- Fix ontology RAG pipeline: embeddings API, chunker provenance,

and query concurrency

- Fix ontology embeddings to use correct response shape from embed()
  API (returns list of vectors, not list of list of vectors).
- Simplify chunker URI logic to append /c{index} to parent ID
  instead of parsing page/doc URI structure which was fragile.

- Add provenance tracking and librarian integration to token
  chunker, matching recursive chunker capabilities.

- Add configurable concurrency (default 10) to Cassandra, Qdrant,
  and embeddings query services.

tests/unit/test_chunking/test_token_chunker.py

@@ -176,6 +176,9 @@ class TestTokenChunkerSimple(IsolatedAsyncioTestCase):
 
         processor = Processor(**config)
 
+        # Mock save_child_document to avoid librarian producer interactions
+        processor.save_child_document = AsyncMock(return_value="chunk-id")
+
         # Mock message with TextDocument
         mock_message = MagicMock()
         mock_text_doc = MagicMock()
@@ -191,11 +194,13 @@ class TestTokenChunkerSimple(IsolatedAsyncioTestCase):
         # Mock consumer and flow with parameter overrides
         mock_consumer = MagicMock()
         mock_producer = AsyncMock()
+        mock_triples_producer = AsyncMock()
         mock_flow = MagicMock()
         mock_flow.side_effect = lambda param: {
             "chunk-size": 400,
             "chunk-overlap": 40,
-            "output": mock_producer
+            "output": mock_producer,
+            "triples": mock_triples_producer,
         }.get(param)
 
         # Act

trustgraph-base/trustgraph/base/document_embeddings_query_service.py

@@ -17,12 +17,14 @@ from . producer_spec import ProducerSpec
 logger = logging.getLogger(__name__)
 
 default_ident = "doc-embeddings-query"
+default_concurrency = 10
 
 class DocumentEmbeddingsQueryService(FlowProcessor):
 
     def __init__(self, **params):
 
         id = params.get("id")
+        concurrency = params.get("concurrency", default_concurrency)
 
         super(DocumentEmbeddingsQueryService, self).__init__(
             **params | { "id": id }
@@ -32,7 +34,8 @@ class DocumentEmbeddingsQueryService(FlowProcessor):
             ConsumerSpec(
                 name = "request",
                 schema = DocumentEmbeddingsRequest,
-                handler = self.on_message
+                handler = self.on_message,
+                concurrency = concurrency,
             )
         )
 
@@ -83,6 +86,13 @@ class DocumentEmbeddingsQueryService(FlowProcessor):
 
         FlowProcessor.add_args(parser)
 
+        parser.add_argument(
+            '-c', '--concurrency',
+            type=int,
+            default=default_concurrency,
+            help=f'Number of concurrent requests (default: {default_concurrency})'
+        )
+
 def run():
 
     Processor.launch(default_ident, __doc__)

trustgraph-base/trustgraph/base/graph_embeddings_query_service.py

@@ -17,12 +17,14 @@ from . producer_spec import ProducerSpec
 logger = logging.getLogger(__name__)
 
 default_ident = "graph-embeddings-query"
+default_concurrency = 10
 
 class GraphEmbeddingsQueryService(FlowProcessor):
 
     def __init__(self, **params):
 
         id = params.get("id")
+        concurrency = params.get("concurrency", default_concurrency)
 
         super(GraphEmbeddingsQueryService, self).__init__(
             **params | { "id": id }
@@ -32,7 +34,8 @@ class GraphEmbeddingsQueryService(FlowProcessor):
             ConsumerSpec(
                 name = "request",
                 schema = GraphEmbeddingsRequest,
-                handler = self.on_message
+                handler = self.on_message,
+                concurrency = concurrency,
             )
         )
 
@@ -83,6 +86,13 @@ class GraphEmbeddingsQueryService(FlowProcessor):
 
         FlowProcessor.add_args(parser)
 
+        parser.add_argument(
+            '-c', '--concurrency',
+            type=int,
+            default=default_concurrency,
+            help=f'Number of concurrent requests (default: {default_concurrency})'
+        )
+
 def run():
 
