diff --git a/README.md b/README.md
index 38d1c39b..c366a3d9 100644
--- a/README.md
+++ b/README.md
@@ -3,7 +3,7 @@
-[](https://pypi.org/project/trustgraph/)  
+[](https://pypi.org/project/trustgraph/) [](LICENSE) 
[](https://discord.gg/sQMwkRz5GX) [](https://deepwiki.com/trustgraph-ai/trustgraph)
@@ -11,89 +11,44 @@
-# Write context once. Run agents anywhere.
+# The agent runtime platform
-Stop rebuilding context from scratch. TrustGraph treats context as a holon — a modular, independent whole that naturally snaps into a larger domain-wide intelligence layer. By deploying context as holonic context graphs, TrustGraph powers multi-tenant agent workflows, dramatically reduces token consumption, and aligns with semantic web standards (RDF, OWL, SKOS, SHACL). Version your context, share it across teams, and scale with full provenance.
+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.
-## What TrustGraph Does
-
-TrustGraph is a complete holonic context harness for all LLMs. It provides the full infrastructure layer underneath your agents: knowledge ingestion, structured storage, graph-grounded retrieval, agent orchestration, and a full LLM inferencing stack.
-
-TrustGraph relies on absolutely no 3rd party services aside from optional API integrations to cloud-hosted LLMs. Whether you are using Anthropic's or OpenAI's API, or self-hosting Qwen3.7 via vLLM, TrustGraph handles it all with pre-built API connectors and a full LLM inferencing stack to enrich the models with a sovereign, private holonic system that grounds your agents in reality.
-
-## The Problem: Why Agents Break
-
-When you build an AI agent today, you spend most of your time fighting context:
-
-- **RAG retrieves fragments, not meaning**. Chunks of text have no structure. Relationships between facts are invisible. Your agent guesses at the connections.
-
-- **Context is disposable**. What the agent learned in one session is gone in the next. There is no persistent, structured knowledge layer underneath.
-
-- **Answers aren't traceable**. You can't explain why the agent said what it said, which means you can't trust it in production.
-
-- **Knowledge can't be reused**. You rebuild the same context pipelines for every new project, every new agent, every new environment.
-
-These aren't retrieval problems. They are structural problems. Context needs to be organized, versioned, and composable — exactly the way software infrastructure is.
-
-## The Solution: A Holonic Context System
-The philosopher Arthur Koestler coined the word [holon](https://en.wikipedia.org/wiki/Holon_(philosophy)) to describe something that is simultaneously a whole in itself and a part of something larger. A fact is whole. It is also part of a domain. A domain is whole. It is also part of an organization's knowledge.
-
-AI agents break down because this holonic structure is never built. Context gets shoved into flat text windows, scattered across vector stores, or hardwired into one-off prompts. Facts lose their relationships.
-
-TrustGraph solves this by organizing your domain into holonic context graphs. Entities, relationships, and evidence are treated as first-class objects. Every agent query is grounded against these holons—marrying symbolic graph structures with vector embeddings. Every answer carries provenance. Every fact is traceable.
-
-## Context Cores: Knowledge as a First-Class Citizen
-
-A Context Core is the deployable unit of knowledge in TrustGraph. It packages everything an agent needs to reason reliably over a domain into a single, portable artifact.
-
-### What's inside a Context Core
-- **Ontology** — your domain schema and entity mappings
-- **Holon** — entities, relationships, and supporting evidence
-- **Embeddings** — vector indexes for fast semantic entry-point lookup
-- **Provenance** — where every fact came from, when, and how it was derived
-- **Retrieval policies** — traversal rules, freshness controls, authority ranking
-
-Context Cores decouple what agents know from how agents are deployed. Build once. Run in Docker locally, Kubernetes in production, or on any cloud. Pin a version. Roll back. Promote across environments. This is context engineering — and it works because knowledge is finally treated like the infrastructure it is.
-
-## Explainability: Trust Your Agents in Production
-LLMs are black boxes, and traditional RAG makes it worse. When an agent pulls flat text chunks from a vector store, you have no idea how it connected those fragments to form an answer. You cannot ship agents to production if you can't explain why they said what they said.
-
-### How TrustGraph makes agents explainable:
-
-- **Traceable Reasoning Paths**: Instead of guessing at connections between text chunks, TrustGraph traverses explicit relationship paths in the holonic context graph. You can inspect exactly which entities, relationships, and sub-graphs were pulled into the LLM's context window to generate a given response.
-- **Fact-Level Provenance**: Every node and edge in the graph carries strict provenance. When an agent makes a claim, you can trace it back to the exact source document, the time it was ingested, and the extraction method used to derive it.
-- **No Black-Box Guesses**: By grounding the LLM in a structured, symbolic graph, you eliminate the hallucinations that occur when models are forced to infer relationships from unstructured text. If a fact isn't in the graph, the agent doesn't use it.
-
-TrustGraph doesn't just give you answers - it gives you the receipt. Every fact is traceable, every connection is visible, and every output is verifiable.
-
-## Workspaces, Collections, and Flows
-
-TrustGraph has a [three-level system](https://docs.trustgraph.ai/overview/workspaces) for organizing and isolating knowledge.
-
-A `Workspace` is the outermost boundary — a fully isolated tenancy scope where all data, users, configuration, and pipelines live independently from every other workspace. Isolation is structural: enforced at the pub/sub queue, storage, and API gateway layers, not by trusting a field in a message body.
-
-Within a workspace, a `Collection` groups related holons, graph structures, embeddings, and documents together — think of it as a dedicated shelf in a library, scoped to a specific domain, project, or customer.
-
-A `Flow` is a running data processing pipeline that defines how raw data moves through ingestion, extraction, structuring, and storage — the assembly line that turns documents into queryable knowledge. Together, the three layers let you run multiple isolated tenants on a single deployment, separate knowledge by domain within each tenant, and process that knowledge through fully configurable pipelines — all without restarting the system or rebuilding your infrastructure.
-
-## The Full Stack
-TrustGraph is not a wrapper around a graph database. It is the complete backend for production agentic systems.
-
-- **Holonic context graph engine**: automated entity and relationship extraction, ontology-driven graph construction, graph-grounded retrieval for explainable outputs
-- **Multi-model database**: tabular/relational, key-value, document, graph, vectors, images, video, and audio — all managed in Cassandra and S3-compatible Garage
-- **Out-of-the-box RAG pipelines**: DocumentRAG, GraphRAG, and OntologyRAG ready to deploy
-- **Fully agentic orchestration**: single or multi-agent, ReAct, Plan-then-Execute, Supervisor patterns, and MCP integration
-- **3D Knowledge Explorer**: interactive graph visualization with BFS neighborhood extraction and edge pulse animation
-- **Automated data ingest**: quick ingest with semantic similarity or ontology-structured precision retrieval
-- **Run anywhere**: Docker/Podman locally, Kubernetes in the cloud
-
-All major LLMs — Anthropic, Cohere, Gemini, Mistral, OpenAI, and more via API.
-
-vLLM, Ollama, TGI, LM Studio, and Llamafiles for fully local inferencing.
-
-Verified cloud deployments for Alibaba Cloud, AWS, Azure, GCP, OVHcloud, and Scaleway.
+The platform:
+- [x] Multi-model and multimodal database system
+ - [x] Tabular/relational, key-value
+ - [x] Document, graph, and vectors
+ - [x] Images, video, and audio
+- [x] Context Graph engine
+ - [x] Automated entity and relationship extraction
+ - [x] Ontology-driven graph construction
+ - [x] Graph-grounded retrieval for explainable outputs
+- [x] Automated data ingest and loading
+ - [x] Quick ingest with semantic similarity retrieval
+ - [x] Ontology structuring for precision retrieval
+- [x] Out-of-the-box RAG pipelines
+ - [x] DocumentRAG
+ - [x] GraphRAG
+ - [x] OntologyRAG
+- [x] 3D GraphViz for exploring context
+- [x] Fully Agentic System
+ - [x] Single or Multi Agent
+ - [x] ReAct, Plan-then-Execute, and Supervisor patterns
+ - [x] MCP integration
+- [x] Run anywhere
+ - [x] Deploy locally with Docker
+ - [x] Deploy in cloud with Kubernetes
+- [x] Support for all major LLMs
+ - [x] API support for Anthropic, Cohere, Gemini, Mistral, OpenAI, and others
+ - [x] Model inferencing with vLLM, Ollama, TGI, LM Studio, and Llamafiles
+- [x] Developer friendly
+ - [x] REST API [Docs](https://docs.trustgraph.ai/reference/apis/rest.html)
+ - [x] Websocket API [Docs](https://docs.trustgraph.ai/reference/apis/websocket.html)
+ - [x] Python API [Docs](https://docs.trustgraph.ai/reference/apis/python)
+ - [x] CLI [Docs](https://docs.trustgraph.ai/reference/cli/)
## No API Keys Required
@@ -107,12 +62,12 @@ Everything else is included.
- [x] Managed Multi-model storage in [Cassandra](https://cassandra.apache.org/_/index.html)
- [x] Managed Vector embedding storage in [Qdrant](https://github.com/qdrant/qdrant)
- [x] Managed File and Object storage in [Garage](https://github.com/deuxfleurs-org/garage) (S3 compatible)
-- [x] Managed High-speed Pub/Sub messaging fabric with [Pulsar](https://github.com/apache/pulsar) or [RabbitMQ](https://www.rabbitmq.com/)
+- [x] Managed High-speed Pub/Sub messaging fabric with [Pulsar](https://github.com/apache/pulsar)
- [x] Complete LLM inferencing stack for open LLMs with [vLLM](https://github.com/vllm-project/vllm), [TGI](https://github.com/huggingface/text-generation-inference), [Ollama](https://github.com/ollama/ollama), [LM Studio](https://github.com/lmstudio-ai), and [Llamafiles](https://github.com/mozilla-ai/llamafile)
## Quickstart
-No need to clone the repo unless you are building from source. TrustGraph deploys as a set of Docker containers. Configure it on the command line in one step:
+There's no need to clone this repo, unless you want to build from source. TrustGraph is a fully containerized app that deploys as a set of Docker containers. To configure TrustGraph on the command line:
```
npx @trustgraph/config
@@ -123,39 +78,44 @@ The config process will generate an app config that can be run locally with Dock
- Deployment instructions as `INSTALLATION.md`
-
For a browser based configuration, try the [Configuration Terminal](https://config-ui.demo.trustgraph.ai/).
-## Watch What is a Holonic Context Graph?
+## Watch What is a Context Graph?
[](https://www.youtube.com/watch?v=gZjlt5WcWB4)
-## Watch Holonic Context Graphs in Action
+## Watch Context Graphs in Action
[](https://www.youtube.com/watch?v=sWc7mkhITIo)
## Getting Started with TrustGraph
- [**Getting Started Guides**](https://docs.trustgraph.ai/getting-started)
+- [**Using the Workbench**](#workbench)
- [**Developer APIs and CLI**](https://docs.trustgraph.ai/reference)
- [**Deployment Guides**](https://docs.trustgraph.ai/deployment)
-## TrustGraph UI
+## Workbench
-
+The **Workbench** provides tools for all major features of TrustGraph. The **Workbench** is on port `8888` by default.
-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
-- **Prompt Editor** — A dedicated prompt editing workflow
+- **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
## TypeScript Library for UIs
@@ -165,6 +125,134 @@ There are 3 libraries for quick UI integration of TrustGraph services.
- [@trustgraph/react-state](https://www.npmjs.com/package/@trustgraph/react-state)
- [@trustgraph/react-provider](https://www.npmjs.com/package/@trustgraph/react-provider)
+## Context Cores
+
+Context Cores are how TrustGraph treats context like code. A Context Core is a **portable, versioned bundle of context** that you can ship between projects and environments, pin in production, and reuse across agents. It packages the “stuff agents need to know” (structured knowledge + embeddings + evidence + policies) into a single artifact, so you can treat context like code: build it, test it, version it, promote it, and roll it back. TrustGraph is built to support this kind of end-to-end context engineering and orchestration workflow.
+
+### What’s inside a Context Core
+A Context Core typically includes:
+- Ontology (your domain schema) and mappings
+- Context Graph (entities, relationships, supporting evidence)
+- Embeddings / vector indexes for fast semantic entry-point lookup
+- Source manifests + provenance (where facts came from, when, and how they were derived)
+- Retrieval policies (traversal rules, freshness, authority ranking)
+
+## Tech Stack
+TrustGraph provides component flexibility to optimize agent workflows.
+
+
+LLM APIs
+
+
+- Anthropic
+- AWS Bedrock
+- AzureAI
+- AzureOpenAI
+- Cohere
+- Google AI Studio
+- Google VertexAI
+- Mistral
+- OpenAI
+
+
+
+LLM Orchestration
+
+
+- LM Studio
+- Llamafiles
+- Ollama
+- TGI
+- vLLM
+
+
+
+Multi-model storage
+
+
+- Apache Cassandra
+
+
+
+VectorDB
+
+
+- Qdrant
+
+
+
+File and Object Storage
+
+
+- Garage
+
+
+
+Observability
+
+
+- Prometheus
+- Grafana
+- Loki
+
+
+
+Data Streaming
+
+
+- Apache Pulsar
+- RabbitMQ
+- Apache Kafka
+
+
+
+Clouds
+
+
+- AWS
+- Azure
+- Google Cloud
+- OVHcloud
+- Scaleway
+
+
+
+## Observability & Telemetry
+
+Once the platform is running, access the Grafana dashboard at:
+
+```
+http://localhost:3000
+```
+
+Default credentials are:
+
+```
+user: admin
+password: admin
+```
+
+The default Grafana dashboard tracks the following:
+
+
+Telemetry
+
+
+- LLM Latency
+- Error Rate
+- Service Request Rates
+- Queue Backlogs
+- Chunking Histogram
+- Error Source by Service
+- Rate Limit Events
+- CPU usage by Service
+- Memory usage by Service
+- Models Deployed
+- Token Throughput (Tokens/second)
+- Cost Throughput (Cost/second)
+
+
+
## Contributing
[Developer's Guide](https://docs.trustgraph.ai/guides/building/introduction.html)
@@ -173,7 +261,7 @@ There are 3 libraries for quick UI integration of TrustGraph services.
**TrustGraph** is licensed under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0).
- Copyright 2024-2026 TrustGraph
+ Copyright 2024-2025 TrustGraph
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
diff --git a/containers/Containerfile.unstructured b/containers/Containerfile.unstructured
index 2b9a18f7..6de8a800 100644
--- a/containers/Containerfile.unstructured
+++ b/containers/Containerfile.unstructured
@@ -7,7 +7,7 @@ FROM docker.io/fedora:42 AS base
ENV PIP_BREAK_SYSTEM_PACKAGES=1
-RUN dnf install -y python3.13 libxcb mesa-libGL poppler-utils && \
+RUN dnf install -y python3.13 libxcb mesa-libGL && \
alternatives --install /usr/bin/python python /usr/bin/python3.13 1 && \
python -m ensurepip --upgrade && \
pip3 install --no-cache-dir --upgrade 'pip>=26.0' 'setuptools>=78.1.1' && \
diff --git a/docs/tech-specs/knowledge-core-completeness.md b/docs/tech-specs/knowledge-core-completeness.md
deleted file mode 100644
index 3ccb41f0..00000000
--- a/docs/tech-specs/knowledge-core-completeness.md
+++ /dev/null
@@ -1,535 +0,0 @@
----
-layout: default
-title: "Knowledge Core Completeness"
-parent: "Tech Specs"
----
-
-# Knowledge Core Completeness
-
-## Overview
-
-Knowledge cores are portable snapshots of extracted knowledge: triples, graph
-embeddings, and document embeddings stored in Cassandra's `knowledge` keyspace.
-They can be downloaded as files, transferred between TrustGraph instances, and
-loaded back into vector and graph stores.
-
-Recent additions to TrustGraph — explainability/provenance and named graphs —
-were not carried through to the knowledge core system. This means that
-exporting and re-importing a core loses provenance links, graph assignments,
-and source material, breaking the explainability chain.
-
-This specification addresses three gaps:
-
-1. **Named graphs not stored** — The `g` (graph name) field on triples is
- silently dropped when writing to the core store and comes back as `None`
- on read.
-2. **Provenance triples not captured** — Provenance triples (PROV-O) are
- generated during extraction and flow to graph stores, but never enter
- the knowledge core store. It is unclear whether they arrive at the store
- in the correct form.
-3. **Source material not included** — Documents, text pages, and chunks in
- the librarian's bucket store are not part of the core. After loading a
- core on a different instance, provenance links to source material point
- at nothing.
-
-## Goals
-
-- **Self-contained cores**: A downloaded knowledge core file contains
- everything needed to reconstruct the full knowledge graph including
- provenance and source attribution on a fresh instance.
-- **Named graph preservation**: Round-tripping a core preserves graph
- assignments on all triples.
-- **Backward compatibility**: Existing core files (without graph names or
- source material) can still be uploaded and loaded. New fields are optional
- on import.
-- **No change to core identity**: A core is still identified by its document
- ID. The additional data is associated with the same core ID.
-- **Minimal file format changes**: Extend the existing msgpack record format
- with new record types rather than restructuring existing ones.
-
-## Background
-
-### Current Lifecycle
-
-```
-Extraction pipeline
- │
- ├─ triples ──────────────────► knowledge core store (Cassandra)
- ├─ graph embeddings ─────────► knowledge core store (Cassandra)
- ├─ document embeddings ──────► knowledge core store (Cassandra)
- ├─ provenance triples ───────► graph store (only)
- └─ source documents ─────────► librarian bucket store (only)
-
-Download: Cassandra ──► knowledge manager ──► API gateway ──► client file
-Upload: client file ──► API gateway ──► knowledge manager ──► Cassandra
-Load: Cassandra ──► knowledge manager ──► Pulsar topics ──► graph/vector stores
-```
-
-### Current Core File Format (msgpack)
-
-A core file is a sequence of concatenated msgpack records. Each record is a
-2-element tuple: `(type_tag, payload)`.
-
-| Type tag | Payload | Description |
-|----------|---------|-------------|
-| `"t"` | `{"m": {id, root, collection}, "t": [triple_dicts]}` | Triple batch |
-| `"ge"` | `{"m": {id, root, collection}, "e": [{entity, vector}]}` | Graph embedding batch |
-
-### What's Missing
-
-#### Named Graphs
-
-The `Triple` dataclass has a `g: str | None` field (graph name IRI), used to
-separate provenance graphs (`urn:graph:source`, `urn:graph:retrieval`) from
-the default graph. However:
-
-- **Cassandra schema** (`knowledge.triples` table): stores a 6-tuple per
- triple `(s_val, s_is_uri, p_val, p_is_uri, o_val, o_is_uri)` — no graph
- field.
-- **`add_triples()`** (`tables/knowledge.py:231`): destructures only `s`,
- `p`, `o` — `g` is discarded.
-- **`get_triples()`** (`tables/knowledge.py:396`): reconstructs `Triple`
- with `g` defaulting to `None`.
-- **Core file format**: triple dicts do not include a graph field.
-
-#### Provenance Triples
-
-Provenance triples are generated in the extraction pipeline
-(`trustgraph-base/trustgraph/provenance/triples.py`) and published to graph
-store topics. They use named graphs (`urn:graph:source`,
-`urn:graph:retrieval`) and PROV-O vocabulary.
-
-The knowledge core store processor (`storage/knowledge/store.py`) listens on
-`triples-input` and `graph-embeddings-input`. Whether provenance triples
-arrive on the same `triples-input` topic or a separate one needs
-verification. Even if they do arrive, the graph name would be lost (per
-above).
-
-#### Source Material
-
-The librarian stores the full document hierarchy in a separate system:
-
-- **Blob store** (S3/MinIO): original documents, text pages, chunks —
- keyed by object UUID under `doc/{object_id}`.
-- **Cassandra `library` keyspace**: document metadata including `id`,
- `kind` (MIME type), `title`, `parent_id`, `document_type`
- (`source`/`extracted`), `object_id` (blob reference).
-
-Provenance triples link extracted facts back to chunk/page/document IDs.
-Those IDs resolve through the librarian. When a core is loaded on a
-different instance, the librarian has no matching documents, so the entire
-provenance chain is broken.
-
-### Key Source Files
-
-| Component | File | Purpose |
-|-----------|------|---------|
-| Core Cassandra schema | `trustgraph-flow/trustgraph/tables/knowledge.py` | Table definitions, read/write |
-| Core manager | `trustgraph-flow/trustgraph/cores/knowledge.py` | API operations, load-to-store |
-| Core store processor | `trustgraph-flow/trustgraph/storage/knowledge/store.py` | Extraction → Cassandra |
-| CLI download | `trustgraph-cli/trustgraph/cli/get_kg_core.py` | Core → msgpack file |
-| CLI upload | `trustgraph-cli/trustgraph/cli/put_kg_core.py` | Msgpack file → core |
-| CLI load | `trustgraph-cli/trustgraph/cli/load_kg_core.py` | Core → graph/vector stores |
-| API client | `trustgraph-base/trustgraph/api/knowledge.py` | Client-side knowledge API |
-| Triple schema | `trustgraph-base/trustgraph/schema/core/primitives.py` | Triple dataclass with `g` field |
-| Provenance generation | `trustgraph-base/trustgraph/provenance/triples.py` | PROV-O triple creation |
-| Librarian | `trustgraph-flow/trustgraph/librarian/librarian.py` | Document storage service |
-| Library tables | `trustgraph-flow/trustgraph/tables/library.py` | Document metadata in Cassandra |
-| Blob store | `trustgraph-flow/trustgraph/librarian/blob_store.py` | S3/MinIO object storage |
-
-## Technical Design
-
-### Change 1: Named Graph Field in Core Storage
-
-#### Cassandra Schema
-
-Extend the `triples` tuple from 6 to 7 elements, adding the graph name:
-
-```
-triples list>
-```
-
-**Migration**: The schema change uses `ALTER TABLE` or is handled by
-creating a new table version. Existing rows with 6-element tuples must be
-handled gracefully on read — if the tuple has 6 elements, treat graph as
-default.
-
-#### Write Path (`add_triples`)
-
-Change `tables/knowledge.py:add_triples()` to include `triple.g`:
-
-```python
-triples = [
- (
- *term_to_tuple(v.s), *term_to_tuple(v.p), *term_to_tuple(v.o),
- v.g or ""
- )
- for v in m.triples
-]
-```
-
-#### Read Path (`get_triples`)
-
-Change `tables/knowledge.py:get_triples()` to restore the graph name:
-
-```python
-Triple(
- s = tuple_to_term(elt[0], elt[1]),
- p = tuple_to_term(elt[2], elt[3]),
- o = tuple_to_term(elt[4], elt[5]),
- g = elt[6] if len(elt) > 6 and elt[6] else None,
-)
-```
-
-The `len(elt) > 6` guard provides backward compatibility with existing
-6-element rows.
