast and scalable search capabilities.

To set up an auto complete search API using Elasticsearch, you would need to do the following:

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1. Install Elasticsearch: You can download and install Elasticsearch from the official website. Once installed, start the Elasticsearch server.
2. Create an index: An index is a collection of documents that have similar characteristics. You can create an index using the Elasticsearch API or using tools like Kibana. For autocomplete, you might want to create an index with only the fields you want to search and autocomplete on.
3. Configure autocomplete: To enable autocomplete, you need to configure Elasticsearch to analyze the text in your fields and create a new field with the analyzed text. You can use the “completion” field type in Elasticsearch to do this.
4. Add documents: Once you have created an index and configured autocomplete, you can add documents to the index. Documents can be added using the Elasticsearch API or tools like Kibana.
5. Query the API: To query the API, you can use the “completion” suggester API in Elasticsearch. This API allows you to suggest autocomplete results based on user input. You can also configure the suggester to return additional information, such as the score or the source document.

Here is an example query for an autocomplete search API using Elasticsearch:

In this example, we are searching for autocomplete suggestions that start with “sea”. The suggester is configured to use the “suggest_field” field in the index, and to return up to 10 suggestions. The “skip_duplicates” parameter is set to true, which means that duplicates will be removed from the result set.

Overall, Elasticsearch provides a fast and scalable way to implement an autocomplete search API. With its powerful indexing and search capabilities, it can handle large volumes of data and provide relevant suggestions to users in real-time.

To use this API, you can create an instance of AutocompleteAPI with your dataset of words, and then call the autocomplete method with a query prefix to retrieve all the words in the dataset that start with that prefix.

For example:

words = [‘apple’, ‘banana’, ‘cherry’, ‘orange’, ‘pear’]

api = AutocompleteAPI(words)

api.autocomplete(‘a’)  # returns [‘apple’]

api.autocomplete(‘b’)  # returns [‘banana’]

api.autocomplete(‘c’)  # returns [‘cherry’]

api.autocomplete(‘o’)  # returns [‘orange’]

api.autocomplete(‘p’)  # returns [‘pear’]

api.autocomplete(‘ch’)  # returns [‘cherry’]

api.autocomplete(‘pe’)  # returns [‘pear’]

api.autocomplete(‘xyz’)  # returns []