trustgraph/trustgraph-base/trustgraph/messaging/translators/agent.py

103 lines
4.1 KiB
Python
Raw Normal View History

from typing import Dict, Any, Tuple
from ...schema import AgentRequest, AgentResponse
from .base import MessageTranslator
from .primitives import TripleTranslator
class AgentRequestTranslator(MessageTranslator):
"""Translator for AgentRequest schema objects"""
Pub/sub abstraction: decouple from Pulsar (#751) Remove Pulsar-specific concepts from application code so that the pub/sub backend is swappable via configuration. Rename translators: - to_pulsar/from_pulsar → decode/encode across all translator classes, dispatch handlers, and tests (55+ files) - from_response_with_completion → encode_with_completion - Remove pulsar.schema.Record from translator base class Queue naming (CLASS:TOPICSPACE:TOPIC): - Replace topic() helper with queue() using new format: flow:tg:name, request:tg:name, response:tg:name, state:tg:name - Queue class implies persistence/TTL (no QoS in names) - Update Pulsar backend map_topic() to parse new format - Librarian queues use flow class (persistent, for chunking) - Config push uses state class (persistent, last-value) - Remove 15 dead topic imports from schema files - Update init_trustgraph.py namespace: config → state Confine Pulsar to pulsar_backend.py: - Delete legacy PulsarClient class from pubsub.py - Move add_args to add_pubsub_args() with standalone flag for CLI tools (defaults to localhost) - PulsarBackendConsumer.receive() catches _pulsar.Timeout, raises standard TimeoutError - Remove Pulsar imports from: async_processor, flow_processor, log_level, all 11 client files, 4 storage writers, gateway service, gateway config receiver - Remove log_level/LoggerLevel from client API - Rewrite tg-monitor-prompts to use backend abstraction - Update tg-dump-queues to use add_pubsub_args Also: pubsub-abstraction.md tech spec covering problem statement, design goals, as-is requirements, candidate broker assessment, approach, and implementation order.
2026-04-01 20:16:53 +01:00
def decode(self, data: Dict[str, Any]) -> AgentRequest:
return AgentRequest(
question=data["question"],
state=data.get("state", None),
group=data.get("group", None),
history=data.get("history", []),
Adding explainability to the ReACT agent (#689) * Added tech spec * Add provenance recording to React agent loop Enables agent sessions to be traced and debugged using the same explainability infrastructure as GraphRAG. Agent traces record: - Session start with query and timestamp - Each iteration's thought, action, arguments, and observation - Final answer with derivation chain Changes: - Add session_id and collection fields to AgentRequest schema - Add agent predicates (TG_THOUGHT, TG_ACTION, etc.) to namespaces - Create agent provenance triple generators in provenance/agent.py - Register explainability producer in agent service - Emit provenance triples during agent execution - Update CLI tools to detect and render agent traces alongside GraphRAG * Updated explainability taxonomy: GraphRAG: tg:Question → tg:Exploration → tg:Focus → tg:Synthesis Agent: tg:Question → tg:Analysis(s) → tg:Conclusion All entities also have their PROV-O type (prov:Activity or prov:Entity). Updated commit message: Add provenance recording to React agent loop Enables agent sessions to be traced and debugged using the same explainability infrastructure as GraphRAG. Entity types follow human reasoning patterns: - tg:Question - the user's query (shared with GraphRAG) - tg:Analysis - each think/act/observe cycle - tg:Conclusion - the final answer Also adds explicit TG types to GraphRAG entities: - tg:Question, tg:Exploration, tg:Focus, tg:Synthesis All types retain their PROV-O base types (prov:Activity, prov:Entity). Changes: - Add session_id and collection fields to AgentRequest schema - Add explainability entity types to namespaces.py - Create agent provenance triple generators - Register explainability producer in agent service - Emit provenance triples during agent execution - Update CLI tools to detect and render both trace types * Document RAG explainability is now complete. Here's a