feat: Add Bitcoin agent and related configurations

Makefile

@@ -108,6 +108,17 @@ chat-powerpoint-agent: .venv
 chat-arxiv-agent: .venv
 	@ docker compose run abi bash -c 'poetry install && poetry run python -m src.core.apps.terminal_agent.main generic_run_agent ArXivAssistant'
 
+# Update Bitcoin Agent command to use the standard module loading approach
+chat-bitcoin-agent: .venv
+	@ docker compose run abi bash -c 'poetry install && poetry run python -m src.core.apps.terminal_agent.main generic_run_agent BitcoinAssistant'
+
+# Bitcoin agent test commands
+test-bitcoin-agent: .venv
+	@ docker compose run abi python -m src.custom.modules.bitcoin.tests.run_price_validation
+
+test-bitcoin-consensus: .venv
+	@ docker compose run abi bash -c 'poetry install && poetry run python -m src.custom.modules.bitcoin.tests.test_price_providers'
+
 .DEFAULT_GOAL := chat-supervisor-agent
 
 .PHONY: all test unit-tests integration-tests lint clean
@@ -137,7 +148,7 @@ fix-lint:
 	black src tests
 	isort src tests
 
-.PHONY: test chat-supervisor-agent chat-support-agent chat-content-agent chat-finance-agent chat-growth-agent chat-opendata-agent chat-operations-agent chat-sales-agent api sh lock add abi-add
+.PHONY: test chat-supervisor-agent chat-support-agent chat-content-agent chat-finance-agent chat-growth-agent chat-opendata-agent chat-operations-agent chat-sales-agent chat-bitcoin-agent api sh lock add abi-add
 
