{ "spec_version": "1.4.0-otel", "timestamp": "2026-04-24T00:13:03.355457Z", "trace_id": "4bf92f3577b34da6a3ce929d0e0e4736", "span_id": "00f067aa0ba902b7", "severity_text": "WARN", "severity_number": 13, "event_name": "swarm.agent.lifecycle.deprecated", "resource": { "service.name": "swarm-orchestrator", "service.version": "4.2.0", "deployment.environment": "production", "node.id": "worker-node-aws-us-east-1a-04" }, "attributes": { "agent.id": "AlternativeDataAgent", "agent.previous_version": "v29.x", "agent.target_version": "v30", "deprecation.reason": "Transitioning to v30 architecture.", "deprecation.action_required": true }, "metrics": { "agent.core_logic.function_count": 8, "agent.capabilities.count": 0 }, "artifacts": { "provenance": { "repository": "git+ssh://repo/core/agents/alternative_data_agent.py", "commit_sha": "a1b2c3d4e5f6g7h8i9j0", "docstring_hash": "sha256:d8c4b12389a..." }, "extracted_capabilities": [], "core_logic_signatures": [ { "name": "__init__", "ast_hash": "sha256:aea8cb10e3c4e6e1", "loc": 23, "source": "def __init__(self, knowledge_base_path=\"knowledge_base/Knowledge_Graph.json\", google_maps_api_key=None):\n \"\"\"\n Initializes the Alternative Data Agent with access to various\n alternative data sources and analytical tools.\n\n Args:\n knowledge_base_path (str): Path to the knowledge base file.\n google_maps_api_key (str, optional): API key for Google Maps.\n \"\"\"\n self.knowledge_base_path = knowledge_base_path\n self.knowledge_base = self._load_knowledge_base()\n\n # Initialize sentiment analyzers\n self.nltk_sentiment_analyzer = SentimentIntensityAnalyzer()\n self.textblob_sentiment_analyzer = TextBlob\n self.vader_sentiment_analyzer = VaderSentiment()\n\n # Initialize connections to external APIs\n self.google_maps_api_key = google_maps_api_key\n if google_maps_api_key:\n self.google_maps_client = googlemaps.Client(key=google_maps_api_key)\n else:\n self.google_maps_client = None" }, { "name": "_load_knowledge_base", "ast_hash": "sha256:b14ef62bf29bfd2a", "loc": 16, "source": "def _load_knowledge_base(self):\n \"\"\"\n Loads the knowledge base from the JSON file.\n\n Returns:\n dict: The knowledge base data.\n \"\"\"\n try:\n with open(self.knowledge_base_path, 'r') as file:\n return json.load(file)\n except FileNotFoundError:\n print(f\"Knowledge base file not found: {self.knowledge_base_path}\")\n return {}\n except json.JSONDecodeError:\n print(f\"Error decoding knowledge base JSON: {self.knowledge_base_path}\")\n return {}" }, { "name": "gather_alternative_data", "ast_hash": "sha256:5618ac1736d3ab1b", "loc": 57, "source_omitted_for_brevity": true, "note": "In a real telemetry pipeline, large functions can be truncated or omitted entirely in favor of the 'ast_hash' to save network bandwidth and storage costs." }, { "name": "analyze_social_media_sentiment", "ast_hash": "sha256:01ebe8c5753769e2", "loc": 29, "source": "def analyze_social_media_sentiment(self, company_name):\n \"\"\"\n Analyzes social media sentiment for a given company.\n\n Args:\n company_name (str): The name of the company.\n\n Returns:\n dict: Social media sentiment analysis results, including\n overall sentiment score and sentiment breakdown\n (positive, negative, neutral).\n \"\"\"\n # --- Developer Notes ---\n # - Gather social media data using the SocialMediaAPI (placeholder)\n # - Perform sentiment analysis using different libraries (NLTK, TextBlob, VADER)\n # - Combine or compare sentiment scores from different libraries\n # - Calculate overall sentiment score and sentiment breakdown\n\n # Placeholder for social media sentiment analysis logic\n # ...