summary of the changes made: Schema Changes: - trustgraph-base/trustgraph/schema/services/retrieval.py: Added explain_id and explain_graph fields to DocumentRagResponse - trustgraph-base/trustgraph/messaging/translators/retrieval.py: Updated translator to handle explainability fields Provenance Changes: - trustgraph-base/trustgraph/provenance/namespaces.py: Added TG_CHUNK_COUNT and TG_SELECTED_CHUNK predicates - trustgraph-base/trustgraph/provenance/uris.py: Added docrag_question_uri, docrag_exploration_uri, docrag_synthesis_uri generators - trustgraph-base/trustgraph/provenance/triples.py: Added docrag_question_triples, docrag_exploration_triples, docrag_synthesis_triples builders - trustgraph-base/trustgraph/provenance/__init__.py: Exported all new Document RAG functions and predicates Service Changes: - trustgraph-flow/trustgraph/retrieval/document_rag/document_rag.py: Added explainability callback support and triple emission at each phase (Question → Exploration → Synthesis) - trustgraph-flow/trustgraph/retrieval/document_rag/rag.py: Registered explainability producer and wired up the callback Documentation: - docs/tech-specs/agent-explainability.md: Added Document RAG entity types and provenance model documentation Document RAG Provenance Model: Question (urn:trustgraph:docrag:{uuid}) │ │ tg:query, prov:startedAtTime │ rdf:type = prov:Activity, tg:Question │ ↓ prov:wasGeneratedBy │ Exploration (urn:trustgraph:docrag:{uuid}/exploration) │ │ tg:chunkCount, tg:selectedChunk (multiple) │ rdf:type = prov:Entity, tg:Exploration │ ↓ prov:wasDerivedFrom │ Synthesis (urn:trustgraph:docrag:{uuid}/synthesis) │ │ tg:content = "The answer..." │ rdf:type = prov:Entity, tg:Synthesis * Specific subtype that makes the retrieval mechanism immediately obvious: System: GraphRAG TG Types on Question: tg:Question, tg:GraphRagQuestion URI Pattern: urn:trustgraph:question:{uuid} ──────────────────────────────────────── System: Document RAG TG Types on Question: tg:Question, tg:DocRagQuestion URI Pattern: urn:trustgraph:docrag:{uuid} ──────────────────────────────────────── System: Agent TG Types on Question: tg:Question, tg:AgentQuestion URI Pattern: urn:trustgraph:agent:{uuid} Files modified: - trustgraph-base/trustgraph/provenance/namespaces.py - Added TG_GRAPH_RAG_QUESTION, TG_DOC_RAG_QUESTION, TG_AGENT_QUESTION - trustgraph-base/trustgraph/provenance/triples.py - Added subtype to question_triples and docrag_question_triples - trustgraph-base/trustgraph/provenance/agent.py - Added subtype to agent_session_triples - trustgraph-base/trustgraph/provenance/__init__.py - Exported new types - docs/tech-specs/agent-explainability.md - Documented the subtypes This allows: - Query all questions: ?q rdf:type tg:Question - Query only GraphRAG: ?q rdf:type tg:GraphRagQuestion - Query only Document RAG: ?q rdf:type tg:DocRagQuestion - Query only Agent: ?q rdf:type tg:AgentQuestion * Fixed tests
2026-03-11 15:28:15 +00:00
collection=data.get("collection", "default"),
streaming=data.get("streaming", False),
session_id=data.get("session_id", ""),
conversation_id=data.get("conversation_id", ""),
pattern=data.get("pattern", ""),
task_type=data.get("task_type", ""),
framing=data.get("framing", ""),
correlation_id=data.get("correlation_id", ""),
parent_session_id=data.get("parent_session_id", ""),
subagent_goal=data.get("subagent_goal", ""),
expected_siblings=data.get("expected_siblings", 0),
)
Pub/sub abstraction: decouple from Pulsar (#751) Remove Pulsar-specific concepts from application code so that the pub/sub backend is swappable via configuration. Rename translators: - to_pulsar/from_pulsar → decode/encode across all translator classes, dispatch handlers, and tests (55+ files) - from_response_with_completion → encode_with_completion - Remove pulsar.schema.Record from translator base class Queue naming (CLASS:TOPICSPACE:TOPIC): - Replace topic() helper with queue() using new format: flow:tg:name, request:tg:name, response:tg:name, state:tg:name - Queue class implies persistence/TTL (no QoS in names) - Update Pulsar backend map_topic() to parse new format - Librarian queues use flow class (persistent, for