Index and retrieve structured data from documents using a graph database.
npx skills add https://github.com/quarkloop/agent --skill indexer-quarkloop
Please help me install this Agent Skill. Skill: indexer Source: https://github.com/quarkloop/agent/tree/main/plugins/services/indexer Command: npx skills add https://github.com/quarkloop/agent --skill indexer-quarkloop
This Skill automates the indexing and retrieval of structured data from various sources, streamlining knowledge management and enhancing search capabilities.
Index all documents in the 'knowledge-base' directory using the indexer skill.
High-intent search queries and answers about installing and using this skill.
Indexing structured data involves storing and organizing facts, entities, relations, and citations for efficient retrieval. This Skill automates that process using vector embeddings and requires a Dgraph database for storage operations.
Yes, you need a graph database like Dgraph. It is required to support the underlying storage and retrieval operations for querying indexed structured data based on vector embeddings and graph contexts.
The best way to retrieve specific information is to index document metadata and text chunks first. You can then perform precise searches using vector embeddings and graph contexts to extract the desired knowledge.
Yes, vector embeddings are supported for knowledge retrieval. The system queries indexed data by combining these vector embeddings with graph contexts to retrieve relevant information from your structured sources.
You can store and organize document metadata, text chunks, facts, entities, relations, and citations. This structured data is indexed to streamline knowledge management and enhance search capabilities.
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