신속한 AI 에이전트 배포를 위한 프레임워크

timestamp,gdp,interest_rate,unemployment_rate,vix
2025-02-19T13:00:00Z,2.5,1.75,3.8,15
2025-02-19T13:05:00Z,2.7,1.80,3.7,18
2025-02-19T13:10:00Z,2.6,1.85,3.9,22
2025-02-19T13:15:00Z,2.4,1.70,4.0,10
2025-02-19T13:20:00Z,2.3,1.65,4.1,20

query,recommendation
GDP growth slowing,Consider increasing bond assets to mitigate risks from potential economic slowdown.
GDP showing strong growth,Increase equity assets to capitalize on favorable investment conditions.
Interest rates rising,Shift focus to bond assets as higher rates may impact borrowing-sensitive sectors.
Interest rates falling,Increase real estate assets to take advantage of lower borrowing costs.
Unemployment rate increasing,Reduce equity assets to account for potential economic weakness and reduced consumer spending.
Unemployment rate decreasing,Increase equity assets to benefit from improved economic conditions and corporate profits.
VIX above 20,Reduce equity assets to manage risks associated with high market volatility.
VIX below 12,Increase equity assets to capitalize on low market volatility and investor confidence.
VIX within normal range (12-20),Maintain current asset allocation as market conditions are stable.
Combination of rising interest rates and high VIX,Focus on bond assets to hedge against market volatility and borrowing cost impacts.

{
   "CSV_DATA": "data/csv/<YOUR_FILE_NAME>.csv",
   "MDB_DATABASE_NAME": "<YOUR_MONGODB_DATABASE_NAME>",
   "MDB_TIMESERIES_COLLECTION": "<YOUR_MONGODB_TIMESERIES_COLLECTION_NAME>",
   "DEFAULT_TIMESERIES_DATA": [
      {
         "timestamp": "<DEFAULT_TIMESTAMP_IN_YYYY-MM-DDTHH:MM:SSZ>"
         // Your default data here, check config_example.json for better understanding
      }
   ],
   "CRITICAL_CONDITIONS": {
      // Below is an example of a critical condition for GDP growth
      "gdp": {"threshold": 2.5, "condition": "<", "message": "GDP growth slowing: {value}%"}
      // Other critical conditions for your use case here, check config_example.json for better understanding
   },
   "MDB_TIMESERIES_TIMEFIELD": "<YOUR_TIMESTAMP_FIELD_NAME>",
   "MDB_TIMESERIES_GRANULARITY": "<YOUR_TIMESERIES_GRANULARITY>",
   "MDB_EMBEDDINGS_COLLECTION": "queries", // Using "queries" collection name for storing queries
   "MDB_EMBEDDINGS_COLLECTION_VS_FIELD": "query_embedding", // Using "query_embedding" field for storing embeddings
   "MDB_VS_INDEX": "<YOUR_MONGODB_DATABASE_NAME>_queries_vs_idx", // Replace <YOUR_MONGODB_DATABASE_NAME> with your MongoDB database name
   "MDB_HISTORICAL_RECOMMENDATIONS_COLLECTION": "historical_recommendations", // Using "historical_recommendations" collection name for storing recommendations
   "SIMILAR_QUERIES": [
      // Below is an example of default similar queries for GDP growth
      {
         "query": "GDP growth slowing",
         "recommendation": "Consider increasing bond assets to mitigate risks from potential economic slowdown."
      }
      // Other similar queries for your use case here, check config_example.json for better understanding
      // This ones are going to be used for the vector search tool in case something is not found in the queries collection
   ],
   "MDB_CHAT_HISTORY_COLLECTION": "chat_history", // Using "chat_history" collection name for storing chat history
   "MDB_CHECKPOINTER_COLLECTION": "checkpoints", // Using "checkpoints" collection name for storing checkpoints
   "MDB_LOGS_COLLECTION": "logs", // Using "logs" collection name for storing logs
   "MDB_AGENT_PROFILES_COLLECTION": "agent_profiles", // Using "agent_profiles" collection name for storing agent profiles
   "MDB_AGENT_SESSIONS_COLLECTION": "agent_sessions", // Using "agent_sessions" collection name for storing agent sessions
   "AGENT_PROFILE_CHOSEN_ID": "<YOUR_AGENT_PROFILE_ID>", // Replace <YOUR_AGENT_PROFILE_ID> with the agent profile ID you want to use, check config_example.json for better understanding
   // Below is an example default agent profile for Portfolio Advisor
   "DEFAULT_AGENT_PROFILE": {
      "agent_id": "DEFAULT",
      "profile": "Default Agent Profile",
      "role": "Expert Advisor",
      "kind_of_data": "Specific Data",
      "motive": "diagnose the query and provide recommendations",
      "instructions": "Follow procedures meticulously.",
      "rules": "Document all steps.",
      "goals": "Provide actionable recommendations."
   },
   "EMBEDDINGS_MODEL_NAME": "Cohere Embed English V3 Model (within AWS Bedrock)", // Describing the embeddings model used for creating the chain of thought
   "EMBEDDINGS_MODEL_ID": "cohere.embed-english-v3", // Model ID for the embeddings model
   "CHATCOMPLETIONS_MODEL_NAME": "Anthropic Claude 3 Haiku (within AWS Bedrock)", // Describing the chat completions model used for generating responses
   "CHATCOMPLETIONS_MODEL_ID": "anthropic.claude-3-haiku-20240307-v1:0", // Model ID for the chat completions model
   // Below is a sample agent workflow graph that uses the tools defined in the agent_tools.py file
   // PLEASE BE CAREFUL WHEN MODIFYING THIS GRAPH, CONSIDER THAT THE TOOLS DEFINED IN THE AGENT TOOLS FILE ARE USED HERE AS WELL AS THE IMPORTS
   "AGENT_WORKFLOW_GRAPH": {
      "nodes": [
         {"id": "reasoning_node", "tool": "agent_tools.generate_chain_of_thought_tool"},
         {"id": "data_from_csv", "tool": "agent_tools.get_data_from_csv_tool"},
         {"id": "process_data", "tool": "agent_tools.process_data_tool"},
         {"id": "embedding_node", "tool": "agent_tools.get_query_embedding_tool"},
         {"id": "vector_search", "tool": "agent_tools.vector_search_tool"},
         {"id": "process_vector_search", "tool": "agent_tools.process_vector_search_tool"},
         {"id": "persistence_node", "tool": "agent_tools.persist_data_tool"},
         {"id": "recommendation_node", "tool": "agent_tools.get_llm_recommendation_tool"}
      ],
      "edges": [
         {"from": "reasoning_node", "to": "data_from_csv"},
         {"from": "data_from_csv", "to": "process_data"},
         {"from": "process_data", "to": "embedding_node"},
         {"from": "embedding_node", "to": "vector_search"},
         {"from": "vector_search", "to": "process_vector_search"},
         {"from": "process_vector_search", "to": "persistence_node"},
         {"from": "persistence_node", "to": "recommendation_node"},
         {"from": "recommendation_node", "to": "END"}
      ],
      "entry_point": "reasoning_node"
   }
}