chunking) - Config push uses state class (persistent, last-value) - Remove 15 dead topic imports from schema files - Update init_trustgraph.py namespace: config → state Confine Pulsar to pulsar_backend.py: - Delete legacy PulsarClient class from pubsub.py - Move add_args to add_pubsub_args() with standalone flag for CLI tools (defaults to localhost) - PulsarBackendConsumer.receive() catches _pulsar.Timeout, raises standard TimeoutError - Remove Pulsar imports from: async_processor, flow_processor, log_level, all 11 client files, 4 storage writers, gateway service, gateway config receiver - Remove log_level/LoggerLevel from client API - Rewrite tg-monitor-prompts to use backend abstraction - Update tg-dump-queues to use add_pubsub_args Also: pubsub-abstraction.md tech spec covering problem statement, design goals, as-is requirements, candidate broker assessment, approach, and implementation order.
2026-04-01 20:16:53 +01:00
def encode(self, obj: AgentRequest) -> Dict[str, Any]:
return {
"question": obj.question,
"state": obj.state,
"group": obj.group,
"history": obj.history,
Adding explainability to the ReACT agent (#689) * Added tech spec * Add provenance recording to React agent loop Enables agent sessions to be traced and debugged using the same explainability infrastructure as GraphRAG. Agent traces record: - Session start with query and timestamp - Each iteration's thought, action, arguments, and observation - Final answer with derivation chain Changes: - Add session_id and collection fields to AgentRequest schema - Add agent predicates (TG_THOUGHT, TG_ACTION, etc.) to namespaces - Create agent provenance triple generators in provenance/agent.py - Register explainability producer in agent service - Emit provenance triples during agent execution - Update CLI tools to detect and render agent traces alongside GraphRAG * Updated explainability taxonomy: GraphRAG: tg:Question → tg:Exploration → tg:Focus → tg:Synthesis Agent: tg:Question → tg:Analysis(s) → tg:Conclusion All entities also have their PROV-O type (prov:Activity or prov:Entity). Updated commit message: Add provenance recording to React agent loop Enables agent sessions to be traced and debugged using the same explainability infrastructure as GraphRAG. Entity types follow human reasoning patterns: - tg:Question - the user's query (shared with GraphRAG) - tg:Analysis - each think/act/observe cycle - tg:Conclusion - the final answer Also adds explicit TG types to GraphRAG entities: - tg:Question, tg:Exploration, tg:Focus, tg:Synthesis All types retain their PROV-O base types (prov:Activity, prov:Entity). Changes: - Add session_id and collection fields to AgentRequest schema - Add explainability entity types to namespaces.py - Create agent provenance triple generators - Register explainability producer in agent service - Emit provenance triples during agent execution - Update CLI tools to detect and render both trace types * Document RAG explainability is now complete. Here's a summary of the changes made: Schema Changes: - trustgraph-base/trustgraph/schema/services/retrieval.py: Added explain_id and explain_graph fields to DocumentRagResponse - trustgraph-base/trustgraph/messaging/translators/retrieval.py: Updated translator to handle explainability fields Provenance Changes: - trustgraph-base/trustgraph/provenance/namespaces.py: Added TG_CHUNK_COUNT and TG_SELECTED_CHUNK predicates - trustgraph-base/trustgraph/provenance/uris.py: Added docrag_question_uri, docrag_exploration_uri, docrag_synthesis_uri generators - trustgraph-base/trustgraph/provenance/triples.py: Added docrag_question_triples, docrag_exploration_triples, docrag_synthesis_triples builders - trustgraph-base/trustgraph/provenance/__init__.py: Exported all new Document RAG functions and predicates Service Changes: - trustgraph-flow/trustgraph/retrieval/document_rag/document_rag.py: Added explainability callback support and triple emission at each phase (Question → Exploration → Synthesis) - trustgraph-flow/trustgraph/retrieval/document_rag/rag.py: Registered explainability producer and wired up the callback Documentation: - docs/tech-specs/agent-explainability.md: Added Document RAG entity types and provenance model documentation Document RAG Provenance Model: Question (urn:trustgraph:docrag:{uuid}) │ │ tg:query, prov:startedAtTime │ rdf:type = prov:Activity, tg:Question │ ↓ prov:wasGeneratedBy │ Exploration (urn:trustgraph:docrag:{uuid}/exploration) │ │ tg:chunkCount, tg:selectedChunk (multiple) │ rdf:type = prov:Entity, tg:Exploration │ ↓ prov:wasDerivedFrom │ Synthesis (urn:trustgraph:docrag:{uuid}/synthesis) │ │ tg:content = "The answer..." │ rdf:type = prov:Entity, tg:Synthesis * Specific subtype that makes the retrieval mechanism immediately obvious: System: GraphRAG TG Types on Question: tg:Question, tg:GraphRagQuestion URI Pattern: urn:trustgraph:question:{uuid} ──────────────────────────────────────── System: Document RAG TG Types on Question: tg:Question, tg:DocRagQuestion URI Pattern: urn:trustgraph:docrag:{uuid} ──────────────────────────────────────── System: Agent TG Types on Question: tg:Question, tg:AgentQuestion URI Pattern: urn:trustgraph:agent:{uuid} Files modified: - trustgraph-base/trustgraph/provenance/namespaces.py - Added TG_GRAPH_RAG_QUESTION, TG_DOC_RAG_QUESTION, TG_AGENT_QUESTION - trustgraph-base/trustgraph/provenance/triples.py - Added subtype to question_triples and docrag_question_triples - trustgraph-base/trustgraph/provenance/agent.py - Added subtype to agent_session_triples - trustgraph-base/trustgraph/provenance/__init__.py - Exported new types - docs/tech-specs/agent-explainability.md - Documented the subtypes This allows: - Query all questions: ?q rdf:type tg:Question - Query only GraphRAG: ?q rdf:type tg:GraphRagQuestion - Query only Document RAG: ?q rdf:type tg:DocRagQuestion - Query only Agent: ?q rdf:type tg:AgentQuestion * Fixed tests
2026-03-11 15:28:15 +00:00
"collection": getattr(obj, "collection", "default"),
"streaming": getattr(obj, "streaming", False),
"session_id": getattr(obj, "session_id", ""),
"conversation_id": getattr(obj, "conversation_id", ""),
"pattern": getattr(obj, "pattern", ""),
"task_type": getattr(obj, "task_type", ""),
"framing": getattr(obj, "framing", ""),
"correlation_id": getattr(obj, "correlation_id", ""),
"parent_session_id": getattr(obj, "parent_session_id", ""),
"subagent_goal": getattr(obj, "subagent_goal", ""),
"expected_siblings": getattr(obj, "expected_siblings", 0),
}
class AgentResponseTranslator(MessageTranslator):
"""Translator for AgentResponse schema objects"""
def __init__(self):
self.triple_translator = TripleTranslator()
Pub/sub abstraction: decouple from Pulsar (#751) Remove Pulsar-specific concepts from application code so that the pub/sub backend is swappable via configuration. Rename translators: - to_pulsar/from_pulsar → decode/encode across all translator classes, dispatch handlers, and tests (55+ files) - from_response_with_completion → encode_with_completion - Remove pulsar.schema.Record from translator base class Queue naming (CLASS:TOPICSPACE:TOPIC): - Replace topic() helper with queue() using new format: flow:tg:name, request:tg:name, response:tg:name, state:tg:name - Queue class implies persistence/TTL (no QoS in names) - Update Pulsar backend map_topic() to parse new format - Librarian queues use flow class (persistent, for chunking) - Config push uses state class (persistent, last-value) - Remove 15 dead topic imports from schema files - Update init_trustgraph.py namespace: config → state Confine Pulsar to pulsar_backend.py: - Delete legacy PulsarClient class from pubsub.py - Move add_args to add_pubsub_args() with standalone flag for CLI tools (defaults to localhost) - PulsarBackendConsumer.receive() catches _pulsar.Timeout, raises standard TimeoutError - Remove Pulsar imports from: async_processor, flow_processor, log_level, all 11 client files, 4 storage writers, gateway service, gateway config receiver - Remove log_level/LoggerLevel from client API - Rewrite tg-monitor-prompts to use backend abstraction - Update tg-dump-queues to use add_pubsub_args Also: pubsub-abstraction.md tech spec covering problem statement, design goals, as-is requirements, candidate broker assessment, approach, and implementation order.
