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Autonomous RAG

Autonomous RAG systems make intelligent decisions about when and how to retrieve information, using reasoning to guide the retrieval process.

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Overview

Autonomous RAG features:
  • Self-directed retrieval: Agent decides when to retrieve
  • Reasoning integration: Think through queries before retrieval
  • Adaptive strategies: Adjust retrieval based on context
  • Tool orchestration: Combine multiple retrieval tools

Reasoning Agent

Uses ReAct pattern for step-by-step reasoning

PgVector Integration

PostgreSQL vector extension for scalable retrieval

Query Planning

Plans retrieval strategy before execution

Self-Correction

Validates and refines retrieval results

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Implementation

See the Agentic RAG page for complete implementation details including Agno framework integration and autonomous retrieval patterns.

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Key Features

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ReAct Pattern

# Agent reasons about retrieval needs
agent = Agent(
    model=OpenAI(id="gpt-4o"),
    tools=[retrieval_tool, search_tool],
    reasoning=True,
    markdown=True
)

# Agent decides when to retrieve
response = agent.run("Analyze the impact of climate change")

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Adaptive Retrieval

The agent adapts its retrieval strategy based on:
  • Query complexity
  • Initial result quality
  • Context requirements
  • Tool availability
Autonomous RAG excels at complex queries requiring multi-step reasoning.

Agentic RAG

Complete autonomous RAG implementation

Corrective RAG

Add self-correction to your RAG system

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