Merge pull request #550 from jupyter-naas/546-enhance-linkedin-module-with-mutual-connections-search-and-invitation-feature

feat: enhance linkedin module with mutual connections search

Author: FlorentRvl

config.yaml

@@ -36,16 +36,18 @@ modules:
     enabled: true
   - path: src/core/modules/qwen
     enabled: true
+  - path: src/core/modules/templatablesparqlquery
+    enabled: true
   - path: src/marketplace/modules/applications/algolia
     enabled: false
   - path: src/marketplace/modules/applications/arxiv
     enabled: false
   - path: src/marketplace/modules/applications/git
-    enabled: false
+    enabled: true
   - path: src/marketplace/modules/applications/github
     enabled: true
   - path: src/marketplace/modules/applications/google_search
-    enabled: false
+    enabled: true
   - path: src/marketplace/modules/applications/linkedin
     enabled: true
   - path: src/marketplace/modules/applications/naas

lib/abi/utils/Module.py

@@ -35,7 +35,6 @@ def __init__(self, module_path: str, module_import_path: str, imported_module: A
         self.triggers = []
         self.ontologies = []
         self.agents = []
-        self.loaded = False
 
     def check_requirements(self):
         if hasattr(self.imported_module, "requirements"):
@@ -44,16 +43,14 @@ def check_requirements(self):
                 raise SystemExit(f"Application crashed due to module loading failure: {self.module_import_path}")
 
     def load(self):
-        if self.loaded is False:
-            try:
-                self.check_requirements()
-                self.__load_agents()
-                self.__load_triggers()
-                self.__load_ontologies()
-                self.loaded = True
-            except Exception as e:
-                logger.error(f"❌ Critical error loading module {self.module_import_path}: {e}")
-                raise SystemExit(f"Application crashed due to module loading failure: {self.module_import_path}")
+        try:
+            self.check_requirements()
+            # self.__load_agents()
+            self.__load_triggers()
+            self.__load_ontologies()
+        except Exception as e:
+            logger.error(f"❌ Critical error loading module {self.module_import_path}: {e}")
+            raise SystemExit(f"Application crashed due to module loading failure: {self.module_import_path}")
 
     def load_agents(self):
         try:

src/__init__.py

@@ -196,10 +196,15 @@ def load_modules():
                     topic, event_type, callback, background
                 )
 
-
+    logger.debug("Loading on_initialized")
     for module in _modules:
         module.on_initialized()
 
+    logger.debug("Loading agents")
+    for module in _modules:
+        logger.debug(f"Loading agents for module {module.module_import_path}")
+        module.load_agents()
+
     return _modules
 
 services = LazyLoader(lambda: init_services(config, secret))

src/api.py

@@ -10,7 +10,7 @@
 import subprocess
 import os
 from abi import logger
-from src.__modules__ import get_modules
+from src import modules
 
 # Authentication
 from fastapi.security import OAuth2PasswordRequestForm
@@ -20,8 +20,6 @@
 from src.openapi_doc import TAGS_METADATA, API_LANDING_HTML
 from src import config
 
-# Automatic loading of agents from modules
-
 # Init API
 TITLE = config.api_title
 DESCRIPTION = config.api_description
@@ -190,9 +188,6 @@ def root():
     return API_LANDING_HTML.replace("[TITLE]", TITLE).replace("[LOGO_NAME]", logo_name)
 
 # Add agents to the API
-modules = get_modules(config)
-
-# Collect all agents first
 all_agents: list = []
 for module in modules:
     for agent in module.agents:

src/core/modules/__templates__/agents/TemplateAgent.py

@@ -71,7 +71,7 @@ def create_agent(
     tools: list = []
 