-
-#### Core File Format
-
-Extend triple dicts in the `"t"` record to include the graph name:
-
-```python
-# In get_kg_core.py write_triple — each triple dict gains "g" key
-{"s": ..., "p": ..., "o": ..., "g": "urn:graph:source"}
-```
-
-On read (`put_kg_core.py`), treat missing `"g"` key as default graph for
-backward compatibility with old core files.
-
-### Change 2: Provenance Triples in Cores
-
-#### Investigation Required
-
-Before implementation, verify:
-
-1. Whether provenance triples arrive on the `triples-input` topic that the
- knowledge core store processor already listens on.
-2. If not, which topic they use, and whether the store processor should
- subscribe to it.
-
-#### If provenance triples already arrive at the store
-
-The only change needed is Change 1 (named graphs) — the provenance triples
-are already being stored, just without their graph name. Once graph names
-are preserved, provenance triples will round-trip correctly.
-
-#### If provenance triples do NOT arrive at the store
-
-Two options:
-
-**Option A — Route provenance to the existing store topic**: Configure the
-flow so provenance triples are published to the same `triples-input` topic.
-This is the simpler approach and keeps the store processor unchanged.
-
-**Option B — Add a subscription**: Add a new `ConsumerSpec` in the store
-processor for the provenance topic. This keeps provenance routing
-independent but adds complexity.
-
-Recommendation: Option A, unless there is a reason provenance triples are
-intentionally kept off the core store topic.
-
-### Change 3: Source Material in Cores
-
-This is the largest change. The goal is that when a core is loaded on a
-fresh instance, provenance links to source material resolve.
-
-#### Architecture
-
-Source material is **not stored in the knowledge core tables**. It lives in
-the librarian (Cassandra `library` keyspace + S3/MinIO blob store) and is
-fetched on demand via the librarian's existing service API.
-
-The knowledge manager acts as a **client of the librarian service** — it
-calls the librarian's request/response API over pub/sub to retrieve document
-metadata and content. It does not access the library's Cassandra tables or
-blob store directly.
-
-#### Transport
-
-The librarian's pub/sub API already handles chunking of large documents.
-This chunking is designed to be websocket-friendly, so library content
-flowing through the API gateway to external clients does not require
-re-chunking. The API gateway remains a transport layer.
-
-```
-Download:
- Knowledge manager ──pub/sub──► Librarian (fetch metadata + content)
- Knowledge manager ──pub/sub──► API gateway ──websocket──► Client
-
-Upload:
- Client ──websocket──► API gateway ──pub/sub──► Knowledge manager
- Knowledge manager ──pub/sub──► Librarian (store metadata + content)
-```
-
-#### What to Include
-
-The provenance chain links facts → chunks → pages → documents. For the
-chain to resolve, the core must include:
-
-1. **Document metadata** — the library record for each document in the
- hierarchy (id, kind, title, parent_id, document_type, etc.)
-2. **Document content** — the blob data for each document (original file,
- extracted text pages, text chunks)
-
-Including the full hierarchy is necessary because:
-- A user viewing provenance needs to traverse fact → chunk → page → document
-- The chunk text is needed to show what text a fact was extracted from
-- The page text provides broader context
-- The original document is needed for full source attribution
-
-#### Size Implications
-
-Source material will significantly increase core file sizes. A rough model:
-
-| Component | Typical size per document |
-|-----------|-------------------------|
-| Triples + embeddings (current) | 1-10 MB |
-| Chunk text (all chunks) | ~same as original document |
-| Page text (all pages) | ~same as original document |
-| Original document (PDF, etc.) | Varies widely (KB to hundreds of MB) |
-
-For a 10 MB PDF, the core could grow from ~5 MB to ~25 MB (original +
-derived text + existing data). For large document sets, cores could become
-very large.
-
-**Decision needed**: Whether to include original documents or just derived
-text (pages + chunks). Including only derived text still allows provenance
-display but loses the ability to serve the original file.
-
-#### New Core File Record Types
-
-Add new msgpack record types for library content:
-
-| Type tag | Payload | Description |
-|----------|---------|-------------|
-| `"lm"` | `{"id", "kind", "title", "parent_id", "document_type", "comments", "tags", "metadata"}` | Library document metadata |
-| `"lb"` | `{"id", "data"}` | Library document blob content (chunked by pub/sub layer) |
-
-These are emitted after the existing `"t"` and `"ge"` records during
-download and processed during upload.
-
-#### Download Path
-
-Extend `KnowledgeManager.get_kg_core()` to:
-
-1. Stream triples and graph embeddings from the core store (existing
- behavior).
-2. Use the librarian service API to retrieve documents associated with
- this core ID:
- a. Fetch the root document metadata and content.
- b. Use `list-children` to discover child documents (pages, chunks).
- c. Recursively fetch metadata and content for each child.
-3. Stream each document as `"lm"` (metadata) and `"lb"` (content) records.
-
-The knowledge manager gains the librarian service as a pub/sub dependency.
-Large document content is chunked by the librarian's existing pub/sub
-transport — the knowledge manager receives and forwards these chunks without
-buffering the full blob in memory.
-
-#### Upload Path
-
-Extend `KnowledgeManager.put_kg_core()` to handle the new record types:
-
-1. For `"lm"` records: call the librarian service API to create/update
- the document metadata.
-2. For `"lb"` records: call the librarian service API to store the
- document content.
-
-Parent-child relationships are preserved because `parent_id` is stored in
-the metadata. Documents should be processed in hierarchy order (parent
-before child) to satisfy any ordering constraints.
-
-#### Load Path
-
-The load path (`_load_kg_core`) publishes triples and embeddings to Pulsar
-topics for ingestion into graph/vector stores. Source material does not need
-to flow through the load path — it is already in the librarian after the
-upload step and can be accessed directly by services that need it.
-
-No changes to the load path for source material.
-
-#### CLI Changes
-
-**`tg-get-kg-core`**: Add handling for `"lm"` and `"lb"` record types in
-the file writer.
-
-**`tg-put-kg-core`**: Add handling for `"lm"` and `"lb"` record types in
-the file reader. Send library records to the knowledge manager alongside
-triple/embedding records.
-
-#### Associating Documents with Cores
-
-The core ID is `metadata.root`, which is the root document ID from the
-librarian. This provides a natural join: the core's root document and all
-its children (pages, chunks) are the source material for that core.
-
-The librarian's `list-children` API provides the child documents. A
-recursive traversal from the root document collects the full hierarchy.
-
-### API Changes
-
-#### KnowledgeResponse Schema
-
-Add optional fields to `KnowledgeResponse` for library data:
-
-```python
-@dataclass
-class KnowledgeResponse:
- error: Error | None = None
- ids: list | None = None
- eos: bool = False
- triples: Triples | None = None
- graph_embeddings: GraphEmbeddings | None = None
- document_embeddings: DocumentEmbeddings | None = None
- library_metadata: LibraryMetadata | None = None # new
- library_blob: LibraryBlob | None = None # new
-```
-
-#### New Schema Types
-
-```python
-@dataclass
-class LibraryMetadata:
- id: str
- kind: str | None = None
- title: str | None = None
- parent_id: str | None = None
- document_type: str | None = None
- comments: str | None = None
- tags: list[str] | None = None
- metadata: list[Triple] | None = None
-
-@dataclass
-class LibraryBlob:
- id: str
- data: bytes
-```
-
-#### Socket API
-
-The existing streaming protocol for `get-kg-core` / `put-kg-core` carries
-these new fields naturally — responses already stream multiple record types.
-
-### Dependencies Between Changes
-
-```
-Change 1 (named graphs) ◄── Change 2 depends on this
- │
- └── Change 2 (provenance triples)
- │
- └── Change 3 (source material) is independent
-```
-
-Change 1 is a prerequisite for Change 2 (provenance triples use named
-graphs). Change 3 is independent and can be implemented in parallel.
-
-## Security Considerations
-
-- **Workspace isolation**: Core download/upload must respect workspace
- boundaries. Source material from the librarian must only be included if
- it belongs to the same workspace as the core. This is already enforced
- by the existing workspace-scoped queries.
-- **Large blob transfer**: Streaming large documents through the API
- is handled by the librarian's existing pub/sub chunking, which is
- designed to be websocket-friendly. No additional chunking layer is
- needed.
-- **Cross-instance trust**: When uploading a core from an external source,
- the library content should be treated as untrusted input. Document
- metadata and blob content should be validated before insertion.
-
-## Performance Considerations
-
-- **Core file size**: Including source material will significantly increase
- core file sizes. Consider adding a flag to download/upload commands to
- optionally exclude source material for use cases where only the knowledge
- graph is needed.
-- **Streaming**: All paths already use streaming (paged Cassandra queries,
- msgpack record-at-a-time). Library content should follow the same pattern.
-- **Cassandra schema migration**: Changing the tuple width in the `triples`
- table requires careful handling. Cassandra frozen tuples cannot be altered
- in place — a migration strategy is needed (see Migration Plan).
-
-## Testing Strategy
-
-- **Unit tests**: Triple round-trip with graph name (write → read →
- verify `g` field preserved). Backward compatibility with 6-element tuples.
-- **Integration tests**: Full lifecycle — extract with provenance → download
- core → upload to fresh instance → load → verify provenance chain resolves.
-- **File format tests**: Read old-format core files (no graph name, no
- library records) and verify they load without error.
-- **Library inclusion tests**: Download core with source material → upload →
- verify documents accessible through librarian.
-
-## Migration Plan
-
-### Cassandra Schema
-
-The `triples` table stores tuples in a `list>` column. Cassandra
-does not support altering the type of an existing column. Options:
-
-**Option A — New table**: Create a `triples_v2` table with the 7-element
-tuple. Migrate data from `triples` to `triples_v2`. The read path checks
-both tables during a transition period, then the old table is dropped.
-
-**Option B — Dual read**: Keep the existing table. The read path handles
-both 6-element and 7-element tuples by checking length. New writes use
-7-element tuples. This works if Cassandra accepts variable-length tuples in
-a list — **needs verification**.
-
-**Option C — Separate graph column**: Instead of extending the tuple, add a
-parallel `graphs list` column where `graphs[i]` corresponds to
-`triples[i]`. This avoids tuple migration entirely but requires keeping the
-two lists in sync.
-
-Recommendation: Verify Option B first (simplest). Fall back to Option A if
-Cassandra rejects mixed tuple lengths.
-
-### Core File Format
-
-Backward compatible by design:
-- Old files lack `"g"` in triple dicts and have no `"lm"`/`"lb"` records →
- handled by defaults.
-- New files read by old code → old code ignores unknown record types (the
- existing `read_message` raises on unknown types, so this needs a small
- fix to skip unknown types gracefully).
-
-## Open Questions
-
-1. **Provenance topic routing**: Do provenance triples currently arrive at
- the `triples-input` topic consumed by the knowledge core store? If not,
- what topic are they on?
-
-2. **Include original documents?**: Should cores include the original
- uploaded document (e.g. PDF), or only derived text (pages + chunks)?
- Including originals makes cores fully self-contained but potentially
- very large. Excluding them preserves provenance text display but loses
- the ability to serve the original file.
-
-3. **Optional source material**: Should there be a flag on download/upload
- to include or exclude source material? This would let users choose
- between compact cores (knowledge only) and complete cores (knowledge +
- sources).
-
-4. **Cassandra tuple migration**: Can Cassandra handle mixed-length tuples
- in a `list>` column, or is a table migration required?
-
-5. **Document embedding cores**: DE cores are managed alongside KG cores.
- Do they need the same treatment (source material inclusion)? The
- document embeddings reference chunk IDs — the same provenance chain
- applies.
-
-6. **Core versioning**: Should the core file include a version marker so
- readers can distinguish old-format from new-format files without
- trial-and-error parsing?
-
-## References
-
-- Extraction-time provenance: `docs/tech-specs/extraction-time-provenance.md`
-- Query-time explainability: `docs/tech-specs/query-time-explainability.md`
-- Agent explainability: `docs/tech-specs/agent-explainability.md`
-- Data ownership model: `docs/tech-specs/data-ownership-model.md`
diff --git a/tests/unit/test_base/test_cassandra_config.py b/tests/unit/test_base/test_cassandra_config.py
index fe8a8379..a291434d 100644
--- a/tests/unit/test_base/test_cassandra_config.py
+++ b/tests/unit/test_base/test_cassandra_config.py
@@ -409,57 +409,4 @@ class TestEdgeCases:
assert hosts == ['mixed-host']
assert username is None # Stays None
- assert password == 'mixed-pass'
-
-
-class TestReplicationFactorParamPath:
-
- def test_explicit_kwarg(self):
- with patch.dict(os.environ, {}, clear=True):
- _, _, _, _, rf = resolve_cassandra_config(
- replication_factor=3,
- )
- assert rf == 3
-
- def test_kwarg_overrides_env(self):
- with patch.dict(os.environ, {'CASSANDRA_REPLICATION_FACTOR': '5'}, clear=True):
- _, _, _, _, rf = resolve_cassandra_config(
- replication_factor=3,
- )
- assert rf == 3
-
- def test_env_fallback_when_kwarg_none(self):
- with patch.dict(os.environ, {'CASSANDRA_REPLICATION_FACTOR': '5'}, clear=True):
- _, _, _, _, rf = resolve_cassandra_config(
- replication_factor=None,
- )
- assert rf == 5
-
- def test_default_when_no_kwarg_no_env(self):
- with patch.dict(os.environ, {}, clear=True):
- _, _, _, _, rf = resolve_cassandra_config()
- assert rf == 1
-
- def test_params_dict_path(self):
- with patch.dict(os.environ, {}, clear=True):
- params = {'cassandra_replication_factor': 3}
- _, _, _, _, rf = resolve_cassandra_config(
- replication_factor=params.get('cassandra_replication_factor'),
- )
- assert rf == 3
-
- def test_params_dict_overrides_env(self):
- with patch.dict(os.environ, {'CASSANDRA_REPLICATION_FACTOR': '5'}, clear=True):
- params = {'cassandra_replication_factor': 3}
- _, _, _, _, rf = resolve_cassandra_config(
- replication_factor=params.get('cassandra_replication_factor'),
- )
- assert rf == 3
-
- def test_params_dict_missing_falls_to_env(self):
- with patch.dict(os.environ, {'CASSANDRA_REPLICATION_FACTOR': '5'}, clear=True):
- params = {}
- _, _, _, _, rf = resolve_cassandra_config(
- replication_factor=params.get('cassandra_replication_factor'),
- )
- assert rf == 5
\ No newline at end of file
+ assert password == 'mixed-pass'
\ No newline at end of file
diff --git a/tests/unit/test_base/test_qdrant_config.py b/tests/unit/test_base/test_qdrant_config.py
deleted file mode 100644
index dbbe4214..00000000
--- a/tests/unit/test_base/test_qdrant_config.py
+++ /dev/null
@@ -1,136 +0,0 @@
-
-import os
-import pytest
-from unittest.mock import patch
-
-from trustgraph.base.qdrant_config import (
- get_qdrant_defaults,
- resolve_qdrant_config,
-)
-
-
-class TestGetQdrantDefaults:
-
- def test_defaults_with_no_env_vars(self):
- with patch.dict(os.environ, {}, clear=True):
- defaults = get_qdrant_defaults()
- assert defaults['url'] == 'http://localhost:6333'
- assert defaults['api_key'] is None
- assert defaults['replication_factor'] == 1
- assert defaults['shard_number'] == 1
-
- def test_defaults_from_env(self):
- env = {
- 'QDRANT_URL': 'http://qdrant:6333',
- 'QDRANT_API_KEY': 'secret',
- 'QDRANT_REPLICATION_FACTOR': '3',
- 'QDRANT_SHARD_NUMBER': '5',
- }
- with patch.dict(os.environ, env, clear=True):
- defaults = get_qdrant_defaults()
- assert defaults['url'] == 'http://qdrant:6333'
- assert defaults['api_key'] == 'secret'
- assert defaults['replication_factor'] == 3
- assert defaults['shard_number'] == 5
-
-
-class TestResolveQdrantConfig:
-
- def test_defaults(self):
- with patch.dict(os.environ, {}, clear=True):
- url, api_key, rf, sn = resolve_qdrant_config()
- assert url == 'http://localhost:6333'
- assert api_key is None
- assert rf == 1
- assert sn == 1
-
- def test_explicit_kwargs(self):
- with patch.dict(os.environ, {}, clear=True):
- url, api_key, rf, sn = resolve_qdrant_config(
- url='http://custom:6333',
- api_key='key',
- replication_factor=3,
- shard_number=5,
- )
- assert url == 'http://custom:6333'
- assert api_key == 'key'
- assert rf == 3
- assert sn == 5
-
- def test_kwargs_override_env(self):
- env = {
- 'QDRANT_URL': 'http://env:6333',
- 'QDRANT_REPLICATION_FACTOR': '10',
- 'QDRANT_SHARD_NUMBER': '10',
- }
- with patch.dict(os.environ, env, clear=True):
- url, _, rf, sn = resolve_qdrant_config(
- url='http://explicit:6333',
- replication_factor=3,
- shard_number=5,
- )
- assert url == 'http://explicit:6333'
- assert rf == 3
- assert sn == 5
-
- def test_env_fallback_when_kwargs_none(self):
- env = {
- 'QDRANT_URL': 'http://env:6333',
- 'QDRANT_REPLICATION_FACTOR': '3',
- 'QDRANT_SHARD_NUMBER': '5',
- }
- with patch.dict(os.environ, env, clear=True):
- url, _, rf, sn = resolve_qdrant_config()
- assert url == 'http://env:6333'
- assert rf == 3
- assert sn == 5
-
- def test_params_dict_path(self):
- with patch.dict(os.environ, {}, clear=True):
- params = {
- 'store_uri': 'http://params:6333',
- 'api_key': 'pkey',
- 'qdrant_replication_factor': 3,
- 'qdrant_shard_number': 5,
- }
- url, api_key, rf, sn = resolve_qdrant_config(
- url=params.get('store_uri'),
- api_key=params.get('api_key'),
- replication_factor=params.get('qdrant_replication_factor'),
- shard_number=params.get('qdrant_shard_number'),
- )
- assert url == 'http://params:6333'
- assert api_key == 'pkey'
- assert rf == 3
- assert sn == 5
-
- def test_params_dict_overrides_env(self):
- env = {
- 'QDRANT_REPLICATION_FACTOR': '10',
- 'QDRANT_SHARD_NUMBER': '10',
- }
- with patch.dict(os.environ, env, clear=True):
- params = {
- 'qdrant_replication_factor': 3,
- 'qdrant_shard_number': 5,
- }
- _, _, rf, sn = resolve_qdrant_config(
- replication_factor=params.get('qdrant_replication_factor'),
- shard_number=params.get('qdrant_shard_number'),
- )
- assert rf == 3
- assert sn == 5
-
- def test_params_dict_missing_falls_to_env(self):
- env = {
- 'QDRANT_REPLICATION_FACTOR': '3',
- 'QDRANT_SHARD_NUMBER': '5',
- }
- with patch.dict(os.environ, env, clear=True):
- params = {}
- _, _, rf, sn = resolve_qdrant_config(
- replication_factor=params.get('qdrant_replication_factor'),
- shard_number=params.get('qdrant_shard_number'),
- )
- assert rf == 3
- assert sn == 5
diff --git a/tests/unit/test_cores/test_knowledge_manager.py b/tests/unit/test_cores/test_knowledge_manager.py
index 7797c9be..8f73dcc6 100644
--- a/tests/unit/test_cores/test_knowledge_manager.py
+++ b/tests/unit/test_cores/test_knowledge_manager.py
@@ -11,12 +11,7 @@ from unittest.mock import AsyncMock, Mock, patch, MagicMock
from unittest.mock import call
from trustgraph.cores.knowledge import KnowledgeManager
-from trustgraph.schema import (
- KnowledgeResponse, Triples, GraphEmbeddings, Metadata, Triple, Term,
- EntityEmbeddings, IRI, LITERAL,
- LibraryMetadata, LibraryBlob,
- LibrarianResponse, DocumentMetadata,
-)
+from trustgraph.schema import KnowledgeResponse, Triples, GraphEmbeddings, Metadata, Triple, Term, EntityEmbeddings, IRI, LITERAL
@pytest.fixture
@@ -378,252 +373,11 @@ class TestKnowledgeManagerOtherMethods:
mock_respond = AsyncMock()
await knowledge_manager.delete_kg_core(mock_request, mock_respond, "test-user")
-
+
# Verify table store was called correctly
knowledge_manager.table_store.delete_kg_core.assert_called_once_with("test-user", "test-doc-id")
-
+
# Verify response
mock_respond.assert_called_once()
response = mock_respond.call_args[0][0]
- assert response.error is None
-
-
-class TestKnowledgeManagerLibraryDownload:
- """Test get_kg_core streaming of library documents."""
