Using A Large Language Model (LLM)

SearchBlox Enterprise Search integrates Large Language Model (LLM) capabilities to automatically enrich document metadata, including:

• Titles - Context-aware document naming
• Descriptions - Concise content summaries
• Keywords - Optimized search terms

This AI-driven enhancement applies to all documents within a collection, improving both search accuracy and content discoverability.

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Prerequisites:

• Download and install SearchBlox Service.
• Set-up the configurations by creating a collection and indexing documents.
• Install Python 3.11.0 or Python 3.12.4.

Note: For production deployments with large document volumes, consider dedicated AI processing servers to ensure optimal performance.

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Technical Guide:

SearchBlox leverages advanced LLM technology to automatically enhance document metadata, improving search relevance and content discoverability.

Core Technology Stack

1. LLM Model: Llama-2-based architecture
2. Search Engine: OpenSearch backend integration
3. Data Format: JSON payloads for metadata transmission

• Retrieving and Processing: The model retrieves all indexed documents directly from OpenSearch, the underlying search engine within SearchBlox.
• Generating Enhanced Metadata:
  • Collect content from each document of a Collection
  • Pass the content through the LLM with a prompt.
  • The LLM processes the collected data and generates relevant titles, descriptions, and up to 20 keyword
• Seamless Integration:
  • The generated metadata is sent back to Opensearch in JSON format.
  • It is updated within the document's metadata using document's uid.
  • Improvements are immediately reflected in the search results.

Generate Titles, Descriptions and Keywords using LLM

Using a Large Language Models (LLMs), we can now create titles, descriptions and keywords for any document.

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NOTE:

• Large Language Models (LLMs) improves the relevance of the search results, even with the poor quality metadata.
• The new titles, descriptions, and keywords are generated systematically using LLMs, making them more comprehensive.

To generate titles, descriptions and keywords for Text/Images based on Llama-2-based model click on the following links:

• Generate Titles, Descriptions and Keywords For Text
• Generating Titles and Descriptions for Images