 # Build module - copies components to the module directory
 build-module:
@@ -165,7 +176,7 @@ build-module:
 	echo "Creating module README..." && \
 	echo "# $$MODULE_NAME Module\n\nDescription of your module and its purpose.\n\n## Components\n\n- Integrations\n- Workflows\n- Pipelines\n- Ontologies\n- Assistants\n\n## Usage\n\nHow to use this module.\n" > src/custom/modules/$$MODULE_NAME/README.md && \
 	echo "Creating example assistant..." && \
-	echo "from langchain_openai import ChatOpenAI\nfrom abi.services.agent.Agent import Agent, AgentConfiguration, AgentSharedState\nfrom src import secret, services\n\nNAME = \"$$MODULE_NAME_PASCAL Assistant\"\nDESCRIPTION = \"A brief description of what your assistant does.\"\nMODEL = \"o3-mini\"  # Or another appropriate model\nTEMPERATURE = 1\nAVATAR_URL = \"https://example.com/avatar.png\"\nSYSTEM_PROMPT = \"\"\"You are the $$MODULE_NAME_PASCAL Assistant. Your role is to help users with tasks related to $$MODULE_NAME.\n\nYou can perform the following tasks:\n- Task 1\n- Task 2\n- Task 3\n\nAlways be helpful, concise, and focus on solving the user's problem.\"\"\"\n\ndef create_agent(shared_state: AgentSharedState = None) -> Agent:\n    \"\"\"Creates a new instance of the $$MODULE_NAME_PASCAL Assistant.\"\"\"\n    # Configure the underlying chat model\n    llm = ChatOpenAI(\n        model=MODEL,\n        temperature=TEMPERATURE,\n        api_key=secret.get_openai_api_key()\n    )\n    \n    # Configure the agent\n    config = AgentConfiguration(\n        name=NAME,\n        description=DESCRIPTION,\n        model=MODEL,\n        temperature=TEMPERATURE,\n        system_prompt=SYSTEM_PROMPT,\n        avatar_url=AVATAR_URL,\n        shared_state=shared_state or AgentSharedState(),\n    )\n    \n    # Create and return the agent\n    agent = Agent(llm=llm, config=config)\n    \n    # Add tools to the agent (uncomment and modify as needed)\n    # workflow = YourWorkflow(YourWorkflowConfiguration())\n    # agent.add_tools(workflow.as_tools())\n    \n    return agent\n\n# For testing purposes\nif __name__ == \"__main__\":\n    agent = create_agent()\n    agent.run(\"Hello, I need help with $$MODULE_NAME\")\n" > src/custom/modules/$$MODULE_NAME/assistants/$${MODULE_NAME_PASCAL}Assistant.py && \
+	echo "from langchain_openai import ChatOpenAI\nfrom abi.services.agent.Agent import Agent, AgentConfiguration, AgentSharedState, MemorySaver\nfrom src import secret, services\n\nNAME = \"$$MODULE_NAME_PASCAL Assistant\"\nDESCRIPTION = \"A brief description of what your assistant does.\"\nMODEL = \"o3-mini\"  # Or another appropriate model\nTEMPERATURE = 1\nAVATAR_URL = \"https://example.com/avatar.png\"\nSYSTEM_PROMPT = \"\"\"You are the $$MODULE_NAME_PASCAL Assistant. Your role is to help users with tasks related to $$MODULE_NAME.\n\nYou can perform the following tasks:\n- Task 1\n- Task 2\n- Task 3\n\nAlways be helpful, concise, and focus on solving the user's problem.\"\"\"\n\ndef create_agent(shared_state: AgentSharedState = None) -> Agent:\n    \"\"\"Creates a new instance of the $$MODULE_NAME_PASCAL Assistant.\"\"\"\n    # Configure the underlying chat model\n    chat_model = ChatOpenAI(\n        model=MODEL,\n        temperature=TEMPERATURE,\n        api_key=secret.get('OPENAI_API_KEY')\n    )\n    \n    # Create minimal configuration\n    config = AgentConfiguration(\n        system_prompt=SYSTEM_PROMPT,\n        avatar_url=AVATAR_URL\n    )\n    \n    # Create shared state if not provided\n    if shared_state is None:\n        shared_state = AgentSharedState()\n    \n    # Create and return the agent\n    agent = Agent(\n        name=NAME,\n        description=DESCRIPTION,\n        chat_model=chat_model,  # Using chat_model instead of llm\n        tools=[],  # Empty tools list\n        agents=[],  # No sub-agents\n        state=shared_state,\n        configuration=config,\n        memory=MemorySaver()\n    )\n    \n    return agent\n\n# For testing purposes\nif __name__ == \"__main__\":\n    agent = create_agent()\n    agent.run(\"Hello, I need help with $$MODULE_NAME\")\n" > src/custom/modules/$$MODULE_NAME/assistants/$${MODULE_NAME_PASCAL}Assistant.py && \
 	echo "Creating example workflow..." && \
 	echo "from pydantic import BaseModel, Field\nfrom typing import Optional, List, Dict, Any\nfrom fastapi import APIRouter\nfrom langchain_core.tools import StructuredTool\n\nclass $${MODULE_NAME_PASCAL}WorkflowConfiguration(BaseModel):\n    \"\"\"Configuration for the $$MODULE_NAME_PASCAL Workflow.\"\"\"\n    # Add configuration parameters here\n    api_key: Optional[str] = Field(None, description=\"API key for external service\")\n\nclass $${MODULE_NAME_PASCAL}WorkflowParameters(BaseModel):\n    \"\"\"Parameters for running the $$MODULE_NAME_PASCAL Workflow.\"\"\"\n    # Add input parameters here\n    query: str = Field(..., description=\"Query to process\")\n    max_results: int = Field(10, description=\"Maximum number of results to return\")\n\nclass $${MODULE_NAME_PASCAL}WorkflowResult(BaseModel):\n    \"\"\"Result of the $$MODULE_NAME_PASCAL Workflow.\"\"\"\n    # Define the structure of the workflow results\n    results: List[Dict[str, Any]] = Field(default_factory=list, description=\"List of results\")\n    count: int = Field(0, description=\"Number of results found\")\n\nclass $${MODULE_NAME_PASCAL}Workflow:\n    \"\"\"A workflow for $$MODULE_NAME operations.\"\"\"\n    \n    def __init__(self, configuration: $${MODULE_NAME_PASCAL}WorkflowConfiguration):\n        self.__configuration = configuration\n    \n    def as_tools(self) -> list[StructuredTool]:\n        \"\"\"Returns a list of LangChain tools for this workflow.\"\"\"\n        return [StructuredTool(\n            name=\"$${MODULE_NAME}_workflow\",\n            description=\"Runs the $$MODULE_NAME_PASCAL workflow with the given parameters\",\n            func=lambda **kwargs: self.run($${MODULE_NAME_PASCAL}WorkflowParameters(**kwargs)),\n            args_schema=$${MODULE_NAME_PASCAL}WorkflowParameters\n        )]\n    \n    def as_api(self, router: APIRouter) -> None:\n        \"\"\"Adds API endpoints for this workflow to the given router.