2026-04-01 20:16:53 +01:00
def decode(self, data: Dict[str, Any]) -> AgentResponse:
raise NotImplementedError("Response translation to Pulsar not typically needed")
Pub/sub abstraction: decouple from Pulsar (#751) Remove Pulsar-specific concepts from application code so that the pub/sub backend is swappable via configuration. Rename translators: - to_pulsar/from_pulsar → decode/encode across all translator classes, dispatch handlers, and tests (55+ files) - from_response_with_completion → encode_with_completion - Remove pulsar.schema.Record from translator base class Queue naming (CLASS:TOPICSPACE:TOPIC): - Replace topic() helper with queue() using new format: flow:tg:name, request:tg:name, response:tg:name, state:tg:name - Queue class implies persistence/TTL (no QoS in names) - Update Pulsar backend map_topic() to parse new format - Librarian queues use flow class (persistent, for chunking) - Config push uses state class (persistent, last-value) - Remove 15 dead topic imports from schema files - Update init_trustgraph.py namespace: config → state Confine Pulsar to pulsar_backend.py: - Delete legacy PulsarClient class from pubsub.py - Move add_args to add_pubsub_args() with standalone flag for CLI tools (defaults to localhost) - PulsarBackendConsumer.receive() catches _pulsar.Timeout, raises standard TimeoutError - Remove Pulsar imports from: async_processor, flow_processor, log_level, all 11 client files, 4 storage writers, gateway service, gateway config receiver - Remove log_level/LoggerLevel from client API - Rewrite tg-monitor-prompts to use backend abstraction - Update tg-dump-queues to use add_pubsub_args Also: pubsub-abstraction.md tech spec covering problem statement, design goals, as-is requirements, candidate broker assessment, approach, and implementation order.
2026-04-01 20:16:53 +01:00
def encode(self, obj: AgentResponse) -> Dict[str, Any]:
result = {}
Add agent explainability instrumentation and unify envelope field naming (#795) Addresses recommendations from the UX developer's agent experience report. Adds provenance predicates, DAG structure changes, error resilience, and a published OWL ontology. Explainability additions: - Tool candidates: tg:toolCandidate on Analysis events lists the tools visible to the LLM for each iteration (names only, descriptions in config) - Termination reason: tg:terminationReason on Conclusion/Synthesis events (final-answer, plan-complete, subagents-complete) - Step counter: tg:stepNumber on iteration events - Pattern decision: new tg:PatternDecision entity in the DAG between session and first iteration, carrying tg:pattern and tg:taskType - Latency: tg:llmDurationMs on Analysis events, tg:toolDurationMs on Observation events - Token counts on events: tg:inToken/tg:outToken/tg:llmModel on Grounding, Focus, Synthesis, and Analysis events - Tool/parse errors: tg:toolError on Observation events with tg:Error mixin type. Parse failures return as error observations instead of crashing the agent, giving it a chance to retry. Envelope unification: - Rename chunk_type to message_type across AgentResponse schema, translator, SDK types, socket clients, CLI, and all tests. Agent and RAG services now both use message_type on the wire. Ontology: - specs/ontology/trustgraph.ttl — OWL vocabulary covering all 26 classes, 7 object properties, and 36+ datatype properties including new predicates. DAG structure tests: - tests/unit/test_provenance/test_dag_structure.py verifies the wasDerivedFrom chain for GraphRAG, DocumentRAG, and all three agent patterns (react, plan, supervisor) including the pattern-decision link.
