Autonomous RAG

Self-directed retrieval agent with reasoning and adaptive retrieval strategies

Autonomous RAG

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

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

Implementation

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

Key Features

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")

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.

Related Examples

Agentic RAG

Complete autonomous RAG implementation

Corrective RAG

Add self-correction to your RAG system