f74040adf3
- Update ConversationRecorder to include source and nodeId metadata in transcripts for better context tracking. - Introduce optional variable handling in DynamicVariableStore, allowing for unset variables to be rendered as empty without raising errors. - Refactor WorkflowBrain to apply turn configurations and manage interaction policies dynamically, improving agent responsiveness. - Implement tests to ensure proper handling of updated session variables and workflow metadata in various scenarios.
349 lines
13 KiB
Python
349 lines
13 KiB
Python
from __future__ import annotations
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import unittest
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from models import AssistantConfig, RuntimeModelResource
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from services.pipecat.service_factory import config_with_resource
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from services.node_specs import graph_references, normalize_graph, validate_graph
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from services.runtime_variables import DynamicVariableStore, prepare_dynamic_config
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from services.workflow_engine import WorkflowEngine
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def valid_graph():
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return {
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"specVersion": 3,
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"settings": {"globalPrompt": "服务 {{customer}}"},
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"nodes": [
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{"id": "start", "type": "start", "data": {"name": "Start"}},
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{
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"id": "agent",
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"type": "agent",
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"data": {"name": "Agent", "prompt": "处理订单", "contextPolicy": "fresh"},
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},
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{"id": "end", "type": "end", "data": {"name": "End", "scope": "flow"}},
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],
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"edges": [
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{
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"id": "begin",
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"source": "start",
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"target": "agent",
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"data": {"mode": "always", "priority": 0},
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},
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{
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"id": "paid",
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"source": "agent",
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"target": "end",
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"data": {
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"mode": "expression",
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"priority": 10,
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"expression": {
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"combinator": "and",
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"rules": [
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{"variable": "order_status", "operator": "eq", "value": "paid"}
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],
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},
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},
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},
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],
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}
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class WorkflowGraphTests(unittest.TestCase):
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def test_agent_entry_mode_defaults_and_validation(self):
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graph = valid_graph()
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normalized = normalize_graph(graph)
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agent = next(node for node in normalized["nodes"] if node["type"] == "agent")
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self.assertEqual(agent["data"]["entryMode"], "wait_user")
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self.assertEqual(agent["data"]["entrySpeech"], "")
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self.assertTrue(agent["data"]["inheritGlobalConfig"])
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self.assertEqual(agent["data"]["contextPolicy"], "fresh")
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agent["data"]["entryMode"] = "fixed_speech"
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self.assertTrue(
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any("固定进入语不能为空" in error for error in validate_graph(normalized))
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)
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agent["data"]["entrySpeech"] = "您好，{{customer}}"
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self.assertEqual(validate_graph(normalized), [])
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def test_voice_resource_creates_isolated_runtime_config(self):
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base = AssistantConfig(type="workflow", asr="default", voice="default")
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asr = RuntimeModelResource(
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id="asr_1",
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capability="ASR",
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interface_type="openai-asr",
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values={"modelId": "sensevoice", "language": "zh", "apiUrl": "https://asr.test"},
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secrets={"apiKey": "secret"},
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)
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resolved = config_with_resource(base, asr)
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self.assertEqual(resolved.asr, "sensevoice")
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self.assertEqual(resolved.stt_api_key, "secret")
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self.assertEqual(base.asr, "default")
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llm = RuntimeModelResource(
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id="llm_1",
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capability="LLM",
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interface_type="openai-llm",
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values={"modelId": "deepseek-chat", "apiUrl": "https://llm.test/v1"},
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secrets={"apiKey": "llm-secret"},
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)
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llm_resolved = config_with_resource(base, llm)
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self.assertEqual(llm_resolved.model, "deepseek-chat")
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self.assertEqual(llm_resolved.llm_api_key, "llm-secret")
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def test_global_and_custom_agent_references_are_preserved(self):
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graph = valid_graph()
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graph["settings"].update(
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{
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"defaultLlmResourceId": "llm_global",
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"defaultAsrResourceId": "asr_global",
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"defaultTtsResourceId": "tts_global",
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"toolIds": ["tool_global"],
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"knowledgeBaseId": "kb_global",
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}
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)
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agent = next(node for node in graph["nodes"] if node["type"] == "agent")
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agent["data"].update(
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{
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"inheritGlobalConfig": False,
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"llmResourceId": "llm_agent",
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"asrResourceId": "asr_agent",
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"ttsResourceId": "tts_agent",