2026-04-13 16:16:42 +01:00
if obj.message_type:
result["message_type"] = obj.message_type
if obj.content:
result["content"] = obj.content
result["end_of_message"] = getattr(obj, "end_of_message", False)
result["end_of_dialog"] = getattr(obj, "end_of_dialog", False)
if getattr(obj, "message_id", ""):
result["message_id"] = obj.message_id
Add unified explainability support and librarian storage for (#693) Add unified explainability support and librarian storage for all retrieval engines Implements consistent explainability/provenance tracking across GraphRAG, DocumentRAG, and Agent retrieval engines. All large content (answers, thoughts, observations) is now stored in librarian rather than as inline literals in the knowledge graph. Explainability API: - New explainability.py module with entity classes (Question, Exploration, Focus, Synthesis, Analysis, Conclusion) and ExplainabilityClient - Quiescence-based eventual consistency handling for trace fetching - Content fetching from librarian with retry logic CLI updates: - tg-invoke-graph-rag -x/--explainable flag returns explain_id - tg-invoke-document-rag -x/--explainable flag returns explain_id - tg-invoke-agent -x/--explainable flag returns explain_id - tg-list-explain-traces uses new explainability API - tg-show-explain-trace handles all three trace types Agent provenance: - Records session, iterations (think/act/observe), and conclusion - Stores thoughts and observations in librarian with document references - New predicates: tg:thoughtDocument, tg:observationDocument DocumentRAG provenance: - Records question, exploration (chunk retrieval), and synthesis - Stores answers in librarian with document references Schema changes: - AgentResponse: added explain_id, explain_graph fields - RetrievalResponse: added explain_id, explain_graph fields - agent_iteration_triples: supports thought_document_id, observation_document_id Update tests.
2026-03-12 21:40:09 +00:00
# Include explainability fields if present
explain_id = getattr(obj, "explain_id", None)
if explain_id:
result["explain_id"] = explain_id
explain_graph = getattr(obj, "explain_graph", None)
if explain_graph is not None:
result["explain_graph"] = explain_graph
# Include explain_triples for explain messages
explain_triples = getattr(obj, "explain_triples", [])
if explain_triples:
result["explain_triples"] = [
self.triple_translator.encode(t) for t in explain_triples
]
# Always include error if present
if hasattr(obj, 'error') and obj.error and obj.error.message:
result["error"] = {"message": obj.error.message, "code": obj.error.code}
Expose LLM token usage across all service layers (#782) Expose LLM token usage (in_token, out_token, model) across all service layers Propagate token counts from LLM services through the prompt, text-completion, graph-RAG, document-RAG, and agent orchestrator pipelines to the API gateway and Python SDK. All fields are Optional — None means "not available", distinguishing from a real zero count. Key changes: - Schema: Add in_token/out_token/model to TextCompletionResponse, PromptResponse, GraphRagResponse, DocumentRagResponse, AgentResponse - TextCompletionClient: New TextCompletionResult return type. Split into text_completion() (non-streaming) and text_completion_stream() (streaming with per-chunk handler callback) - PromptClient: New PromptResult with response_type (text/json/jsonl), typed fields (text/object/objects), and token usage. All callers updated. - RAG services: Accumulate token usage across all prompt calls (extract-concepts, edge-scoring, edge-reasoning, synthesis). Non-streaming path sends single combined response instead of chunk + end_of_session. - Agent orchestrator: UsageTracker accumulates tokens across meta-router, pattern prompt calls, and react reasoning. Attached to end_of_dialog. - Translators: Encode token fields when not None (is not None, not truthy) - Python SDK: RAG and text-completion methods return TextCompletionResult (non-streaming) or RAGChunk/AgentAnswer with token fields (streaming) - CLI: --show-usage flag on tg-invoke-llm, tg-invoke-prompt, tg-invoke-graph-rag, tg-invoke-document-rag, tg-invoke-agent