\n\n return {\n \"overall_sentiment\": 0.75, # Example overall sentiment score\n \"sentiment_breakdown\": {\n \"positive\": 0.8,\n \"negative\": 0.1,\n \"neutral\": 0.1\n }\n }" }, { "name": "analyze_web_traffic", "ast_hash": "sha256:fe50c107088a0877", "loc": 25, "source": "def analyze_web_traffic(self, company_name):\n \"\"\"\n Analyzes web traffic data for a given company.\n\n Args:\n company_name (str): The name of the company.\n\n Returns:\n dict: Web traffic analysis results, including website visits,\n page views, and other relevant metrics.\n \"\"\"\n # --- Developer Notes ---\n # - Gather web traffic data using the WebTrafficAPI (placeholder)\n # - Analyze website traffic trends and patterns\n # - Identify key metrics (e.g., unique visitors, page views, bounce rate)\n # - Correlate web traffic data with other data sources (e.g., social media sentiment)\n\n # Placeholder for web traffic analysis logic\n # ...\n\n return {\n \"website_visits\": 1000000, # Example website visits\n \"page_views\": 5000000, # Example page views\n # ... other metrics\n }" }, { "name": "analyze_satellite_imagery", "ast_hash": "sha256:63abe9c0a55b53f8", "loc": 25, "source": "def analyze_satellite_imagery(self, company_name):\n \"\"\"\n Analyzes satellite imagery data for a given company.\n\n Args:\n company_name (str): The name of the company.\n\n Returns:\n dict: Satellite imagery analysis results, including insights\n on manufacturing activity, retail store traffic, etc.\n \"\"\"\n # --- Developer Notes ---\n # - Gather satellite imagery data using the SatelliteImageryAPI (placeholder)\n # - Analyze images of manufacturing plants to estimate production levels\n # - Analyze images of retail store parking lots to estimate customer traffic\n # - Identify changes in infrastructure or land use\n\n # Placeholder for satellite imagery analysis logic\n # ...\n\n return {\n \"manufacturing_activity\": \"Increasing\", # Example analysis result\n \"retail_store_traffic\": \"Decreasing\", # Example analysis result\n # ... other insights\n }" }, { "name": "analyze_foot_traffic", "ast_hash": "sha256:610fba1efb1d8cb3", "loc": 25, "source": "def analyze_foot_traffic(self, company_name):\n \"\"\"\n Analyzes foot traffic data for a given company.\n\n Args:\n company_name (str): The name of the company.\n\n Returns:\n dict: Foot traffic analysis results, including customer\n traffic patterns and trends for retail stores.\n \"\"\"\n # --- Developer Notes ---\n # - Gather foot traffic data using the FootTrafficAPI (placeholder)\n # - Analyze foot traffic patterns in retail stores\n # - Identify peak hours and days\n # - Correlate foot traffic with sales data\n\n # Placeholder for foot traffic analysis logic\n # ...\n\n return {\n \"peak_hours\": \"12pm-2pm\", # Example analysis result\n \"peak_days\": [\"Saturday\", \"Sunday\"], # Example analysis result\n # ... other insights\n }" }, { "name": "analyze_shipping_data", "ast_hash": "sha256:8968e435335a5f08", "loc": 25, "source": "def analyze_shipping_data(self, company_name):\n \"\"\"\n Analyzes shipping data for a given company.\n\n Args:\n company_name (str): The name of the company.\n\n Returns:\n dict: Shipping data analysis results, including insights\n on supply chain efficiency, shipping volume, etc.\n \"\"\"\n # --- Developer Notes ---\n # - Gather shipping data using the ShippingDataAPI (placeholder)\n # - Analyze shipping volume and trends\n # - Identify potential bottlenecks or delays in the supply chain\n # - Estimate delivery times and shipping costs\n\n # Placeholder for shipping data analysis logic\n # ...\n\n return {\n \"shipping_volume\": \"Increasing\", # Example analysis result\n \"supply_chain_efficiency\": \"Stable\", # Example analysis result\n # ... other insights\n }" } ] } }