-
- @pytest.fixture
- def manager_with_librarian(self, mock_flow_config):
- with patch('trustgraph.cores.knowledge.KnowledgeTableStore'):
- mock_librarian = AsyncMock()
- manager = KnowledgeManager(
- cassandra_host=["localhost"],
- cassandra_username="test_user",
- cassandra_password="test_pass",
- keyspace="test_keyspace",
- flow_config=mock_flow_config,
- librarian=mock_librarian,
- )
- manager.table_store = AsyncMock()
- return manager
-
- @pytest.mark.asyncio
- async def test_get_kg_core_streams_library_docs(self, manager_with_librarian):
- mock_request = Mock()
- mock_request.id = "root-doc"
- mock_respond = AsyncMock()
-
- manager_with_librarian.table_store.get_triples = AsyncMock()
- manager_with_librarian.table_store.get_graph_embeddings = AsyncMock()
-
- root_meta = DocumentMetadata(
- id="root-doc", kind="application/pdf", title="Test PDF",
- document_type="source",
- )
- child_meta = DocumentMetadata(
- id="chunk-1", kind="text/plain", title="Chunk 1",
- parent_id="root-doc", document_type="chunk",
- )
-
- manager_with_librarian.librarian.fetch_document_metadata.return_value = root_meta
- manager_with_librarian.librarian.request.return_value = LibrarianResponse(
- document_metadatas=[child_meta],
- )
- manager_with_librarian.librarian.fetch_document_content.side_effect = [
- b"cm9vdCBjb250ZW50",
- b"Y2h1bmsgY29udGVudA==",
- ]
-
- await manager_with_librarian.get_kg_core(
- mock_request, mock_respond, "test-user"
- )
-
- responses = [c[0][0] for c in mock_respond.call_args_list]
-
- lm_responses = [r for r in responses if r.library_metadata is not None]
- lb_responses = [r for r in responses if r.library_blob is not None]
- eos_responses = [r for r in responses if r.eos is True]
-
- assert len(lm_responses) == 2
- assert lm_responses[0].library_metadata.id == "root-doc"
- assert lm_responses[0].library_metadata.document_type == "source"
- assert lm_responses[1].library_metadata.id == "chunk-1"
- assert lm_responses[1].library_metadata.parent_id == "root-doc"
-
- assert len(lb_responses) == 2
- assert lb_responses[0].library_blob.id == "root-doc"
- assert lb_responses[0].library_blob.data == b"cm9vdCBjb250ZW50"
- assert lb_responses[1].library_blob.id == "chunk-1"
-
- assert len(eos_responses) == 1
-
- @pytest.mark.asyncio
- async def test_get_kg_core_no_librarian_skips_library(self, mock_flow_config):
- with patch('trustgraph.cores.knowledge.KnowledgeTableStore'):
- manager = KnowledgeManager(
- cassandra_host=["localhost"],
- cassandra_username="u", cassandra_password="p",
- keyspace="ks", flow_config=mock_flow_config,
- )
- manager.table_store = AsyncMock()
- manager.table_store.get_triples = AsyncMock()
- manager.table_store.get_graph_embeddings = AsyncMock()
-
- mock_request = Mock()
- mock_request.id = "doc-1"
- mock_respond = AsyncMock()
-
- await manager.get_kg_core(mock_request, mock_respond, "w")
-
- responses = [c[0][0] for c in mock_respond.call_args_list]
- assert all(r.library_metadata is None for r in responses)
- assert all(r.library_blob is None for r in responses)
-
- @pytest.mark.asyncio
- async def test_get_kg_core_librarian_metadata_failure_is_graceful(
- self, manager_with_librarian,
- ):
- mock_request = Mock()
- mock_request.id = "missing-doc"
- mock_respond = AsyncMock()
-
- manager_with_librarian.table_store.get_triples = AsyncMock()
- manager_with_librarian.table_store.get_graph_embeddings = AsyncMock()
- manager_with_librarian.librarian.fetch_document_metadata.side_effect = (
- RuntimeError("not found")
- )
-
- await manager_with_librarian.get_kg_core(
- mock_request, mock_respond, "test-user"
- )
-
- responses = [c[0][0] for c in mock_respond.call_args_list]
- assert all(r.library_metadata is None for r in responses)
- assert any(r.eos for r in responses)
-
-
-class TestKnowledgeManagerLibraryUpload:
- """Test put_kg_core handling of library metadata and blob records."""
-
- @pytest.fixture
- def manager_with_librarian(self, mock_flow_config):
- with patch('trustgraph.cores.knowledge.KnowledgeTableStore'):
- mock_librarian = AsyncMock()
- manager = KnowledgeManager(
- cassandra_host=["localhost"],
- cassandra_username="u", cassandra_password="p",
- keyspace="ks", flow_config=mock_flow_config,
- librarian=mock_librarian,
- )
- manager.table_store = AsyncMock()
- return manager
-
- @pytest.mark.asyncio
- async def test_put_metadata_then_blob_calls_librarian(
- self, manager_with_librarian,
- ):
- mock_respond = AsyncMock()
- manager_with_librarian.librarian.request.return_value = LibrarianResponse()
-
- # First call: metadata
- req_meta = Mock()
- req_meta.triples = None
- req_meta.graph_embeddings = None
- req_meta.library_metadata = LibraryMetadata(
- id="doc-1", kind="application/pdf", title="Test",
- document_type="source",
- )
- req_meta.library_blob = None
- await manager_with_librarian.put_kg_core(req_meta, mock_respond, "ws")
-
- # Metadata is buffered, librarian not called yet
- manager_with_librarian.librarian.request.assert_not_called()
-
- # Second call: blob
- req_blob = Mock()
- req_blob.triples = None
- req_blob.graph_embeddings = None
- req_blob.library_metadata = None
- req_blob.library_blob = LibraryBlob(
- id="doc-1", data=b"dGVzdA==",
- )
- await manager_with_librarian.put_kg_core(req_blob, mock_respond, "ws")
-
- # Now librarian should have been called with add-document
- manager_with_librarian.librarian.request.assert_called_once()
- call_args = manager_with_librarian.librarian.request.call_args[0][0]
- assert call_args.operation == "add-document"
- assert call_args.document_metadata.id == "doc-1"
- assert call_args.document_metadata.kind == "application/pdf"
- assert call_args.content == b"dGVzdA=="
-
- @pytest.mark.asyncio
- async def test_put_child_document_uses_add_child_operation(
- self, manager_with_librarian,
- ):
- mock_respond = AsyncMock()
- manager_with_librarian.librarian.request.return_value = LibrarianResponse()
-
- req_meta = Mock()
- req_meta.triples = None
- req_meta.graph_embeddings = None
- req_meta.library_metadata = LibraryMetadata(
- id="chunk-1", kind="text/plain", title="Chunk",
- parent_id="doc-1", document_type="chunk",
- )
- req_meta.library_blob = None
- await manager_with_librarian.put_kg_core(req_meta, mock_respond, "ws")
-
- req_blob = Mock()
- req_blob.triples = None
- req_blob.graph_embeddings = None
- req_blob.library_metadata = None
- req_blob.library_blob = LibraryBlob(id="chunk-1", data=b"Y2h1bms=")
- await manager_with_librarian.put_kg_core(req_blob, mock_respond, "ws")
-
- call_args = manager_with_librarian.librarian.request.call_args[0][0]
- assert call_args.operation == "add-child-document"
- assert call_args.document_metadata.parent_id == "doc-1"
-
- @pytest.mark.asyncio
- async def test_put_blob_without_metadata_logs_warning(
- self, manager_with_librarian,
- ):
- mock_respond = AsyncMock()
-
- req_blob = Mock()
- req_blob.triples = None
- req_blob.graph_embeddings = None
- req_blob.library_metadata = None
- req_blob.library_blob = LibraryBlob(id="orphan", data=b"data")
- await manager_with_librarian.put_kg_core(req_blob, mock_respond, "ws")
-
- # Librarian should not be called for orphan blob
- manager_with_librarian.librarian.request.assert_not_called()
-
- @pytest.mark.asyncio
- async def test_put_existing_document_is_graceful(
- self, manager_with_librarian,
- ):
- mock_respond = AsyncMock()
- manager_with_librarian.librarian.request.side_effect = RuntimeError(
- "Document already exists"
- )
-
- req_meta = Mock()
- req_meta.triples = None
- req_meta.graph_embeddings = None
- req_meta.library_metadata = LibraryMetadata(
- id="doc-1", kind="application/pdf", title="Test",
- document_type="source",
- )
- req_meta.library_blob = None
- await manager_with_librarian.put_kg_core(req_meta, mock_respond, "ws")
-
- req_blob = Mock()
- req_blob.triples = None
- req_blob.graph_embeddings = None
- req_blob.library_metadata = None
- req_blob.library_blob = LibraryBlob(id="doc-1", data=b"data")
- await manager_with_librarian.put_kg_core(req_blob, mock_respond, "ws")
-
- # Should not raise — "already exists" is handled gracefully
\ No newline at end of file
+ assert response.error is None
\ No newline at end of file
diff --git a/tests/unit/test_decoding/test_pdf_decoder.py b/tests/unit/test_decoding/test_pdf_decoder.py
index 641a9d78..04807b20 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"%PDF-1.7\nfake pdf content"
+ pdf_content = b"fake pdf content"
pdf_base64 = base64.b64encode(pdf_content).decode('utf-8')
# Mock PyPDFLoader
@@ -88,55 +88,13 @@ 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"%PDF-1.7\nfake pdf content"
+ pdf_content = b"fake pdf content"
pdf_base64 = base64.b64encode(pdf_content).decode('utf-8')
mock_loader = MagicMock()
@@ -168,7 +126,7 @@ 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"%PDF-1.7\nfake pdf content"
+ pdf_content = b"fake pdf content"
pdf_base64 = base64.b64encode(pdf_content).decode('utf-8')
mock_loader = MagicMock()
diff --git a/tests/unit/test_embeddings/test_huggingface_dynamic_model.py b/tests/unit/test_embeddings/test_huggingface_dynamic_model.py
index 65837323..aef6fc92 100644
--- a/tests/unit/test_embeddings/test_huggingface_dynamic_model.py
+++ b/tests/unit/test_embeddings/test_huggingface_dynamic_model.py
@@ -18,7 +18,7 @@ from trustgraph.embeddings.hf.hf import Processor
class TestHuggingFaceDynamicModelLoading(IsolatedAsyncioTestCase):
"""Test HuggingFace dynamic model loading and caching"""
- @patch('langchain_huggingface.HuggingFaceEmbeddings')
+ @patch('trustgraph.embeddings.hf.hf.HuggingFaceEmbeddings')
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
@patch('trustgraph.base.embeddings_service.EmbeddingsService.__init__')
async def test_default_model_loaded_on_init(self, mock_embeddings_init, mock_async_init, mock_hf_class):
@@ -39,7 +39,7 @@ class TestHuggingFaceDynamicModelLoading(IsolatedAsyncioTestCase):
assert processor.cached_model_name == "test-model"
assert processor.embeddings is not None
- @patch('langchain_huggingface.HuggingFaceEmbeddings')
+ @patch('trustgraph.embeddings.hf.hf.HuggingFaceEmbeddings')
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
@patch('trustgraph.base.embeddings_service.EmbeddingsService.__init__')
async def test_model_caching_avoids_reload(self, mock_embeddings_init, mock_async_init, mock_hf_class):
@@ -63,7 +63,7 @@ class TestHuggingFaceDynamicModelLoading(IsolatedAsyncioTestCase):
mock_hf_class.assert_not_called()
assert processor.cached_model_name == "test-model"
- @patch('langchain_huggingface.HuggingFaceEmbeddings')
+ @patch('trustgraph.embeddings.hf.hf.HuggingFaceEmbeddings')
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
@patch('trustgraph.base.embeddings_service.EmbeddingsService.__init__')
async def test_model_reload_on_name_change(self, mock_embeddings_init, mock_async_init, mock_hf_class):
@@ -84,7 +84,7 @@ class TestHuggingFaceDynamicModelLoading(IsolatedAsyncioTestCase):
mock_hf_class.assert_called_once_with(model_name="different-model")
assert processor.cached_model_name == "different-model"
- @patch('langchain_huggingface.HuggingFaceEmbeddings')
+ @patch('trustgraph.embeddings.hf.hf.HuggingFaceEmbeddings')
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
@patch('trustgraph.base.embeddings_service.EmbeddingsService.__init__')
async def test_on_embeddings_uses_default_model(self, mock_embeddings_init, mock_async_init, mock_hf_class):
@@ -107,7 +107,7 @@ class TestHuggingFaceDynamicModelLoading(IsolatedAsyncioTestCase):
assert processor.cached_model_name == "test-model" # Still using default
assert result == [[0.1, 0.2, 0.3, 0.4, 0.5]]
- @patch('langchain_huggingface.HuggingFaceEmbeddings')
+ @patch('trustgraph.embeddings.hf.hf.HuggingFaceEmbeddings')
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
@patch('trustgraph.base.embeddings_service.EmbeddingsService.__init__')
async def test_on_embeddings_uses_specified_model(self, mock_embeddings_init, mock_async_init, mock_hf_class):
@@ -130,7 +130,7 @@ class TestHuggingFaceDynamicModelLoading(IsolatedAsyncioTestCase):
assert processor.cached_model_name == "custom-model"
mock_hf_instance.embed_documents.assert_called_once_with(["test text"])
- @patch('langchain_huggingface.HuggingFaceEmbeddings')
+ @patch('trustgraph.embeddings.hf.hf.HuggingFaceEmbeddings')
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
@patch('trustgraph.base.embeddings_service.EmbeddingsService.__init__')
async def test_multiple_model_switches(self, mock_embeddings_init, mock_async_init, mock_hf_class):
@@ -164,7 +164,7 @@ class TestHuggingFaceDynamicModelLoading(IsolatedAsyncioTestCase):
assert call_count_after_b == initial_call_count + 2 # Reload for model-b
assert call_count_after_a_again == initial_call_count + 3 # Reload back to model-a
- @patch('langchain_huggingface.HuggingFaceEmbeddings')
+ @patch('trustgraph.embeddings.hf.hf.HuggingFaceEmbeddings')
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
@patch('trustgraph.base.embeddings_service.EmbeddingsService.__init__')
async def test_none_model_uses_default(self, mock_embeddings_init, mock_async_init, mock_hf_class):
@@ -187,7 +187,7 @@ class TestHuggingFaceDynamicModelLoading(IsolatedAsyncioTestCase):
assert mock_hf_class.call_count == initial_count
assert processor.cached_model_name == "test-model"
- @patch('langchain_huggingface.HuggingFaceEmbeddings')
+ @patch('trustgraph.embeddings.hf.hf.HuggingFaceEmbeddings')
@patch('trustgraph.base.async_processor.AsyncProcessor.__init__')
@patch('trustgraph.base.embeddings_service.EmbeddingsService.__init__')
async def test_initialization_without_model_uses_default(self, mock_embeddings_init, mock_async_init, mock_hf_class):
diff --git a/tests/unit/test_query/test_rows_cassandra_query.py b/tests/unit/test_query/test_rows_cassandra_query.py
index fb385f43..b61500a4 100644
--- a/tests/unit/test_query/test_rows_cassandra_query.py
+++ b/tests/unit/test_query/test_rows_cassandra_query.py
@@ -333,8 +333,8 @@ class TestUnifiedTableQueries:
"""Test queries against the unified rows table"""
@pytest.mark.asyncio
- @patch('trustgraph.query.rows.cassandra.service.async_execute_paged', new_callable=AsyncMock)
- async def test_query_with_index_match(self, mock_async_execute_paged):
+ @patch('trustgraph.query.rows.cassandra.service.async_execute', new_callable=AsyncMock)
+ async def test_query_with_index_match(self, mock_async_execute):
"""Test query execution with matching index"""
processor = MagicMock()
processor.session = MagicMock()
@@ -344,10 +344,10 @@ class TestUnifiedTableQueries:
processor.find_matching_index = Processor.find_matching_index.__get__(processor, Processor)
processor.query_cassandra = Processor.query_cassandra.__get__(processor, Processor)
- # Mock async_execute_paged to return test data (list of pages)
+ # Mock async_execute to return test data
mock_row = MagicMock()
mock_row.data = {"id": "123", "name": "Test Product", "category": "electronics"}
- mock_async_execute_paged.return_value = [[mock_row]]
+ mock_async_execute.return_value = [mock_row]
schema = RowSchema(
name="products",
@@ -370,10 +370,10 @@ class TestUnifiedTableQueries:
# Verify Cassandra was connected and queried
processor.connect_cassandra.assert_called_once()
- mock_async_execute_paged.assert_called_once()
+ mock_async_execute.assert_called_once()
# Verify query structure - should query unified rows table
- call_args = mock_async_execute_paged.call_args
+ call_args = mock_async_execute.call_args
query = call_args[0][1]
params = call_args[0][2]
@@ -394,8 +394,8 @@ class TestUnifiedTableQueries:
assert results[0]["category"] == "electronics"
@pytest.mark.asyncio
- @patch('trustgraph.query.rows.cassandra.service.async_scan', new_callable=AsyncMock)
- async def test_query_without_index_match(self, mock_async_scan):
+ @patch('trustgraph.query.rows.cassandra.service.async_execute', new_callable=AsyncMock)
+ async def test_query_without_index_match(self, mock_async_execute):
"""Test query execution without matching index (scan mode)"""
processor = MagicMock()
processor.session = MagicMock()
@@ -406,10 +406,12 @@ class TestUnifiedTableQueries:
processor._matches_filters = Processor._matches_filters.__get__(processor, Processor)
processor.query_cassandra = Processor.query_cassandra.__get__(processor, Processor)
- # Mock async_scan to return filtered test data
+ # Mock async_execute to return test data
mock_row1 = MagicMock()
mock_row1.data = {"id": "1", "name": "Product A", "price": "100"}
- mock_async_scan.return_value = [mock_row1]
+ mock_row2 = MagicMock()
+ mock_row2.data = {"id": "2", "name": "Product B", "price": "200"}
+ mock_async_execute.return_value = [mock_row1, mock_row2]
schema = RowSchema(
name="products",
@@ -430,16 +432,13 @@ class TestUnifiedTableQueries:
limit=10
)
- # Verify async_scan was called
- mock_async_scan.assert_called_once()
-
- # Verify query structure
- call_args = mock_async_scan.call_args
+ # Query should use ALLOW FILTERING for scan
+ call_args = mock_async_execute.call_args
query = call_args[0][1]
assert "ALLOW FILTERING" in query
- # Should return filtered results
+ # Should post-filter results
assert len(results) == 1
assert results[0]["name"] == "Product A"
diff --git a/tests/unit/test_reliability/test_null_embedding_protection.py b/tests/unit/test_reliability/test_null_embedding_protection.py
index 41d0f88b..dbe06b40 100644
--- a/tests/unit/test_reliability/test_null_embedding_protection.py
+++ b/tests/unit/test_reliability/test_null_embedding_protection.py
@@ -259,8 +259,6 @@ class TestGraphEmbeddingsNullProtection:
proc.collection_exists = MagicMock(return_value=True)
proc._cache_lock = asyncio.Lock()
proc._known_collections = set()
- proc.replication_factor = 1
- proc.shard_number = 1
msg = MagicMock()
msg.metadata.collection = "graphs"
diff --git a/tests/unit/test_tables/test_knowledge_table_store.py b/tests/unit/test_tables/test_knowledge_table_store.py
index 2d058733..59d15b45 100644
--- a/tests/unit/test_tables/test_knowledge_table_store.py
+++ b/tests/unit/test_tables/test_knowledge_table_store.py
@@ -35,9 +35,9 @@ def _make_store():
class TestGetGraphEmbeddings:
@pytest.mark.asyncio
- @patch('trustgraph.tables.knowledge.async_execute_paged', new_callable=AsyncMock)
+ @patch('trustgraph.tables.knowledge.async_execute', new_callable=AsyncMock)
async def test_row_converts_to_entity_embeddings_with_singular_vector(
- self, mock_async_execute_paged
+ self, mock_async_execute
):
"""
Cassandra rows return entities as a list of [entity_tuple, vector]
@@ -57,7 +57,7 @@ class TestGetGraphEmbeddings:
store = _make_store()
store.cassandra = Mock()
store.get_graph_embeddings_stmt = Mock()
- mock_async_execute_paged.return_value = [[fake_row]]
+ mock_async_execute.return_value = [fake_row]
received = []
@@ -66,7 +66,7 @@ class TestGetGraphEmbeddings:
await store.get_graph_embeddings("alice", "doc-1", receiver)
- mock_async_execute_paged.assert_called_once_with(
+ mock_async_execute.assert_called_once_with(
store.cassandra,
store.get_graph_embeddings_stmt,
("alice", "doc-1"),
@@ -96,8 +96,8 @@ class TestGetGraphEmbeddings:
assert ge.entities[2].entity.value == "a literal entity"
@pytest.mark.asyncio
- @patch('trustgraph.tables.knowledge.async_execute_paged', new_callable=AsyncMock)
- async def test_empty_entities_blob_yields_empty_list(self, mock_async_execute_paged):
+ @patch('trustgraph.tables.knowledge.async_execute', new_callable=AsyncMock)
+ async def test_empty_entities_blob_yields_empty_list(self, mock_async_execute):
"""row[3] being None / empty must produce a GraphEmbeddings with
no entities, not raise."""
fake_row = (None, None, None, None)
@@ -105,7 +105,7 @@ class TestGetGraphEmbeddings:
store = _make_store()
store.cassandra = Mock()
store.get_graph_embeddings_stmt = Mock()
- mock_async_execute_paged.return_value = [[fake_row]]
+ mock_async_execute.return_value = [fake_row]
received = []
@@ -118,8 +118,8 @@ class TestGetGraphEmbeddings:
assert received[0].entities == []
@pytest.mark.asyncio
- @patch('trustgraph.tables.knowledge.async_execute_paged', new_callable=AsyncMock)
- async def test_multiple_rows_each_emit_one_message(self, mock_async_execute_paged):
+ @patch('trustgraph.tables.knowledge.async_execute', new_callable=AsyncMock)
+ async def test_multiple_rows_each_emit_one_message(self, mock_async_execute):
fake_rows = [
(None, None, None, [
(("http://example.org/a", True), [1.0]),
@@ -132,7 +132,7 @@ class TestGetGraphEmbeddings:
store = _make_store()
store.cassandra = Mock()
store.get_graph_embeddings_stmt = Mock()
- mock_async_execute_paged.return_value = [fake_rows]
+ mock_async_execute.return_value = fake_rows
received = []
@@ -153,9 +153,9 @@ class TestGetTriples:
the same Metadata construction. Cover it for parity."""
@pytest.mark.asyncio
- @patch('trustgraph.tables.knowledge.async_execute_paged', new_callable=AsyncMock)
- async def test_row_converts_to_triples(self, mock_async_execute_paged):
- # row[3] is a list of (s_val, s_uri, p_val, p_uri, o_val, o_uri, graph)
+ @patch('trustgraph.tables.knowledge.async_execute', new_callable=AsyncMock)
+ async def test_row_converts_to_triples(self, mock_async_execute):
+ # row[3] is a list of (s_val, s_uri, p_val, p_uri, o_val, o_uri)
fake_row = (
None, None, None,
[
@@ -163,7 +163,6 @@ class TestGetTriples:
"http://example.org/alice", True,
"http://example.org/knows", True,
"http://example.org/bob", True,
- "urn:graph:source",
),
],
)
@@ -171,7 +170,7 @@ class TestGetTriples:
store = _make_store()
store.cassandra = Mock()
store.get_triples_stmt = Mock()
- mock_async_execute_paged.return_value = [[fake_row]]
+ mock_async_execute.return_value = [fake_row]
received = []
@@ -192,33 +191,3 @@ class TestGetTriples:
assert t.s.iri == "http://example.org/alice"
assert t.p.iri == "http://example.org/knows"
assert t.o.iri == "http://example.org/bob"
- assert t.g == "urn:graph:source"
-
- @pytest.mark.asyncio
- @patch('trustgraph.tables.knowledge.async_execute_paged', new_callable=AsyncMock)
- async def test_empty_graph_name_becomes_none(self, mock_async_execute_paged):
- fake_row = (
- None, None, None,
- [
- (
- "http://example.org/alice", True,
- "http://example.org/knows", True,
- "http://example.org/bob", True,
- "",
- ),
- ],
- )
-
- store = _make_store()
- store.cassandra = Mock()
- store.get_triples_stmt = Mock()
- mock_async_execute_paged.return_value = [[fake_row]]
-
- received = []
-
- async def receiver(msg):
- received.append(msg)
-
- await store.get_triples("w", "d", receiver)
-
- assert received[0].triples[0].g is None
diff --git a/tests/unit/test_translators/test_knowledge_translator_roundtrip.py b/tests/unit/test_translators/test_knowledge_translator_roundtrip.py
index af128f23..437b83c8 100644
--- a/tests/unit/test_translators/test_knowledge_translator_roundtrip.py
+++ b/tests/unit/test_translators/test_knowledge_translator_roundtrip.py
@@ -1,6 +1,5 @@
"""
-Round-trip unit tests for KnowledgeRequestTranslator and
-KnowledgeResponseTranslator.