\"\"\"\n        @router.post(\"/$${MODULE_NAME_PASCAL}Workflow\")\n        def run(parameters: $${MODULE_NAME_PASCAL}WorkflowParameters):\n            return self.run(parameters)\n    \n    def run(self, parameters: $${MODULE_NAME_PASCAL}WorkflowParameters) -> $${MODULE_NAME_PASCAL}WorkflowResult:\n        \"\"\"Runs the workflow with the given parameters.\"\"\"\n        # Implement your workflow logic here\n        # This is a placeholder implementation\n        \n        # Example placeholder implementation\n        results = [\n            {\"id\": 1, \"name\": \"Result 1\", \"value\": \"Sample data 1\"},\n            {\"id\": 2, \"name\": \"Result 2\", \"value\": \"Sample data 2\"},\n        ]\n        \n        # Take only as many results as requested\n        results = results[:parameters.max_results]\n        \n        return $${MODULE_NAME_PASCAL}WorkflowResult(\n            results=results,\n            count=len(results)\n        )\n\n# For testing purposes\nif __name__ == \"__main__\":\n    config = $${MODULE_NAME_PASCAL}WorkflowConfiguration()\n    workflow = $${MODULE_NAME_PASCAL}Workflow(config)\n    result = workflow.run($${MODULE_NAME_PASCAL}WorkflowParameters(query=\"test query\"))\n    print(result)\n" > src/custom/modules/$$MODULE_NAME/workflows/$${MODULE_NAME_PASCAL}Workflow.py && \
 	echo "Creating example pipeline..." && \
@@ -181,3 +192,7 @@ build-module:
 	echo "Creating sample test file..." && \
 	echo "import unittest\nfrom unittest.mock import MagicMock, patch\n\nclass Test$${MODULE_NAME_PASCAL}Module(unittest.TestCase):\n    \"\"\"Test suite for the $$MODULE_NAME_PASCAL module.\"\"\"\n    \n    def setUp(self):\n        \"\"\"Set up test fixtures.\"\"\"\n        pass\n    \n    def tearDown(self):\n        \"\"\"Tear down test fixtures.\"\"\"\n        pass\n    \n    def test_assistant(self):\n        \"\"\"Test the $$MODULE_NAME_PASCAL Assistant.\"\"\"\n        try:\n            from ..assistants.$${MODULE_NAME_PASCAL}Assistant import create_agent\n            \n            # Test agent creation\n            agent = create_agent()\n            self.assertIsNotNone(agent)\n            \n            # Additional tests for the assistant would go here\n        except ImportError:\n            self.skipTest(\"Assistant not implemented yet\")\n    \n    def test_workflow(self):\n        \"\"\"Test the $$MODULE_NAME_PASCAL Workflow.\"\"\"\n        try:\n            from ..workflows.$${MODULE_NAME_PASCAL}Workflow import $${MODULE_NAME_PASCAL}Workflow, $${MODULE_NAME_PASCAL}WorkflowConfiguration, $${MODULE_NAME_PASCAL}WorkflowParameters\n            \n            # Test workflow initialization\n            config = $${MODULE_NAME_PASCAL}WorkflowConfiguration()\n            workflow = $${MODULE_NAME_PASCAL}Workflow(config)\n            self.assertIsNotNone(workflow)\n            \n            # Test workflow execution\n            params = $${MODULE_NAME_PASCAL}WorkflowParameters(query=\"test\")\n            result = workflow.run(params)\n            self.assertIsNotNone(result)\n            \n            # Test tool creation\n            tools = workflow.as_tools()\n            self.assertTrue(len(tools) > 0)\n        except ImportError:\n            self.skipTest(\"Workflow not implemented yet\")\n    \n    def test_pipeline(self):\n        \"\"\"Test the $$MODULE_NAME_PASCAL Pipeline.\"\"\"\n        try:\n            from ..pipelines.$${MODULE_NAME_PASCAL}Pipeline import $${MODULE_NAME_PASCAL}Pipeline, $${MODULE_NAME_PASCAL}PipelineConfiguration, $${MODULE_NAME_PASCAL}PipelineParameters\n            from abi.services.ontology_store import OntologyStoreService\n            \n            # Create a mock ontology store\n            mock_store = MagicMock(spec=OntologyStoreService)\n            \n            # Test pipeline initialization\n            config = $${MODULE_NAME_PASCAL}PipelineConfiguration(ontology_store=mock_store)\n            pipeline = $${MODULE_NAME_PASCAL}Pipeline(config)\n            self.assertIsNotNone(pipeline)\n            \n            # Test pipeline execution\n            params = $${MODULE_NAME_PASCAL}PipelineParameters(entity_id=\"123\")\n            result = pipeline.run(params)\n            self.assertIsNotNone(result)\n            \n            # Test if result is a graph\n            self.assertTrue(hasattr(result, 'serialize'))\n        except ImportError:\n            self.skipTest(\"Pipeline not implemented yet\")\n    \n    def test_integration(self):\n        \"\"\"Test the $$MODULE_NAME_PASCAL Integration.\"\"\"\n        try:\n            from ..integrations.$${MODULE_NAME_PASCAL}Integration import $${MODULE_NAME_PASCAL}Integration, $${MODULE_NAME_PASCAL}IntegrationConfiguration, $${MODULE_NAME_PASCAL}SearchParameters\n            from pydantic import SecretStr\n            \n            # Test integration initialization\n            config = $${MODULE_NAME_PASCAL}IntegrationConfiguration(api_key=SecretStr(\"test_key\"))\n            integration = $${MODULE_NAME_PASCAL}Integration(config)\n            self.assertIsNotNone(integration)\n            \n            # Test search function\n            params = $${MODULE_NAME_PASCAL}SearchParameters(query=\"test\")\n            results = integration.search(params)\n            self.assertIsNotNone(results)\n            self.assertTrue(isinstance(results, list))\n        except ImportError:\n            self.skipTest(\"Integration not implemented yet\")\n\nif __name__ == '__main__':\n    unittest.main()" > src/custom/modules/$$MODULE_NAME/tests/test_module.py && \
 	echo "Module '$$MODULE_NAME' built successfully in src/custom/modules/$$MODULE_NAME/"
+
+# Add debug target
+debug-agents: .venv
+	@ docker compose run abi bash -c 'poetry install && poetry run python -m src.debug_agents'