     Processor.launch(default_ident, __doc__)

trustgraph-flow/trustgraph/chunking/recursive/chunker.py

@@ -12,8 +12,7 @@ from ... schema import TextDocument, Chunk, Metadata, Triples
 from ... base import ChunkingService, ConsumerSpec, ProducerSpec
 
 from ... provenance import (
-    page_uri, chunk_uri_from_page, chunk_uri_from_doc,
+    derived_entity_triples,
-    derived_entity_triples, document_uri,
     set_graph, GRAPH_SOURCE,
 )
 
@@ -114,22 +113,9 @@ class Processor(ChunkingService):
         texts = text_splitter.create_documents([text])
 
         # Get parent document ID for provenance linking
+        # This could be a page URI (doc/p3) or document URI (doc) - we don't need to parse it
         parent_doc_id = v.document_id or v.metadata.id
 
-        # Determine if parent is a page (from PDF) or source document (text)
-        # Check if parent_doc_id contains "/p" which indicates a page
-        is_from_page = "/p" in parent_doc_id
-
-        # Extract the root document ID for chunk URI generation
-        if is_from_page:
-            # Parent is a page like "doc123/p3", extract page number
-            parts = parent_doc_id.rsplit("/p", 1)
-            root_doc_id = parts[0]
-            page_num = int(parts[1]) if len(parts) > 1 else 1
-        else:
-            root_doc_id = parent_doc_id
-            page_num = None
-
         # Track character offset for provenance
         char_offset = 0
 
@@ -138,15 +124,11 @@ class Processor(ChunkingService):
 
             logger.debug(f"Created chunk of size {len(chunk.page_content)}")
 
-            # Generate chunk document ID
+            # Generate chunk document ID by appending /c{index} to parent
-            if is_from_page:
+            # Works for both page URIs (doc/p3 -> doc/p3/c1) and doc URIs (doc -> doc/c1)
-                chunk_doc_id = f"{root_doc_id}/p{page_num}/c{chunk_index}"
+            chunk_doc_id = f"{parent_doc_id}/c{chunk_index}"
-                chunk_uri = chunk_uri_from_page(root_doc_id, page_num, chunk_index)
+            chunk_uri = chunk_doc_id  # URI is same as document ID
-                parent_uri = page_uri(root_doc_id, page_num)
+            parent_uri = parent_doc_id
-            else:
-                chunk_doc_id = f"{root_doc_id}/c{chunk_index}"
-                chunk_uri = chunk_uri_from_doc(root_doc_id, chunk_index)
-                parent_uri = document_uri(root_doc_id)
 
             chunk_content = chunk.page_content.encode("utf-8")
             chunk_length = len(chunk.page_content)

trustgraph-flow/trustgraph/chunking/token/chunker.py

@@ -8,9 +8,18 @@ import logging
 from langchain_text_splitters import TokenTextSplitter
 from prometheus_client import Histogram
 
-from ... schema import TextDocument, Chunk
+from ... schema import TextDocument, Chunk, Metadata, Triples
 from ... base import ChunkingService, ConsumerSpec, ProducerSpec
 
+from ... provenance import (
+    derived_entity_triples,
+    set_graph, GRAPH_SOURCE,
+)
+
+# Component identification for provenance
+COMPONENT_NAME = "token-chunker"
+COMPONENT_VERSION = "1.0.0"
+
 # Module logger
 logger = logging.getLogger(__name__)
 
@@ -24,7 +33,7 @@ class Processor(ChunkingService):
         id = params.get("id", default_ident)
         chunk_size = params.get("chunk_size", 250)
         chunk_overlap = params.get("chunk_overlap", 15)
-        
+
         super(Processor, self).__init__(
             **params | { "id": id }
         )
@@ -62,6 +71,13 @@ class Processor(ChunkingService):
             )
         )
 
+        self.register_specification(
+            ProducerSpec(
+                name = "triples",
+                schema = Triples,
+            )
+        )
+
         logger.info("Token chunker initialized")
 
     async def on_message(self, msg, consumer, flow):
@@ -94,21 +110,82 @@ class Processor(ChunkingService):
 
         texts = text_splitter.create_documents([text])
 
