Third backend alongside Pulsar and RabbitMQ. Topics map 1:1 to Kafka
topics, subscriptions map to consumer groups. Response/notify uses
unique consumer groups with correlation ID filtering. Topic lifecycle
managed via AdminClient with class-based retention.
Initial code drop: Needs major integration testing
The RabbitMQ backend used a single topic exchange per topicspace
with routing keys to differentiate logical topics. This meant the
flow service had to manually create named queues for every
processor-topic pair, including producer-side topics — creating
phantom queues that accumulated unread message copies indefinitely.
Replace with one fanout exchange per logical topic. Consumers now
declare and bind their own queues on connect. The flow service
manages topic lifecycle (create/delete exchanges) rather than queue
lifecycle, and only collects unique topic identifiers instead of
per-processor (topic, subscription) pairs.
Backend API: create_queue/delete_queue/ensure_queue replaced with
create_topic/delete_topic/ensure_topic (subscription parameter
removed).
feat: separate flow service from config service with explicit queue
lifecycle management
The flow service is now an independent service that owns the lifecycle
of flow and blueprint queues. System services own their own queues.
Consumers never create queues.
Flow service separation:
- New service at trustgraph-flow/trustgraph/flow/service/
- Uses async ConfigClient (RequestResponse pattern) to talk to config
service
- Config service stripped of all flow handling
Queue lifecycle management:
- PubSubBackend protocol gains create_queue, delete_queue,
queue_exists, ensure_queue — all async
- RabbitMQ: implements via pika with asyncio.to_thread internally
- Pulsar: stubs for future admin REST API implementation
- Consumer _connect() no longer creates queues (passive=True for named
queues)
- System services call ensure_queue on startup
- Flow service creates queues on flow start, deletes on flow stop
- Flow service ensures queues for pre-existing flows on startup
Two-phase flow stop:
- Phase 1: set flow status to "stopping", delete processor config
entries
- Phase 2: retry queue deletion, then delete flow record
Config restructure:
- active-flow config replaced with processor:{name} types
- Each processor has its own config type, each flow variant is a key
- Flow start/stop use batch put/delete — single config push per
operation
- FlowProcessor subscribes to its own type only
Blueprint format:
- Processor entries split into topics and parameters dicts
- Flow interfaces use {"flow": "topic"} instead of bare strings
- Specs (ConsumerSpec, ProducerSpec, etc.) read from
definition["topics"]
Tests updated
asyncio.iscoroutinefunction is deprecated since Python 3.14 and slated for
removal in 3.16. The inspect equivalent has an identical signature and return
semantics. Replaces 8 call sites across 3 modules to silence DeprecationWarnings
reported in #818.
Python 3.14 deprecates datetime.utcnow(). Replace all 9 occurrences with
datetime.now(timezone.utc) and normalize the output to preserve the existing
ISO-8601 "Z"-suffixed format so downstream parsers are unaffected.
Fixes#814
feat: add type hints to all public functions in trustgraph/base
Add type annotations to 23 modules covering:
- Metrics classes (ConsumerMetrics, ProducerMetrics, etc.)
- Spec classes (ConsumerSpec, ProducerSpec, SubscriberSpec, etc.)
- Service classes with add_args() and run() methods
- Utility functions (logging, pubsub, clients)
- AsyncProcessor methods
All 93 public functions now fully typed.
Refs #785
* refactor: deduplicate imports and move __future__ after docstrings
Addresses review feedback on PR #803:
- Remove duplicate 'from argparse import ArgumentParser' across 12 files
- Move 'from __future__ import annotations' to line 1 in all files
- Clean up excessive blank lines
Add class-level docstrings to five public classes in trustgraph-base:
Flow, LlmService, ConsumerMetrics, ToolClient, and TriplesStoreService.
Each docstring summarises the class's role in the system to aid
discoverability for new contributors.
Signed-off-by: Jenkins, Kenneth Alexander <kjenkins60@gatech.edu>
- Silence pika, cassandra etc. logging at INFO (too much chatter)
- Add per processor log tags so that logs can be understood in
processor group.
- Deal with RabbitMQ lag weirdness
- Added more processor group examples
Processor group implementation: A wrapper to launch multiple
processors in a single processor
- trustgraph-base/trustgraph/base/processor_group.py — group runner
module. run_group(config) is the async body; run() is the
endpoint. Loads JSON or YAML config, validates that every entry
has a unique params.id, instantiates each class via importlib,
shares one TaskGroup, mirrors AsyncProcessor.launch's retry loop
and Prometheus startup.
- trustgraph-base/pyproject.toml — added [project.scripts] block
with processor-group = "trustgraph.base.processor_group:run".
Key behaviours:
- Unique id enforced up front — missing or duplicate params.id fails
fast with a clear error, preventing the Prometheus Info label
collision we flagged.
- No registry — dotted class path is the identifier; any
AsyncProcessor descendant importable at runtime is packable.
- YAML import is lazy — only pulled in if the config file ends in
.yaml/.yml, so JSON-only users don't need PyYAML installed.
- Single Prometheus server — start_http_server runs once at
startup, before the retry loop, matching launch()'s pattern.
- Retry loop — same shape as AsyncProcessor.launch: catches
ExceptionGroup from TaskGroup, logs, sleeps 4s,
retries. Fail-group semantics (one processor dying tears down the
group) — simple and surfaces bugs, as discussed.
Example config:
processors:
- class: trustgraph.extract.kg.definitions.extract.Processor
params:
id: kg-extract-definitions
- class: trustgraph.chunking.recursive.Processor
params:
id: chunker-recursive
Run with processor-group -c group.yaml.
Native CLI i18n: The TrustGraph CLI has built-in translation support
that dynamically loads language strings. You can test and use
different languages by simply passing the --lang flag (e.g., --lang
es for Spanish, --lang ru for Russian) or by configuring your
environment's LANG variable.
Automated Docs Translations: This PR introduces autonomously
translated Markdown documentation into several target languages,
including Spanish, Swahili, Portuguese, Turkish, Hindi, Hebrew,
Arabic, Simplified Chinese, and Russian.
Fix test suite Prometheus registry pollution and remove default coverage
The test_metrics.py fixture (added in 4e63dbda) deleted
class-level metric singleton attributes without restoring them. This
stripped the hasattr() guards that prevent duplicate Prometheus
registration, so every subsequent Processor construction in the test
run raised ValueError: Duplicated timeseries. Fix by saving and
restoring the original attributes around each test.
Remove --cov=trustgraph from pytest.ini defaults — the coverage
import hooks interact badly with the trustgraph namespace package,
causing duplicate class loading. Coverage can still be requested
explicitly via the command line.
- add unit tests for base metrics, logging, spec, parameter_spec,
and flow modules
- add a lightweight test-only module loader so these tests can run
without optional runtime dependencies
- fix Parameter.start/stop to accept self
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.
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
* Fix Metadata/EntityEmbeddings schema migration tail and add regression tests (#776)
The Metadata dataclass dropped its `metadata: list[Triple]` field
and EntityEmbeddings/ChunkEmbeddings settled on a singular
`vector: list[float]` field, but several call sites kept passing
`Metadata(metadata=...)` and `EntityEmbeddings(vectors=...)`. The
bugs were latent until a websocket client first hit
`/api/v1/flow/default/import/entity-contexts`, at which point the
dispatcher TypeError'd on construction.
Production fixes (5 call sites on the same migration tail):
* trustgraph-flow gateway dispatchers entity_contexts_import.py
and graph_embeddings_import.py — drop the stale
Metadata(metadata=...) kwarg; switch graph_embeddings_import
to the singular `vector` wire key.
* trustgraph-base messaging translators knowledge.py and
document_loading.py — fix decode side to read the singular
`"vector"` key, matching what their own encode sides have
always written.
