Tri-Database Chatbot V1

April 15, 2025 · View on GitHub

A foundational infrastructure for a multi-purpose chatbot system with intent-based routing and mock database integrations.This project implements a skeletal blueprint for an advanced chat bot system which connects to 3 types of database in order to effectively handle all kinds of complex business transactions. It uses a Kubernetes architecture and will eventually support containerized databases implemented in Rust.

Table of Contents

  1. Project Overview
  2. System Architecture
  3. Database Mocks Architecture
  4. Mock Database Integration
  5. Implementation Details
  6. Development Guidelines
  7. Development Setup
  8. Testing Framework and Results
  9. Known Limitations
  10. Troubleshooting Guide

1. Project Overview

This project implements a skeletal blueprint for a chatbot system, focusing on establishing foundational "utility lines" of connectivity using Python-based service foundations and Test-Driven Development (TDD).

Core Features

  • Intent-based query routing
  • Multiple mock database integrations
  • Asynchronous request handling
  • Health monitoring system
  • Test-driven development approach

3. Database Mocks Architecture

Neo4j Mock (Port 8002)

The Neo4j mock implements a graph database simulation using NetworkX, providing a lightweight alternative to a full Neo4j instance while maintaining essential functionality.

Architecture Components

  • NetworkX-based Graph Engine
    • In-memory graph structure
    • Basic CRUD operations for nodes and relationships
    • Support for simple graph traversal operations
    • Property storage on nodes and edges

Query Interface

  • Cypher-like Query Parser
    • Support for basic MATCH, CREATE, SET operations
    • Pattern matching functionality
    • Property filtering
    • Result formatting matching Neo4j's response structure

Core Capabilities

# Example Query Support
CREATE (n:Person {name: 'John'})
MATCH (n:Person) WHERE n.name = 'John' RETURN n
MATCH (n)-[r:KNOWS]->(m) RETURN n, r, m

Weaviate Mock (Port 8003)

The Weaviate mock simulates vector search operations using NumPy, providing efficient similarity search capabilities without requiring a full Weaviate installation.

Architecture Components

  • NumPy Vector Operations
    • Vector storage and manipulation
    • Cosine similarity calculations
    • KNN search implementation
    • Efficient batch operations

Core Features

  • Vector Search Engine
    • Vector embedding storage
    • Similarity search operations
    • Basic filtering capabilities
    • Result ranking and scoring

Performance Optimization

  • In-memory vector storage
  • Batch processing support
  • Indexed similarity calculations
  • Optimized NumPy operations

Relational Mock (Port 8004)

The relational database mock leverages SQLAlchemy Core for SQL operations, providing a robust foundation for relational data operations.

Architecture Components

  • SQLAlchemy Core Implementation
    • Table definitions using SQLAlchemy models
    • Transaction management
    • Connection pooling
    • Query builder interface

Schema Management

  • Alembic Integration
    • Version-controlled schema changes
    • Migration scripts
    • Schema rollback capability
    • Database initialization scripts

Core Features

  • Query Processing
    • CRUD operations
    • Transaction support
    • Join operations
    • Aggregation functions

4. Mock Database Integration

Connection Patterns with MCP

sequenceDiagram
    participant CS as Chatbot Service
    participant MCP as MCP Service
    participant DB as Database Mock

    CS->>MCP: Query Request
    MCP->>MCP: Route Selection
    MCP->>DB: Formatted Query
    DB->>DB: Process Query
    DB->>MCP: Standard Response
    MCP->>CS: Formatted Response

Query Routing Implementation

  • Intent-based routing logic
  • Database selection criteria
  • Load balancing considerations
  • Failover handling

Response Format Standardization

{
  "status": "success",
  "data": {
    "result": [
      /* database-specific result */
    ],
    "metadata": {
      "db_type": "neo4j|weaviate|relational",
      "query_time": "0.123s",
      "node_count": 10
    }
  }
}

Error Handling Approaches

  • Connection failures
  • Query timeout handling
  • Invalid query detection
  • Resource exhaustion handling

Performance Considerations

  • Connection pooling
  • Query optimization
  • Caching strategies
  • Resource management

5. Implementation Details

Code Structure

mock_services/
├── neo4j_mock/
│   ├── graph_engine.py
│   ├── query_parser.py
│   └── response_formatter.py
├── weaviate_mock/
│   ├── vector_store.py
│   ├── similarity_engine.py
│   └── result_ranker.py
└── relational_mock/
    ├── schema.py
    ├── migrations/
    └── query_builder.py

Interface Contracts

  • Standardized API endpoints
  • Request/response schemas
  • Error reporting format
  • Health check interfaces

Test Coverage Details

  • Unit test organization
  • Integration test suites
  • Performance benchmarks
  • Coverage requirements

Mock Data Management

  • Data generation strategies
  • Seeding mechanisms
  • Data consistency rules
  • Reset capabilities

6. Development Guidelines

Local Setup Instructions

  1. Clone the repository
  2. Install dependencies
  3. Configure environment
  4. Initialize mock databases

Testing Procedures

  1. Unit testing guidelines
  2. Integration testing process
  3. Performance testing
  4. Coverage reporting

Common Troubleshooting

  • Database connection issues
  • Query parsing errors
  • Performance bottlenecks
  • Environment setup problems

Extension Points

  • Custom query handlers
  • New database mock integration
  • Enhanced routing logic
  • Advanced features