Here’s the translation to American English:
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In the field of data analysis, the integration of artificial intelligence technologies is becoming increasingly crucial, especially with platforms like Amazon OpenSearch. This tool not only allows for searching vast volumes of data but also addresses the growing need to enrich information before indexing. For example, when processing log files with IP addresses, obtaining the corresponding geographical location becomes essential; in other cases, such as analyzing customer comments, identifying the language of the texts is fundamental.
Traditional ingestion pipelines, which rely on external processes to enrich data, can complicate system efficiency and risk its operation. To simplify this process, OpenSearch has introduced various third-party machine learning connectors that facilitate data enrichment.
One of the standout connectors is Amazon Comprehend, which enables the detection of document languages through the implementation of the LangDetect API. Another relevant connector is Amazon Bedrock, which allows the use of the Amazon Titan Text Embeddings v2 model, enhancing semantic search capabilities in documents written in multiple languages.
To illustrate these functionalities, an Amazon SageMaker notebook is used along with an AWS CloudFormation template, providing users with all the resources needed to replicate the process in their environment. Configuring OpenSearch to access Amazon Comprehend requires ensuring that the system has the appropriate permissions through an IAM role, which must be correctly mapped to allow the use of the language detection API.
Additionally, an ingestion pipeline is provided that integrates the Amazon Comprehend API, automatically adding language information to documents at the time of indexing. This implementation demonstrates how OpenSearch can effectively combine third-party machine learning models, enhancing both search and analysis capabilities.
The Amazon Bedrock connector particularly excels in performing multilingual semantic searches using the embeddings model to generate text vectors from documents in various languages. This process is carried out through a workflow that includes loading documents into dataframes and creating an index that stores both the corresponding vectors and the original text along with its English translation.
The use of these connectors not only optimizes system architecture but also reduces the required infrastructure, facilitating maintenance and scalability. Furthermore, operational cost efficiency is achieved by eliminating the need to manage multiple endpoints and simplifying billing.
With these innovations, Amazon OpenSearch establishes itself as an indispensable tool for those looking to not only store and search data but also enrich its content, enabling informed decisions based on accurate and contextual information.
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Feel free to ask if you need any more translations or adjustments!
via: MiMub in Spanish
