Retriever Agent
(Redirected from Data Acquisition Agent)
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A Retriever Agent is a vertical ai agent that specializes in information acquisition and knowledge access within multi-agent workflows by locating and providing relevant information for task completion.
- AKA: Information Retrieval Agent, Knowledge Access Agent, Data Acquisition Agent.
- Context:
- It can typically execute Retriever Agent Information Search across retriever agent multiple data sources and retriever agent knowledge bases to locate retriever agent relevant content.
- It can typically perform Retriever Agent Content Filtering to identify retriever agent relevant information for retriever agent specific querys and retriever agent task requirements.
- It can typically implement Retriever Agent Semantic Search using retriever agent vector embeddings and retriever agent similarity matching for retriever agent contextual relevance.
- It can typically provide Retriever Agent Context Assembly combining retriever agent multiple information sources into retriever agent coherent responses.
- It can typically optimize Retriever Agent Search Strategy based on retriever agent query type and retriever agent information requirements within retriever agent multi-agent workflows.
- It can often implement Retriever Agent Caching Mechanisms to improve retriever agent response time for retriever agent frequent querys and retriever agent repeated requests.
- It can often provide Retriever Agent Source Validation ensuring retriever agent information quality and retriever agent content reliability for retriever agent downstream agents.
- It can range from being a Simple Retriever Agent to being a Complex Retriever Agent, depending on its retriever agent search capability.
- It can range from being a Single-Source Retriever Agent to being a Multi-Source Retriever Agent, depending on its retriever agent data access scope.
- ...
- Examples:
- Retriever Agent Search Methods, such as:
- Retriever Agent Keyword Search using retriever agent text matching and retriever agent boolean logic.
- Retriever Agent Semantic Search leveraging retriever agent vector similarity and retriever agent embedding models.
- Retriever Agent Hybrid Search combining retriever agent multiple approaches for retriever agent comprehensive retrieval.
- Retriever Agent Graph Search navigating retriever agent knowledge graphs and retriever agent entity relationships.
- Retriever Agent Data Source Types, such as:
- Retriever Agent Document Database accessing retriever agent structured documents and retriever agent content repository.
- Retriever Agent Web Search querying retriever agent external web sources and retriever agent online databases.
- Retriever Agent API Integration connecting to retriever agent external services and retriever agent data providers.
- Retriever Agent Application Patterns, such as:
- ...
- Retriever Agent Search Methods, such as:
- Counter-Examples:
- Planner Agent, which focuses on strategic planning rather than information acquisition.
- Retrieval Augmented Generation, which provides RAG capability rather than a specialized agent role.
- Executor Agent, which performs actions rather than retrieving information.
- See: Multi-Agent Workflow, Vertical AI Agent, Agent Role Specialization, Planner Agent, Information Retrieval System.