From 6dfa47aac8661dc50944f166e687a9c99ca90448 Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Sat, 30 May 2026 17:07:19 -0700 Subject: [PATCH 1/3] Revise README for semantic infrastructure terminology (#962) Updated the README to reflect changes in terminology and improve clarity regarding the platform's features. --- README.md | 32 +++++++++++++++----------------- 1 file changed, 15 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index c366a3d9..1edccff6 100644 --- a/README.md +++ b/README.md @@ -11,11 +11,11 @@ trustgraph-ai%2Ftrustgraph | Trendshift -# The agent runtime platform +# The semantic infrastructure for agents -TrustGraph is an agent runtime platform built around context graphs — structured, queryable representations of your domain knowledge that ground every agent query in verified, explainable facts in private deployments with sovereign control. The platform is the full stack for agentic systems: context graphs, memory, retrieval, orchestration, and inference for precision-critical agent workloads. +TrustGraph is a comprehensive semantic infrastructure for agents built around context graphs — structured, queryable representations of your domain knowledge that ground every agent query in verified, explainable facts in private deployments with sovereign control. The platform is the full stack for agentic systems: context graphs, memory, retrieval, orchestration, and inference for deterministic agent workloads. The platform: - [x] Multi-model and multimodal database system @@ -99,23 +99,21 @@ For a browser based configuration, try the [Configuration Terminal](https://conf - [**Developer APIs and CLI**](https://docs.trustgraph.ai/reference) - [**Deployment Guides**](https://docs.trustgraph.ai/deployment) -## Workbench +## Context Graph UI -The **Workbench** provides tools for all major features of TrustGraph. The **Workbench** is on port `8888` by default. +Image -- **Vector Search**: Search the installed knowledge bases -- **Agentic, GraphRAG and LLM Chat**: Chat interface for agents, GraphRAG queries, or direct to LLMs -- **Relationships**: Analyze deep relationships in the installed knowledge bases -- **Graph Visualizer**: 3D GraphViz of the installed knowledge bases -- **Library**: Staging area for installing knowledge bases -- **Flow Classes**: Workflow preset configurations -- **Flows**: Create custom workflows and adjust LLM parameters during runtime -- **Knowledge Cores**: Manage resuable knowledge bases -- **Prompts**: Manage and adjust prompts during runtime -- **Schemas**: Define custom schemas for structured data knowledge bases -- **Ontologies**: Define custom ontologies for unstructured data knowledge bases -- **Agent Tools**: Define tools with collections, knowledge cores, MCP connections, and tool groups -- **MCP Tools**: Connect to MCP servers +The UI provides tools for all major features of TrustGraph. The UI deploys on port `8888` by default. + +- **Agent Console** — Query your agents directly with streaming responses and live explainability event tracking, so you can watch reasoning unfold in real time +- **GraphRAG View** — Interactive graph RAG queries with a visual explainability DAG and inline provenance display, making it easy to see exactly where answers came from +- **Context Explorer** — An interactive 3D context graph explorer with dynamic graph loading, BFS neighborhood extraction, edge pulse animation, and multiple navigation views +- **Document Ingestion** — A complete upload and submission workflow with page and chunk inspection and document structure browsing +- **Ontology Workbench** — A full ontology editor with class and property trees, OWL/XML and Turtle import/export with round-trip fidelity, circular dependency detection, and safe-delete confirmation dialogs +- **Schema Workbench** — Interactive schema management with list, create, edit, and delete operations including field and index management +- **Flow Management** — Flow creation and detail views with configurable parameters, temperature controls, and grouped storage layout +- **Workspace UX** — Workspace selection and management surfaced directly in the interface +- **Prompt Editor** — A dedicated prompt editing workflow ## TypeScript Library for UIs From 97453d9b8319910c4f9d1860a6752b5a9c9f53ed Mon Sep 17 00:00:00 2001 From: Jack Colquitt <126733989+JackColquitt@users.noreply.github.com> Date: Mon, 1 Jun 2026 14:08:30 -0700 Subject: [PATCH 2/3] Change project title to 'The semantic deployment platform' (#968) Updated the project title in the README. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1edccff6..b66edc70 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ trustgraph-ai%2Ftrustgraph | Trendshift -# The semantic infrastructure for agents +# The semantic deployment platform From 28a51c244f60c9fe189aa972e1fd58940cd44c64 Mon Sep 17 00:00:00 2001 From: Jacob Molz Date: Tue, 9 Jun 2026 11:37:10 -0400 Subject: [PATCH 3/3] fix: reject invalid PDF decoder input (#977) --- tests/unit/test_decoding/test_pdf_decoder.py | 48 ++++++++++++++- .../trustgraph/decoding/pdf/pdf_decoder.py | 60 +++++++++++-------- 2 files changed, 79 insertions(+), 29 deletions(-) diff --git a/tests/unit/test_decoding/test_pdf_decoder.py b/tests/unit/test_decoding/test_pdf_decoder.py index 04807b20..641a9d78 100644 --- a/tests/unit/test_decoding/test_pdf_decoder.py +++ b/tests/unit/test_decoding/test_pdf_decoder.py @@ -49,7 +49,7 @@ class TestPdfDecoderProcessor(IsolatedAsyncioTestCase): async def test_on_message_success(self, mock_pdf_loader_class, mock_producer, mock_consumer): """Test successful PDF processing""" # Mock PDF content - pdf_content = b"fake pdf content" + pdf_content = b"%PDF-1.7\nfake pdf content" pdf_base64 = base64.b64encode(pdf_content).decode('utf-8') # Mock PyPDFLoader @@ -88,13 +88,55 @@ class TestPdfDecoderProcessor(IsolatedAsyncioTestCase): # Verify triples were sent for each page (provenance) assert mock_triples_flow.send.call_count == 2 + @patch('trustgraph.base.librarian_client.Consumer') + @patch('trustgraph.base.librarian_client.Producer') + @patch('trustgraph.decoding.pdf.pdf_decoder.PyPDFLoader') + @patch('trustgraph.base.async_processor.AsyncProcessor', MockAsyncProcessor) + async def test_on_message_rejects_librarian_content_that_is_not_pdf(self, mock_pdf_loader_class, mock_producer, mock_consumer): + """Test rejecting non-PDF content before invoking the PDF loader""" + html_content = b"Not found" + html_base64 = base64.b64encode(html_content) + + mock_metadata = Metadata(id="test-doc") + mock_document = Document(metadata=mock_metadata, document_id="doc-123") + mock_msg = MagicMock() + mock_msg.value.return_value = mock_document + + mock_output_flow = AsyncMock() + mock_triples_flow = AsyncMock() + mock_flow = MagicMock(side_effect=lambda name: { + "output": mock_output_flow, + "triples": mock_triples_flow, + }.get(name)) + mock_flow.librarian.fetch_document_metadata = AsyncMock( + return_value=MagicMock(kind="application/pdf") + ) + mock_flow.librarian.fetch_document_content = AsyncMock( + return_value=html_base64 + ) + mock_flow.librarian.save_child_document = AsyncMock() + + config = { + 'id': 'test-pdf-decoder', + 'taskgroup': AsyncMock() + } + + processor = Processor(**config) + + await processor.on_message(mock_msg, None, mock_flow) + + mock_pdf_loader_class.assert_not_called() + mock_output_flow.send.assert_not_called() + mock_triples_flow.send.assert_not_called() + mock_flow.librarian.save_child_document.assert_not_called() + @patch('trustgraph.base.librarian_client.Consumer') @patch('trustgraph.base.librarian_client.Producer') @patch('trustgraph.decoding.pdf.pdf_decoder.PyPDFLoader') @patch('trustgraph.base.async_processor.AsyncProcessor', MockAsyncProcessor) async def test_on_message_empty_pdf(self, mock_pdf_loader_class, mock_producer, mock_consumer): """Test handling of empty PDF""" - pdf_content = b"fake pdf content" + pdf_content = b"%PDF-1.7\nfake pdf content" pdf_base64 = base64.b64encode(pdf_content).decode('utf-8') mock_loader = MagicMock() @@ -126,7 +168,7 @@ class TestPdfDecoderProcessor(IsolatedAsyncioTestCase): @patch('trustgraph.base.async_processor.AsyncProcessor', MockAsyncProcessor) async def test_on_message_unicode_content(self, mock_pdf_loader_class, mock_producer, mock_consumer): """Test handling of unicode content in PDF""" - pdf_content = b"fake pdf content" + pdf_content = b"%PDF-1.7\nfake pdf content" pdf_base64 = base64.b64encode(pdf_content).decode('utf-8') mock_loader = MagicMock() diff --git a/trustgraph-flow/trustgraph/decoding/pdf/pdf_decoder.py b/trustgraph-flow/trustgraph/decoding/pdf/pdf_decoder.py index ca242265..ae393028 100755 --- a/trustgraph-flow/trustgraph/decoding/pdf/pdf_decoder.py +++ b/trustgraph-flow/trustgraph/decoding/pdf/pdf_decoder.py @@ -32,6 +32,10 @@ logger = logging.getLogger(__name__) default_ident = "document-decoder" +def _looks_like_pdf(content): + return content.lstrip().startswith(b"%PDF-") + + class Processor(FlowProcessor): def __init__(self, **params): @@ -94,33 +98,37 @@ class Processor(FlowProcessor): ) return - with tempfile.NamedTemporaryFile(delete_on_close=False, suffix='.pdf') as fp: + # Check if we should fetch from librarian or use inline data + if v.document_id: + # Fetch from librarian via Pulsar + logger.info(f"Fetching document {v.document_id} from librarian...") + + content = await flow.librarian.fetch_document_content( + document_id=v.document_id, + + ) + + # Content is base64 encoded + if isinstance(content, str): + content = content.encode('utf-8') + decoded_content = base64.b64decode(content) + + logger.info(f"Fetched {len(decoded_content)} bytes from librarian") + else: + # Use inline data (backward compatibility) + decoded_content = base64.b64decode(v.data) + + if not _looks_like_pdf(decoded_content): + logger.error( + f"Document {v.metadata.id} is not valid PDF content. " + f"Ignoring document." + ) + return + + with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as fp: temp_path = fp.name - - # Check if we should fetch from librarian or use inline data - if v.document_id: - # Fetch from librarian via Pulsar - logger.info(f"Fetching document {v.document_id} from librarian...") - fp.close() - - content = await flow.librarian.fetch_document_content( - document_id=v.document_id, - - ) - - # Content is base64 encoded - if isinstance(content, str): - content = content.encode('utf-8') - decoded_content = base64.b64decode(content) - - with open(temp_path, 'wb') as f: - f.write(decoded_content) - - logger.info(f"Fetched {len(decoded_content)} bytes from librarian") - else: - # Use inline data (backward compatibility) - fp.write(base64.b64decode(v.data)) - fp.close() + fp.write(decoded_content) + fp.close() global PyPDFLoader if PyPDFLoader is None: