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d23248f065
Uncouple Copilot task execution from the REST API server. This should help performance and scalability, and allows task execution to continue regardless of the state of the user's connection. - Resolves #12023 ### Changes 🏗️ - Add `backend.copilot.executor`->`CoPilotExecutor` (setup similar to `backend.executor`->`ExecutionManager`). This executor service uses RabbitMQ-based task distribution, and sticks with the existing Redis Streams setup for task output. It uses a cluster lock mechanism to ensure a task is only executed by one pod, and the `DatabaseManager` for pooled DB access. - Add `backend.data.db_accessors` for automatic choice of direct/proxied DB access Chat requests now flow: API → RabbitMQ → CoPilot Executor → Redis Streams → SSE Client. This enables horizontal scaling of chat processing and isolates long-running LLM operations from the API service. - Move non-API Copilot stuff into `backend.copilot` (from `backend.api.features.chat`) - Updated import paths for all usages - Move `backend.executor.database` to `backend.data.db_manager` and add methods for copilot executor - Updated import paths for all usages - Make `backend.copilot.db` RPC-compatible (-> DB ops return ~~Prisma~~ Pydantic models) - Make `backend.data.workspace` RPC-compatible - Make `backend.data.graphs.get_store_listed_graphs` RPC-compatible DX: - Add `copilot_executor` service to Docker setup Config: - Add `Config.num_copilot_workers` (default 5) and `Config.copilot_executor_port` (default 8008) - Remove unused `Config.agent_server_port` > [!WARNING] > **This change adds a new microservice to the system, with entrypoint `backend.copilot.executor`.** > The `docker compose` setup has been updated, but if you run the Platform on something else, you'll have to update your deployment config to include this new service. > > When running locally, the `CoPilotExecutor` uses port 8008 by default. ### Checklist 📋 #### For code changes: - [x] I have clearly listed my changes in the PR description - [x] I have made a test plan - [x] I have tested my changes according to the test plan: - [x] Copilot works - [x] Processes messages when triggered - [x] Can use its tools #### For configuration changes: - [x] `.env.default` is updated or already compatible with my changes - [x] `docker-compose.yml` is updated or already compatible with my changes - [x] I have included a list of my configuration changes in the PR description (under **Changes**) --------- Co-authored-by: Zamil Majdy <zamil.majdy@agpt.co>
340 lines
12 KiB
Python
340 lines
12 KiB
Python
"""CustomizeAgentTool - Customizes marketplace/template agents using natural language."""
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import logging
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from typing import Any
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from backend.api.features.store.exceptions import AgentNotFoundError
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from backend.copilot.model import ChatSession
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from backend.data.db_accessors import store_db as get_store_db
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from .agent_generator import (
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AgentGeneratorNotConfiguredError,
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customize_template,
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get_user_message_for_error,
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graph_to_json,
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save_agent_to_library,
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)
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from .base import BaseTool
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from .models import (
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AgentPreviewResponse,
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AgentSavedResponse,
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ClarificationNeededResponse,
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ClarifyingQuestion,
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ErrorResponse,
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ToolResponseBase,
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)
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logger = logging.getLogger(__name__)
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class CustomizeAgentTool(BaseTool):
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"""Tool for customizing marketplace/template agents using natural language."""
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@property
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def name(self) -> str:
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return "customize_agent"
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@property
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def description(self) -> str:
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return (
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"Customize a marketplace or template agent using natural language. "
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"Takes an existing agent from the marketplace and modifies it based on "
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"the user's requirements before adding to their library."
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)
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@property
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def requires_auth(self) -> bool:
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return True
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@property
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def is_long_running(self) -> bool:
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return True
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@property
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def parameters(self) -> dict[str, Any]:
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return {
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"type": "object",
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"properties": {
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"agent_id": {
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"type": "string",
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"description": (
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"The marketplace agent ID in format 'creator/slug' "
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"(e.g., 'autogpt/newsletter-writer'). "
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"Get this from find_agent results."
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),
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},
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"modifications": {
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"type": "string",
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"description": (
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"Natural language description of how to customize the agent. "
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"Be specific about what changes you want to make."
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),
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},
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"context": {
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"type": "string",
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"description": (
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"Additional context or answers to previous clarifying questions."
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),
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},
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"save": {
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"type": "boolean",
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"description": (
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"Whether to save the customized agent to the user's library. "
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"Default is true. Set to false for preview only."
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),
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"default": True,
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},
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},
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"required": ["agent_id", "modifications"],
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}
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async def _execute(
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self,
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user_id: str | None,
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session: ChatSession,
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**kwargs,
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) -> ToolResponseBase:
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"""Execute the customize_agent tool.
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Flow:
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1. Parse the agent ID to get creator/slug
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2. Fetch the template agent from the marketplace
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3. Call customize_template with the modification request
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4. Preview or save based on the save parameter
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"""
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agent_id = kwargs.get("agent_id", "").strip()
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modifications = kwargs.get("modifications", "").strip()
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context = kwargs.get("context", "")
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save = kwargs.get("save", True)
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session_id = session.session_id if session else None
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if not agent_id:
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return ErrorResponse(
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message="Please provide the marketplace agent ID (e.g., 'creator/agent-name').",
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error="missing_agent_id",
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session_id=session_id,
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)
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if not modifications:
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return ErrorResponse(
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message="Please describe how you want to customize this agent.",
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error="missing_modifications",
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session_id=session_id,
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)
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# Parse agent_id in format "creator/slug"
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parts = [p.strip() for p in agent_id.split("/")]
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if len(parts) != 2 or not parts[0] or not parts[1]:
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return ErrorResponse(
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message=(
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f"Invalid agent ID format: '{agent_id}'. "
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"Expected format is 'creator/agent-name' "
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"(e.g., 'autogpt/newsletter-writer')."