2026-04-13 14:38:34 +01:00
if obj.in_token is not None:
result["in_token"] = obj.in_token
if obj.out_token is not None:
result["out_token"] = obj.out_token
if obj.model is not None:
result["model"] = obj.model
return result
Pub/sub abstraction: decouple from Pulsar (#751) Remove Pulsar-specific concepts from application code so that the pub/sub backend is swappable via configuration. Rename translators: - to_pulsar/from_pulsar → decode/encode across all translator classes, dispatch handlers, and tests (55+ files) - from_response_with_completion → encode_with_completion - Remove pulsar.schema.Record from translator base class Queue naming (CLASS:TOPICSPACE:TOPIC): - Replace topic() helper with queue() using new format: flow:tg:name, request:tg:name, response:tg:name, state:tg:name - Queue class implies persistence/TTL (no QoS in names) - Update Pulsar backend map_topic() to parse new format - Librarian queues use flow class (persistent, for chunking) - Config push uses state class (persistent, last-value) - Remove 15 dead topic imports from schema files - Update init_trustgraph.py namespace: config → state Confine Pulsar to pulsar_backend.py: - Delete legacy PulsarClient class from pubsub.py - Move add_args to add_pubsub_args() with standalone flag for CLI tools (defaults to localhost) - PulsarBackendConsumer.receive() catches _pulsar.Timeout, raises standard TimeoutError - Remove Pulsar imports from: async_processor, flow_processor, log_level, all 11 client files, 4 storage writers, gateway service, gateway config receiver - Remove log_level/LoggerLevel from client API - Rewrite tg-monitor-prompts to use backend abstraction - Update tg-dump-queues to use add_pubsub_args Also: pubsub-abstraction.md tech spec covering problem statement, design goals, as-is requirements, candidate broker assessment, approach, and implementation order.
2026-04-01 20:16:53 +01:00
def encode_with_completion(self, obj: AgentResponse) -> Tuple[Dict[str, Any], bool]:
"""Returns (response_dict, is_final)"""
is_final = getattr(obj, 'end_of_dialog', False)
Pub/sub abstraction: decouple from Pulsar (#751) Remove Pulsar-specific concepts from application code so that the pub/sub backend is swappable via configuration. Rename translators: - to_pulsar/from_pulsar → decode/encode across all translator classes, dispatch handlers, and tests (55+ files) - from_response_with_completion → encode_with_completion - Remove pulsar.schema.Record from translator base class Queue naming (CLASS:TOPICSPACE:TOPIC): - Replace topic() helper with queue() using new format: flow:tg:name, request:tg:name, response:tg:name, state:tg:name - Queue class implies persistence/TTL (no QoS in names) - Update Pulsar backend map_topic() to parse new format - Librarian queues use flow class (persistent, for chunking) - Config push uses state class (persistent, last-value) - Remove 15 dead topic imports from schema files - Update init_trustgraph.py namespace: config → state Confine Pulsar to pulsar_backend.py: - Delete legacy PulsarClient class from pubsub.py - Move add_args to add_pubsub_args() with standalone flag for CLI tools (defaults to localhost) - PulsarBackendConsumer.receive() catches _pulsar.Timeout, raises standard TimeoutError - Remove Pulsar imports from: async_processor, flow_processor, log_level, all 11 client files, 4 storage writers, gateway service, gateway config receiver - Remove log_level/LoggerLevel from client API - Rewrite tg-monitor-prompts to use backend abstraction - Update tg-dump-queues to use add_pubsub_args Also: pubsub-abstraction.md tech spec covering problem statement, design goals, as-is requirements, candidate broker assessment, approach, and implementation order.
2026-04-01 20:16:53 +01:00
return self.encode(obj), is_final