+Round-trip unit tests for KnowledgeRequestTranslator.
Regression coverage: a previous version of the decode side constructed
EntityEmbeddings(vectors=...) — the schema field is `vector` (singular),
@@ -16,13 +15,9 @@ Triples breaks the test.
import pytest
-from trustgraph.messaging.translators.knowledge import (
- KnowledgeRequestTranslator,
- KnowledgeResponseTranslator,
-)
+from trustgraph.messaging.translators.knowledge import KnowledgeRequestTranslator
from trustgraph.schema import (
KnowledgeRequest,
- KnowledgeResponse,
GraphEmbeddings,
EntityEmbeddings,
Triples,
@@ -30,8 +25,6 @@ from trustgraph.schema import (
Metadata,
Term,
IRI,
- LibraryMetadata,
- LibraryBlob,
)
@@ -152,161 +145,3 @@ class TestKnowledgeRequestTranslatorTriples:
assert t.s.iri == "http://example.org/alice"
assert t.p.iri == "http://example.org/knows"
assert t.o.iri == "http://example.org/bob"
-
-
-class TestKnowledgeRequestTranslatorLibrary:
-
- def test_roundtrip_preserves_library_metadata(self, translator):
- request = KnowledgeRequest(
- operation="put-kg-core",
- id="doc-1",
- library_metadata=LibraryMetadata(
- id="doc-1",
- kind="application/pdf",
- title="Test Document",
- parent_id="",
- document_type="source",
- comments="test comments",
- tags=["tag1", "tag2"],
- ),
- )
-
- encoded = translator.encode(request)
- assert "library-metadata" in encoded
- lm = encoded["library-metadata"]
- assert lm["id"] == "doc-1"
- assert lm["kind"] == "application/pdf"
- assert lm["title"] == "Test Document"
- assert lm["parent-id"] == ""
- assert lm["document-type"] == "source"
- assert lm["comments"] == "test comments"
- assert lm["tags"] == ["tag1", "tag2"]
-
- decoded = translator.decode(encoded)
- assert decoded.library_metadata is not None
- assert decoded.library_metadata.id == "doc-1"
- assert decoded.library_metadata.kind == "application/pdf"
- assert decoded.library_metadata.title == "Test Document"
- assert decoded.library_metadata.parent_id == ""
- assert decoded.library_metadata.document_type == "source"
- assert decoded.library_metadata.comments == "test comments"
- assert decoded.library_metadata.tags == ["tag1", "tag2"]
-
- def test_roundtrip_preserves_child_document_metadata(self, translator):
- request = KnowledgeRequest(
- operation="put-kg-core",
- id="doc-1",
- library_metadata=LibraryMetadata(
- id="chunk-1",
- kind="text/plain",
- title="Chunk 1",
- parent_id="doc-1",
- document_type="chunk",
- ),
- )
-
- encoded = translator.encode(request)
- decoded = translator.decode(encoded)
-
- assert decoded.library_metadata.parent_id == "doc-1"
- assert decoded.library_metadata.document_type == "chunk"
-
- def test_roundtrip_preserves_library_blob(self, translator):
- request = KnowledgeRequest(
- operation="put-kg-core",
- id="doc-1",
- library_blob=LibraryBlob(
- id="doc-1",
- data=b"SGVsbG8gV29ybGQ=",
- ),
- )
-
- encoded = translator.encode(request)
- assert "library-blob" in encoded
- assert encoded["library-blob"]["id"] == "doc-1"
- assert encoded["library-blob"]["data"] == "SGVsbG8gV29ybGQ="
-
- decoded = translator.decode(encoded)
- assert decoded.library_blob is not None
- assert decoded.library_blob.id == "doc-1"
- assert decoded.library_blob.data == "SGVsbG8gV29ybGQ="
-
- def test_absent_library_fields_decode_as_none(self, translator):
- decoded = translator.decode({
- "operation": "get-kg-core",
- "id": "doc-1",
- })
- assert decoded.library_metadata is None
- assert decoded.library_blob is None
-
-
-class TestKnowledgeResponseTranslatorLibrary:
-
- @pytest.fixture
- def response_translator(self):
- return KnowledgeResponseTranslator()
-
- def test_encode_library_metadata(self, response_translator):
- response = KnowledgeResponse(
- ids=None,
- library_metadata=LibraryMetadata(
- id="doc-1",
- kind="application/pdf",
- title="Test",
- parent_id="",
- document_type="source",
- comments="",
- tags=[],
- ),
- )
- encoded = response_translator.encode(response)
- assert "library-metadata" in encoded
- assert encoded["library-metadata"]["id"] == "doc-1"
- assert encoded["library-metadata"]["kind"] == "application/pdf"
- assert encoded["library-metadata"]["document-type"] == "source"
-
- def test_encode_library_blob_bytes_to_string(self, response_translator):
- response = KnowledgeResponse(
- ids=None,
- library_blob=LibraryBlob(
- id="doc-1",
- data=b"dGVzdCBkYXRh",
- ),
- )
- encoded = response_translator.encode(response)
- assert "library-blob" in encoded
- assert encoded["library-blob"]["id"] == "doc-1"
- assert encoded["library-blob"]["data"] == "dGVzdCBkYXRh"
- assert isinstance(encoded["library-blob"]["data"], str)
-
- def test_encode_library_blob_string_passthrough(self, response_translator):
- response = KnowledgeResponse(
- ids=None,
- library_blob=LibraryBlob(
- id="doc-1",
- data="already-a-string",
- ),
- )
- encoded = response_translator.encode(response)
- assert encoded["library-blob"]["data"] == "already-a-string"
-
- def test_library_metadata_is_not_final(self, response_translator):
- response = KnowledgeResponse(
- ids=None,
- library_metadata=LibraryMetadata(id="doc-1"),
- )
- _, is_final = response_translator.encode_with_completion(response)
- assert is_final is False
-
- def test_library_blob_is_not_final(self, response_translator):
- response = KnowledgeResponse(
- ids=None,
- library_blob=LibraryBlob(id="doc-1", data=b"data"),
- )
- _, is_final = response_translator.encode_with_completion(response)
- assert is_final is False
-
- def test_eos_is_final(self, response_translator):
- response = KnowledgeResponse(eos=True)
- _, is_final = response_translator.encode_with_completion(response)
- assert is_final is True
diff --git a/trustgraph-base/trustgraph/api/api.py b/trustgraph-base/trustgraph/api/api.py
index 0190d3f5..9074bac1 100644
--- a/trustgraph-base/trustgraph/api/api.py
+++ b/trustgraph-base/trustgraph/api/api.py
@@ -337,7 +337,7 @@ class Api:
from . bulk_client import BulkClient
# Extract base URL (remove api/v1/ suffix)
base_url = self.url.rsplit("api/v1/", 1)[0].rstrip("/")
- self._bulk_client = BulkClient(base_url, self.timeout, self.token, workspace=self.workspace)
+ self._bulk_client = BulkClient(base_url, self.timeout, self.token)
return self._bulk_client
def metrics(self):
@@ -462,7 +462,7 @@ class Api:
from . async_bulk_client import AsyncBulkClient
# Extract base URL (remove api/v1/ suffix)
base_url = self.url.rsplit("api/v1/", 1)[0].rstrip("/")
- self._async_bulk_client = AsyncBulkClient(base_url, self.timeout, self.token, workspace=self.workspace)
+ self._async_bulk_client = AsyncBulkClient(base_url, self.timeout, self.token)
return self._async_bulk_client
def async_metrics(self):
diff --git a/trustgraph-base/trustgraph/api/async_bulk_client.py b/trustgraph-base/trustgraph/api/async_bulk_client.py
index f93ab667..9a6a49c3 100644
--- a/trustgraph-base/trustgraph/api/async_bulk_client.py
+++ b/trustgraph-base/trustgraph/api/async_bulk_client.py
@@ -9,11 +9,10 @@ from . types import Triple
class AsyncBulkClient:
"""Asynchronous bulk operations client"""
- def __init__(self, url: str, timeout: int, token: Optional[str], workspace: str = "default") -> None:
+ def __init__(self, url: str, timeout: int, token: Optional[str]) -> None:
self.url: str = self._convert_to_ws_url(url)
self.timeout: int = timeout
self.token: Optional[str] = token
- self.workspace: str = workspace
def _convert_to_ws_url(self, url: str) -> str:
"""Convert HTTP URL to WebSocket URL"""
@@ -26,21 +25,11 @@ class AsyncBulkClient:
else:
return f"ws://{url}"
- def _build_ws_url(self, path: str) -> str:
- """Build a WebSocket URL with token and workspace query params."""
- ws_url = f"{self.url}{path}"
- params = []
- if self.token:
- params.append(f"token={self.token}")
- if self.workspace:
- params.append(f"workspace={self.workspace}")
- if params:
- ws_url = f"{ws_url}?{'&'.join(params)}"
- return ws_url
-
async def import_triples(self, flow: str, triples: AsyncIterator[Triple], **kwargs: Any) -> None:
"""Bulk import triples via WebSocket"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/import/triples")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/import/triples"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for triple in triples:
@@ -53,7 +42,9 @@ class AsyncBulkClient:
async def export_triples(self, flow: str, **kwargs: Any) -> AsyncIterator[Triple]:
"""Bulk export triples via WebSocket"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/export/triples")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/export/triples"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for raw_message in websocket:
@@ -66,7 +57,9 @@ class AsyncBulkClient:
async def import_graph_embeddings(self, flow: str, embeddings: AsyncIterator[Dict[str, Any]], **kwargs: Any) -> None:
"""Bulk import graph embeddings via WebSocket"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/import/graph-embeddings")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/import/graph-embeddings"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for embedding in embeddings:
@@ -74,7 +67,9 @@ class AsyncBulkClient:
async def export_graph_embeddings(self, flow: str, **kwargs: Any) -> AsyncIterator[Dict[str, Any]]:
"""Bulk export graph embeddings via WebSocket"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/export/graph-embeddings")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/export/graph-embeddings"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for raw_message in websocket:
@@ -82,7 +77,9 @@ class AsyncBulkClient:
async def import_document_embeddings(self, flow: str, embeddings: AsyncIterator[Dict[str, Any]], **kwargs: Any) -> None:
"""Bulk import document embeddings via WebSocket"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/import/document-embeddings")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/import/document-embeddings"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for embedding in embeddings:
@@ -90,7 +87,9 @@ class AsyncBulkClient:
async def export_document_embeddings(self, flow: str, **kwargs: Any) -> AsyncIterator[Dict[str, Any]]:
"""Bulk export document embeddings via WebSocket"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/export/document-embeddings")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/export/document-embeddings"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for raw_message in websocket:
@@ -98,7 +97,9 @@ class AsyncBulkClient:
async def import_entity_contexts(self, flow: str, contexts: AsyncIterator[Dict[str, Any]], **kwargs: Any) -> None:
"""Bulk import entity contexts via WebSocket"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/import/entity-contexts")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/import/entity-contexts"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for context in contexts:
@@ -106,7 +107,9 @@ class AsyncBulkClient:
async def export_entity_contexts(self, flow: str, **kwargs: Any) -> AsyncIterator[Dict[str, Any]]:
"""Bulk export entity contexts via WebSocket"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/export/entity-contexts")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/export/entity-contexts"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for raw_message in websocket:
@@ -114,7 +117,9 @@ class AsyncBulkClient:
async def import_rows(self, flow: str, rows: AsyncIterator[Dict[str, Any]], **kwargs: Any) -> None:
"""Bulk import rows via WebSocket"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/import/rows")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/import/rows"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for row in rows:
diff --git a/trustgraph-base/trustgraph/api/async_socket_client.py b/trustgraph-base/trustgraph/api/async_socket_client.py
index 7b38a4b1..d18bee34 100644
--- a/trustgraph-base/trustgraph/api/async_socket_client.py
+++ b/trustgraph-base/trustgraph/api/async_socket_client.py
@@ -30,7 +30,6 @@ class AsyncSocketClient:
self.timeout = timeout
self.token = token
self.workspace = workspace
- self._workspace_explicit = workspace != "default"
self._request_counter = 0
self._socket = None
self._connect_cm = None
@@ -93,8 +92,7 @@ class AsyncSocketClient:
)
if resp.get("type") == "auth-ok":
- if not self._workspace_explicit:
- self.workspace = resp.get("workspace", self.workspace)
+ self.workspace = resp.get("workspace", self.workspace)
elif resp.get("type") == "auth-failed":
await self._socket.close()
raise ProtocolException(
diff --git a/trustgraph-base/trustgraph/api/bulk_client.py b/trustgraph-base/trustgraph/api/bulk_client.py
index ae185240..0e49fc4e 100644
--- a/trustgraph-base/trustgraph/api/bulk_client.py
+++ b/trustgraph-base/trustgraph/api/bulk_client.py
@@ -34,7 +34,7 @@ class BulkClient:
Note: For true async support, use AsyncBulkClient instead.
"""
- def __init__(self, url: str, timeout: int, token: Optional[str], workspace: str = "default") -> None:
+ def __init__(self, url: str, timeout: int, token: Optional[str]) -> None:
"""
Initialize synchronous bulk client.
@@ -42,12 +42,10 @@ class BulkClient:
url: Base URL for TrustGraph API (HTTP/HTTPS will be converted to WS/WSS)
timeout: WebSocket timeout in seconds
token: Optional bearer token for authentication
- workspace: Workspace for data isolation
"""
self.url: str = self._convert_to_ws_url(url)
self.timeout: int = timeout
self.token: Optional[str] = token
- self.workspace: str = workspace
def _convert_to_ws_url(self, url: str) -> str:
"""Convert HTTP URL to WebSocket URL"""
@@ -60,18 +58,6 @@ class BulkClient:
else:
return f"ws://{url}"
- def _build_ws_url(self, path: str) -> str:
- """Build a WebSocket URL with token and workspace query params."""
- ws_url = f"{self.url}{path}"
- params = []
- if self.token:
- params.append(f"token={self.token}")
- if self.workspace:
- params.append(f"workspace={self.workspace}")
- if params:
- ws_url = f"{ws_url}?{'&'.join(params)}"
- return ws_url
-
def _run_async(self, coro: Coroutine[Any, Any, Any]) -> Any:
"""Run async coroutine synchronously"""
try:
@@ -130,7 +116,9 @@ class BulkClient:
metadata: Optional[Dict[str, Any]], batch_size: int
) -> None:
"""Async implementation of triple import"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/import/triples")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/import/triples"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
if metadata is None:
metadata = {"id": "", "metadata": [], "collection": "default"}
@@ -206,7 +194,9 @@ class BulkClient:
async def _export_triples_async(self, flow: str) -> Iterator[Triple]:
"""Async implementation of triple export"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/export/triples")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/export/triples"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for raw_message in websocket:
@@ -248,7 +238,9 @@ class BulkClient:
async def _import_graph_embeddings_async(self, flow: str, embeddings: Iterator[Dict[str, Any]]) -> None:
"""Async implementation of graph embeddings import"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/import/graph-embeddings")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/import/graph-embeddings"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
for embedding in embeddings:
@@ -304,7 +296,9 @@ class BulkClient:
async def _export_graph_embeddings_async(self, flow: str) -> Iterator[Dict[str, Any]]:
"""Async implementation of graph embeddings export"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/export/graph-embeddings")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/export/graph-embeddings"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for raw_message in websocket:
@@ -342,7 +336,9 @@ class BulkClient:
async def _import_document_embeddings_async(self, flow: str, embeddings: Iterator[Dict[str, Any]]) -> None:
"""Async implementation of document embeddings import"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/import/document-embeddings")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/import/document-embeddings"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
for embedding in embeddings:
@@ -398,7 +394,9 @@ class BulkClient:
async def _export_document_embeddings_async(self, flow: str) -> Iterator[Dict[str, Any]]:
"""Async implementation of document embeddings export"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/export/document-embeddings")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/export/document-embeddings"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for raw_message in websocket:
@@ -448,7 +446,9 @@ class BulkClient:
metadata: Optional[Dict[str, Any]], batch_size: int
) -> None:
"""Async implementation of entity contexts import"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/import/entity-contexts")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/import/entity-contexts"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
if metadata is None:
metadata = {"id": "", "metadata": [], "collection": "default"}
@@ -522,7 +522,9 @@ class BulkClient:
async def _export_entity_contexts_async(self, flow: str) -> Iterator[Dict[str, Any]]:
"""Async implementation of entity contexts export"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/export/entity-contexts")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/export/entity-contexts"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
async for raw_message in websocket:
@@ -560,7 +562,9 @@ class BulkClient:
async def _import_rows_async(self, flow: str, rows: Iterator[Dict[str, Any]]) -> None:
"""Async implementation of rows import"""
- ws_url = self._build_ws_url(f"/api/v1/flow/{flow}/import/rows")
+ ws_url = f"{self.url}/api/v1/flow/{flow}/import/rows"
+ if self.token:
+ ws_url = f"{ws_url}?token={self.token}"
async with websockets.connect(ws_url, ping_interval=20, ping_timeout=self.timeout) as websocket:
for row in rows:
diff --git a/trustgraph-base/trustgraph/api/socket_client.py b/trustgraph-base/trustgraph/api/socket_client.py
index 91bc67a1..6eeb95ff 100644
--- a/trustgraph-base/trustgraph/api/socket_client.py
+++ b/trustgraph-base/trustgraph/api/socket_client.py
@@ -167,8 +167,7 @@ class SocketClient:
)
if resp.get("type") == "auth-ok":
- if self.workspace == "default":
- self.workspace = resp.get("workspace", self.workspace)
+ self.workspace = resp.get("workspace", self.workspace)
elif resp.get("type") == "auth-failed":
await self._socket.close()
raise ProtocolException(
@@ -502,7 +501,6 @@ class SocketClient:
def put_kg_core(
self, id: str, triples=None, graph_embeddings=None,
- library_metadata=None, library_blob=None,
) -> Dict[str, Any]:
request = {
"operation": "put-kg-core",
@@ -513,10 +511,6 @@ class SocketClient:
request["triples"] = triples
if graph_embeddings is not None:
request["graph-embeddings"] = graph_embeddings
- if library_metadata is not None:
- request["library-metadata"] = library_metadata
- if library_blob is not None:
- request["library-blob"] = library_blob
return self._send_request_sync("knowledge", None, request)
def get_de_core(self, id: str) -> Iterator[Dict[str, Any]]:
diff --git a/trustgraph-base/trustgraph/base/cassandra_config.py b/trustgraph-base/trustgraph/base/cassandra_config.py
index b2e36fbd..78505c68 100644
--- a/trustgraph-base/trustgraph/base/cassandra_config.py
+++ b/trustgraph-base/trustgraph/base/cassandra_config.py
@@ -103,19 +103,35 @@ def resolve_cassandra_config(
host: Optional[str] = None,
username: Optional[str] = None,
password: Optional[str] = None,
- default_keyspace: Optional[str] = None,
- replication_factor: Optional[int] = None,
+ default_keyspace: Optional[str] = None
) -> Tuple[List[str], Optional[str], Optional[str], Optional[str], int]:
+ """
+ Resolve Cassandra configuration from various sources.
+
+ Can accept either argparse args object or explicit parameters.
+ Converts host string to list format for Cassandra driver.
+
+ Args:
+ args: Optional argparse namespace with cassandra_host, cassandra_username, cassandra_password, cassandra_keyspace, cassandra_replication_factor
+ host: Optional explicit host parameter (overrides args)
+ username: Optional explicit username parameter (overrides args)
+ password: Optional explicit password parameter (overrides args)
+ default_keyspace: Optional default keyspace if not specified elsewhere
+
+ Returns:
+ tuple: (hosts_list, username, password, keyspace, replication_factor)
+ """
+ # If args provided, extract values
keyspace = None
+ replication_factor = 1
if args is not None:
host = host or getattr(args, 'cassandra_host', None)
username = username or getattr(args, 'cassandra_username', None)
password = password or getattr(args, 'cassandra_password', None)
keyspace = getattr(args, 'cassandra_keyspace', None)
- replication_factor = replication_factor or getattr(
- args, 'cassandra_replication_factor', None
- )
+ replication_factor = getattr(args, 'cassandra_replication_factor', 1)
+ # Apply defaults if still None
defaults = get_cassandra_defaults()
host = host or defaults['host']
username = username or defaults['username']
diff --git a/trustgraph-base/trustgraph/base/logging.py b/trustgraph-base/trustgraph/base/logging.py
index ff10c140..9bf599b1 100644
--- a/trustgraph-base/trustgraph/base/logging.py
+++ b/trustgraph-base/trustgraph/base/logging.py
@@ -11,7 +11,6 @@ Supports dual output to console and Loki for centralized log aggregation.
import contextvars
import logging
import logging.handlers
-import uuid
from argparse import ArgumentParser
from queue import Queue
from typing import Any
@@ -133,12 +132,14 @@ def setup_logging(args: dict[str, Any]) -> None:
try:
from logging_loki import LokiHandler
- instance_id = str(uuid.uuid4())[:8]
-
+ # Create Loki handler with optional authentication. The
+ # processor label is NOT baked in here — it's stamped onto
+ # each record by _ProcessorIdFilter reading the task-local
+ # contextvar, and logging_loki's emitter reads record.tags
+ # to build per-record Loki labels.
