Implementação rápida do agente de IA

{
   "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"
   }
}