* trustgraph-flow tables/knowledge.py — fix Cassandra row
deserialiser to construct EntityEmbeddings(vector=...)
instead of vectors=.
* trustgraph-flow gateway core_import/core_export — switch the
kg-core msgpack wire format to the singular `"v"`/`"vector"`
key and drop the dead `m["m"]` envelope field that referenced
the removed Metadata.metadata triples list (it was a
guaranteed KeyError on the export side).
Defense-in-depth regression coverage (32 new tests across 7 files):
* tests/contract/test_schema_field_contracts.py — pin the field
set of Metadata, EntityEmbeddings, ChunkEmbeddings,
EntityContext so any future schema rename fails CI loudly
with a clear diff.
* tests/unit/test_translators/test_knowledge_translator_roundtrip.py
and test_document_embeddings_translator_roundtrip.py -
encode→decode round-trip the affected translators end to end,
locking in the singular `"vector"` wire key.
* tests/unit/test_gateway/test_entity_contexts_import_dispatcher.py
and test_graph_embeddings_import_dispatcher.py — exercise the
websocket dispatchers' receive() path with realistic
payloads, the direct regression test for the original
production crash.
* tests/unit/test_gateway/test_core_import_export_roundtrip.py
— pack/unpack the kg-core msgpack format through the real
dispatcher classes (with KnowledgeRequestor mocked),
including a full export→import round-trip.
* tests/unit/test_tables/test_knowledge_table_store.py —
exercise the Cassandra row → schema conversion via __new__ to
bypass the live cluster connection.
Also fixes an unrelated leaked-coroutine RuntimeWarning in
test_gateway/test_service.py::test_run_method_calls_web_run_app: the
mocked aiohttp.web.run_app now closes the coroutine that Api.run() hands
it, mirroring what the real run_app would do, instead of leaving it for
the GC to complain about.
* Fix RabbitMQ request/response race and chunker Flow API drift
Two unrelated regressions surfaced after the v2.2 queue class
refactor. Bundled here because both are small and both block
production.
1. Request/response race against ephemeral RabbitMQ response
queues
Commit feeb92b3 switched response/notify queues to per-subscriber
auto-delete exclusive queues. That fixed orphaned-queue
accumulation but introduced a setup race: Subscriber.start()
created the run() task and returned immediately, while the
underlying RabbitMQ consumer only declared and bound its queue
lazily on the first receive() call. RequestResponse.request()
therefore published the request before any queue was bound to the
matching routing key, and the broker dropped the reply. Symptoms:
"Failed to fetch config on notify" / "Request timeout exception"
repeating roughly every 10s in api-gateway, document-embeddings
and any other service exercising the config notify path.
Fix:
* Add ensure_connected() to the BackendConsumer protocol;
implement it on RabbitMQBackendConsumer (calls _connect
synchronously, declaring and binding the queue) and as a
no-op on PulsarBackendConsumer (Pulsar's client.subscribe is
already synchronous at construction).
* Convert Subscriber's readiness signal from a non-existent
Event to an asyncio.Future created in start(). run() calls
consumer.ensure_connected() immediately after
create_consumer() and sets _ready.set_result(None) on first
successful bind. start() awaits the future via asyncio.wait
so it returns only once the consumer is fully bound. Any
reply published after start() returns is therefore guaranteed
to land in a bound queue.
* First-attempt connection failures call
_ready.set_exception(e) and exit run() so start() unblocks
with the error rather than hanging forever — the existing
higher-level retry pattern in fetch_and_apply_config takes
over from there. Runtime failures after a successful start
still go through the existing retry-with-backoff path.
* Update the two existing graceful-shutdown tests that
monkey-patch Subscriber.run with a custom coroutine to honor
the new contract by signalling _ready themselves.
* Add tests/unit/test_base/test_subscriber_readiness.py with
five regression tests pinning the readiness contract:
ensure_connected must be called before start() returns;
start() must block while ensure_connected runs
(race-condition guard with a threading.Event gate);
first-attempt create_consumer and ensure_connected failures
must propagate to start() instead of hanging;
ensure_connected must run before any receive() call.
