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
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
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.
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
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
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
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.
Introduce an agent orchestrator service that supports three
execution patterns (ReAct, plan-then-execute, supervisor) with
LLM-based meta-routing to select the appropriate pattern and task
type per request. Update the agent schema to support
orchestration fields (correlation, sub-agents, plan steps) and
remove legacy response fields (answer, thought, observation).
Add universal document decoder with multi-format support
using 'unstructured'.
New universal decoder service powered by the unstructured
library, handling DOCX, XLSX, PPTX, HTML, Markdown, CSV, RTF,
ODT, EPUB and more through a single service. Tables are preserved
as HTML markup for better downstream extraction. Images are
stored in the librarian but excluded from the text
pipeline. Configurable section grouping strategies
(whole-document, heading, element-type, count, size) for non-page
formats. Page-based formats (PDF, PPTX, XLSX) are automatically
grouped by page.
All four decoders (PDF, Mistral OCR, Tesseract OCR, universal)
now share the "document-decoder" ident so they are
interchangeable. PDF-only decoders fetch document metadata to
check MIME type and gracefully skip unsupported formats.
Librarian changes: removed MIME type whitelist validation so any
document format can be ingested. Simplified routing so text/plain
goes to text-load and everything else goes to document-load.
Removed dual inline/streaming data paths — documents always use
document_id for content retrieval.
New provenance entity types (tg:Section, tg:Image) and metadata
predicates (tg:elementTypes, tg:tableCount, tg:imageCount) for
richer explainability.
Universal decoder is in its own package (trustgraph-unstructured)
and container image (trustgraph-unstructured).
Replace per-triple provenance reification with subgraph model
Extraction provenance previously created a full reification (statement
URI, activity, agent) for every single extracted triple, producing ~13
provenance triples per knowledge triple. Since each chunk is processed
by a single LLM call, this was both redundant and semantically
inaccurate.
Now one subgraph object is created per chunk extraction, with
tg:contains linking to each extracted triple. For 20 extractions from
a chunk this reduces provenance from ~260 triples to ~33.
- Rename tg:reifies -> tg:contains, stmt_uri -> subgraph_uri
- Replace triple_provenance_triples() with subgraph_provenance_triples()
- Refactor kg-extract-definitions and kg-extract-relationships to
generate provenance once per chunk instead of per triple
- Add subgraph provenance to kg-extract-ontology and kg-extract-agent
(previously had none)
- Update CLI tools and tech specs to match
Also rename tg-show-document-hierarchy to tg-show-extraction-provenance.
Added extra typing for extraction provenance, fixed extraction prov CLI
Add explainability CLI tools for debugging provenance data
- tg-show-document-hierarchy: Display document → page → chunk → edge
hierarchy by traversing prov:wasDerivedFrom relationships
- tg-list-explain-traces: List all GraphRAG sessions with questions
and timestamps from the retrieval graph
- tg-show-explain-trace: Show full explainability cascade for a
GraphRAG session (question → exploration → focus → synthesis)
These tools query the source and retrieval graphs to help debug
and explore provenance/explainability data stored during document
processing and GraphRAG queries.
The metadata field (list of triples) in the pipeline Metadata class
was redundant. Document metadata triples already flow directly from
librarian to triple-store via emit_document_provenance() - they don't
need to pass through the extraction pipeline.
Additionally, chunker and PDF decoder were overwriting metadata to []
anyway, so any metadata passed through the pipeline was being
discarded.