loki_handler_kwargs = {
'url': loki_url,
'version': "1",
- 'tags': {'instance': instance_id},
}
if loki_username and loki_password:
diff --git a/trustgraph-base/trustgraph/base/qdrant_config.py b/trustgraph-base/trustgraph/base/qdrant_config.py
deleted file mode 100644
index f3e015ca..00000000
--- a/trustgraph-base/trustgraph/base/qdrant_config.py
+++ /dev/null
@@ -1,87 +0,0 @@
-
-import os
-import argparse
-from typing import Optional, Any, Tuple
-
-
-def get_qdrant_defaults() -> dict:
- return {
- 'url': os.getenv('QDRANT_URL', 'http://localhost:6333'),
- 'api_key': os.getenv('QDRANT_API_KEY'),
- 'replication_factor': int(os.getenv('QDRANT_REPLICATION_FACTOR', '1')),
- 'shard_number': int(os.getenv('QDRANT_SHARD_NUMBER', '1')),
- }
-
-
-def add_qdrant_args(parser: argparse.ArgumentParser) -> None:
- defaults = get_qdrant_defaults()
-
- url_help = f"Qdrant URL (default: {defaults['url']})"
- if 'QDRANT_URL' in os.environ:
- url_help += " [from QDRANT_URL]"
-
- api_key_help = "Qdrant API key"
- if defaults['api_key']:
- api_key_help += " (default: )"
- if 'QDRANT_API_KEY' in os.environ:
- api_key_help += " [from QDRANT_API_KEY]"
-
- replication_help = f"Qdrant collection replication factor (default: {defaults['replication_factor']})"
- if 'QDRANT_REPLICATION_FACTOR' in os.environ:
- replication_help += " [from QDRANT_REPLICATION_FACTOR]"
-
- shard_help = f"Qdrant collection shard number (default: {defaults['shard_number']})"
- if 'QDRANT_SHARD_NUMBER' in os.environ:
- shard_help += " [from QDRANT_SHARD_NUMBER]"
-
- parser.add_argument(
- '--store-uri',
- default=defaults['url'],
- help=url_help,
- )
-
- parser.add_argument(
- '--api-key',
- default=defaults['api_key'],
- help=api_key_help,
- )
-
- parser.add_argument(
- '--qdrant-replication-factor',
- type=int,
- default=defaults['replication_factor'],
- help=replication_help,
- )
-
- parser.add_argument(
- '--qdrant-shard-number',
- type=int,
- default=defaults['shard_number'],
- help=shard_help,
- )
-
-
-def resolve_qdrant_config(
- args: Optional[Any] = None,
- url: Optional[str] = None,
- api_key: Optional[str] = None,
- replication_factor: Optional[int] = None,
- shard_number: Optional[int] = None,
-) -> Tuple[str, Optional[str], int, int]:
- if args is not None:
- url = url or getattr(args, 'store_uri', None)
- api_key = api_key or getattr(args, 'api_key', None)
- replication_factor = replication_factor or getattr(
- args, 'qdrant_replication_factor', None
- )
- shard_number = shard_number or getattr(
- args, 'qdrant_shard_number', None
- )
-
- defaults = get_qdrant_defaults()
- url = url or defaults['url']
- api_key = api_key or defaults['api_key']
- replication_factor = replication_factor or defaults['replication_factor']
- shard_number = shard_number or defaults['shard_number']
-
- return url, api_key, replication_factor, shard_number
diff --git a/trustgraph-base/trustgraph/messaging/translators/knowledge.py b/trustgraph-base/trustgraph/messaging/translators/knowledge.py
index 3f09b41b..3830bf59 100644
--- a/trustgraph-base/trustgraph/messaging/translators/knowledge.py
+++ b/trustgraph-base/trustgraph/messaging/translators/knowledge.py
@@ -2,8 +2,7 @@ from typing import Dict, Any, Tuple, Optional
from ...schema import (
KnowledgeRequest, KnowledgeResponse, Triples, GraphEmbeddings,
DocumentEmbeddings, ChunkEmbeddings,
- Metadata, EntityEmbeddings,
- LibraryMetadata, LibraryBlob,
+ Metadata, EntityEmbeddings
)
from .base import MessageTranslator
from .primitives import ValueTranslator, SubgraphTranslator
@@ -62,27 +61,6 @@ class KnowledgeRequestTranslator(MessageTranslator):
]
)
- library_metadata = None
- if "library-metadata" in data:
- lm = data["library-metadata"]
- library_metadata = LibraryMetadata(
- id=lm.get("id", ""),
- kind=lm.get("kind", ""),
- title=lm.get("title", ""),
- parent_id=lm.get("parent-id", ""),
- document_type=lm.get("document-type", ""),
- comments=lm.get("comments", ""),
- tags=lm.get("tags", []),
- )
-
- library_blob = None
- if "library-blob" in data:
- lb = data["library-blob"]
- library_blob = LibraryBlob(
- id=lb.get("id", ""),
- data=lb.get("data", b""),
- )
-
return KnowledgeRequest(
operation=data.get("operation"),
id=data.get("id"),
@@ -91,8 +69,6 @@ class KnowledgeRequestTranslator(MessageTranslator):
triples=triples,
graph_embeddings=graph_embeddings,
document_embeddings=document_embeddings,
- library_metadata=library_metadata,
- library_blob=library_blob,
)
def encode(self, obj: KnowledgeRequest) -> Dict[str, Any]:
@@ -149,26 +125,6 @@ class KnowledgeRequestTranslator(MessageTranslator):
],
}
- if obj.library_metadata:
- result["library-metadata"] = {
- "id": obj.library_metadata.id,
- "kind": obj.library_metadata.kind,
- "title": obj.library_metadata.title,
- "parent-id": obj.library_metadata.parent_id,
- "document-type": obj.library_metadata.document_type,
- "comments": obj.library_metadata.comments,
- "tags": obj.library_metadata.tags,
- }
-
- if obj.library_blob:
- data = obj.library_blob.data
- if isinstance(data, bytes):
- data = data.decode("utf-8")
- result["library-blob"] = {
- "id": obj.library_blob.id,
- "data": data,
- }
-
return result
@@ -238,32 +194,6 @@ class KnowledgeResponseTranslator(MessageTranslator):
}
}
- # Streaming library metadata response
- if obj.library_metadata:
- return {
- "library-metadata": {
- "id": obj.library_metadata.id,
- "kind": obj.library_metadata.kind,
- "title": obj.library_metadata.title,
- "parent-id": obj.library_metadata.parent_id,
- "document-type": obj.library_metadata.document_type,
- "comments": obj.library_metadata.comments,
- "tags": obj.library_metadata.tags,
- }
- }
-
- # Streaming library blob response
- if obj.library_blob:
- data = obj.library_blob.data
- if isinstance(data, bytes):
- data = data.decode("utf-8")
- return {
- "library-blob": {
- "id": obj.library_blob.id,
- "data": data,
- }
- }
-
# End of stream marker
if obj.eos is True:
return {"eos": True}
@@ -279,9 +209,7 @@ class KnowledgeResponseTranslator(MessageTranslator):
is_final = (
obj.ids is not None or # List response
obj.eos is True or # End of stream
- (not obj.triples and not obj.graph_embeddings
- and not obj.document_embeddings
- and not obj.library_metadata and not obj.library_blob) # Empty response
+ (not obj.triples and not obj.graph_embeddings and not obj.document_embeddings) # Empty response
)
return response, is_final
\ No newline at end of file
diff --git a/trustgraph-base/trustgraph/schema/knowledge/knowledge.py b/trustgraph-base/trustgraph/schema/knowledge/knowledge.py
index 4353065b..a3879103 100644
--- a/trustgraph-base/trustgraph/schema/knowledge/knowledge.py
+++ b/trustgraph-base/trustgraph/schema/knowledge/knowledge.py
@@ -21,21 +21,6 @@ from .embeddings import GraphEmbeddings, DocumentEmbeddings
# <- ()
# <- (error)
-@dataclass
-class LibraryMetadata:
- id: str = ""
- kind: str = ""
- title: str = ""
- parent_id: str = ""
- document_type: str = ""
- comments: str = ""
- tags: list[str] = field(default_factory=list)
-
-@dataclass
-class LibraryBlob:
- id: str = ""
- data: bytes = b""
-
@dataclass
class KnowledgeRequest:
# get-kg-core, delete-kg-core, list-kg-cores, put-kg-core
@@ -59,10 +44,6 @@ class KnowledgeRequest:
# put-de-core
document_embeddings: DocumentEmbeddings | None = None
- # put-kg-core (source material)
- library_metadata: LibraryMetadata | None = None
- library_blob: LibraryBlob | None = None
-
@dataclass
class KnowledgeResponse:
error: Error | None = None
@@ -71,8 +52,6 @@ class KnowledgeResponse:
triples: Triples | None = None
graph_embeddings: GraphEmbeddings | None = None
document_embeddings: DocumentEmbeddings | None = None
- library_metadata: LibraryMetadata | None = None
- library_blob: LibraryBlob | None = None
knowledge_request_queue = queue('knowledge', cls='request')
knowledge_response_queue = queue('knowledge', cls='response')
diff --git a/trustgraph-cli/trustgraph/cli/get_document_content.py b/trustgraph-cli/trustgraph/cli/get_document_content.py
index f4d44cca..62fa7ca2 100644
--- a/trustgraph-cli/trustgraph/cli/get_document_content.py
+++ b/trustgraph-cli/trustgraph/cli/get_document_content.py
@@ -5,7 +5,7 @@ Gets document content from the library by document ID.
import argparse
import os
import sys
-import requests
+from trustgraph.api import Api
default_url = os.getenv("TRUSTGRAPH_URL", 'http://localhost:8088/')
default_token = os.getenv("TRUSTGRAPH_TOKEN", None)
@@ -13,29 +13,15 @@ default_workspace = os.getenv("TRUSTGRAPH_WORKSPACE", "default")
def get_content(url, document_id, output_file, token=None, workspace="default"):
- stream_url = url.rstrip("/") + "/api/v1/document-stream"
+ api = Api(url, token=token, workspace=workspace).library()
- params = {
- "document-id": document_id,
- "workspace": workspace,
- }
-
- headers = {}
- if token:
- headers["Authorization"] = f"Bearer {token}"
-
- resp = requests.get(stream_url, params=params, headers=headers, stream=True)
- resp.raise_for_status()
+ content = api.get_document_content(id=document_id)
if output_file:
- total = 0
with open(output_file, 'wb') as f:
- for chunk in resp.iter_content(chunk_size=65536):
- f.write(chunk)
- total += len(chunk)
- print(f"Written {total} bytes to {output_file}")
+ f.write(content)
+ print(f"Written {len(content)} bytes to {output_file}")
else:
- content = resp.content
try:
text = content.decode('utf-8')
print(text)
diff --git a/trustgraph-cli/trustgraph/cli/get_kg_core.py b/trustgraph-cli/trustgraph/cli/get_kg_core.py
index 2ff1a3cc..b4f37b81 100644
--- a/trustgraph-cli/trustgraph/cli/get_kg_core.py
+++ b/trustgraph-cli/trustgraph/cli/get_kg_core.py
@@ -47,31 +47,6 @@ def write_ge(f, data):
)
f.write(msgpack.packb(msg, use_bin_type=True))
-def write_library_metadata(f, data):
- msg = (
- "lm",
- {
- "i": data["id"],
- "k": data.get("kind", ""),
- "t": data.get("title", ""),
- "p": data.get("parent-id", ""),
- "d": data.get("document-type", ""),
- "c": data.get("comments", ""),
- "g": data.get("tags", []),
- }
- )
- f.write(msgpack.packb(msg, use_bin_type=True))
-
-def write_library_blob(f, data):
- msg = (
- "lb",
- {
- "i": data["id"],
- "d": data.get("data", b""),
- }
- )
- f.write(msgpack.packb(msg, use_bin_type=True))
-
def fetch(url, workspace, id, output, token=None):
api = Api(url=url, token=token, workspace=workspace)
@@ -80,8 +55,6 @@ def fetch(url, workspace, id, output, token=None):
try:
ge = 0
t = 0
- lm = 0
- lb = 0
with open(output, "wb") as f:
@@ -95,15 +68,7 @@ def fetch(url, workspace, id, output, token=None):
ge += 1
write_ge(f, response["graph-embeddings"])
- if "library-metadata" in response:
- lm += 1
- write_library_metadata(f, response["library-metadata"])
-
- if "library-blob" in response:
- lb += 1
- write_library_blob(f, response["library-blob"])
-
- print(f"Got: {t} triple, {ge} GE, {lm} library metadata, {lb} library blob messages.")
+ print(f"Got: {t} triple, {ge} GE messages.")
finally:
socket.close()
diff --git a/trustgraph-cli/trustgraph/cli/load_structured_data.py b/trustgraph-cli/trustgraph/cli/load_structured_data.py
index 5649a5ae..3cd2a229 100644
--- a/trustgraph-cli/trustgraph/cli/load_structured_data.py
+++ b/trustgraph-cli/trustgraph/cli/load_structured_data.py
@@ -78,7 +78,7 @@ def load_structured_data(
logger.info("Step 1: Analyzing data to discover best matching schema...")
# Step 1: Auto-discover schema (reuse discover_schema logic)
- discovered_schema = _auto_discover_schema(api_url, input_file, sample_chars, flow, logger, token=token, workspace=workspace)
+ discovered_schema = _auto_discover_schema(api_url, input_file, sample_chars, flow, logger, workspace=workspace)
if not discovered_schema:
logger.error("Failed to discover suitable schema automatically")
print("❌ Could not automatically determine the best schema for your data.")
@@ -90,7 +90,7 @@ def load_structured_data(
# Step 2: Auto-generate descriptor
logger.info("Step 2: Generating descriptor configuration...")
- auto_descriptor = _auto_generate_descriptor(api_url, input_file, discovered_schema, sample_chars, flow, logger, token=token, workspace=workspace)
+ auto_descriptor = _auto_generate_descriptor(api_url, input_file, discovered_schema, sample_chars, flow, logger, workspace=workspace)
if not auto_descriptor:
logger.error("Failed to generate descriptor automatically")
print("❌ Could not automatically generate descriptor configuration.")
@@ -172,7 +172,7 @@ def load_structured_data(
logger.info(f"Sample chars: {sample_chars} characters")
# Use the helper function to discover schema (get raw response for display)
- response = _auto_discover_schema(api_url, input_file, sample_chars, flow, logger, return_raw_response=True, token=token, workspace=workspace)
+ response = _auto_discover_schema(api_url, input_file, sample_chars, flow, logger, return_raw_response=True, workspace=workspace)
if response:
# Debug: print response type and content
@@ -203,7 +203,7 @@ def load_structured_data(
# If no schema specified, discover it first
if not schema_name:
logger.info("No schema specified, auto-discovering...")
- schema_name = _auto_discover_schema(api_url, input_file, sample_chars, flow, logger, token=token, workspace=workspace)
+ schema_name = _auto_discover_schema(api_url, input_file, sample_chars, flow, logger, workspace=workspace)
if not schema_name:
print("Error: Could not determine schema automatically.")
print("Please specify a schema using --schema-name or run --discover-schema first.")
@@ -213,7 +213,7 @@ def load_structured_data(
logger.info(f"Target schema: {schema_name}")
# Generate descriptor using helper function
- descriptor = _auto_generate_descriptor(api_url, input_file, schema_name, sample_chars, flow, logger, token=token, workspace=workspace)
+ descriptor = _auto_generate_descriptor(api_url, input_file, schema_name, sample_chars, flow, logger, workspace=workspace)
if descriptor:
# Output the generated descriptor
@@ -293,7 +293,7 @@ def load_structured_data(
# Send to TrustGraph
print(f"🚀 Importing {len(output_records)} records to TrustGraph...")
- imported_count = _send_to_trustgraph(output_records, api_url, flow, batch_size, token=token, workspace=workspace)
+ imported_count = _send_to_trustgraph(output_records, api_url, flow, batch_size, token=token)
# Get summary info from descriptor
format_info = descriptor.get('format', {})
@@ -603,7 +603,7 @@ def _send_to_trustgraph(rows, api_url, flow, batch_size=1000, token=None, worksp
# Helper functions for auto mode
-def _auto_discover_schema(api_url, input_file, sample_chars, flow, logger, return_raw_response=False, token=None, workspace="default"):
+def _auto_discover_schema(api_url, input_file, sample_chars, flow, logger, return_raw_response=False, workspace="default"):
"""Auto-discover the best matching schema for the input data
Args:
@@ -626,7 +626,7 @@ def _auto_discover_schema(api_url, input_file, sample_chars, flow, logger, retur
# Import API modules
from trustgraph.api import Api
from trustgraph.api.types import ConfigKey
- api = Api(api_url, token=token, workspace=workspace)
+ api = Api(api_url, workspace=workspace)
config_api = api.config()
# Get available schemas
@@ -707,7 +707,7 @@ def _auto_discover_schema(api_url, input_file, sample_chars, flow, logger, retur
return None
-def _auto_generate_descriptor(api_url, input_file, schema_name, sample_chars, flow, logger, token=None, workspace="default"):
+def _auto_generate_descriptor(api_url, input_file, schema_name, sample_chars, flow, logger, workspace="default"):
"""Auto-generate descriptor configuration for the discovered schema"""
try:
# Read sample data
@@ -717,7 +717,7 @@ def _auto_generate_descriptor(api_url, input_file, schema_name, sample_chars, fl
# Import API modules
from trustgraph.api import Api
from trustgraph.api.types import ConfigKey
- api = Api(api_url, token=token, workspace=workspace)
+ api = Api(api_url, workspace=workspace)
config_api = api.config()
# Get schema definition
diff --git a/trustgraph-cli/trustgraph/cli/put_kg_core.py b/trustgraph-cli/trustgraph/cli/put_kg_core.py
index f4e0b3dd..fe0981a5 100644
--- a/trustgraph-cli/trustgraph/cli/put_kg_core.py
+++ b/trustgraph-cli/trustgraph/cli/put_kg_core.py
@@ -40,23 +40,6 @@ def read_message(unpacked, id):
},
"triples": msg["t"],
}
- elif unpacked[0] == "lm":
- msg = unpacked[1]
- return "lm", {
- "id": msg["i"],
- "kind": msg.get("k", ""),
- "title": msg.get("t", ""),
- "parent-id": msg.get("p", ""),
- "document-type": msg.get("d", ""),
- "comments": msg.get("c", ""),
- "tags": msg.get("g", []),
- }
- elif unpacked[0] == "lb":
- msg = unpacked[1]
- return "lb", {
- "id": msg["i"],
- "data": msg.get("d", b""),
- }
else:
raise RuntimeError("Unpacked unexpected messsage type", unpacked[0])
@@ -68,8 +51,6 @@ def put(url, workspace, id, input, token=None):
try:
ge = 0
t = 0
- lm = 0
- lb = 0
with open(input, "rb") as f:
@@ -92,18 +73,10 @@ def put(url, workspace, id, input, token=None):
t += 1
socket.put_kg_core(id, triples=msg)
- elif kind == "lm":
- lm += 1
- socket.put_kg_core(id, library_metadata=msg)
-
- elif kind == "lb":
- lb += 1
- socket.put_kg_core(id, library_blob=msg)
-
else:
raise RuntimeError("Unexpected message kind", kind)
- print(f"Put: {t} triple, {ge} GE, {lm} library metadata, {lb} library blob messages.")
+ print(f"Put: {t} triple, {ge} GE messages.")
finally:
socket.close()
diff --git a/trustgraph-cli/trustgraph/cli/set_prompt.py b/trustgraph-cli/trustgraph/cli/set_prompt.py
index 2feaba00..dbf9c326 100644
--- a/trustgraph-cli/trustgraph/cli/set_prompt.py
+++ b/trustgraph-cli/trustgraph/cli/set_prompt.py
@@ -119,8 +119,7 @@ def main():
raise RuntimeError("Can't use --system with other args")
set_system(
- url=args.api_url, system=args.system, token=args.token,
- workspace=args.workspace,
+ url=args.api_url, system=args.system, token=args.token
)
else:
diff --git a/trustgraph-cli/trustgraph/cli/show_flow_blueprints.py b/trustgraph-cli/trustgraph/cli/show_flow_blueprints.py
index c1aea836..4924c925 100644
--- a/trustgraph-cli/trustgraph/cli/show_flow_blueprints.py
+++ b/trustgraph-cli/trustgraph/cli/show_flow_blueprints.py
@@ -105,7 +105,7 @@ async def fetch_data(client, workspace):
return blueprint_names, blueprints, param_type_defs
async def _show_flow_blueprints_async(url, token=None, workspace="default"):
- async with AsyncSocketClient(url, timeout=60, token=token, workspace=workspace) as client:
+ async with AsyncSocketClient(url, timeout=60, token=token) as client:
return await fetch_data(client, workspace)
def show_flow_blueprints(url, token=None, workspace="default"):
diff --git a/trustgraph-cli/trustgraph/cli/show_flows.py b/trustgraph-cli/trustgraph/cli/show_flows.py
index b8a30c44..6e9479f9 100644
--- a/trustgraph-cli/trustgraph/cli/show_flows.py
+++ b/trustgraph-cli/trustgraph/cli/show_flows.py
@@ -213,7 +213,7 @@ async def fetch_show_flows(client, workspace):
async def _show_flows_async(url, token=None, workspace="default"):
- async with AsyncSocketClient(url, timeout=60, token=token, workspace=workspace) as client:
+ async with AsyncSocketClient(url, timeout=60, token=token) as client:
return await fetch_show_flows(client, workspace)
def show_flows(url, token=None, workspace="default"):
diff --git a/trustgraph-cli/trustgraph/cli/show_parameter_types.py b/trustgraph-cli/trustgraph/cli/show_parameter_types.py
index b0b25f3d..67d6e823 100644
--- a/trustgraph-cli/trustgraph/cli/show_parameter_types.py
+++ b/trustgraph-cli/trustgraph/cli/show_parameter_types.py
@@ -15,7 +15,6 @@ import json
default_url = os.getenv("TRUSTGRAPH_URL", 'http://localhost:8088/')
default_token = os.getenv("TRUSTGRAPH_TOKEN", None)
-default_workspace = os.getenv("TRUSTGRAPH_WORKSPACE", "default")
def format_enum_values(enum_list):
"""
@@ -126,11 +125,11 @@ async def fetch_single_param_type(client, param_type_name):
return json.loads(values[0].get("value", "{}"))
return None
-def show_parameter_types(url, token=None, workspace="default"):
+def show_parameter_types(url, token=None):
"""Show all parameter type definitions."""
async def _fetch():
- async with AsyncSocketClient(url, timeout=60, token=token, workspace=workspace) as client:
+ async with AsyncSocketClient(url, timeout=60, token=token) as client:
return await fetch_all_param_types(client)
param_type_names, param_type_defs = asyncio.run(_fetch())
@@ -154,11 +153,11 @@ def show_parameter_types(url, token=None, workspace="default"):
))
print()
-def show_specific_parameter_type(url, param_type_name, token=None, workspace="default"):
+def show_specific_parameter_type(url, param_type_name, token=None):
"""Show a specific parameter type definition."""