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"toolIds": ["tool_agent"],
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"knowledgeBaseId": "kb_agent",
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}
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)
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refs = graph_references(graph)
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self.assertEqual(
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refs["model_resources"],
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{
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"llm_global",
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"asr_global",
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"tts_global",
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"llm_agent",
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"asr_agent",
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"tts_agent",
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},
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)
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self.assertEqual(refs["tools"], {"tool_global", "tool_agent"})
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self.assertEqual(refs["knowledge_bases"], {"kb_global", "kb_agent"})
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def test_existing_agent_override_disables_implicit_inheritance(self):
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graph = valid_graph()
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agent = next(node for node in graph["nodes"] if node["type"] == "agent")
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agent["data"]["toolIds"] = ["legacy_tool"]
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normalized = normalize_graph(graph)
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normalized_agent = next(
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node for node in normalized["nodes"] if node["type"] == "agent"
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)
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self.assertFalse(normalized_agent["data"]["inheritGlobalConfig"])
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def test_inherited_agent_ignores_stale_custom_references(self):
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graph = valid_graph()
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agent = next(node for node in graph["nodes"] if node["type"] == "agent")
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agent["data"].update(
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{
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"inheritGlobalConfig": True,
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"llmResourceId": "stale_llm",
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"asrResourceId": "stale_asr",
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"ttsResourceId": "stale_tts",
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"toolIds": ["stale_tool"],
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"knowledgeBaseId": "stale_kb",
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}
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)
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refs = graph_references(graph)
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self.assertNotIn("stale_llm", refs["model_resources"])
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self.assertNotIn("stale_tool", refs["tools"])
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self.assertNotIn("stale_kb", refs["knowledge_bases"])
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def test_agent_effective_config_inherits_then_switches_to_override(self):
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graph = valid_graph()
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graph["settings"].update(
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{
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"defaultLlmResourceId": "llm_global",
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"defaultAsrResourceId": "asr_global",
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"defaultTtsResourceId": "tts_global",
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"toolIds": ["tool_global"],
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"knowledgeBaseId": "kb_global",
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"knowledgeMode": "on_demand",
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"knowledgeTopN": 8,
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"knowledgeScoreThreshold": 0.4,
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"enableInterrupt": False,
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"turnConfig": {
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"bargeIn": {"strategy": "transcription"},
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"turnDetection": {"strategy": "silence", "silenceTimeoutSecs": 1.2},
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},
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}
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)
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engine = WorkflowEngine(graph)
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inherited = engine.agent_stage_config("agent")
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self.assertEqual(inherited.llm_resource_id, "llm_global")
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self.assertEqual(inherited.tool_ids, ("tool_global",))
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self.assertEqual(inherited.knowledge_mode, "on_demand")
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self.assertFalse(inherited.enable_interrupt)
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self.assertEqual(
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inherited.turn_config["bargeIn"]["strategy"],
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"transcription",
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)
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engine.data("agent").update(
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{
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"inheritGlobalConfig": False,
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"llmResourceId": "llm_agent",
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"toolIds": ["tool_agent"],
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"knowledgeBaseId": "",
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"enableInterrupt": True,
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"turnConfig": {
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"bargeIn": {"strategy": "vad"},
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"turnDetection": {"strategy": "smart_turn"},
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},
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}
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)
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custom = engine.agent_stage_config("agent")
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self.assertEqual(custom.llm_resource_id, "llm_agent")
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self.assertEqual(custom.tool_ids, ("tool_agent",))
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self.assertEqual(custom.knowledge_mode, "disabled")
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self.assertTrue(custom.enable_interrupt)
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self.assertEqual(
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custom.turn_config["turnDetection"]["strategy"],
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"smart_turn",
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)
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def test_start_agent_and_handoff_may_have_no_outgoing_edge(self):
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terminal_graphs = [
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{
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"specVersion": 3,
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"settings": {},
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"nodes": [{"id": "start", "type": "start", "data": {}}],
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"edges": [],
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},
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{
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"specVersion": 3,
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"settings": {},
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"nodes": [
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{"id": "start", "type": "start", "data": {}},
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{
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"id": "agent",