     # Get tools from intentmapping
-    from src.core.modules.abi import get_tools
+    from src.core.modules.templatablesparqlquery import get_tools
     templates_tools = [
         "search_class"
     ]

src/core/modules/abi/README.md

@@ -1,40 +1,62 @@
 # Abi Agent Module
 
-> **Multi-Agent Orchestrator and Strategic Advisory System**
+> **Multi-Agent Orchestrator and Knowledge Graph Management System**
 
-The Abi agent is the **central coordinator** for the ABI ecosystem, managing specialized AI agents and knowledge graph operations while providing strategic advisory capabilities.
+The Abi module is the **central coordination hub** for the ABI ecosystem, featuring a sophisticated multi-agent orchestrator, specialized knowledge graph agents, and comprehensive ontology management capabilities.
 
 ## 🎯 Purpose & Role
 
-**Abi** (*Agentic Brain Infrastructure*) orchestrates multi-agent workflows by:
+**Abi** (*Agentic Brain Infrastructure*) provides comprehensive AI orchestration and knowledge management through:
 
-- **Coordinating** specialized AI agents (ChatGPT, Claude, Mistral, Gemini, Grok, Llama, Perplexity)
-- **Managing** conversation context and agent transitions
-- **Routing** requests based on weighted decision hierarchy
-- **Providing** strategic advisory through direct consultation
-- **Supporting** multilingual interactions (English/French)
-- **Integrating** knowledge graph exploration and SPARQL querying
+- **Multi-Agent Orchestration**: Central coordinator managing specialized AI agents (ChatGPT, Claude, Mistral, Gemini, Grok, Llama, Perplexity, Qwen, DeepSeek, Gemma)
+- **Knowledge Graph Operations**: Complete CRUD operations for semantic data management
+- **Ontology Engineering**: BFO-compliant entity extraction and SPARQL generation
+- **Intelligent Routing**: Weighted decision hierarchy with context preservation
+- **Multilingual Support**: Native French/English interactions with cultural awareness
+- **Production Integration**: Event-driven triggers and YAML ontology publishing
 
 ## 🏗️ Architecture
 
 ### Directory Structure
 ```
 src/core/modules/abi/
 ├── agents/                  # Agent implementations
-│   ├── AbiAgent.py         # Main orchestrator
-│   ├── AbiAgent_test.py    # Test suite
-│   ├── EntitytoSPARQLAgent.py
-│   ├── KnowledgeGraphBuilderAgent.py
-│   └── OntologyEngineerAgent.py
+│   ├── AbiAgent.py         # Main multi-agent orchestrator
+│   ├── AbiAgent_test.py    # Comprehensive test suite
+│   ├── EntitytoSPARQLAgent.py        # BFO entity extraction & SPARQL generation
+│   ├── EntitytoSPARQLAgent_test.py   # Entity extraction tests
+│   ├── KnowledgeGraphBuilderAgent.py # Triple store CRUD operations
+│   ├── KnowledgeGraphBuilderAgent_test.py # KG builder tests
+│   ├── OntologyEngineerAgent.py      # BFO ontology engineering
+│   └── OntologyEngineerAgent_test.py # Ontology engineering tests
 ├── models/                  # Model configurations
-│   ├── o3_mini.py          # OpenAI o3-mini (cloud)
-│   └── qwen3_8b.py         # Qwen3 8B (local/Ollama)
-├── workflows/               # Business logic workflows
-├── pipelines/               # Data processing pipelines
-├── ontologies/              # Ontology definitions
-├── sandbox/                 # Experimental scripts
-├── triggers.py              # Event-driven triggers
-├── mappings.py              # Visualization mappings
+│   ├── o3_mini.py          # OpenAI o3-mini (cloud, temp=1.0)
+│   └── qwen3_8b.py         # Qwen3 8B (local/Ollama, temp=0.7)
+├── workflows/               # Business logic workflows (15 implementations)
+│   ├── AgentRecommendationWorkflow.py     # AI agent recommendation engine
+│   ├── ConvertOntologyGraphToYamlWorkflow.py # Graph to YAML conversion
+│   ├── CreateClassOntologyYamlWorkflow.py    # Class ontology publishing
+│   ├── CreateIndividualOntologyYamlWorkflow.py # Individual ontology publishing
+│   ├── ExportGraphInstancesToExcelWorkflow.py # Data export capabilities
+│   ├── GetObjectPropertiesFromClassWorkflow.py # Property retrieval
+│   ├── GetSubjectGraphWorkflow.py            # Entity graph exploration
+│   ├── SearchIndividualWorkflow.py           # Semantic search
+│   └── TemplatableSparqlQuery.py            # SPARQL query templating
+├── pipelines/               # Data processing pipelines (18 implementations)
+│   ├── AddIndividualPipeline.py        # Entity creation
+│   ├── InsertDataSPARQLPipeline.py     # SPARQL data insertion
+│   ├── MergeIndividualsPipeline.py     # Entity merging
+│   ├── RemoveIndividualPipeline.py     # Entity deletion
+│   ├── UpdateDataPropertyPipeline.py   # Property updates
+│   └── Update*Pipeline.py              # Specialized update pipelines
+├── ontologies/              # Ontology definitions (4-level hierarchy)
+│   ├── top-level/          # BFO foundational ontologies
+│   ├── mid-level/          # Common Core Ontologies
+│   ├── domain-level/       # Domain-specific ontologies
+│   └── application-level/  # Use-case specific ontologies
+├── sandbox/                 # Experimental scripts (8 utilities)
+├── triggers.py              # Production event-driven triggers
+├── mappings.py              # Knowledge graph visualization colors
 └── __init__.py              # Module initialization
 ```
 