Changes:
- Remove metadata field from Metadata dataclass
(schema/core/metadata.py)
- Update all Metadata instantiations to remove metadata=[]
parameter
- Remove metadata handling from translators (document_loading,
knowledge)
- Remove metadata consumption from extractors (ontology, agent)
- Update gateway serializers and import handlers
- Update all unit, integration, and contract tests
Schema fix (dtype/lang clustering key):
- Add dtype and lang to PRIMARY KEY in quads_by_entity table
- Add otype, dtype, lang to PRIMARY KEY in quads_by_collection table
- Fixes deduplication bug where literals with same value but different
datatype or language tag were collapsed (e.g., "thing" vs "thing"@en)
- Update delete_collection to pass new clustering columns
- Update tech spec to reflect new schema
Graph filter semantics (simplified, no wildcard constant):
- g=None means all graphs (no filter)
- g="" means default graph only
- g="uri" means specific named graph
- Remove GRAPH_WILDCARD usage from EntityCentricKnowledgeGraph
- Fix service.py streaming and non-streaming paths
- Fix CLI to preserve empty string for -g '' argument
Base Service (trustgraph-base/trustgraph/base/embeddings_service.py):
- Changed on_request to use request.texts
FastEmbed Processor
(trustgraph-flow/trustgraph/embeddings/fastembed/processor.py):
- on_embeddings(texts, model=None) now processes full batch efficiently
- Returns [[v.tolist()] for v in vecs] - list of vector sets
Ollama Processor (trustgraph-flow/trustgraph/embeddings/ollama/processor.py):
- on_embeddings(texts, model=None) passes list directly to Ollama
- Returns [[embedding] for embedding in embeds.embeddings]
EmbeddingsClient (trustgraph-base/trustgraph/base/embeddings_client.py):
- embed(texts, timeout=300) accepts list of texts
Tests Updated:
- test_fastembed_dynamic_model.py - 4 tests updated for new interface
- test_ollama_dynamic_model.py - 4 tests updated for new interface
Updated CLI, SDK and APIs
Tech spec
BlobStore (trustgraph-flow/trustgraph/librarian/blob_store.py):
- get_stream() - yields document content in chunks for streaming retrieval
- create_multipart_upload() - initializes S3 multipart upload, returns
upload_id
- upload_part() - uploads a single part, returns etag
- complete_multipart_upload() - finalizes upload with part etags
- abort_multipart_upload() - cancels and cleans up
Cassandra schema (trustgraph-flow/trustgraph/tables/library.py):
- New upload_session table with 24-hour TTL
- Index on user for listing sessions
- Prepared statements for all operations
- Methods: create_upload_session(), get_upload_session(),
update_upload_session_chunk(), delete_upload_session(),
list_upload_sessions()
- Schema extended with UploadSession, UploadProgress, and new
request/response fields
- Librarian methods: begin_upload, upload_chunk, complete_upload,
abort_upload, get_upload_status, list_uploads
- Service routing for all new operations
- Python SDK with transparent chunked upload:
- add_document() auto-switches to chunked for files > 10MB
- Progress callback support (on_progress)
- get_pending_uploads(), get_upload_status(), abort_upload(),
resume_upload()
- Document table: Added parent_id and document_type columns with index
- Document schema (knowledge/document.py): Added document_id field for
streaming retrieval
- Librarian operations:
- add-child-document for extracted PDF pages
- list-children to get child documents
- stream-document for chunked content retrieval
- Cascade delete removes children when parent is deleted
- list-documents filters children by default
- PDF decoder (decoding/pdf/pdf_decoder.py): Updated to stream large
documents from librarian API to temp file
- Librarian service (librarian/service.py): Sends document_id instead of
content for large PDFs (>2MB)
- Deprecated tools (load_pdf.py, load_text.py): Added deprecation
warnings directing users to tg-add-library-document +
tg-start-library-processing
Remove load_pdf and load_text utils
Move chunker/librarian comms to base class
Updating tests
* Changed schema for Value -> Term, majorly breaking change
* Following the schema change, Value -> Term into all processing
* Updated Cassandra for g, p, s, o index patterns (7 indexes)
* Reviewed and updated all tests
* Neo4j, Memgraph and FalkorDB remain broken, will look at once settled down
* Removed legacy storage management cruft. Tidied tech specs.
* Fix deletion of last collection
* Storage processor ignores data on the queue which is for a deleted collection
* Updated tests
* Tweak object store parameters to be more generic for other S3-type store integration
* Update librarian to have region & SSL params
* Update MinIO migration tech spec
* Plugin architecture for messaging fabric
* Schemas use a technology neutral expression
* Schemas strictness has uncovered some incorrect schema use which is fixed
* Tech spec
* Address multi-tenant queue option problems in CLI
* Modified collection service to use config
* Changed storage management to use the config service definition
* Tech spec
* Python CLI utilities updated to use the API including streaming features
* Added type safety to Python API
* Completed missing auth token support in CLI
* Tidy up duplicate tech specs in doc directory
* Streaming LLM text-completion service tech spec.
* text-completion and prompt interfaces
* streaming change applied to all LLMs, so far tested with VertexAI
* Skip Pinecone unit tests, upstream module issue is affecting things, tests are passing again
* Added agent streaming, not working and has broken tests
* Onto-rag tech spec
* New processor kg-extract-ontology, use 'ontology' objects from config to guide triple extraction
* Also entity contexts
* Integrate with ontology extractor from workbench
This is first phase, the extraction is tested and working, also GraphRAG with the extracted knowledge works
