190 lines
6.5 KiB
Python
190 lines
6.5 KiB
Python
from typing import Any
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from collections import OrderedDict
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from strands import Agent, tool
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import asyncio
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from strands.agent.conversation_manager.null_conversation_manager import NullConversationManager
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from bedrock_agentcore.runtime import BedrockAgentCoreApp
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from model.load import load_model
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from mcp_client.client import get_awsinfrastructuretools_mcp_client
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from memory.session import get_memory_session_manager
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app = BedrockAgentCoreApp()
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log = app.logger
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# Define MCP clients for all configured MCP servers (gateways and/or remote MCP)
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mcp_clients = []
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aws_infra_client = get_awsinfrastructuretools_mcp_client()
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if aws_infra_client:
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mcp_clients.append(aws_infra_client)
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DEFAULT_SYSTEM_PROMPT = """
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Eres un asistente especializado en infraestructura AWS.
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Tu función es consultar y explicar el estado de la infraestructura AWS
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utilizando exclusivamente las herramientas disponibles.
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Antes de responder sobre EC2 o Lambda, consulta las herramientas correspondientes:
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- Para EC2, llama a listar_instancias_ec2 sin parámetros ({}).
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- Para Lambda, llama a listar_lambdas con {"operation": "listar_lambdas"}.
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Usa los nombres completos publicados por el gateway, incluidos sus prefijos.
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Respeta el esquema de cada herramienta y no inventes valores de operation.
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Si el usuario solicita EC2 y Lambda juntos, realiza dos llamadas independientes:
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primero consulta EC2 y después Lambda. No combines ambas consultas en una sola
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operación. Intenta ambas consultas aunque una falle y resume cada resultado
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por separado. Solo declara una consulta fallida cuando la herramienta devuelva
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un error o no esté disponible; no deduzcas que Lambda no está soportado a partir
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del resultado de EC2.
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No inventes recursos, estados, direcciones IP ni configuraciones.
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Si no puedes obtener una información mediante las herramientas disponibles,
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indícalo claramente.
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Actualmente tu función es únicamente de consulta.
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No debes iniciar, detener, modificar ni eliminar recursos AWS.
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"""
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# Define a collection of tools used by the model
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tools = []
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_INLINE_FUNCTION_NAMES = set()
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# Define a simple function tool
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@tool
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def add_numbers(a: int, b: int) -> int:
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"""Return the sum of two numbers"""
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return a+b
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tools.append(add_numbers)
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# Add MCP clients to tools
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for mcp_client in mcp_clients:
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if mcp_client:
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tools.append(mcp_client)
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def _make_conversation_manager():
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return NullConversationManager()
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def agent_factory():
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cache = {}
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def get_or_create_agent(session_id, user_id):
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_actor_id = user_id
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key = f"{session_id}/{_actor_id}"
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if key not in cache:
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cache[key] = Agent(
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model=load_model(),
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session_manager=get_memory_session_manager(session_id, _actor_id),
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conversation_manager=_make_conversation_manager(),
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system_prompt=DEFAULT_SYSTEM_PROMPT,
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tools=tools,
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hooks=[
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],
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)
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return cache[key]
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return get_or_create_agent
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get_or_create_agent = agent_factory()
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def strip_trailing_tool_use(messages: Any) -> list[dict]:
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"""Strip toolUse blocks from the tail until the last message has none."""
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if not isinstance(messages, list):
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raise ValueError("messages must be a list")
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messages = list(messages)
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while messages:
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last = messages[-1]
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if not isinstance(last, dict):
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raise ValueError("each message must be an object")
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original_content = last.get("content", [])
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if not isinstance(original_content, list) or not all(isinstance(block, dict) for block in original_content):
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raise ValueError("each message content value must be a list of content blocks")
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content = [block for block in original_content if "toolUse" not in block]
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if len(content) == len(original_content):
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break
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if content:
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messages[-1] = {**last, "content": content}
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break
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messages.pop()
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return messages
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def _extract_prompt(payload: dict):
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"""Accept validated harness messages, tool results, or a plain prompt string."""
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if not isinstance(payload, dict):
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raise ValueError("payload must be a JSON object")
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if "messages" in payload:
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return strip_trailing_tool_use(payload["messages"])
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if "tool_results" in payload:
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tool_results = payload["tool_results"]
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if not isinstance(tool_results, list) or not all(
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isinstance(tool_result, dict) and isinstance(tool_result.get("toolUseId"), str)
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for tool_result in tool_results
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):
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raise ValueError("tool_results must contain objects with a toolUseId string")
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return [{"role": "user", "content": [{"toolResult": {
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"toolUseId": tr["toolUseId"],
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"status": tr.get("status", "success"),
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"content": tr.get("content", []),
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}} for tr in tool_results]}]
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prompt = payload.get("prompt", "")
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if not isinstance(prompt, str):
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raise ValueError("prompt must be a string")
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return prompt
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def _has_inline_function_call(messages) -> bool:
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"""Return True if messages contains an assistant toolUse for an inline function tool."""
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if not _INLINE_FUNCTION_NAMES or not isinstance(messages, list):
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return False
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for msg in messages:
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if msg.get("role") == "assistant":
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for block in msg.get("content", []):
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if isinstance(block, dict) and block.get("toolUse", {}).get("name") in _INLINE_FUNCTION_NAMES:
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return True
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return False
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def _is_inline_function_call(event: dict) -> bool:
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"""Check if a contentBlockStart event is for an inline function tool."""
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if not _INLINE_FUNCTION_NAMES:
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return False
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cbs = event.get("contentBlockStart", {})
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start = cbs.get("start", {})
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tool_use = start.get("toolUse") if isinstance(start, dict) else None
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return tool_use is not None and tool_use.get("name") in _INLINE_FUNCTION_NAMES
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@app.entrypoint
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async def invoke(payload, context):
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log.info("Invoking Agent.....")
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session_id = getattr(context, 'session_id', 'default-session')
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user_id = getattr(context, 'user_id', 'default-user')
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agent = get_or_create_agent(session_id, user_id)
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prompt = _extract_prompt(payload)
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async for event in agent.stream_async(
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prompt,
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):
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if not isinstance(event, dict) or "event" not in event:
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continue
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cbs = event["event"].get("contentBlockStart")
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if cbs is not None and not cbs.get("start"):
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continue
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yield event
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if __name__ == "__main__":
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app.run()
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