AgentSkillsCN

agentic-rag

构建智能体RAG系统的策略。通过引入自主智能体，突破静态检索的局限，实现自适应源选择、查询扩展，以及多步推理能力。

SKILL.md
--- frontmatter
name: agentic-rag
description: strategies for building Agentic RAG systems. Use this to move beyond static retrieval by using autonomous agents for adaptive source selection, query expansion, and multi-step reasoning.

Agentic RAG Strategies

Goal

Transform traditional, static Retrieval-Augmented Generation (RAG) into a dynamic, agentic process that actively reasons about how and where to find information.

Core Capabilities

1. Adaptive Retrieval

  • •Concept: Instead of a single pass against a vector database, the agent dynamically selects the best knowledge source based on the context.
  • •Mechanism: The agent evaluates the query ambiguity and chooses between multiple data stores (e.g., "PDFs" vs. "Web Search" vs. "Structured DB").

2. Multi-Step Reasoning

  • •Concept: For complex queries, the agent breaks the problem down into logical steps and retrieves information sequentially.
  • •Workflow:
    1. •Decompose: Break "Compare the revenue of Company A and Company B" into two sub-queries.
    2. •Retrieve: Fetch revenue for Company A.
    3. •Retrieve: Fetch revenue for Company B.
    4. •Synthesize: Combine both facts into a final answer.

3. Context-Aware Query Expansion

  • •Concept: The agent doesn't just search for the user's raw query. It generates multiple refined search terms to increase recall.
  • •Benefit: Captures synonyms, related concepts, and specific terminology that the user might have missed.

Optimization Techniques

  • •Self-Correction: Implement an Evaluator Agent that reviews retrieved chunks for relevance before generating an answer. If the data is poor, it triggers a new search with better terms.
  • •Better Search: Enhance the underlying engine with semantic chunking (keeping topics together) and re-ranking (using a second model to order results by quality).