2. Chunker Flow parameter lookup using the wrong attribute
trustgraph-base/trustgraph/base/chunking_service.py was reading
flow.parameters.get("chunk-size") and chunk-overlap, but the Flow
class has no `parameters` attribute — parameter lookup is exposed
through Flow.__call__ (flow("chunk-size") returns the resolved
value or None). The exception was caught and logged as a
WARNING, so chunking continued with the default sizes and any
configured chunk-size / chunk-overlap was silently ignored:
chunker - WARNING - Could not parse chunk-size parameter:
'Flow' object has no attribute 'parameters'
The chunker tests didn't catch this because they constructed
mock_flow = MagicMock() and configured
mock_flow.parameters.get.side_effect = ..., which is the same
phantom attribute MagicMock auto-creates on demand. Tests and
production agreed on the wrong API.
Fix: switch chunking_service.py to flow("chunk-size") /
flow("chunk-overlap"). Update both chunker test files to mock the
__call__ side_effect instead of the phantom parameters.get,
merging parameter values into the existing flow() lookup the
on_message tests already used for producer resolution.
The Metadata dataclass dropped its `metadata: list[Triple]` field
and EntityEmbeddings/ChunkEmbeddings settled on a singular
`vector: list[float]` field, but several call sites kept passing
`Metadata(metadata=...)` and `EntityEmbeddings(vectors=...)`. The
bugs were latent until a websocket client first hit
`/api/v1/flow/default/import/entity-contexts`, at which point the
dispatcher TypeError'd on construction.
Production fixes (5 call sites on the same migration tail):
* trustgraph-flow gateway dispatchers entity_contexts_import.py
and graph_embeddings_import.py — drop the stale
Metadata(metadata=...) kwarg; switch graph_embeddings_import
to the singular `vector` wire key.
* trustgraph-base messaging translators knowledge.py and
document_loading.py — fix decode side to read the singular
`"vector"` key, matching what their own encode sides have
always written.
* trustgraph-flow tables/knowledge.py — fix Cassandra row
deserialiser to construct EntityEmbeddings(vector=...)
instead of vectors=.
* trustgraph-flow gateway core_import/core_export — switch the
kg-core msgpack wire format to the singular `"v"`/`"vector"`
key and drop the dead `m["m"]` envelope field that referenced
the removed Metadata.metadata triples list (it was a
guaranteed KeyError on the export side).
Defense-in-depth regression coverage (32 new tests across 7 files):
* tests/contract/test_schema_field_contracts.py — pin the field
set of Metadata, EntityEmbeddings, ChunkEmbeddings,
EntityContext so any future schema rename fails CI loudly
with a clear diff.
* tests/unit/test_translators/test_knowledge_translator_roundtrip.py
and test_document_embeddings_translator_roundtrip.py -
encode→decode round-trip the affected translators end to end,
locking in the singular `"vector"` wire key.
* tests/unit/test_gateway/test_entity_contexts_import_dispatcher.py
and test_graph_embeddings_import_dispatcher.py — exercise the
websocket dispatchers' receive() path with realistic
payloads, the direct regression test for the original
production crash.
* tests/unit/test_gateway/test_core_import_export_roundtrip.py
— pack/unpack the kg-core msgpack format through the real
dispatcher classes (with KnowledgeRequestor mocked),
including a full export→import round-trip.
* tests/unit/test_tables/test_knowledge_table_store.py —
exercise the Cassandra row → schema conversion via __new__ to
bypass the live cluster connection.
Also fixes an unrelated leaked-coroutine RuntimeWarning in
test_gateway/test_service.py::test_run_method_calls_web_run_app: the
mocked aiohttp.web.run_app now closes the coroutine that Api.run() hands
it, mirroring what the real run_app would do, instead of leaving it for
the GC to complain about.