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),
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error="invalid_agent_id_format",
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session_id=session_id,
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)
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creator_username, agent_slug = parts
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store_db = get_store_db()
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# Fetch the marketplace agent details
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try:
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agent_details = await store_db.get_store_agent_details(
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username=creator_username, agent_name=agent_slug
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)
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except AgentNotFoundError:
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return ErrorResponse(
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message=(
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f"Could not find marketplace agent '{agent_id}'. "
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"Please check the agent ID and try again."
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),
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error="agent_not_found",
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session_id=session_id,
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)
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except Exception as e:
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logger.error(f"Error fetching marketplace agent {agent_id}: {e}")
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return ErrorResponse(
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message="Failed to fetch the marketplace agent. Please try again.",
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error="fetch_error",
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session_id=session_id,
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)
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if not agent_details.store_listing_version_id:
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return ErrorResponse(
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message=(
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f"The agent '{agent_id}' does not have an available version. "
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"Please try a different agent."
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),
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error="no_version_available",
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session_id=session_id,
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)
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# Get the full agent graph
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try:
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graph = await store_db.get_agent(agent_details.store_listing_version_id)
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template_agent = graph_to_json(graph)
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except Exception as e:
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logger.error(f"Error fetching agent graph for {agent_id}: {e}")
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return ErrorResponse(
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message="Failed to fetch the agent configuration. Please try again.",
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error="graph_fetch_error",
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session_id=session_id,
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)
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# Call customize_template
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try:
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result = await customize_template(
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template_agent=template_agent,
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modification_request=modifications,
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context=context,
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)
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except AgentGeneratorNotConfiguredError:
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return ErrorResponse(
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message=(
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"Agent customization is not available. "
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"The Agent Generator service is not configured."
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),
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error="service_not_configured",
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session_id=session_id,
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)
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except Exception as e:
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logger.error(f"Error calling customize_template for {agent_id}: {e}")
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return ErrorResponse(
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message=(
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"Failed to customize the agent due to a service error. "
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"Please try again."
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),
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error="customization_service_error",
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session_id=session_id,
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)
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if result is None:
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return ErrorResponse(
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message=(
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"Failed to customize the agent. "
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"The agent generation service may be unavailable or timed out. "
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"Please try again."
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),
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error="customization_failed",
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session_id=session_id,
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)
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# Handle error response
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if isinstance(result, dict) and result.get("type") == "error":
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error_msg = result.get("error", "Unknown error")
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error_type = result.get("error_type", "unknown")
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user_message = get_user_message_for_error(
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error_type,
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operation="customize the agent",
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llm_parse_message=(
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"The AI had trouble customizing the agent. "
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"Please try again or simplify your request."
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),
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validation_message=(
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"The customized agent failed validation. "
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"Please try rephrasing your request."
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),
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error_details=error_msg,
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)
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return ErrorResponse(
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message=user_message,
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error=f"customization_failed:{error_type}",
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session_id=session_id,
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)
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# Handle clarifying questions
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if isinstance(result, dict) and result.get("type") == "clarifying_questions":
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questions = result.get("questions") or []
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if not isinstance(questions, list):
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logger.error(
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f"Unexpected clarifying questions format: {type(questions)}"
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)
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questions = []
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return ClarificationNeededResponse(
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message=(
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"I need some more information to customize this agent. "
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"Please answer the following questions:"
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),
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questions=[
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ClarifyingQuestion(
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question=q.get("question", ""),
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keyword=q.get("keyword", ""),
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example=q.get("example"),
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)
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for q in questions
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if isinstance(q, dict)
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],
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session_id=session_id,
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)
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# Result should be the customized agent JSON
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if not isinstance(result, dict):
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logger.error(f"Unexpected customize_template response type: {type(result)}")
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return ErrorResponse(
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message="Failed to customize the agent due to an unexpected response.",
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error="unexpected_response_type",
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session_id=session_id,
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)
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customized_agent = result
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agent_name = customized_agent.get(
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"name", f"Customized {agent_details.agent_name}"
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)
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agent_description = customized_agent.get("description", "")
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nodes = customized_agent.get("nodes")
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links = customized_agent.get("links")
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node_count = len(nodes) if isinstance(nodes, list) else 0
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link_count = len(links) if isinstance(links, list) else 0
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if not save:
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return AgentPreviewResponse(
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message=(
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f"I've customized the agent '{agent_details.agent_name}'. "
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f"The customized agent has {node_count} blocks. "
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f"Review it and call customize_agent with save=true to save it."
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),
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agent_json=customized_agent,
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agent_name=agent_name,
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description=agent_description,
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node_count=node_count,
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link_count=link_count,
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session_id=session_id,
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)
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if not user_id:
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return ErrorResponse(
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message="You must be logged in to save agents.",
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error="auth_required",
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session_id=session_id,
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)
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# Save to user's library
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try:
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created_graph, library_agent = await save_agent_to_library(
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customized_agent, user_id, is_update=False
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)
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return AgentSavedResponse(
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message=(
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f"Customized agent '{created_graph.name}' "
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f"(based on '{agent_details.agent_name}') "
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f"has been saved to your library!"
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),
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agent_id=created_graph.id,
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agent_name=created_graph.name,
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library_agent_id=library_agent.id,
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library_agent_link=f"/library/agents/{library_agent.id}",
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agent_page_link=f"/build?flowID={created_graph.id}",
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session_id=session_id,
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)
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except Exception as e:
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logger.error(f"Error saving customized agent: {e}")
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return ErrorResponse(
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message="Failed to save the customized agent. Please try again.",
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error="save_failed",
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session_id=session_id,
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)
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