agents.rag.db_rag.graph_db.example¶

Example usage of the Graph Database RAG Agent.

This module demonstrates various ways to use the GraphDBRAGAgent for querying Neo4j databases with natural language. It includes basic usage, advanced configurations, error handling, and different execution modes.

The examples cover:

  • Basic agent usage with default configuration

  • Custom domain configuration

  • Streaming execution with progress tracking

  • Batch query processing

  • Error handling and debugging

  • Performance monitoring

To run these examples:

  1. Ensure Neo4j is running and accessible

  2. Set environment variables for Neo4j connection

  3. Run: python example.py

Examples

Basic execution:

$ export NEO4J_URI="bolt://localhost:7687"
$ export NEO4J_USER="neo4j"
$ export NEO4J_PASSWORD="your-password"
$ python example.py

Note

These examples assume you have a Neo4j database with movie data. Adjust the queries and domain configuration for your specific use case.

Functions¶

async_example()

Demonstrate asynchronous execution of the agent.

basic_example()

Demonstrate basic usage of GraphDBRAGAgent.

batch_processing_example()

Demonstrate batch processing of multiple queries.

custom_domain_example()

Demonstrate agent configuration for a custom domain.

error_handling_example()

Demonstrate error handling and recovery strategies.

main()

Run all examples with a menu interface.

performance_monitoring_example()

Demonstrate performance monitoring and optimization.

run_all_examples()

Run all examples in sequence.

streaming_example()

Demonstrate streaming execution with progress tracking.

Module Contents¶

async agents.rag.db_rag.graph_db.example.async_example()¶

Demonstrate asynchronous execution of the agent.

This example shows how to use the agent asynchronously for better performance in async applications.

Example Output:
>>> asyncio.run(async_example())
 Async Execution Example
=====================================

Processing 3 queries concurrently


Query 1 completed: “What are the top rated movies?” Query 2 completed: “Who directed The Godfather?” Query 3 completed: “Which actors have won an Oscar?”

Total time: 2.8s (vs ~7s sequential)

agents.rag.db_rag.graph_db.example.basic_example()¶

Demonstrate basic usage of GraphDBRAGAgent.

This example shows the simplest way to use the agent with default configuration. It relies on environment variables for Neo4j connection.

Example Output:
>>> basic_example()
Basic GraphDB RAG Agent Example
=====================================

Question: What is the movie with the highest rating?

Processing


Answer: The highest rated movie is “The Shawshank Redemption” with a rating of 9.3.

Execution Details:

  • Cypher Query: MATCH (m:Movie) RETURN m.title, m.rating ORDER BY m.rating DESC LIMIT 1

  • Processing Time: 2.3 seconds

  • Steps: [‘check_domain_relevance’, ‘generate_query’, ‘validate_query’, ‘execute_query’, ‘generate_answer’]

agents.rag.db_rag.graph_db.example.batch_processing_example()¶

Demonstrate batch processing of multiple queries.

This example shows how to efficiently process multiple queries and collect statistics about the execution.

Example Output:
>>> batch_processing_example()
 Batch Processing Example
=====================================

Processing 5 queries


  1. “What are the top 5 rated movies?”

    Success (2.1s)

  2. “Who acted in The Godfather?”

    Success (1.8s)

  3. “What’s the weather?”

    Out of domain (0.5s)

Batch Statistics:

  • Total Queries: 5

  • Successful: 4 (80%)

  • Failed: 1 (20%)

  • Average Time: 1.7s

  • Total Time: 8.5s

agents.rag.db_rag.graph_db.example.custom_domain_example()¶

Demonstrate agent configuration for a custom domain.

This example shows how to configure the agent for a specific domain with custom examples and categories.

Example Output:
>>> custom_domain_example()
 Custom Domain (Healthcare) Example
=====================================

Configuring agent for healthcare domain


Testing domain relevance: - “Which patients have diabetes?” - Accepted - “What’s the weather today?” - Rejected (out of domain)

Processing healthcare query
 Answer: The following patients have been diagnosed with diabetes: John Smith, Mary Johnson, and Robert Williams.

agents.rag.db_rag.graph_db.example.error_handling_example()¶

Demonstrate error handling and recovery strategies.

This example shows how the agent handles various error conditions including invalid queries, connection issues, and schema mismatches.

Example Output:
>>> error_handling_example()
 Error Handling Example
=====================================

Testing various error scenarios


  1. Invalid Cypher syntax: Initial query had errors: [‘Syntax error at line 1’]

    Successfully corrected and executed

  2. Non-existent labels:

    Handled gracefully: Label ‘NonExistentNode’ not in schema

  3. Complex nested query:

    Successfully validated and executed

agents.rag.db_rag.graph_db.example.main()¶

Run all examples with a menu interface.

This function provides an interactive menu to run different examples demonstrating various features of the GraphDBRAGAgent.

Examples

Running the main function:

$ python example.py

 GraphDB RAG Agent Examples
=============================

Select an example to run:
1. Basic Usage
2. Streaming Execution
3. Custom Domain Configuration
4. Batch Processing
5. Error Handling
6. Performance Monitoring
7. Async Execution
8. Run All Examples
0. Exit

Enter your choice (0-8):
agents.rag.db_rag.graph_db.example.performance_monitoring_example()¶

Demonstrate performance monitoring and optimization.

This example shows how to monitor agent performance and identify bottlenecks in the workflow.

Example Output:
>>> performance_monitoring_example()
 Performance Monitoring Example
=====================================

Running performance analysis


Performance Metrics:

Step | Time (s) | % of Total ————————-|----------|———— check_domain_relevance | 0.3 | 12% generate_query | 1.2 | 48% validate_query | 0.4 | 16% execute_query | 0.3 | 12% generate_answer | 0.3 | 12% ————————-|----------|———— Total | 2.5 | 100%

Optimization Suggestions:

  • Query generation is the bottleneck (48% of time)

  • Consider caching frequent queries

  • Use more specific examples for faster generation

agents.rag.db_rag.graph_db.example.run_all_examples()¶

Run all examples in sequence.

This function executes all example functions to demonstrate the full capabilities of the GraphDBRAGAgent.

agents.rag.db_rag.graph_db.example.streaming_example()¶

Demonstrate streaming execution with progress tracking.

This example shows how to use the streaming interface to monitor the agent’s progress through each step of the workflow.

Example Output:
>>> streaming_example()
 Streaming GraphDB RAG Agent Example
=====================================

Question: Who directed The Matrix?

Step: check_domain_relevance

Domain check passed

Step: generate_query

Generated Cypher: MATCH (p:Person)-[:DIRECTED]->(m:Movie {title: ‘The Matrix’}) RETURN p.name

Step: validate_query

Query validation passed

Step: execute_query

Query executed successfully Results: [{‘p.name’: ‘Lana Wachowski’},

{‘p.name’: ‘Lilly Wachowski’}]

Step: generate_answer

Final answer generated

Final Answer: The Matrix was directed by Lana Wachowski and Lilly Wachowski.