Derive consumer behaviour from queue class, remove
consumer_type parameter
The queue class prefix (flow, request, response, notify) now
fully determines consumer behaviour in both RabbitMQ and Pulsar
backends. Added 'notify' class for ephemeral broadcast (config
push notifications). Response and notify classes always create
per-subscriber auto-delete queues, eliminating orphaned queues
that accumulated on service restarts.
Change init-trustgraph to set up the 'notify' namespace in
Pulsar instead of old hangover 'state'.
Fixes 'stuck backlog' on RabbitMQ config notification queue.
The triples client returns Uri/Literal (str subclasses), not Term
objects. _quoted_triple() treated all values as IRIs, so literal
objects like skos:definition values were mistyped in focus
provenance events, and trace_source_documents could not match
them in the store.
Added to_term() to convert Uri/Literal back to Term, threaded a
term_map from follow_edges_batch through
get_subgraph/get_labelgraph into uri_map, and updated
_quoted_triple to accept Term objects directly.
Subscriber resilience: recreate consumer after connection failure
- Move consumer creation from Subscriber.start() into the run() loop,
matching the pattern used by Consumer. If the connection drops and the
consumer is closed in the finally block, the loop now recreates it on
the next iteration instead of spinning forever on a None consumer.
Consumer thread safety:
- Dedicated ThreadPoolExecutor per consumer so all pika operations
(create, receive, acknowledge, negative_acknowledge) run on the
same thread — pika BlockingConnection is not thread-safe
- Applies to both Consumer and Subscriber classes
Config handler type audit — fix four mismatched type registrations:
- librarian: was ["librarian"] (non-existent type), now ["flow",
"active-flow"] (matches config["flow"] that the handler reads)
- cores/service: was ["kg-core"], now ["flow"] (reads
config["flow"])
- metering/counter: was ["token-costs"], now ["token-cost"]
(singular)
- agent/mcp_tool: was ["mcp-tool"], now ["mcp"] (reads
config["mcp"])
Update tests
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
Additional explainability test
- Fix agent react and orchestrator services appending bare methods
to config_handlers instead of using register_config_handler() —
caused 'method object is not subscriptable' on config notify
- Add exc_info to config fetch retry logging for proper tracebacks
- Remove debug print statements from collection management
dispatcher and translator
- Disable RabbitMQ heartbeats (heartbeat=0) to prevent broker
closing idle producer connections that can't process heartbeat
frames from BlockingConnection
Replace the config push mechanism that broadcast the full config
blob on a 'state' class pub/sub queue with a lightweight notify
signal containing only the version number and affected config
types. Processors fetch the full config via request/response from
the config service when notified.
This eliminates the need for the pub/sub 'state' queue class and
stateful pub/sub services entirely. The config push queue moves
from 'state' to 'flow' class — a simple transient signal rather
than a retained message. This solves the RabbitMQ
late-subscriber problem where restarting processes never received
the current config because their fresh queue had no historical
messages.
Key changes:
- ConfigPush schema: config dict replaced with types list
- Subscribe-then-fetch startup with retry: processors subscribe
to notify queue, fetch config via request/response, then
process buffered notifies with version comparison to avoid race
conditions
- register_config_handler() accepts optional types parameter so
handlers only fire when their config types change
- Short-lived config request/response clients to avoid subscriber
contention on non-persistent response topics
- Config service passes affected types through put/delete/flow
operations
- Gateway ConfigReceiver rewritten with same notify pattern and
retry loop
Tests updated
New tests:
- register_config_handler: without types, with types, multiple
types, multiple handlers
- on_config_notify: old/same version skipped, irrelevant types
skipped (version still updated), relevant type triggers fetch,
handler without types always called, mixed handler filtering,
empty types invokes all, fetch failure handled gracefully
- fetch_config: returns config+version, raises on error response,
stops client even on exception
- fetch_and_apply_config: applies to all handlers on startup,
retries on failure
* fix: prevent duplicate dispatcher creation race condition in invoke_global_service
Concurrent coroutines could all pass the `if key in self.dispatchers` check
before any of them wrote the result back, because `await dispatcher.start()`
yields to the event loop. This caused multiple Pulsar consumers to be created
on the same shared subscription, distributing responses round-robin and
dropping ~2/3 of them — manifesting as a permanent spinner in the Workbench UI.
