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c6ef354290
Provenance triples are now included directly in explain messages from GraphRAG, DocumentRAG, and Agent services, eliminating the need for follow-up knowledge graph queries to retrieve explainability details. Each explain message in the response stream now carries: - explain_id: root URI for this provenance step (unchanged) - explain_graph: named graph where triples are stored (unchanged) - explain_triples: the actual provenance triples for this step (new) Changes across the stack: - Schema: added explain_triples field to GraphRagResponse, DocumentRagResponse, and AgentResponse - Services: all explain message call sites pass triples through (graph_rag, document_rag, agent react, agent orchestrator) - Translators: encode explain_triples via TripleTranslator for gateway wire format - Python SDK: ProvenanceEvent now includes parsed ExplainEntity and raw triples; expanded event_type detection - CLI: invoke_graph_rag, invoke_agent, invoke_document_rag use inline entity when available, fall back to graph query - Tech specs updated
70 lines
3.1 KiB
Python
70 lines
3.1 KiB
Python
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from dataclasses import dataclass, field
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from typing import Optional
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from ..core.primitives import Error, Triple
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############################################################################
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# Prompt services, abstract the prompt generation
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@dataclass
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class PlanStep:
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goal: str = ""
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tool_hint: str = "" # Suggested tool for this step
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depends_on: list[int] = field(default_factory=list) # Indices of prerequisite steps
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status: str = "pending" # pending, running, completed, failed
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result: str = "" # Result of step execution
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@dataclass
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class AgentStep:
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thought: str = ""
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action: str = ""
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arguments: dict[str, str] = field(default_factory=dict)
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observation: str = ""
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user: str = "" # User context for the step
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step_type: str = "" # "react", "plan", "execute", "decompose", "synthesise"
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plan: list[PlanStep] = field(default_factory=list) # Plan steps (for plan-then-execute)
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subagent_results: dict[str, str] = field(default_factory=dict) # Subagent results keyed by goal
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@dataclass
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class AgentRequest:
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question: str = ""
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state: str = ""
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group: list[str] | None = None
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history: list[AgentStep] = field(default_factory=list)
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user: str = "" # User context for multi-tenancy
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collection: str = "default" # Collection for provenance traces
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streaming: bool = False # Enable streaming response delivery (default false)
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session_id: str = "" # For provenance tracking across iterations
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# Orchestration fields
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conversation_id: str = "" # Groups related requests into a conversation
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pattern: str = "" # Selected pattern: "react", "plan-then-execute", "supervisor"
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task_type: str = "" # Task type from config: "general", "research", etc.
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framing: str = "" # Domain framing text injected into prompts
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correlation_id: str = "" # Links fan-out subagents to parent for fan-in
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parent_session_id: str = "" # Session ID of the supervisor that spawned this subagent
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subagent_goal: str = "" # Specific goal for a subagent (set by supervisor)
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expected_siblings: int = 0 # Number of sibling subagents in this fan-out
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@dataclass
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class AgentResponse:
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# Streaming-first design
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chunk_type: str = "" # "thought", "action", "observation", "answer", "explain", "error"
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content: str = "" # The actual content (interpretation depends on chunk_type)
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end_of_message: bool = False # Current chunk type (thought/action/etc.) is complete
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end_of_dialog: bool = False # Entire agent dialog is complete
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# Explainability fields
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explain_id: str | None = None # Root URI for this explain step
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explain_graph: str | None = None # Named graph (e.g., urn:graph:retrieval)
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explain_triples: list[Triple] = field(default_factory=list) # Provenance triples for this step
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# Orchestration fields
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message_id: str = "" # Unique ID for this response message
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error: Error | None = None
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############################################################################
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