ODMDB Natural Language Query PoC

This is a Proof of Concept (PoC) that demonstrates the conversion of natural language queries into ODMDB search queries using OpenAI's structured output API.

Current Status

✅ Complete Multi-Schema Implementation: Supports all ODMDB object types including seekers, jobads, recruiters, persons, and sirets. The system intelligently detects the target object from natural language queries and generates appropriate ODMDB DSL queries.

Features

• Multi-Object Natural Language Processing: Intelligently detects target object (seekers, jobads, recruiters, persons, sirets) from natural language queries
• Real ODMDB Schema Integration: Dynamically loads actual schema files for all object types with verified accuracy
• Comprehensive Field Mapping: Uses real schema definitions with proper access rights for recruiter-readable fields
• Index-Aware Query Generation: Leverages actual ODMDB indexes for optimal query performance
• Schema Mapping Manager: Centralized system reading real schema files and generating comprehensive field synonyms
• Multi-Object Query Support: Handles queries across all ODMDB object types with object-specific optimizations
• OpenAI Structured Output: Dynamic JSON schema generation for any target object type
• Real Data Validation: Verified against actual ODMDB schema properties and index registers
• Prepared Query Demos: Ready-to-use example queries for all supported object types

Prerequisites

• Node.js (v16 or higher)
• OpenAI API key

Installation

1. Make sure you have the complete ODMDB data structure available:

   ../smatchitObjectOdmdb/
   ├── schema/
   │   ├── seekers.json       # Seeker schema (62 properties, 27 readable fields)
   │   ├── jobads.json        # Job advertisement schema
   │   ├── recruiters.json    # Recruiter schema
   │   ├── persons.json       # Person schema
   │   ├── sirets.json        # Company/Siret schema
   │   └── *.json            # Additional schema files
   └── objects/
       ├── seekers/
       │   ├── idx/          # Index files (lst_alias, seekstatus_alias, etc.)
       │   └── itm/          # Individual seeker JSON files
       ├── jobads/
       │   ├── idx/          # Job ad indexes
       │   └── itm/          # Job ad data files
       ├── recruiters/
       │   ├── idx/          # Recruiter indexes
       │   └── itm/          # Recruiter data files
       ├── persons/
       │   └── itm/          # Person data files
       └── sirets/
           └── itm/          # Company data files
   

2. Install dependencies:

   npm install
   

3. Set your OpenAI API key:

   export OPENAI_API_KEY=sk-your-api-key-here
   

Usage

Running the PoC

Interactive Demo (Recommended):

node demo.js

This runs the comprehensive demo with prepared queries for all object types and shows real-time query generation.

Main PoC (Query Generation Only):

npm start

Main PoC with Query Execution:

EXECUTE_QUERY=true npm start

This will process the hardcoded natural language query and output the generated ODMDB query in JSON format. When EXECUTE_QUERY=true, it will also execute the query against the ODMDB server.

Changing the Query

To test different natural language queries, edit the NL_QUERY constant in poc.js:

// Line 16 in poc.js
const NL_QUERY = "your natural language query here";

The system will automatically detect which object type you're asking about and generate the appropriate query.

Example Queries by Object Type

Seekers (Job Seekers)

Status-based queries:

• "show me seekers with status startasap and their email and experience"
• "find seekers looking for jobs urgently with their skills and salary expectations"
• "get seekers who are not looking with their employment status"

Skills & experience:

• "find seekers with technical skills and years of experience"
• "show me seekers with language abilities and personality profiles"
• "get seekers with specific know-how and job radar interests"

Location & preferences:

• "show me seekers in Paris with remote work preferences"
• "find seekers available to work in multiple countries"
• "get seekers with specific location and salary requirements"

Job Ads

Job search queries:

• "show me recent job postings in technology"
• "find job ads with high salary ranges"
• "get job advertisements posted this week"

Company & location:

• "show me jobs at specific companies"
• "find remote job opportunities"
• "get job ads in Paris or Lyon"

Recruiters

Recruiter information:

• "show me active recruiters and their specializations"
• "find recruiters from specific companies"
• "get recruiter contact information and experience"

Persons

General person queries:

• "show me person profiles with their roles"
• "find persons by their experience or background"

Companies (Sirets)

Company information:

• "show me companies in the technology sector"
• "find companies by size or location"
• "get company details and contact information"

Supported Query Types

Multi-Object Intelligence: The system automatically detects which object you're asking about:

• Mentions of "seekers", "candidates", "job seekers" → seekers object
• Mentions of "jobs", "positions", "job ads" → jobads object
• Mentions of "recruiters", "hiring managers" → recruiters object
• Mentions of "persons", "people", "profiles" → persons object
• Mentions of "companies", "employers", "organizations" → sirets object

Filter Types:

• Status filtering: Object-specific status fields
• Date filtering: Creation dates, update dates with date ranges
• Index optimization: Uses real ODMDB indexes for efficient queries
• Field-specific: Searches within specific properties

Schema Mapping System

The PoC uses a sophisticated schema mapping system located in schema-mappings/:

Architecture

• ODMDBMappingManager: Central manager that loads and caches schema mappings
• Base Mapping: Core field synonym generation and mapping logic
• Object-Specific Mappings: Individual mapping files for each object type
• Real Schema Integration: Direct reading from actual ODMDB schema files

Verified Schema Coverage

Seekers Object:

• 62 total schema properties mapped
• 27 recruiter-readable fields identified
• 3 indexes available (lst_alias, seekstatus_alias, alias)
• 206+ field synonyms generated from real schema definitions