Apply a double-checked asyncio.Lock in both `invoke_global_service` and
`invoke_flow_service` so only one dispatcher is ever created per service key.
* test: add concurrent-dispatch tests for race condition fix
Add asyncio.gather-based tests that verify invoke_global_service and
invoke_flow_service create exactly one dispatcher under concurrent calls,
preventing the duplicate Pulsar consumer bug.
SPARQL 1.1 query service wrapping pub/sub triples interface
Add a backend-agnostic SPARQL query service that parses SPARQL
queries using rdflib, decomposes them into triple pattern lookups
via the existing TriplesClient pub/sub interface, and performs
in-memory joins, filters, and projections.
Includes:
- SPARQL parser, algebra evaluator, expression evaluator, solution
sequence operations (BGP, JOIN, OPTIONAL, UNION, FILTER, BIND,
VALUES, GROUP BY, ORDER BY, LIMIT/OFFSET, DISTINCT, aggregates)
- FlowProcessor service with TriplesClientSpec
- Gateway dispatcher, request/response translators, API spec
- Python SDK method (FlowInstance.sparql_query)
- CLI command (tg-invoke-sparql-query)
- Tech spec (docs/tech-specs/sparql-query.md)
New unit tests for SPARQL query
Adds a RabbitMQ backend as an alternative to Pulsar, selectable via
PUBSUB_BACKEND=rabbitmq. Both backends implement the same PubSubBackend
protocol — no application code changes needed to switch.
RabbitMQ topology:
- Single topic exchange per topicspace (e.g. 'tg')
- Routing key derived from queue class and topic name
- Shared consumers: named queue bound to exchange (competing, round-robin)
- Exclusive consumers: anonymous auto-delete queue (broadcast, each gets
every message). Used by Subscriber and config push consumer.
- Thread-local producer connections (pika is not thread-safe)
- Push-based consumption via basic_consume with process_data_events
for heartbeat processing
Consumer model changes:
- Consumer class creates one backend consumer per concurrent task
(required for pika thread safety, harmless for Pulsar)
- Consumer class accepts consumer_type parameter
- Subscriber passes consumer_type='exclusive' for broadcast semantics
- Config push consumer uses consumer_type='exclusive' so every
processor instance receives config updates
- handle_one_from_queue receives consumer as parameter for correct
per-connection ack/nack
LibrarianClient:
- New shared client class replacing duplicated librarian request-response
code across 6+ services (chunking, decoders, RAG, etc.)
- Uses stream-document instead of get-document-content for fetching
document content in 1MB chunks (avoids broker message size limits)
- Standalone object (self.librarian = LibrarianClient(...)) not a mixin
- get-document-content marked deprecated in schema and OpenAPI spec
Serialisation:
- Extracted dataclass_to_dict/dict_to_dataclass to shared
serialization.py (used by both Pulsar and RabbitMQ backends)
Librarian queues:
- Changed from flow class (persistent) back to request/response class
now that stream-document eliminates large single messages
- API upload chunk size reduced from 5MB to 3MB to stay under broker
limits after base64 encoding
Factory and CLI:
- get_pubsub() handles 'rabbitmq' backend with RabbitMQ connection params
- add_pubsub_args() includes RabbitMQ options (host, port, credentials)
- add_pubsub_args(standalone=True) defaults to localhost for CLI tools
- init_trustgraph skips Pulsar admin setup for non-Pulsar backends
- tg-dump-queues and tg-monitor-prompts use backend abstraction
- BaseClient and ConfigClient accept generic pubsub config
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.