@@ -65,23 +87,31 @@ graph TD
 | Variable | Values | Default | Description |
 |----------|--------|---------|-------------|
 | `AI_MODE` | `cloud` \| `local` | `cloud` | Model deployment mode |
-| `OPENAI_API_KEY` | API key | Required | For cloud model (o3-mini) |
-| `NAAS_API_KEY` | API key | Optional | For production ontology triggers |
+| `OPENAI_API_KEY` | API key | Required | For cloud models (o3-mini, gpt-4o) |
+| `NAAS_API_KEY` | API key | Optional | For production ontology triggers & YAML publishing |
+| `ENV` | `dev` \| `prod` | `dev` | Environment mode (affects trigger activation) |
 
 ### Model Selection
 
 The agent automatically selects models based on `AI_MODE`:
 
 ```python
-# Cloud Mode (default)
+# Cloud Mode (default) - Creative orchestration
 AI_MODE=cloud  # Uses OpenAI o3-mini with temperature=1.0
 
-# Local Mode  
+# Local Mode - Privacy-focused with stable performance
 AI_MODE=local  # Uses Ollama qwen3:8b with temperature=0.7
 
-# Error handling
+# Model Selection Logic:
+# - AbiAgent: Uses AI_MODE selection (o3-mini vs qwen3:8b)
+# - EntitytoSPARQLAgent: Uses o3-mini (cloud only)
+# - KnowledgeGraphBuilderAgent: Uses gpt-4o (cloud only)
+# - OntologyEngineerAgent: Uses o3-mini (cloud only)
+
+# Error Handling:
 # Missing OPENAI_API_KEY in cloud mode → Agent creation fails
-# Missing Ollama in local mode → Agent creation fails
+# Missing Ollama in local mode → AbiAgent creation fails
+# Specialized agents require cloud mode and OpenAI API key
 ```
 
 ## 🚀 Usage
@@ -92,32 +122,32 @@ AI_MODE=local  # Uses Ollama qwen3:8b with temperature=0.7
 # Start Abi agent (default target)
 make chat-abi-agent
 