async def _fetch():
- async with AsyncSocketClient(url, timeout=60, token=token, workspace=workspace) as client:
+ async with AsyncSocketClient(url, timeout=60, token=token) as client:
return await fetch_single_param_type(client, param_type_name)
param_type_def = asyncio.run(_fetch())
@@ -194,12 +193,6 @@ def main():
help='Authentication token (default: $TRUSTGRAPH_TOKEN)',
)
- parser.add_argument(
- '-w', '--workspace',
- default=default_workspace,
- help=f'Workspace (default: {default_workspace})',
- )
-
parser.add_argument(
'-t', '--type',
help='Show only the specified parameter type',
@@ -209,9 +202,9 @@ def main():
try:
if args.type:
- show_specific_parameter_type(args.api_url, args.type, args.token, workspace=args.workspace)
+ show_specific_parameter_type(args.api_url, args.type, args.token)
else:
- show_parameter_types(args.api_url, args.token, workspace=args.workspace)
+ show_parameter_types(args.api_url, args.token)
except Exception as e:
print("Exception:", e, flush=True)
diff --git a/trustgraph-flow/trustgraph/config/service/service.py b/trustgraph-flow/trustgraph/config/service/service.py
index 725f1106..c5fac198 100644
--- a/trustgraph-flow/trustgraph/config/service/service.py
+++ b/trustgraph-flow/trustgraph/config/service/service.py
@@ -83,8 +83,7 @@ class Processor(AsyncProcessor):
host=cassandra_host,
username=cassandra_username,
password=cassandra_password,
- default_keyspace="config",
- replication_factor=params.get("cassandra_replication_factor"),
+ default_keyspace="config"
)
# Store resolved configuration
diff --git a/trustgraph-flow/trustgraph/cores/knowledge.py b/trustgraph-flow/trustgraph/cores/knowledge.py
index 6f017c43..f1fa53f5 100644
--- a/trustgraph-flow/trustgraph/cores/knowledge.py
+++ b/trustgraph-flow/trustgraph/cores/knowledge.py
@@ -1,7 +1,6 @@
from .. schema import KnowledgeResponse, Error, Triples, GraphEmbeddings
-from .. schema import DocumentEmbeddings, LibraryMetadata, LibraryBlob
-from .. schema import LibrarianRequest, DocumentMetadata
+from .. schema import DocumentEmbeddings
from .. knowledge import hash
from .. exceptions import RequestError
from .. tables.knowledge import KnowledgeTableStore
@@ -19,7 +18,7 @@ class KnowledgeManager:
def __init__(
self, cassandra_host, cassandra_username, cassandra_password,
- keyspace, flow_config, librarian=None, replication_factor=1,
+ keyspace, flow_config, replication_factor=1,
):
self.table_store = KnowledgeTableStore(
@@ -27,9 +26,6 @@ class KnowledgeManager:
replication_factor
)
- self.librarian = librarian
- self._pending_library_metadata = {}
-
self.loader_queue = asyncio.Queue(maxsize=20)
self.background_task = None
self.flow_config = flow_config
@@ -90,9 +86,6 @@ class KnowledgeManager:
publish_ge,
)
- if self.librarian:
- await self._stream_library_docs(request.id, respond)
-
logger.debug("Knowledge core retrieval complete")
await respond(
@@ -129,12 +122,6 @@ class KnowledgeManager:
workspace, request.graph_embeddings
)
- if request.library_metadata and self.librarian:
- await self._put_library_metadata(request.library_metadata, workspace)
-
- if request.library_blob and self.librarian:
- await self._put_library_blob(request.library_blob, workspace)
-
await respond(
KnowledgeResponse(
error = None,
@@ -263,112 +250,6 @@ class KnowledgeManager:
await self.loader_queue.put((request, respond, workspace))
- async def _stream_library_docs(self, document_id, respond):
-
- try:
- root_meta = await self.librarian.fetch_document_metadata(
- document_id
- )
- except Exception as e:
- logger.warning(f"Could not fetch library metadata for {document_id}: {e}")
- return
-
- if root_meta is None:
- return
-
- await self._stream_one_doc(root_meta, respond)
-
- try:
- resp = await self.librarian.request(
- LibrarianRequest(
- operation="list-children",
- document_id=document_id,
- )
- )
- except Exception as e:
- logger.warning(f"Could not list children for {document_id}: {e}")
- return
-
- for child_meta in resp.document_metadatas:
- await self._stream_one_doc(child_meta, respond)
-
- async def _stream_one_doc(self, doc_meta, respond):
-
- lm = LibraryMetadata(
- id=doc_meta.id,
- kind=doc_meta.kind,
- title=doc_meta.title,
- parent_id=doc_meta.parent_id,
- document_type=doc_meta.document_type,
- comments=doc_meta.comments,
- tags=doc_meta.tags or [],
- )
-
- await respond(
- KnowledgeResponse(library_metadata=lm)
- )
-
- try:
- content = await self.librarian.fetch_document_content(
- doc_meta.id
- )
- except Exception as e:
- logger.warning(f"Could not fetch content for {doc_meta.id}: {e}")
- return
-
- await respond(
- KnowledgeResponse(
- library_blob=LibraryBlob(
- id=doc_meta.id,
- data=content,
- )
- )
- )
-
- async def _put_library_metadata(self, lm, workspace):
- self._pending_library_metadata[lm.id] = lm
-
- async def _put_library_blob(self, lb, workspace):
-
- lm = self._pending_library_metadata.pop(lb.id, None)
- if lm is None:
- logger.warning(
- f"Received library blob for {lb.id} with no preceding metadata"
- )
- return
-
- doc_meta = DocumentMetadata(
- id=lm.id,
- kind=lm.kind,
- title=lm.title,
- parent_id=lm.parent_id,
- document_type=lm.document_type,
- comments=lm.comments,
- tags=lm.tags or [],
- )
-
- if lm.parent_id:
- operation = "add-child-document"
- else:
- operation = "add-document"
-
- try:
- await self.librarian.request(
- LibrarianRequest(
- operation=operation,
- document_id=lm.id,
- document_metadata=doc_meta,
- content=lb.data,
- )
- )
- except RuntimeError as e:
- if "already exists" in str(e):
- logger.debug(f"Library document {lm.id} already exists, skipping")
- else:
- logger.warning(f"Could not save library document {lm.id}: {e}")
- except Exception as e:
- logger.warning(f"Could not save library document {lm.id}: {e}")
-
async def core_loader(self):
logger.info("Knowledge background processor running...")
diff --git a/trustgraph-flow/trustgraph/cores/service.py b/trustgraph-flow/trustgraph/cores/service.py
index 5c50c207..a04e42ca 100755
--- a/trustgraph-flow/trustgraph/cores/service.py
+++ b/trustgraph-flow/trustgraph/cores/service.py
@@ -12,7 +12,6 @@ import logging
from .. base import WorkspaceProcessor, Consumer, Producer, Publisher, Subscriber
from .. base import ConsumerMetrics, ProducerMetrics
from .. base.cassandra_config import add_cassandra_args, resolve_cassandra_config
-from .. base import LibrarianClient
from .. schema import KnowledgeRequest, KnowledgeResponse, Error
from .. schema import knowledge_request_queue, knowledge_response_queue
@@ -61,8 +60,7 @@ class Processor(WorkspaceProcessor):
host=cassandra_host,
username=cassandra_username,
password=cassandra_password,
- default_keyspace="knowledge",
- replication_factor=params.get("cassandra_replication_factor"),
+ default_keyspace="knowledge"
)
self.cassandra_host = hosts
@@ -79,17 +77,12 @@ class Processor(WorkspaceProcessor):
}
)
- self.librarian_client = LibrarianClient(
- id=id, backend=self.pubsub, taskgroup=self.taskgroup,
- )
-
self.knowledge = KnowledgeManager(
cassandra_host = self.cassandra_host,
cassandra_username = self.cassandra_username,
cassandra_password = self.cassandra_password,
keyspace = keyspace,
flow_config = self,
- librarian = self.librarian_client,
replication_factor = replication_factor,
)
@@ -163,7 +156,6 @@ class Processor(WorkspaceProcessor):
async def start(self):
await super(Processor, self).start()
- await self.librarian_client.start()
async def on_knowledge_config(self, workspace, config, version):
diff --git a/trustgraph-flow/trustgraph/decoding/mistral_ocr/processor.py b/trustgraph-flow/trustgraph/decoding/mistral_ocr/processor.py
index 40ecac8a..f214111d 100755
--- a/trustgraph-flow/trustgraph/decoding/mistral_ocr/processor.py
+++ b/trustgraph-flow/trustgraph/decoding/mistral_ocr/processor.py
@@ -219,14 +219,7 @@ class Processor(FlowProcessor):
source_doc_id = v.document_id or v.metadata.id
# Run OCR, get per-page markdown
- try:
- pages = self.ocr(blob)
- except Exception as e:
- logger.error(
- f"Failed to decode PDF {source_doc_id}: "
- f"{type(e).__name__}: {e}"
- )
- return
+ pages = self.ocr(blob)
for markdown, page_num in pages:
diff --git a/trustgraph-flow/trustgraph/decoding/pdf/pdf_decoder.py b/trustgraph-flow/trustgraph/decoding/pdf/pdf_decoder.py
index ae393028..209153f6 100755
--- a/trustgraph-flow/trustgraph/decoding/pdf/pdf_decoder.py
+++ b/trustgraph-flow/trustgraph/decoding/pdf/pdf_decoder.py
@@ -32,10 +32,6 @@ 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):
@@ -98,37 +94,33 @@ class Processor(FlowProcessor):
)
return
- # 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:
+ with tempfile.NamedTemporaryFile(delete_on_close=False, suffix='.pdf') as fp:
temp_path = fp.name
- fp.write(decoded_content)
- fp.close()
+
+ # 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()
global PyPDFLoader
if PyPDFLoader is None:
@@ -137,15 +129,7 @@ class Processor(FlowProcessor):
)
PyPDFLoader = _cls
loader = PyPDFLoader(temp_path)
- try:
- pages = loader.load()
- except Exception as e:
- source_doc_id = v.document_id or v.metadata.id
- logger.error(
- f"Failed to decode PDF {source_doc_id}: "
- f"{type(e).__name__}: {e}"
- )
- return
+ pages = loader.load()
# Get the source document ID
source_doc_id = v.document_id or v.metadata.id
diff --git a/trustgraph-flow/trustgraph/direct/cassandra_kg.py b/trustgraph-flow/trustgraph/direct/cassandra_kg.py
index f1e4a577..d7abd1a9 100644
--- a/trustgraph-flow/trustgraph/direct/cassandra_kg.py
+++ b/trustgraph-flow/trustgraph/direct/cassandra_kg.py
@@ -6,7 +6,7 @@ import logging
from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
from cassandra.query import BatchStatement, SimpleStatement
-import ssl
+from ssl import SSLContext, PROTOCOL_TLSv1_2
from ..tables.cassandra_async import async_execute
@@ -41,15 +41,13 @@ class KnowledgeGraph:
def __init__(
self, hosts=None,
- keyspace="trustgraph", username=None, password=None,
- replication_factor=1,
+ keyspace="trustgraph", username=None, password=None
):
if hosts is None:
hosts = ["localhost"]
self.keyspace = keyspace
- self.replication_factor = replication_factor
self.username = username
# 7-table schema for quads with full query pattern support
@@ -70,7 +68,7 @@ class KnowledgeGraph:
self.collection_metadata_table = "collection_metadata"
if username and password:
- ssl_context = ssl.create_default_context()
+ ssl_context = SSLContext(PROTOCOL_TLSv1_2)
auth_provider = PlainTextAuthProvider(username=username, password=password)
self.cluster = Cluster(hosts, auth_provider=auth_provider, ssl_context=ssl_context)
else:
@@ -94,7 +92,7 @@ class KnowledgeGraph:
create keyspace if not exists {self.keyspace}
with replication = {{
'class' : 'SimpleStrategy',
- 'replication_factor' : {self.replication_factor}
+ 'replication_factor' : 1
}};
""")
@@ -541,15 +539,13 @@ class EntityCentricKnowledgeGraph:
def __init__(
self, hosts=None,
- keyspace="trustgraph", username=None, password=None,
- replication_factor=1,
+ keyspace="trustgraph", username=None, password=None
):
if hosts is None:
hosts = ["localhost"]
self.keyspace = keyspace
- self.replication_factor = replication_factor
self.username = username
# 2-table entity-centric schema
@@ -560,7 +556,7 @@ class EntityCentricKnowledgeGraph:
self.collection_metadata_table = "collection_metadata"
if username and password:
- ssl_context = ssl.create_default_context()
+ ssl_context = SSLContext(PROTOCOL_TLSv1_2)
auth_provider = PlainTextAuthProvider(username=username, password=password)
self.cluster = Cluster(hosts, auth_provider=auth_provider, ssl_context=ssl_context)
else:
@@ -584,7 +580,7 @@ class EntityCentricKnowledgeGraph:
create keyspace if not exists {self.keyspace}
with replication = {{
'class' : 'SimpleStrategy',
- 'replication_factor' : {self.replication_factor}
+ 'replication_factor' : 1
}};
""")
diff --git a/trustgraph-flow/trustgraph/gateway/dispatch/core_export.py b/trustgraph-flow/trustgraph/gateway/dispatch/core_export.py
index 90080cc4..6696afbe 100644
--- a/trustgraph-flow/trustgraph/gateway/dispatch/core_export.py
+++ b/trustgraph-flow/trustgraph/gateway/dispatch/core_export.py
@@ -73,39 +73,6 @@ class CoreExport:
enc = msgpack.packb(msg)
await response.write(enc)
- if "library-metadata" in resp:
-
- data = resp["library-metadata"]
- msg = (
- "lm",
- {
- "i": data["id"],
- "k": data.get("kind", ""),
- "t": data.get("title", ""),
- "p": data.get("parent-id", ""),
- "d": data.get("document-type", ""),
- "c": data.get("comments", ""),
- "g": data.get("tags", []),
- }
- )
-
- enc = msgpack.packb(msg)
- await response.write(enc)
-
- if "library-blob" in resp:
-
- data = resp["library-blob"]
- msg = (
- "lb",
- {
- "i": data["id"],
- "d": data.get("data", b""),
- }
- )
-
- enc = msgpack.packb(msg, use_bin_type=True)
- await response.write(enc)
-
await kr.process(
{
"operation": "get-kg-core",
diff --git a/trustgraph-flow/trustgraph/gateway/dispatch/core_import.py b/trustgraph-flow/trustgraph/gateway/dispatch/core_import.py
index bf660def..d03d4efd 100644
--- a/trustgraph-flow/trustgraph/gateway/dispatch/core_import.py
+++ b/trustgraph-flow/trustgraph/gateway/dispatch/core_import.py
@@ -79,39 +79,6 @@ class CoreImport:
await kr.process(msg)
- elif unpacked[0] == "lm":
- msg = unpacked[1]
- msg = {
- "operation": "put-kg-core",
- "workspace": workspace,
- "id": id,
- "library-metadata": {
- "id": msg["i"],
- "kind": msg.get("k", ""),
- "title": msg.get("t", ""),
- "parent-id": msg.get("p", ""),
- "document-type": msg.get("d", ""),
- "comments": msg.get("c", ""),
- "tags": msg.get("g", []),
- }
- }
-
- await kr.process(msg)
-
- elif unpacked[0] == "lb":
- msg = unpacked[1]
- msg = {
- "operation": "put-kg-core",
- "workspace": workspace,
- "id": id,
- "library-blob": {
- "id": msg["i"],
- "data": msg.get("d", b""),
- }
- }
-
- await kr.process(msg)
-
except Exception as e:
logger.error(f"Core import exception: {e}", exc_info=True)
await error(str(e))
diff --git a/trustgraph-flow/trustgraph/gateway/dispatch/document_stream.py b/trustgraph-flow/trustgraph/gateway/dispatch/document_stream.py
index 74b4d7df..2992d99f 100644
--- a/trustgraph-flow/trustgraph/gateway/dispatch/document_stream.py
+++ b/trustgraph-flow/trustgraph/gateway/dispatch/document_stream.py
@@ -3,7 +3,6 @@ import asyncio
import uuid
import logging
from . librarian import LibrarianRequestor
-from ... schema import librarian_request_queue, librarian_response_queue
# Module logger
logger = logging.getLogger(__name__)
@@ -24,13 +23,10 @@ class DocumentStreamExport:
response = await ok()
- uid = str(uuid.uuid4())
lr = LibrarianRequestor(
backend=self.backend,
- consumer="api-gateway-doc-stream-" + uid,
- subscriber="api-gateway-doc-stream-" + uid,
- request_queue=f"{librarian_request_queue}:{workspace}",
- response_queue=f"{librarian_response_queue}:{workspace}",
+ consumer="api-gateway-doc-stream-" + str(uuid.uuid4()),
+ subscriber="api-gateway-doc-stream-" + str(uuid.uuid4()),
)
try:
diff --git a/trustgraph-flow/trustgraph/gateway/dispatch/mux.py b/trustgraph-flow/trustgraph/gateway/dispatch/mux.py
index 9b119f8e..bdbd18d8 100644
--- a/trustgraph-flow/trustgraph/gateway/dispatch/mux.py
+++ b/trustgraph-flow/trustgraph/gateway/dispatch/mux.py
@@ -4,8 +4,6 @@ import queue
import uuid
import logging
-from ..capabilities import PUBLIC, AUTHENTICATED
-
# Module logger
logger = logging.getLogger(__name__)
@@ -158,41 +156,37 @@ class Mux:
})
return
- # Resolve workspace (default-fill from the caller's
- # bound workspace). Workspace resolution applies to all
- # operations regardless of capability level.
+ # Resolve workspace first (default-fill from the caller's
+ # bound workspace), then ask the regime to authorise the
+ # service-level capability against the matched
+ # operation's resource shape.
try:
await enforce_workspace(data, self.identity, self.auth)
if isinstance(inner, dict):
await enforce_workspace(inner, self.identity, self.auth)
- # Authorisation: capability sentinels short-circuit
- # the regime call; capability strings go through
- # authorise().
- if op.capability not in (PUBLIC, AUTHENTICATED):
- if data.get("flow"):
- resource = {
- "workspace": data.get("workspace", ""),
- "flow": data.get("flow", ""),
- }
- parameters = {}
- else:
- # Build a minimal RequestContext so the matched
- # operation's own extractors decide resource
- # and parameters — same path the HTTP
- # endpoints take.
- from ..registry import RequestContext
- ctx = RequestContext(
- body=inner if isinstance(inner, dict) else {},
- match_info={},
- identity=self.identity,
- )
- resource = op.extract_resource(ctx)
- parameters = op.extract_parameters(ctx)
-
- await self.auth.authorise(
- self.identity, op.capability, resource, parameters,
+ if data.get("flow"):
+ resource = {
+ "workspace": data.get("workspace", ""),
+ "flow": data.get("flow", ""),
+ }
+ parameters = {}
+ else:
+ # Build a minimal RequestContext so the matched
+ # operation's own extractors decide resource and
+ # parameters — same path the HTTP endpoints take.
+ from ..registry import RequestContext
+ ctx = RequestContext(
+ body=inner if isinstance(inner, dict) else {},
+ match_info={},
+ identity=self.identity,
)
+ resource = op.extract_resource(ctx)
+ parameters = op.extract_parameters(ctx)
+
+ await self.auth.authorise(
+ self.identity, op.capability, resource, parameters,
+ )
except _web.HTTPNotFound:
await self.ws.send_json({
"id": request_id,
@@ -294,8 +288,6 @@ class Mux:
await self.maybe_tidy_workers(workers)
async def responder(resp, fin):
- if self.ws is None:
- return
await self.ws.send_json({
"id": id,
"response": resp,
@@ -329,8 +321,6 @@ class Mux:
)
except Exception as e:
- if self.ws is None:
- return
await self.ws.send_json({
"id": id,
"error": {"message": str(e), "type": "error"},
diff --git a/trustgraph-flow/trustgraph/gateway/endpoint/socket.py b/trustgraph-flow/trustgraph/gateway/endpoint/socket.py
index af6183db..f53ad73b 100644
--- a/trustgraph-flow/trustgraph/gateway/endpoint/socket.py
+++ b/trustgraph-flow/trustgraph/gateway/endpoint/socket.py
@@ -117,10 +117,8 @@ class SocketEndpoint:
running = Running()
- params = dict(request.query)
- params.update(request.match_info)
dispatcher = await self.dispatcher(
- ws, running, params
+ ws, running, request.match_info
)
worker_task = tg.create_task(
diff --git a/trustgraph-flow/trustgraph/iam/service/service.py b/trustgraph-flow/trustgraph/iam/service/service.py
index b2f3976d..8ce22757 100644
--- a/trustgraph-flow/trustgraph/iam/service/service.py
+++ b/trustgraph-flow/trustgraph/iam/service/service.py
@@ -101,7 +101,6 @@ class Processor(AsyncProcessor):
username=cassandra_username,
password=cassandra_password,
default_keyspace="iam",
- replication_factor=params.get("cassandra_replication_factor"),
)
self.cassandra_host = hosts
diff --git a/trustgraph-flow/trustgraph/librarian/librarian.py b/trustgraph-flow/trustgraph/librarian/librarian.py
index cc5f0bdf..1c4d010e 100644
--- a/trustgraph-flow/trustgraph/librarian/librarian.py
+++ b/trustgraph-flow/trustgraph/librarian/librarian.py
@@ -162,9 +162,6 @@ class Librarian:
request.document_id
)
- if object_id is None:
- raise RequestError(f"Document not found: {request.document_id}")
-
content = await self.blob_store.get(
object_id
)
diff --git a/trustgraph-flow/trustgraph/librarian/service.py b/trustgraph-flow/trustgraph/librarian/service.py
index 4d3efbfb..cc5efdae 100755
--- a/trustgraph-flow/trustgraph/librarian/service.py
+++ b/trustgraph-flow/trustgraph/librarian/service.py
@@ -8,7 +8,6 @@ import asyncio
import base64
import json
import logging
-import os
from datetime import datetime
from .. base import WorkspaceProcessor, Consumer, Producer, Publisher, Subscriber
@@ -55,16 +54,6 @@ default_object_store_access_key = "object-user"
default_object_store_secret_key = "object-password"
default_object_store_use_ssl = False
default_object_store_region = None
-
-# Environment variables consulted as a fallback when the
-# corresponding params field is not set in the processor-group YAML
-# or via CLI. Intended for K8s Secret / env-var injection so
-# credentials never have to live in the YAML (and thus in git).
-ENV_OBJECT_STORE_ENDPOINT = "OBJECT_STORE_ENDPOINT"
-ENV_OBJECT_STORE_ACCESS_KEY = "OBJECT_STORE_ACCESS_KEY"
-ENV_OBJECT_STORE_SECRET_KEY = "OBJECT_STORE_SECRET_KEY"
-ENV_OBJECT_STORE_USE_SSL = "OBJECT_STORE_USE_SSL"
-ENV_OBJECT_STORE_REGION = "OBJECT_STORE_REGION"
default_cassandra_host = "cassandra"
default_min_chunk_size = 1 # No minimum by default (for Garage)
@@ -100,36 +89,22 @@ class Processor(WorkspaceProcessor):
"config_response_queue", default_config_response_queue
)
- # Resolve object-store config. Precedence: explicit params
- # (CLI / processor-group YAML) → environment variable →
- # hardcoded default. The env-var path lets K8s Secrets feed
- # credentials without them appearing in the YAML.
