PraisonAI/examples/rag

RAG Examples

This directory contains comprehensive examples demonstrating RAG (Retrieval Augmented Generation) with PraisonAI Agents.

Core RAG Concepts

Example	Description	Key Concepts
basic_retrieval.py	Fundamental retrieve-then-generate pattern	Knowledge base, context injection
auto_retrieval.py	Intelligent retrieval decisions with AutoRagAgent	Conditional RAG, retrieval policies
chunking_strategies.py	Document splitting approaches	Paragraph, section, fixed-size chunking
hybrid_search.py	Dense + sparse retrieval combination	Semantic search, keyword matching
reranking.py	Two-stage retrieval with reranking	Precision optimization, cross-encoders

Advanced RAG Patterns

Example	Description	Key Concepts
multi_document.py	Synthesizing across multiple sources	Cross-referencing, source diversity
knowledge_graph.py	Relationship-aware retrieval	Entity connections, multi-hop reasoning
external_sources.py	Web, API, and database augmentation	Real-time data, hybrid sources
structured_output.py	Guided and constrained generation	JSON output, Pydantic schemas
retrieval_policies.py	Agent-decided retrieval strategies	Adaptive top-k, priority filtering
citations.py	Source attribution and verification	Inline citations, verification

Application Examples

PDF Q&A (rag_pdf_qa.py)

Basic RAG example showing how to:

• Use Agent with knowledge parameter
• Create explicit RAG pipeline with citations
• Stream responses

python rag_pdf_qa.py

Multi-Agent RAG (rag_multi_agent.py)

Advanced example showing:

• Shared knowledge base across multiple agents
• Collaborative RAG workflow
• Sequential task processing

python rag_multi_agent.py

RAG Evaluation (rag_evaluation.py)

Evaluation example showing:

• Golden query testing
• Retrieval accuracy metrics
• Answer relevance checking

python rag_evaluation.py

Quick Start

Run any example:

# Basic RAG
python basic_retrieval.py

# Auto RAG with intelligent decisions
python auto_retrieval.py

# Hybrid search (dense + sparse)
python hybrid_search.py

# RAG with citations
python citations.py

Installation

pip install "praisonaiagents[knowledge]"

CLI Usage

# Index documents
praisonai rag index ./documents --collection my_docs

# Query
praisonai rag query "What is the main finding?" --collection my_docs

# Interactive chat
praisonai rag chat --collection my_docs

# Evaluate
praisonai rag eval golden_queries.json --collection my_docs

Configuration

Create rag_config.yaml:

knowledge:
  collection: my_docs
  chunking:
    strategy: recursive
    chunk_size: 512

rag:
  top_k: 5
  min_score: 0.3
  include_citations: true

Use with:

praisonai rag query "Question" --config rag_config.yaml