-# Explicit cloud mode
-AI_MODE=cloud make chat-abi-agent
-
-# Local privacy mode (requires Ollama)
-AI_MODE=local make chat-abi-agent
-
-# Alternative generic agent runner
-make chat agent=AbiAgent
+# Alternative agent runners
+make chat agent=AbiAgent                    # Multi-agent orchestrator
+make chat agent=EntitytoSPARQLAgent         # Entity extraction & SPARQL
+make chat agent=KnowledgeGraphBuilderAgent  # Knowledge graph operations
+make chat agent=OntologyEngineerAgent       # BFO ontology engineering
 ```
 
 ### Programmatic Integration
 
 ```python
 from src.core.modules.abi.agents.AbiAgent import create_agent
+from src.core.modules.abi.agents.EntitytoSPARQLAgent import create_agent as create_entity_agent
+from src.core.modules.abi.agents.KnowledgeGraphBuilderAgent import create_agent as create_kg_agent
 
-# Create agent with automatic model selection
-agent = create_agent()
+# Create orchestrator with automatic model selection
+abi_agent = create_agent()
+response = abi_agent.invoke("Route this to the best AI for code generation")
+# Workflow: Context preservation → Classification → Weighted routing to Mistral
 
-# Direct interaction
-response = agent.invoke("Route this to the best AI for code generation")
+# Knowledge graph operations
+kg_agent = create_kg_agent()
+kg_agent.invoke("Add a new organization called 'NaasAI' to the knowledge graph")
 
-# Workflow:
-# 1. Context preservation check
-# 2. Request classification (code generation)
-# 3. Weighted routing to Mistral
-# 4. Response synthesis with "Abi:" prefix
+# Entity extraction and SPARQL generation
+entity_agent = create_entity_agent()
+entity_agent.invoke("Extract entities from: 'John works at Microsoft as a software engineer'")
 ```
 
 ### Interaction Patterns

src/core/modules/abi/__init__.py

@@ -1,35 +1,2 @@
-from src.core.modules.abi.workflows.TemplatableSparqlQuery import load_workflows
-# from abi import logger
-
-workflows: list = []
-tools: list = []
-loaded = False
-
 def requirements():
-    return True
-
-
-def get_workflows():
-    return workflows
-
-
-def get_tools(tool_names: list[str] = []):
-    global loaded
-    if not loaded:
-        load_tools()
-        loaded = True
-
-    if len(tool_names) == 0:
-        return tools
-    else:
-        return [tool for tool in tools if tool.name in tool_names]
-
-
-def load_tools():
-    # logger.debug("Loading Intent Mapping workflows")
-    w = load_workflows()
-    workflows.extend(w)
-    [tools.extend(workflow.as_tools()) for workflow in w]
-    # logger.debug(f"Tools from intentmapping loaded: {len(tools)}")
-    # tool_names = sorted([tool.name for tool in tools])
-    # logger.debug(tool_names)
+    return True

src/core/modules/abi/agents/AbiAgent.py

@@ -217,7 +217,7 @@ class EmptySchema(BaseModel):
     tools.append(knowledge_graph_tool)
 
     # Get tools
-    from src.core.modules.abi import get_tools
+    from src.core.modules.templatablesparqlquery import get_tools
     agent_recommendation_tools = [
         "find_business_proposal_agents",
         "find_coding_agents", 
@@ -384,7 +384,7 @@ class EmptySchema(BaseModel):
                     intent_target=agent.name
                 ))
 
-        if hasattr(agent, 'intents'):
+        if hasattr(agent, 'intents') and agent.name not in agent_intents_map:
             for intent in agent.intents:
                 # Create new intent with target set to agent name
                 new_intent = Intent(

src/core/modules/abi/agents/KnowledgeGraphBuilderAgent.py

@@ -282,7 +282,7 @@ def create_agent(
 
 
     # Add search organizations tools
-    from src.core.modules.abi import get_tools
+    from src.core.modules.templatablesparqlquery import get_tools
     ontology_tools: list = [
         "search_class",
         "count_instances_by_class",