- object_store_endpoint = (
- params.get("object_store_endpoint")
- or os.environ.get(ENV_OBJECT_STORE_ENDPOINT)
- or default_object_store_endpoint
+ object_store_endpoint = params.get("object_store_endpoint", default_object_store_endpoint)
+ object_store_access_key = params.get(
+ "object_store_access_key",
+ default_object_store_access_key
)
- object_store_access_key = (
- params.get("object_store_access_key")
- or os.environ.get(ENV_OBJECT_STORE_ACCESS_KEY)
- or default_object_store_access_key
+ object_store_secret_key = params.get(
+ "object_store_secret_key",
+ default_object_store_secret_key
)
- object_store_secret_key = (
- params.get("object_store_secret_key")
- or os.environ.get(ENV_OBJECT_STORE_SECRET_KEY)
- or default_object_store_secret_key
+ object_store_use_ssl = params.get(
+ "object_store_use_ssl",
+ default_object_store_use_ssl
)
- object_store_use_ssl = params.get("object_store_use_ssl")
- if object_store_use_ssl is None:
- env_ssl = os.environ.get(ENV_OBJECT_STORE_USE_SSL)
- if env_ssl is not None:
- object_store_use_ssl = env_ssl.lower() in ("true", "1", "yes")
- else:
- object_store_use_ssl = default_object_store_use_ssl
- object_store_region = (
- params.get("object_store_region")
- or os.environ.get(ENV_OBJECT_STORE_REGION)
- or default_object_store_region
+ object_store_region = params.get(
+ "object_store_region",
+ default_object_store_region
)
min_chunk_size = params.get(
@@ -146,8 +121,7 @@ class Processor(WorkspaceProcessor):
host=cassandra_host,
username=cassandra_username,
password=cassandra_password,
- default_keyspace="librarian",
- replication_factor=params.get("cassandra_replication_factor"),
+ default_keyspace="librarian"
)
# Store resolved configuration
diff --git a/trustgraph-flow/trustgraph/query/doc_embeddings/qdrant/service.py b/trustgraph-flow/trustgraph/query/doc_embeddings/qdrant/service.py
index de25a139..f6770744 100755
--- a/trustgraph-flow/trustgraph/query/doc_embeddings/qdrant/service.py
+++ b/trustgraph-flow/trustgraph/query/doc_embeddings/qdrant/service.py
@@ -12,33 +12,31 @@ from qdrant_client import QdrantClient
from .... schema import DocumentEmbeddingsResponse, ChunkMatch
from .... schema import Error
from .... base import DocumentEmbeddingsQueryService
-from .... base.qdrant_config import add_qdrant_args, resolve_qdrant_config
# Module logger
logger = logging.getLogger(__name__)
default_ident = "doc-embeddings-query"
+default_store_uri = 'http://localhost:6333'
+
class Processor(DocumentEmbeddingsQueryService):
def __init__(self, **params):
- store_uri = params.get("store_uri")
- api_key = params.get("api_key")
+ store_uri = params.get("store_uri", default_store_uri)
- url, api_key, _, _ = resolve_qdrant_config(
- url=store_uri,
- api_key=api_key,
- )
+ #optional api key
+ api_key = params.get("api_key", None)
super(Processor, self).__init__(
**params | {
- "store_uri": url,
+ "store_uri": store_uri,
"api_key": api_key,
}
)
- self.qdrant = QdrantClient(url=url, api_key=api_key)
+ self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
async def query_document_embeddings(self, workspace, msg):
@@ -87,7 +85,18 @@ class Processor(DocumentEmbeddingsQueryService):
def add_args(parser):
DocumentEmbeddingsQueryService.add_args(parser)
- add_qdrant_args(parser)
+
+ parser.add_argument(
+ '-t', '--store-uri',
+ default=default_store_uri,
+ help=f'Qdrant store URI (default: {default_store_uri})'
+ )
+
+ parser.add_argument(
+ '-k', '--api-key',
+ default=None,
+ help=f'API key for qdrant (default: None)'
+ )
def run():
diff --git a/trustgraph-flow/trustgraph/query/graph_embeddings/qdrant/service.py b/trustgraph-flow/trustgraph/query/graph_embeddings/qdrant/service.py
index aa93925d..167130c9 100755
--- a/trustgraph-flow/trustgraph/query/graph_embeddings/qdrant/service.py
+++ b/trustgraph-flow/trustgraph/query/graph_embeddings/qdrant/service.py
@@ -12,32 +12,31 @@ from qdrant_client import QdrantClient
from .... schema import GraphEmbeddingsResponse, EntityMatch
from .... schema import Error, Term, IRI, LITERAL
from .... base import GraphEmbeddingsQueryService
-from .... base.qdrant_config import add_qdrant_args, resolve_qdrant_config
# Module logger
logger = logging.getLogger(__name__)
default_ident = "graph-embeddings-query"
+default_store_uri = 'http://localhost:6333'
+
class Processor(GraphEmbeddingsQueryService):
def __init__(self, **params):
- store_uri = params.get("store_uri")
- api_key = params.get("api_key")
+ store_uri = params.get("store_uri", default_store_uri)
- url, api_key, _, _ = resolve_qdrant_config(
- url=store_uri, api_key=api_key,
- )
+ #optional api key
+ api_key = params.get("api_key", None)
super(Processor, self).__init__(
**params | {
- "store_uri": url,
+ "store_uri": store_uri,
"api_key": api_key,
}
)
- self.qdrant = QdrantClient(url=url, api_key=api_key)
+ self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
def create_value(self, ent):
if ent.startswith("http://") or ent.startswith("https://"):
@@ -105,7 +104,18 @@ class Processor(GraphEmbeddingsQueryService):
def add_args(parser):
GraphEmbeddingsQueryService.add_args(parser)
- add_qdrant_args(parser)
+
+ parser.add_argument(
+ '-t', '--store-uri',
+ default=default_store_uri,
+ help=f'Qdrant store URI (default: {default_store_uri})'
+ )
+
+ parser.add_argument(
+ '-k', '--api-key',
+ default=None,
+ help=f'API key for qdrant (default: None)'
+ )
def run():
diff --git a/trustgraph-flow/trustgraph/query/ontology/sparql_cassandra.py b/trustgraph-flow/trustgraph/query/ontology/sparql_cassandra.py
index a9005ee4..b7f0f423 100644
--- a/trustgraph-flow/trustgraph/query/ontology/sparql_cassandra.py
+++ b/trustgraph-flow/trustgraph/query/ontology/sparql_cassandra.py
@@ -116,7 +116,7 @@ class CassandraTripleStore(Store if RDFLIB_AVAILABLE else object):
# Create keyspace
self.session.execute(f"""
CREATE KEYSPACE IF NOT EXISTS {self.keyspace}
- WITH replication = {{'class': 'SimpleStrategy', 'replication_factor': {self.cassandra_config.get('replication_factor', 1)}}}
+ WITH replication = {{'class': 'SimpleStrategy', 'replication_factor': 1}}
""")
# Create triples table optimized for SPARQL queries
diff --git a/trustgraph-flow/trustgraph/query/row_embeddings/qdrant/service.py b/trustgraph-flow/trustgraph/query/row_embeddings/qdrant/service.py
index 7e1a5851..1534c044 100644
--- a/trustgraph-flow/trustgraph/query/row_embeddings/qdrant/service.py
+++ b/trustgraph-flow/trustgraph/query/row_embeddings/qdrant/service.py
@@ -19,12 +19,12 @@ from .... schema import (
RowIndexMatch, Error
)
from .... base import FlowProcessor, ConsumerSpec, ProducerSpec
-from .... base.qdrant_config import add_qdrant_args, resolve_qdrant_config
# Module logger
logger = logging.getLogger(__name__)
default_ident = "row-embeddings-query"
+default_store_uri = 'http://localhost:6333'
default_concurrency = 10
@@ -35,17 +35,13 @@ class Processor(FlowProcessor):
id = params.get("id", default_ident)
concurrency = params.get("concurrency", default_concurrency)
- store_uri = params.get("store_uri")
- api_key = params.get("api_key")
-
- url, api_key, _, _ = resolve_qdrant_config(
- url=store_uri, api_key=api_key,
- )
+ store_uri = params.get("store_uri", default_store_uri)
+ api_key = params.get("api_key", None)
super(Processor, self).__init__(
**params | {
"id": id,
- "store_uri": url,
+ "store_uri": store_uri,
"api_key": api_key,
}
)
@@ -66,7 +62,7 @@ class Processor(FlowProcessor):
)
)
- self.qdrant = QdrantClient(url=url, api_key=api_key)
+ self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
def sanitize_name(self, name: str) -> str:
"""Sanitize names for Qdrant collection naming"""
@@ -196,9 +192,21 @@ class Processor(FlowProcessor):
@staticmethod
def add_args(parser):
+ """Add command-line arguments"""
FlowProcessor.add_args(parser)
- add_qdrant_args(parser)
+
+ parser.add_argument(
+ '-t', '--store-uri',
+ default=default_store_uri,
+ help=f'Qdrant store URI (default: {default_store_uri})'
+ )
+
+ parser.add_argument(
+ '-k', '--api-key',
+ default=None,
+ help='API key for Qdrant (default: None)'
+ )
parser.add_argument(
'-c', '--concurrency',
diff --git a/trustgraph-flow/trustgraph/query/rows/cassandra/service.py b/trustgraph-flow/trustgraph/query/rows/cassandra/service.py
index f9868d67..7157daae 100644
--- a/trustgraph-flow/trustgraph/query/rows/cassandra/service.py
+++ b/trustgraph-flow/trustgraph/query/rows/cassandra/service.py
@@ -24,7 +24,7 @@ from .... schema import RowsQueryRequest, RowsQueryResponse, GraphQLError
from .... schema import Error, RowSchema, Field as SchemaField
from .... base import FlowProcessor, ConsumerSpec, ProducerSpec
from .... base.cassandra_config import add_cassandra_args, resolve_cassandra_config
-from .... tables.cassandra_async import async_execute, async_execute_paged, async_scan
+from .... tables.cassandra_async import async_execute
from ... graphql import GraphQLSchemaBuilder, SortDirection
@@ -180,7 +180,7 @@ class Processor(FlowProcessor):
description=field_def.get("description", ""),
required=field_def.get("required", False),
enum_values=field_def.get("enum", []),
- indexed=field_def.get("indexed", False),
+ indexed=field_def.get("indexed", False)
)
fields.append(field)
@@ -232,8 +232,6 @@ class Processor(FlowProcessor):
for index_name in index_names:
if index_name in filters:
value = filters[index_name]
- if value == "" or value is None:
- continue
# Single field index -> single element list
index_value = [str(value)]
return (index_name, index_value)
@@ -284,13 +282,11 @@ class Processor(FlowProcessor):
query += f" LIMIT {limit}"
try:
- pages = await async_execute_paged(
- self.session, query, params
- )
- for page in pages:
- for row in page:
- row_dict = dict(row.data) if row.data else {}
- results.append(row_dict)
+ rows = await async_execute(self.session, query, params)
+ for row in rows:
+ # Convert data map to dict with proper field names
+ row_dict = dict(row.data) if row.data else {}
+ results.append(row_dict)
except Exception as e:
logger.error(f"Failed to query rows: {e}", exc_info=True)
raise
@@ -312,6 +308,8 @@ class Processor(FlowProcessor):
# Query using the first index (arbitrary choice for scan)
primary_index = index_names[0]
+ # We need to scan all values for this index
+ # This requires ALLOW FILTERING or a different approach
query = f"""
SELECT data, source FROM {safe_keyspace}.rows
WHERE collection = %s
@@ -322,18 +320,17 @@ class Processor(FlowProcessor):
params = [collection, schema_name, primary_index]
try:
- def row_filter(row):
- row_dict = dict(row.data) if row.data else {}
- return self._matches_filters(row_dict, filters, row_schema)
+ rows = await async_execute(self.session, query, params)
- matched_rows = await async_scan(
- self.session, query, params,
- row_filter=row_filter,
- limit=limit,
- )
- for row in matched_rows:
+ for row in rows:
row_dict = dict(row.data) if row.data else {}
- results.append(row_dict)
+
+ # Apply post-filters
+ if self._matches_filters(row_dict, filters, row_schema):
+ results.append(row_dict)
+
+ if limit and len(results) >= limit:
+ break
except Exception as e:
logger.error(f"Failed to scan rows: {e}", exc_info=True)
@@ -366,7 +363,7 @@ class Processor(FlowProcessor):
# Parse filter key for operator
if '_' in filter_key:
parts = filter_key.rsplit('_', 1)
- if parts[1] in ['gt', 'gte', 'lt', 'lte', 'contains', 'in', 'not', 'startsWith', 'endsWith', 'not_in']:
+ if parts[1] in ['gt', 'gte', 'lt', 'lte', 'contains', 'in']:
field_name = parts[0]
operator = parts[1]
else:
@@ -403,18 +400,6 @@ class Processor(FlowProcessor):
elif operator == 'in':
if str(row_value) not in [str(v) for v in filter_value]:
return False
- elif operator == 'not':
- if str(row_value) == str(filter_value):
- return False
- elif operator == 'startsWith':
- if not str(row_value).startswith(str(filter_value)):
- return False
- elif operator == 'endsWith':
- if not str(row_value).endswith(str(filter_value)):
- return False
- elif operator == 'not_in':
- if str(row_value) in [str(v) for v in filter_value]:
- return False
except (ValueError, TypeError):
return False
diff --git a/trustgraph-flow/trustgraph/storage/doc_embeddings/qdrant/write.py b/trustgraph-flow/trustgraph/storage/doc_embeddings/qdrant/write.py
index 08d88849..2bfef99c 100644
--- a/trustgraph-flow/trustgraph/storage/doc_embeddings/qdrant/write.py
+++ b/trustgraph-flow/trustgraph/storage/doc_embeddings/qdrant/write.py
@@ -14,36 +14,29 @@ from qdrant_client.models import Distance, VectorParams
from .... base import DocumentEmbeddingsStoreService, CollectionConfigHandler
from .... base import AsyncProcessor, Consumer, Producer
from .... base import ConsumerMetrics, ProducerMetrics
-from .... base.qdrant_config import add_qdrant_args, resolve_qdrant_config
# Module logger
logger = logging.getLogger(__name__)
default_ident = "doc-embeddings-write"
+default_store_uri = 'http://localhost:6333'
+
class Processor(CollectionConfigHandler, DocumentEmbeddingsStoreService):
def __init__(self, **params):
- store_uri = params.get("store_uri")
- api_key = params.get("api_key")
-
- url, api_key, replication_factor, shard_number = resolve_qdrant_config(
- url=store_uri, api_key=api_key,
- replication_factor=params.get("qdrant_replication_factor"),
- shard_number=params.get("qdrant_shard_number"),
- )
+ store_uri = params.get("store_uri", default_store_uri)
+ api_key = params.get("api_key", None)
super(Processor, self).__init__(
**params | {
- "store_uri": url,
+ "store_uri": store_uri,
"api_key": api_key,
}
)
- self.qdrant = QdrantClient(url=url, api_key=api_key)
- self.replication_factor = replication_factor
- self.shard_number = shard_number
+ self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
self._cache_lock = asyncio.Lock()
self._known_collections: set[str] = set()
@@ -68,8 +61,6 @@ class Processor(CollectionConfigHandler, DocumentEmbeddingsStoreService):
vectors_config=VectorParams(
size=dim, distance=Distance.COSINE
),
- replication_factor=self.replication_factor,
- shard_number=self.shard_number,
)
self._known_collections.add(collection_name)
@@ -118,7 +109,18 @@ class Processor(CollectionConfigHandler, DocumentEmbeddingsStoreService):
def add_args(parser):
DocumentEmbeddingsStoreService.add_args(parser)
- add_qdrant_args(parser)
+
+ parser.add_argument(
+ '-t', '--store-uri',
+ default=default_store_uri,
+ help=f'Qdrant URI (default: {default_store_uri})'
+ )
+
+ parser.add_argument(
+ '-k', '--api-key',
+ default=None,
+ help=f'Qdrant API key (default: None)'
+ )
async def create_collection(self, workspace: str, collection: str, metadata: dict):
"""
diff --git a/trustgraph-flow/trustgraph/storage/graph_embeddings/qdrant/write.py b/trustgraph-flow/trustgraph/storage/graph_embeddings/qdrant/write.py
index b6072bdc..13dcdba8 100755
--- a/trustgraph-flow/trustgraph/storage/graph_embeddings/qdrant/write.py
+++ b/trustgraph-flow/trustgraph/storage/graph_embeddings/qdrant/write.py
@@ -14,7 +14,6 @@ from qdrant_client.models import Distance, VectorParams
from .... base import GraphEmbeddingsStoreService, CollectionConfigHandler
from .... base import AsyncProcessor, Consumer, Producer
from .... base import ConsumerMetrics, ProducerMetrics
-from .... base.qdrant_config import add_qdrant_args, resolve_qdrant_config
from .... schema import IRI, LITERAL
# Module logger
@@ -30,34 +29,29 @@ def get_term_value(term):
elif term.type == LITERAL:
return term.value
else:
+ # For blank nodes or other types, use id or value
return term.id or term.value
default_ident = "graph-embeddings-write"
+default_store_uri = 'http://localhost:6333'
+
class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService):
def __init__(self, **params):
- store_uri = params.get("store_uri")
- api_key = params.get("api_key")
-
- url, api_key, replication_factor, shard_number = resolve_qdrant_config(
- url=store_uri, api_key=api_key,
- replication_factor=params.get("qdrant_replication_factor"),
- shard_number=params.get("qdrant_shard_number"),
- )
+ store_uri = params.get("store_uri", default_store_uri)
+ api_key = params.get("api_key", None)
super(Processor, self).__init__(
**params | {
- "store_uri": url,
+ "store_uri": store_uri,
"api_key": api_key,
}
)
- self.qdrant = QdrantClient(url=url, api_key=api_key)
- self.replication_factor = replication_factor
- self.shard_number = shard_number
+ self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
self._cache_lock = asyncio.Lock()
self._known_collections: set[str] = set()
@@ -82,8 +76,6 @@ class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService):
vectors_config=VectorParams(
size=dim, distance=Distance.COSINE
),
- replication_factor=self.replication_factor,
- shard_number=self.shard_number,
)
self._known_collections.add(collection_name)
@@ -136,7 +128,18 @@ class Processor(CollectionConfigHandler, GraphEmbeddingsStoreService):
def add_args(parser):
GraphEmbeddingsStoreService.add_args(parser)
- add_qdrant_args(parser)
+
+ parser.add_argument(
+ '-t', '--store-uri',
+ default=default_store_uri,
+ help=f'Qdrant store URI (default: {default_store_uri})'
+ )
+
+ parser.add_argument(
+ '-k', '--api-key',
+ default=None,
+ help=f'Qdrant API key'
+ )
async def create_collection(self, workspace: str, collection: str, metadata: dict):
"""
diff --git a/trustgraph-flow/trustgraph/storage/knowledge/store.py b/trustgraph-flow/trustgraph/storage/knowledge/store.py
index f6e12a85..162a4057 100644
--- a/trustgraph-flow/trustgraph/storage/knowledge/store.py
+++ b/trustgraph-flow/trustgraph/storage/knowledge/store.py
@@ -27,8 +27,7 @@ class Processor(FlowProcessor):
host=params.get("cassandra_host"),
username=params.get("cassandra_username"),
password=params.get("cassandra_password"),
- default_keyspace='knowledge',
- replication_factor=params.get("cassandra_replication_factor"),
+ default_keyspace='knowledge'
)
super(Processor, self).__init__(
diff --git a/trustgraph-flow/trustgraph/storage/row_embeddings/qdrant/write.py b/trustgraph-flow/trustgraph/storage/row_embeddings/qdrant/write.py
index 4c65edb1..a01629c5 100644
--- a/trustgraph-flow/trustgraph/storage/row_embeddings/qdrant/write.py
+++ b/trustgraph-flow/trustgraph/storage/row_embeddings/qdrant/write.py
@@ -27,12 +27,12 @@ from qdrant_client.models import PointStruct, Distance, VectorParams
from .... schema import RowEmbeddings
from .... base import FlowProcessor, ConsumerSpec
from .... base import CollectionConfigHandler
-from .... base.qdrant_config import add_qdrant_args, resolve_qdrant_config
# Module logger
logger = logging.getLogger(__name__)
default_ident = "row-embeddings-write"
+default_store_uri = 'http://localhost:6333'
class Processor(CollectionConfigHandler, FlowProcessor):
@@ -41,19 +41,13 @@ class Processor(CollectionConfigHandler, FlowProcessor):
id = params.get("id", default_ident)
- store_uri = params.get("store_uri")
- api_key = params.get("api_key")
-
- url, api_key, replication_factor, shard_number = resolve_qdrant_config(
- url=store_uri, api_key=api_key,
- replication_factor=params.get("qdrant_replication_factor"),
- shard_number=params.get("qdrant_shard_number"),
- )
+ store_uri = params.get("store_uri", default_store_uri)
+ api_key = params.get("api_key", None)
super(Processor, self).__init__(
**params | {
"id": id,
- "store_uri": url,
+ "store_uri": store_uri,
"api_key": api_key,
}
)
@@ -69,9 +63,7 @@ class Processor(CollectionConfigHandler, FlowProcessor):
# Register config handler for collection management
self.register_config_handler(self.on_collection_config, types=["collection"])
- self.qdrant = QdrantClient(url=url, api_key=api_key)
- self.replication_factor = replication_factor
- self.shard_number = shard_number
+ self.qdrant = QdrantClient(url=store_uri, api_key=api_key)
self._cache_lock = asyncio.Lock()
self._known_collections: set[str] = set()
@@ -111,8 +103,6 @@ class Processor(CollectionConfigHandler, FlowProcessor):
size=dimension,
distance=Distance.COSINE
),
- replication_factor=self.replication_factor,
- shard_number=self.shard_number,
)
self._known_collections.add(collection_name)
@@ -259,9 +249,21 @@ class Processor(CollectionConfigHandler, FlowProcessor):
@staticmethod
def add_args(parser):
+ """Add command-line arguments"""
FlowProcessor.add_args(parser)
- add_qdrant_args(parser)
+
+ parser.add_argument(
+ '-t', '--store-uri',
+ default=default_store_uri,
+ help=f'Qdrant URI (default: {default_store_uri})'
+ )
+
+ parser.add_argument(
+ '-k', '--api-key',
+ default=None,
+ help='Qdrant API key (default: None)'
+ )
def run():
diff --git a/trustgraph-flow/trustgraph/storage/rows/cassandra/write.py b/trustgraph-flow/trustgraph/storage/rows/cassandra/write.py
index 31fc41a7..65eeee06 100755
--- a/trustgraph-flow/trustgraph/storage/rows/cassandra/write.py
+++ b/trustgraph-flow/trustgraph/storage/rows/cassandra/write.py
@@ -47,18 +47,16 @@ class Processor(CollectionConfigHandler, FlowProcessor):
cassandra_password = params.get("cassandra_password")
# Resolve configuration with environment variable fallback
- hosts, username, password, keyspace, replication_factor = resolve_cassandra_config(
+ hosts, username, password, keyspace, _ = resolve_cassandra_config(
host=cassandra_host,
username=cassandra_username,
- password=cassandra_password,
- replication_factor=params.get("cassandra_replication_factor"),
+ password=cassandra_password
)
# Store resolved configuration with proper names
self.cassandra_host = hosts # Store as list
self.cassandra_username = username
self.cassandra_password = password
- self.replication_factor = replication_factor
# Config key for schemas
self.config_key = params.get("config_type", "schema")
@@ -172,7 +170,7 @@ class Processor(CollectionConfigHandler, FlowProcessor):
description=field_def.get("description", ""),
required=field_def.get("required", False),
enum_values=field_def.get("enum", []),
- indexed=field_def.get("indexed", False),
+ indexed=field_def.get("indexed", False)
)
fields.append(field)
@@ -234,7 +232,7 @@ class Processor(CollectionConfigHandler, FlowProcessor):
CREATE KEYSPACE IF NOT EXISTS {safe_keyspace}
WITH REPLICATION = {{
'class': 'SimpleStrategy',
- 'replication_factor': {self.replication_factor}
+ 'replication_factor': 1
}}
"""
diff --git a/trustgraph-flow/trustgraph/tables/cassandra_async.py b/trustgraph-flow/trustgraph/tables/cassandra_async.py
index fe410a26..2f497748 100644
--- a/trustgraph-flow/trustgraph/tables/cassandra_async.py
+++ b/trustgraph-flow/trustgraph/tables/cassandra_async.py
@@ -27,8 +27,6 @@ Notes:
import asyncio
-from cassandra.query import SimpleStatement
-
async def async_execute(session, query, parameters=None):
"""Execute a CQL statement asynchronously.