+        # Get parent document ID for provenance linking
+        # This could be a page URI (doc/p3) or document URI (doc) - we don't need to parse it
+        parent_doc_id = v.document_id or v.metadata.id
+
+        # Track token offset for provenance (approximate)
+        token_offset = 0
+
         for ix, chunk in enumerate(texts):
+            chunk_index = ix + 1  # 1-indexed
 
             logger.debug(f"Created chunk of size {len(chunk.page_content)}")
 
+            # Generate chunk document ID by appending /c{index} to parent
+            # Works for both page URIs (doc/p3 -> doc/p3/c1) and doc URIs (doc -> doc/c1)
+            chunk_doc_id = f"{parent_doc_id}/c{chunk_index}"
+            chunk_uri = chunk_doc_id  # URI is same as document ID
+            parent_uri = parent_doc_id
+
+            chunk_content = chunk.page_content.encode("utf-8")
+            chunk_length = len(chunk.page_content)
+
+            # Save chunk to librarian as child document
+            await self.save_child_document(
+                doc_id=chunk_doc_id,
+                parent_id=parent_doc_id,
+                user=v.metadata.user,
+                content=chunk_content,
+                document_type="chunk",
+                title=f"Chunk {chunk_index}",
+            )
+
+            # Emit provenance triples (stored in source graph for separation from core knowledge)
+            prov_triples = derived_entity_triples(
+                entity_uri=chunk_uri,
+                parent_uri=parent_uri,
+                component_name=COMPONENT_NAME,
+                component_version=COMPONENT_VERSION,
+                label=f"Chunk {chunk_index}",
+                chunk_index=chunk_index,
+                char_offset=token_offset,  # Note: this is token offset, not char offset
+                char_length=chunk_length,
+                chunk_size=chunk_size,
+                chunk_overlap=chunk_overlap,
+            )
+
+            await flow("triples").send(Triples(
+                metadata=Metadata(
+                    id=chunk_uri,
+                    root=v.metadata.root,
+                    user=v.metadata.user,
+                    collection=v.metadata.collection,
+                ),
+                triples=set_graph(prov_triples, GRAPH_SOURCE),
+            ))
+
+            # Forward chunk ID + content (post-chunker optimization)
             r = Chunk(
-                metadata=v.metadata,
+                metadata=Metadata(
-                chunk=chunk.page_content.encode("utf-8"),
+                    id=chunk_uri,
+                    root=v.metadata.root,
+                    user=v.metadata.user,
+                    collection=v.metadata.collection,
+                ),
+                chunk=chunk_content,
+                document_id=chunk_doc_id,
             )
 
             __class__.chunk_metric.labels(
                 id=consumer.id, flow=consumer.flow
-            ).observe(len(chunk.page_content))
+            ).observe(chunk_length)
 
             await flow("output").send(r)
 
+            # Update token offset (approximate, doesn't account for overlap)
+            token_offset += chunk_size - chunk_overlap
+
         logger.debug("Document chunking complete")
 
     @staticmethod
@@ -120,17 +197,16 @@ class Processor(ChunkingService):
             '-z', '--chunk-size',
             type=int,
             default=250,
-            help=f'Chunk size (default: 250)'
+            help=f'Chunk size in tokens (default: 250)'
         )
 
         parser.add_argument(
             '-v', '--chunk-overlap',
             type=int,
             default=15,
-            help=f'Chunk overlap (default: 15)'
+            help=f'Chunk overlap in tokens (default: 15)'
         )
 
 def run():
 
     Processor.launch(default_ident, __doc__)
-

trustgraph-flow/trustgraph/extract/kg/ontology/extract.py

@@ -149,7 +149,7 @@ class Processor(FlowProcessor):
             # Detect embedding dimension by embedding a test string
             logger.info("Detecting embedding dimension from embeddings service...")
             test_embedding_response = await embeddings_client.embed(["test"])
-            test_embedding = test_embedding_response[0][0]  # Extract first vector from first text
+            test_embedding = test_embedding_response[0]  # Extract first vector
             dimension = len(test_embedding)
             logger.info(f"Detected embedding dimension: {dimension}")
 

trustgraph-flow/trustgraph/extract/kg/ontology/ontology_embedder.py

@@ -153,14 +153,11 @@ class OntologyEmbedder:
             # Get embeddings for batch
             texts = [elem['text'] for elem in batch]
             try:
-                # Single batch embedding call
+                # Single batch embedding call - returns list of vectors
                 embeddings_response = await self.embedding_service.embed(texts)
 
-                # Extract first vector from each text's vector set
-                embeddings_list = [resp[0] for resp in embeddings_response]
-
                 # Convert to numpy array
-                embeddings = np.array(embeddings_list)
+                embeddings = np.array(embeddings_response)
 
                 # Log embedding shape for debugging
                 logger.debug(f"Embeddings shape: {embeddings.shape}, expected: ({len(batch)}, {self.vector_store.dimension})")
@@ -216,9 +213,9 @@ class OntologyEmbedder:
             return None
 
         try:
-            # embed() with single text, extract first vector from first text
+            # embed() with single text, extract first vector
             embedding_response = await self.embedding_service.embed([text])
-            return np.array(embedding_response[0][0])
+            return np.array(embedding_response[0])
         except Exception as e:
             logger.error(f"Failed to embed text: {e}")
             return None
@@ -237,11 +234,9 @@ class OntologyEmbedder:
             return None
 
         try:
-            # Single batch embedding call
+            # Single batch embedding call - returns list of vectors
             embeddings_response = await self.embedding_service.embed(texts)
-            # Extract first vector from each text's vector set
+            return np.array(embeddings_response)
-            embeddings_list = [resp[0] for resp in embeddings_response]
-            return np.array(embeddings_list)
         except Exception as e:
             logger.error(f"Failed to embed texts: {e}")
             return None

trustgraph-flow/trustgraph/query/row_embeddings/qdrant/service.py

@@ -24,6 +24,7 @@ logger = logging.getLogger(__name__)
 
 default_ident = "row-embeddings-query"
 default_store_uri = 'http://localhost:6333'
+default_concurrency = 10
 
 
 class Processor(FlowProcessor):
@@ -31,6 +32,7 @@ class Processor(FlowProcessor):
     def __init__(self, **params):
 
         id = params.get("id", default_ident)
+        concurrency = params.get("concurrency", default_concurrency)
 
         store_uri = params.get("store_uri", default_store_uri)
         api_key = params.get("api_key", None)
@@ -47,7 +49,8 @@ class Processor(FlowProcessor):
             ConsumerSpec(
                 name="request",
                 schema=RowEmbeddingsRequest,
-                handler=self.on_message
+                handler=self.on_message,
+                concurrency=concurrency,
             )
         )
 
@@ -205,6 +208,13 @@ class Processor(FlowProcessor):
             help='API key for Qdrant (default: None)'
         )
 
+        parser.add_argument(
+            '-c', '--concurrency',
+            type=int,
+            default=default_concurrency,
+            help=f'Number of concurrent requests (default: {default_concurrency})'
+        )
+
 
 def run():
     """Entry point for row-embeddings-query-qdrant command"""

trustgraph-flow/trustgraph/query/rows/cassandra/service.py

@@ -30,6 +30,7 @@ from ... graphql import GraphQLSchemaBuilder, SortDirection
 logger = logging.getLogger(__name__)
 
 default_ident = "rows-query"
+default_concurrency = 10
 
 
 class Processor(FlowProcessor):
@@ -37,6 +38,7 @@ class Processor(FlowProcessor):
     def __init__(self, **params):
 
         id = params.get("id", default_ident)
+        concurrency = params.get("concurrency", default_concurrency)
 
         # Get Cassandra parameters
         cassandra_host = params.get("cassandra_host")
@@ -69,7 +71,8 @@ class Processor(FlowProcessor):
             ConsumerSpec(
                 name="request",
                 schema=RowsQueryRequest,
-                handler=self.on_message
+                handler=self.on_message,
+                concurrency=concurrency,
             )
         )
 
@@ -517,6 +520,13 @@ class Processor(FlowProcessor):
             help='Configuration type prefix for schemas (default: schema)'
         )
 
+        parser.add_argument(
+            '-c', '--concurrency',
+            type=int,
+            default=default_concurrency,
+            help=f'Number of concurrent requests (default: {default_concurrency})'
+        )
+
 
 def run():
     """Entry point for rows-query-cassandra command"""