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"type": "agent",
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"data": {"prompt": "持续处理用户问题"},
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},
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],
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"edges": [
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{
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"id": "begin",
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"source": "start",
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"target": "agent",
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"data": {"mode": "always", "priority": 0},
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}
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],
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},
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{
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"specVersion": 3,
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"settings": {},
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"nodes": [
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{"id": "start", "type": "start", "data": {}},
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{"id": "handoff", "type": "handoff", "data": {}},
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],
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"edges": [
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{
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"id": "begin",
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"source": "start",
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"target": "handoff",
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"data": {"mode": "always", "priority": 0},
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}
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],
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},
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]
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for graph in terminal_graphs:
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with self.subTest(node=graph["nodes"][-1]["type"]):
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self.assertEqual(validate_graph(graph), [])
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action_without_exit = {
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"specVersion": 3,
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"settings": {},
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"nodes": [
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{"id": "start", "type": "start", "data": {}},
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{"id": "action", "type": "action", "data": {}},
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],
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"edges": [
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{
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"id": "begin",
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"source": "start",
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"target": "action",
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"data": {"mode": "always", "priority": 0},
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}
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],
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}
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self.assertTrue(
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any(
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"action 的出边不能少于 1" in error
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for error in validate_graph(action_without_exit)
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)
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)
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def test_v2_start_prompt_is_preserved_in_synthetic_agent(self):
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graph = normalize_graph(
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{
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"nodes": [
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{"id": "s", "type": "startCall", "data": {"prompt": "询问需求"}},
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{"id": "e", "type": "endCall", "data": {"prompt": "再见"}},
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],
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"edges": [
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{"id": "done", "source": "s", "target": "e", "data": {"condition": "完成"}}
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],
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}
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)
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migrated = next(node for node in graph["nodes"] if node["type"] == "agent")
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self.assertEqual(migrated["data"]["prompt"], "询问需求")
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self.assertEqual(validate_graph(graph), [])
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def test_rejects_automatic_cycle_and_duplicate_priorities(self):
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graph = valid_graph()
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graph["nodes"].insert(1, {"id": "action", "type": "action", "data": {}})
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graph["edges"] = [
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{"id": "a", "source": "start", "target": "action", "data": {"mode": "always", "priority": 0}},
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{"id": "b", "source": "action", "target": "start", "data": {"mode": "always", "priority": 0}},
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{"id": "c", "source": "agent", "target": "end", "data": {"mode": "always", "priority": 10}},
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]
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errors = validate_graph(graph)
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self.assertTrue(any("无等待循环" in error for error in errors))
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def test_expression_routes_using_session_variables(self):
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cfg = prepare_dynamic_config(
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AssistantConfig(
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type="workflow",
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graph=valid_graph(),
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dynamic_variable_definitions={
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"customer": {"type": "string", "default": "王先生"},
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"order_status": {"type": "string", "default": "pending"},
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},
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),
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{},
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assistant_id="asst_test",
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)
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store = DynamicVariableStore.from_config(cfg)
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engine = WorkflowEngine(cfg.graph)
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self.assertIsNone(engine.deterministic_edge("agent", store, include_default=False))
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store.assign("order_status", "paid")
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self.assertEqual(
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engine.deterministic_edge("agent", store, include_default=False)["target"],
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"end",
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)
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self.assertIn("王先生", engine.prompt_for("agent", store))
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inherited_prompt = engine.prompt_for("agent", store)
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self.assertIn("服务 王先生", inherited_prompt)
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self.assertIn("处理订单", inherited_prompt)
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engine.data("agent")["inheritGlobalConfig"] = False
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custom_prompt = engine.prompt_for("agent", store)
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self.assertNotIn("服务 王先生", custom_prompt)
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self.assertIn("处理订单", custom_prompt)
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if __name__ == "__main__":
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unittest.main()
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