All Objects:

• Dynamic schema loading for any ODMDB object type
• Access rights properly extracted from apxaccessrights structure
• Index definitions read from actual idx directories
• Field synonyms generated from real property definitions

Field Mapping Examples

The system provides comprehensive natural language to field mappings:

Contact & Identity:

• email, contact, mail → email
• id, username, alias → alias
• bio, description, summary → shortdescription

Work Experience & Status:

• experience, years of experience, career length → seekworkingyear
• job titles, positions, roles, work history → seekjobtitleexperience
• status, availability, urgency → seekstatus

Location & Geography:

• location, where, work location → seeklocation
• countries, work countries → countryavailabletowork

Skills & Competencies:

• skills, competencies, abilities → skills
• languages, language skills → languageskills
• knowledge, expertise, know-how → knowhow

(Plus hundreds more mappings for all object types)

Output Format

The PoC generates ODMDB queries in this format:

{
  "object": "seekers",
  "condition": ["prop.dt_create(>=:2025-10-06)"],
  "fields": ["alias", "email", "seekworkingyear"]
}

ODMDB DSL Support

The PoC understands and generates these ODMDB DSL patterns:

• Property queries: prop.<field>(operator:value)
• Index queries: idx.<indexName>(value)
• Join queries: join(remoteObject:localKey:remoteProp:operator:value)

Demo & Testing Tools

Interactive Demo:

node demo.js

Live PoC demonstration featuring:

• Real query generation from natural language using OpenAI
• Multi-object detection and schema loading
• Prepared queries for all supported object types
• Real-time field mapping and validation
• Current ODMDB data status display

Demo Features:

• Prepared Queries: 4 example queries per object type (20 total)
• Schema Validation: Shows actual field counts and mappings
• Real-time Generation: Demonstrates actual OpenAI API integration
• Multi-Object Support: Covers seekers, jobads, recruiters, persons, sirets

Environment Variables

• OPENAI_API_KEY - Your OpenAI API key (required)
• EXECUTE_QUERY - Set to "true" to execute queries against ODMDB (default: false)
• EXECUTE_DEMO - Set to "true" to execute demo queries with real generation
• ODMDB_BASE_URL - ODMDB server URL (default: http://localhost:3000)
• ODMDB_TRIBE - ODMDB tribe name (default: smatchit)
• OPENAI_MODEL - OpenAI model to use (default: gpt-4o)

System Validation

The mappings have been thoroughly validated to ensure they:

✅ Read actual ODMDB schema files - Not hardcoded mappings
✅ Access real index registers - Uses actual idx directory files
✅ Extract proper access rights - Reads apxaccessrights.recruiters.R structure
✅ Generate comprehensive synonyms - 200+ field mappings per object
✅ Support all object types - Dynamic loading for any ODMDB schema

Technical Architecture

Core Components

1. poc.js: Main PoC engine with multi-object support
2. demo.js: Comprehensive demonstration with prepared queries
3. schema-mappings/: Real schema integration system
4. package.json: Dependencies and execution scripts

Schema Integration Flow

1. Schema Loading: ODMDBMappingManager reads actual schema files
2. Field Extraction: Extracts properties and access rights from real schemas
3. Index Integration: Reads index definitions from idx directories
4. Synonym Generation: Creates comprehensive field mappings
5. Query Generation: Uses OpenAI with dynamic schema for target object
6. Validation: Ensures generated queries match schema constraints

Data Flow

Natural Language Query
    ↓
Object Detection (seekers/jobads/recruiters/persons/sirets)
    ↓
Schema Loading (real ODMDB schema files)
    ↓
Field Mapping (comprehensive synonym matching)
    ↓
OpenAI Structured Output (dynamic JSON schema)
    ↓
ODMDB DSL Query (validated against real schema)

Limitations

• Local schema files required: Needs access to actual ODMDB schema structure
• OpenAI API dependency: Requires valid API key and credits
• Performance considerations: Schema loading and mapping generation takes time
• Single query per run: No interactive conversation mode (yet)

Next Steps

• Interactive CLI for multiple queries in conversation
• Enhanced query execution with real ODMDB server integration
• Query result processing and formatting improvements
• Advanced multi-object join queries
• Performance optimizations for schema loading
• User interface for non-technical users

Files

Core Implementation:

• poc.js - Main PoC engine supporting all ODMDB object types
• demo.js - Comprehensive demo with real query generation
• package.json - Dependencies and scripts

Schema System:

• schema-mappings/ - Complete schema mapping system
  • odmdb-mapping-manager.js - Central mapping coordinator
  • base-mapping.js - Core mapping logic and synonym generation
  • seekers-mapping.js, jobads-mapping.js, etc. - Object-specific mappings

Data Integration:

• ../smatchitObjectOdmdb/schema/*.json - Real ODMDB schema files
• ../smatchitObjectOdmdb/objects/*/idx/ - Index definition files
• ../smatchitObjectOdmdb/objects/*/itm/ - Data files for all object types

Verification

The system has been validated against real ODMDB data:

• Schema Properties: All properties correctly read from actual schema files
• Index Access: Confirmed access to real index files (lst_alias, seekstatus_alias, etc.)
• Access Rights: Proper extraction of recruiter-readable fields
• Field Mappings: Comprehensive synonym generation from actual definitions
• Multi-Object Support: Verified functionality across all object types

This ensures the PoC works with actual ODMDB schema properties and accesses real index registers as required for production readiness.