docs/storage/local.md

@@ -27,18 +27,18 @@ storage/
 │   ├── intermediate/         # Temporary processing results
 │   └── output/               # Final data products
 │
-├── triple_store/             # Semantic data storage
+├── triplestore/             # Semantic data storage
 │   ├── ontologies/           # Ontology definitions (.owl, .rdf)
 │   └── triples/              # RDF triple data (.ttl)
 │
-└── vector_store/             # Vector embeddings
+└── vectorstore/             # Vector embeddings
     ├── embeddings/           # Raw vector data
     ├── indexes/              # Vector search indexes
     └── metadata/             # Associated metadata
 
 ### Triple Store Structure
 
-The `triple_store/` directory follows semantic web standards:
+The `triplestore/` directory follows semantic web standards:
 
 - **ontologies/**: Contains schema definitions and ontology models
   - `.owl` files define formal ontologies with classes, properties, and rules
@@ -50,7 +50,7 @@ The `triple_store/` directory follows semantic web standards:
 
 ### Vector Store Structure
 
-The `vector_store/` directory is optimized for machine learning applications:
+The `vectorstore/` directory is optimized for machine learning applications:
 
 - **embeddings/**: Contains raw vector data, typically in binary formats
   - Organized by model and dimension (e.g., `bert-base-768d/`)
@@ -86,7 +86,7 @@ storage_service = ObjectStorageFactory.ObjectStorageServiceFS("/path/to/storage"
 
 # Basic operations
 # Store a file
-storage_service.put_object("triple_store/triples", "people.ttl", ttl_content)
+storage_service.put_object("triplestore/triples", "people.ttl", ttl_content)
 
 # Retrieve a file
 content = storage_service.get_object("data_lake/processed", "customers.json")
@@ -95,7 +95,7 @@ content = storage_service.get_object("data_lake/processed", "customers.json")
 files = storage_service.list_objects("documents/pdf")
 
 # Delete a file
-storage_service.delete_object("vector_store/embeddings", "temp_vectors.bin")
+storage_service.delete_object("vectorstore/embeddings", "temp_vectors.bin")
 ```
 
 ## Synchronization with Remote Storage
@@ -117,11 +117,11 @@ These commands automatically handle the authentication and execute the AWS S3 sy
 1. **Follow the standard directory structure** to ensure consistency and compatibility with other system components.
 
 2. **Use appropriate directories** for different types of data:
-   - Document files → `documents/`
-   - Raw data → `data_lake/raw/`
-   - Processed data → `data_lake/processed/`
-   - RDF triples → `triple_store/triples/`
-   - Vector embeddings → `vector_store/embeddings/`
+   - Document files → `datastore/documents/`
+   - Raw data → `datastore/[module_name]/raw/`
+   - Processed data → `datastore/[module_name]/processed/`
+   - RDF triples → `triplestore/[module_name]/triples/`
+   - Vector embeddings → `vectorstore/[module_name]/embeddings/`
 
 3. **Use consistent naming conventions**:
    - Use lowercase for directories and filenames
@@ -132,9 +132,3 @@ These commands automatically handle the authentication and execute the AWS S3 sy
 4. **Regularly synchronize** with remote storage to ensure data persistence and backup.
 
 5. **Clean up temporary files** to prevent storage bloat and keep the system organized.
-
-## Related Documentation
-
-- [Remote Storage](./remote.md)
-- [Triple Store Architecture](../architecture/triple_store.md)
-- [Vector Embeddings](../machine_learning/embeddings.md)

lib/abi/utils/Module.py

@@ -38,6 +38,11 @@ def load(self):
 
 
     def __load_agents(self):
+        """Loads all agents defined in the module."""
+        # Skip modules with a .skip file
+        if os.path.exists(os.path.join(self.module_path, '.skip')):
+            return
+
         for file in os.listdir(os.path.join(self.module_path, 'assistants')):
             if file.endswith('.py'):
                 assistant_path = self.module_import_path + '.assistants.' + file[:-3]