@@ -78,83 +76,3 @@ def _set_result_if_pending(fut, result):
def _set_exception_if_pending(fut, exc):
if not fut.done():
fut.set_exception(exc)
-
-
-async def async_execute_paged(session, query, parameters=None, fetch_size=5000):
- """Execute a CQL query with page-by-page iteration.
-
- Uses synchronous session.execute() inside run_in_executor so that
- the driver's ResultSet paging works correctly without materialising
- the entire result set in memory.
-
- Returns all pages as a list of lists.
- """
- loop = asyncio.get_running_loop()
-
- if isinstance(query, str):
- stmt = SimpleStatement(query, fetch_size=fetch_size)
- else:
- stmt = query
- stmt.fetch_size = fetch_size
-
- def _fetch_all_pages():
- pages = []
- result_set = session.execute(stmt, parameters)
- while True:
- pages.append(list(result_set.current_rows))
- if result_set.has_more_pages:
- result_set.fetch_next_page()
- else:
- break
- return pages
-
- return await loop.run_in_executor(
- None, _fetch_all_pages
- )
-
-
-async def async_scan(
- session, query, parameters=None, row_filter=None,
- limit=None, fetch_size=5000,
-):
- """Scan a CQL query page-by-page, applying a filter and limit.
-
- Only matching rows accumulate in memory. Each page is discarded
- after processing, so peak memory is bounded by fetch_size plus
- the number of matching rows (capped by limit).
-
- Args:
- session: cassandra.cluster.Session
- query: CQL statement string
- parameters: bind params
- row_filter: callable(row) -> bool, or None to accept all
- limit: max results to return, or None for unlimited
- fetch_size: rows per Cassandra page fetch
-
- Returns:
- List of matching rows.
- """
- loop = asyncio.get_running_loop()
-
- if isinstance(query, str):
- stmt = SimpleStatement(query, fetch_size=fetch_size)
- else:
- stmt = query
- stmt.fetch_size = fetch_size
-
- def _scan():
- results = []
- result_set = session.execute(stmt, parameters)
- while True:
- for row in result_set.current_rows:
- if row_filter is None or row_filter(row):
- results.append(row)
- if limit and len(results) >= limit:
- return results
- if result_set.has_more_pages:
- result_set.fetch_next_page()
- else:
- break
- return results
-
- return await loop.run_in_executor(None, _scan)
diff --git a/trustgraph-flow/trustgraph/tables/config.py b/trustgraph-flow/trustgraph/tables/config.py
index c87cb3b5..74ceb6f4 100644
--- a/trustgraph-flow/trustgraph/tables/config.py
+++ b/trustgraph-flow/trustgraph/tables/config.py
@@ -4,7 +4,7 @@ from .. schema import Metadata, GraphEmbeddings
from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
-import ssl
+from ssl import SSLContext, PROTOCOL_TLSv1_2
import uuid
import time
@@ -33,7 +33,7 @@ class ConfigTableStore:
cassandra_host = [h.strip() for h in cassandra_host.split(',')]
if cassandra_username and cassandra_password:
- ssl_context = ssl.create_default_context()
+ ssl_context = SSLContext(PROTOCOL_TLSv1_2)
auth_provider = PlainTextAuthProvider(
username=cassandra_username, password=cassandra_password
)
diff --git a/trustgraph-flow/trustgraph/tables/iam.py b/trustgraph-flow/trustgraph/tables/iam.py
index b60e9cff..d7bf5e3d 100644
--- a/trustgraph-flow/trustgraph/tables/iam.py
+++ b/trustgraph-flow/trustgraph/tables/iam.py
@@ -15,7 +15,7 @@ import logging
from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
-import ssl
+from ssl import SSLContext, PROTOCOL_TLSv1_2
from . cassandra_async import async_execute
@@ -39,7 +39,7 @@ class IamTableStore:
cassandra_host = [h.strip() for h in cassandra_host.split(",")]
if cassandra_username and cassandra_password:
- ssl_context = ssl.create_default_context()
+ ssl_context = SSLContext(PROTOCOL_TLSv1_2)
auth_provider = PlainTextAuthProvider(
username=cassandra_username, password=cassandra_password,
)
diff --git a/trustgraph-flow/trustgraph/tables/knowledge.py b/trustgraph-flow/trustgraph/tables/knowledge.py
index 53a12b35..cf085fdd 100644
--- a/trustgraph-flow/trustgraph/tables/knowledge.py
+++ b/trustgraph-flow/trustgraph/tables/knowledge.py
@@ -5,7 +5,7 @@ from .. schema import DocumentEmbeddings, ChunkEmbeddings
from cassandra.cluster import Cluster
-from . cassandra_async import async_execute, async_execute_paged
+from . cassandra_async import async_execute
def term_to_tuple(term):
@@ -23,7 +23,7 @@ def tuple_to_term(value, is_uri):
else:
return Term(type=LITERAL, value=value)
from cassandra.auth import PlainTextAuthProvider
-import ssl
+from ssl import SSLContext, PROTOCOL_TLSv1_2
import uuid
import time
@@ -50,7 +50,7 @@ class KnowledgeTableStore:
cassandra_host = [h.strip() for h in cassandra_host.split(',')]
if cassandra_username and cassandra_password:
- ssl_context = ssl.create_default_context()
+ ssl_context = SSLContext(PROTOCOL_TLSv1_2)
auth_provider = PlainTextAuthProvider(
username=cassandra_username, password=cassandra_password
)
@@ -98,8 +98,7 @@ class KnowledgeTableStore:
text, boolean, text, boolean, text, boolean
>>,
triples list>,
PRIMARY KEY ((workspace, document_id), id)
);
@@ -235,8 +234,7 @@ class KnowledgeTableStore:
triples = [
(
- *term_to_tuple(v.s), *term_to_tuple(v.p), *term_to_tuple(v.o),
- v.g or ""
+ *term_to_tuple(v.s), *term_to_tuple(v.p), *term_to_tuple(v.o)
)
for v in m.triples
]
@@ -400,7 +398,7 @@ class KnowledgeTableStore:
logger.debug("Get triples...")
try:
- pages = await async_execute_paged(
+ rows = await async_execute(
self.cassandra,
self.get_triples_stmt,
(workspace, document_id),
@@ -409,31 +407,29 @@ class KnowledgeTableStore:
logger.error("Exception occurred", exc_info=True)
raise
- for page in pages:
- for row in page:
+ for row in rows:
- if row[3]:
- triples = [
- Triple(
- s = tuple_to_term(elt[0], elt[1]),
- p = tuple_to_term(elt[2], elt[3]),
- o = tuple_to_term(elt[4], elt[5]),
- g = elt[6] if elt[6] else None,
- )
- for elt in row[3]
- ]
- else:
- triples = []
-
- await receiver(
- Triples(
- metadata = Metadata(
- id = document_id,
- collection = "default",
- ),
- triples = triples
+ if row[3]:
+ triples = [
+ Triple(
+ s = tuple_to_term(elt[0], elt[1]),
+ p = tuple_to_term(elt[2], elt[3]),
+ o = tuple_to_term(elt[4], elt[5]),
)
+ for elt in row[3]
+ ]
+ else:
+ triples = []
+
+ await receiver(
+ Triples(
+ metadata = Metadata(
+ id = document_id,
+ collection = "default", # FIXME: What to put here?
+ ),
+ triples = triples
)
+ )
logger.debug("Done")
@@ -442,7 +438,7 @@ class KnowledgeTableStore:
logger.debug("Get GE...")
try:
- pages = await async_execute_paged(
+ rows = await async_execute(
self.cassandra,
self.get_graph_embeddings_stmt,
(workspace, document_id),
@@ -451,29 +447,28 @@ class KnowledgeTableStore:
logger.error("Exception occurred", exc_info=True)
raise
- for page in pages:
- for row in page:
+ for row in rows:
- if row[3]:
- entities = [
- EntityEmbeddings(
- entity = tuple_to_term(ent[0][0], ent[0][1]),
- vector = ent[1]
- )
- for ent in row[3]
- ]
- else:
- entities = []
-
- await receiver(
- GraphEmbeddings(
- metadata = Metadata(
- id = document_id,
- collection = "default",
- ),
- entities = entities
+ if row[3]:
+ entities = [
+ EntityEmbeddings(
+ entity = tuple_to_term(ent[0][0], ent[0][1]),
+ vector = ent[1]
)
+ for ent in row[3]
+ ]
+ else:
+ entities = []
+
+ await receiver(
+ GraphEmbeddings(
+ metadata = Metadata(
+ id = document_id,
+ collection = "default", # FIXME: What to put here?
+ ),
+ entities = entities
)
+ )
logger.debug("Done")
@@ -482,7 +477,7 @@ class KnowledgeTableStore:
logger.debug("Get DE...")
try:
- pages = await async_execute_paged(
+ rows = await async_execute(
self.cassandra,
self.get_document_embeddings_stmt,
(workspace, document_id),
@@ -491,29 +486,28 @@ class KnowledgeTableStore:
logger.error("Exception occurred", exc_info=True)
raise
- for page in pages:
- for row in page:
+ for row in rows:
- if row[3]:
- chunks = [
- ChunkEmbeddings(
- chunk_id=ch[0],
- vector=ch[1],
- )
- for ch in row[3]
- ]
- else:
- chunks = []
-
- await receiver(
- DocumentEmbeddings(
- metadata = Metadata(
- id = document_id,
- collection = "default",
- ),
- chunks = chunks
+ if row[3]:
+ chunks = [
+ ChunkEmbeddings(
+ chunk_id=ch[0],
+ vector=ch[1],
)
+ for ch in row[3]
+ ]
+ else:
+ chunks = []
+
+ await receiver(
+ DocumentEmbeddings(
+ metadata = Metadata(
+ id = document_id,
+ collection = "default",
+ ),
+ chunks = chunks
)
+ )
logger.debug("Done")
diff --git a/trustgraph-flow/trustgraph/tables/library.py b/trustgraph-flow/trustgraph/tables/library.py
index 5094e103..58486f0e 100644
--- a/trustgraph-flow/trustgraph/tables/library.py
+++ b/trustgraph-flow/trustgraph/tables/library.py
@@ -24,7 +24,7 @@ from .. exceptions import RequestError
from cassandra.cluster import Cluster
from cassandra.auth import PlainTextAuthProvider
from cassandra.query import BatchStatement
-import ssl
+from ssl import SSLContext, PROTOCOL_TLSv1_2
import uuid
import time
@@ -53,7 +53,7 @@ class LibraryTableStore:
cassandra_host = [h.strip() for h in cassandra_host.split(',')]
if cassandra_username and cassandra_password:
- ssl_context = ssl.create_default_context()
+ ssl_context = SSLContext(PROTOCOL_TLSv1_2)
auth_provider = PlainTextAuthProvider(
username=cassandra_username, password=cassandra_password
)
diff --git a/trustgraph-mcp/trustgraph/mcp_server/mcp.py b/trustgraph-mcp/trustgraph/mcp_server/mcp.py
index 11b975b2..7378db64 100755
--- a/trustgraph-mcp/trustgraph/mcp_server/mcp.py
+++ b/trustgraph-mcp/trustgraph/mcp_server/mcp.py
@@ -8,180 +8,71 @@ import logging
import json
import uuid
import argparse
-from dataclasses import dataclass, field
+from dataclasses import dataclass
from collections.abc import AsyncIterator
from functools import partial
from mcp.server.fastmcp import FastMCP, Context
-from mcp.server.auth.provider import AccessToken, TokenVerifier
-from mcp.server.auth.middleware.auth_context import get_access_token
+from mcp.types import TextContent
+from websockets.asyncio.client import connect
from trustgraph.base.logging import add_logging_args, setup_logging
-from . tg_socket import WebSocketManager, _token_key
-
-logger = logging.getLogger(__name__)
-
-
-# Wire-format Term type codes (match TermTranslator compact keys)
-_TERM_TYPES = {
- "iri": "i",
- "literal": "l",
- "blank": "b",
-}
-
-
-def _make_term(value: str, term_type: str) -> dict:
- """Build a compact-key Term dict for the gateway wire format.
-
- Args:
- value: The term value (IRI string, literal text, or blank node id).
- term_type: One of "iri", "literal", "blank".
- """
- t = _TERM_TYPES.get(term_type)
- if t is None:
- raise ValueError(
- f"Unknown term type '{term_type}' — "
- f"expected one of: {', '.join(_TERM_TYPES)}"
- )
-
- if t == "i":
- return {"t": t, "i": value}
- elif t == "l":
- return {"t": t, "v": value}
- elif t == "b":
- return {"t": t, "d": value}
- return {"t": t}
-
-# ── Security boundary: MCP client → MCP server ──
-# The MCP client authenticates to this server via a Bearer token in the
-# HTTP Authorization header. The SDK's auth middleware extracts and
-# verifies the token before any tool handler runs.
-#
-# We implement a pass-through TokenVerifier: the gateway is the real
-# authority, so we accept any non-empty Bearer token here and forward
-# it to the gateway for validation. The gateway's in-band auth
-# protocol and IAM regime decide whether the token is valid.
-#
-# This means an invalid token will connect to the MCP server but will
-# fail when the first WebSocket auth frame is sent to the gateway.
-# That is intentional — the gateway is the single source of truth.
-
-
-class PassthroughTokenVerifier(TokenVerifier):
- """Accept any non-empty Bearer token and forward it downstream.
-
- The TrustGraph gateway is the authority for token validation, not
- this MCP server. We store the raw token in the AccessToken so that
- tool handlers can retrieve it via ``get_access_token().token`` and
- forward it to the gateway.
- """
-
- async def verify_token(self, token: str) -> AccessToken | None:
- if not token:
- return None
- return AccessToken(
- token=token,
- client_id="mcp-caller",
- scopes=[],
- )
-
+from . tg_socket import WebSocketManager
@dataclass
class AppContext:
- sockets: dict[str, WebSocketManager] = field(default_factory=dict)
- websocket_url: str = ""
-
+ sockets: dict[str, WebSocketManager]
+ websocket_url: str
+ gateway_token: str
@asynccontextmanager
-async def app_lifespan(
- server: FastMCP,
- websocket_url: str = "ws://api-gateway:8088/api/v1/socket",
-) -> AsyncIterator[AppContext]:
- """Manage per-server state: the pool of per-caller WebSocket
- connections to the gateway."""
+async def app_lifespan(server: FastMCP, websocket_url: str = "ws://api-gateway:8088/api/v1/socket", gateway_token: str = "") -> AsyncIterator[AppContext]:
- sockets: dict[str, WebSocketManager] = {}
+ """
+ Manage application lifecycle with type-safe context
+ """
+
+ # Initialize on startup
+ sockets = {}
try:
- yield AppContext(sockets=sockets, websocket_url=websocket_url)
+ yield AppContext(sockets=sockets, websocket_url=websocket_url, gateway_token=gateway_token)
finally:
- logger.info("Shutting down — closing %d WebSocket(s)", len(sockets))
+ # Cleanup on shutdown
+ logging.info("Shutting down context")
- for key, manager in sockets.items():
- try:
- await manager.stop()
- except Exception as e:
- logger.warning("Error closing socket %s: %s", key, e)
+ for k, manager in sockets.items():
+ logging.info(f"Closing socket for {k}")
+ await manager.stop()
- logger.info("Shutdown complete")
+ logging.info("Shutdown complete")
-
-def _require_token() -> str:
- """Extract the caller's Bearer token from the MCP auth context.
-
- Raises RuntimeError if no token is present (the caller did not
- authenticate).
- """
- # ── Security boundary: token extraction ──
- # get_access_token() reads the contextvar set by the SDK's
- # AuthContextMiddleware. The token was placed there by
- # PassthroughTokenVerifier.verify_token() and is the raw Bearer
- # value from the MCP client's Authorization header.
- access = get_access_token()
- if access is None or not access.token:
- raise RuntimeError(
- "Authentication required — send a Bearer token in the "
- "Authorization header"
- )
- return access.token
-
-
-async def get_socket_manager(ctx, token):
- """Return (or create) an authenticated WebSocket for this token.
-
- Each unique token gets its own WebSocket connection so that
- gateway-side identity, workspace binding, and capability scoping
- are preserved per caller.
- """
+async def get_socket_manager(ctx):
lifespan_context = ctx.request_context.lifespan_context
sockets = lifespan_context.sockets
websocket_url = lifespan_context.websocket_url
+ gateway_token = lifespan_context.gateway_token
- key = _token_key(token)
+ if "default" in sockets:
+ logging.info("Return existing socket manager")
+ return sockets["default"]
- if key in sockets:
- manager = sockets[key]
- if manager.socket is not None:
- return manager
- # Socket was closed (e.g. server-side timeout) — reconnect.
- del sockets[key]
+ logging.info(f"Opening socket to {websocket_url}...")
- logger.info("Opening authenticated WebSocket to %s …", websocket_url)
+ # Create manager with empty pending requests
+ manager = WebSocketManager(websocket_url, token=gateway_token)
- manager = WebSocketManager(websocket_url, token=token)
+ # Start reader task with the proper manager
await manager.start()
- # Verify the token is valid by calling whoami. This confirms the
- # gateway accepted the token and gives us the caller's identity.
- try:
- identity = await manager.whoami()
- logger.info(
- "WebSocket ready — caller: %s",
- identity.get("handle", "unknown"),
- )
- except Exception as e:
- await manager.stop()
- raise RuntimeError(
- f"Token rejected by gateway (whoami failed): {e}"
- ) from e
+ sockets["default"] = manager
- sockets[key] = manager
+ logging.info("Return new socket manager")
return manager
-
@dataclass
class EmbeddingsResponse:
vectors: List[List[float]]
@@ -291,23 +182,10 @@ class PutConfigResponse:
class DeleteConfigResponse:
pass
-@dataclass
-class SparqlQueryResponse:
- query_type: str
- variables: List[str]
- bindings: List[Dict[str, Any]]
- ask_result: bool
- triples: List[Dict[str, Any]]
-
-@dataclass
-class GraphQLQueryResponse:
- data: Any
- errors: List[Dict[str, Any]]
-
@dataclass
class GetPromptsResponse:
prompts: List[str]
-
+
@dataclass
class GetPromptResponse:
prompt: Dict[str, Any]
@@ -316,61 +194,31 @@ class GetPromptResponse:
class GetSystemPromptResponse:
prompt: str
-
class McpServer:
- def __init__(
- self,
- host: str = "0.0.0.0",
- port: int = 8000,
- websocket_url: str = "ws://api-gateway:8088/api/v1/socket",
- auth_issuer: str = "",
- auth_resource_url: str = "",
- ):
+ def __init__(self, host: str = "0.0.0.0", port: int = 8000, websocket_url: str = "ws://api-gateway:8088/api/v1/socket", gateway_token: str = ""):
self.host = host
self.port = port
self.websocket_url = websocket_url
+ self.gateway_token = gateway_token
- lifespan_with_url = partial(
- app_lifespan, websocket_url=websocket_url,
- )
-
- # ── Security: MCP-level auth configuration ──
- # The SDK requires AuthSettings whenever a token_verifier is
- # present. The issuer_url tells MCP clients where to obtain
- # tokens; resource_server_url identifies this server in OAuth
- # protected-resource metadata.
- #
- # The PassthroughTokenVerifier accepts any non-empty Bearer
- # token — real validation happens at the gateway. This is
- # intentional: the gateway is the single source of truth for
- # identity and capability checks.
- from mcp.server.auth.settings import AuthSettings
-
- auth_settings = AuthSettings(
- issuer_url=auth_issuer or f"http://{host}:{port}",
- resource_server_url=auth_resource_url or f"http://{host}:{port}",
- )
-
+ # Create a partial function to pass websocket_url to app_lifespan
+ lifespan_with_url = partial(app_lifespan, websocket_url=websocket_url, gateway_token=gateway_token)
+
self.mcp = FastMCP(
- "TrustGraph",
- dependencies=["trustgraph-base"],
- host=self.host,
- port=self.port,
+ "TrustGraph", dependencies=["trustgraph-base"],
+ host=self.host, port=self.port,
lifespan=lifespan_with_url,
- token_verifier=PassthroughTokenVerifier(),
- auth=auth_settings,
)
self._register_tools()
-
+
def _register_tools(self):
"""Register all MCP tools"""
+ # Register all the tools that were previously registered globally
self.mcp.tool()(self.embeddings)
self.mcp.tool()(self.text_completion)
self.mcp.tool()(self.graph_rag)
self.mcp.tool()(self.agent)
self.mcp.tool()(self.triples_query)
- self.mcp.tool()(self.sparql_query)
- self.mcp.tool()(self.graphql_query)
self.mcp.tool()(self.graph_embeddings_query)
self.mcp.tool()(self.get_config_all)
self.mcp.tool()(self.get_config)
@@ -395,69 +243,67 @@ class McpServer:
self.mcp.tool()(self.load_document)
self.mcp.tool()(self.remove_document)
self.mcp.tool()(self.add_processing)
-
+
def run(self):
"""Run the MCP server"""
self.mcp.run(transport="streamable-http")
- async def _get_manager(self, ctx):
- """Get an authenticated WebSocket manager for the current caller.
-
- Extracts the Bearer token from the MCP auth context and returns
- a per-token WebSocket connection to the gateway.
- """
- token = _require_token()
- return await get_socket_manager(ctx, token)
-
async def embeddings(
self,
- texts: List[str],
+ text: str,
flow_id: str | None = None,
- workspace: str | None = None,
ctx: Context = None,
) -> EmbeddingsResponse:
"""
- Generate vector embeddings for the given texts using TrustGraph's embedding models.
-
+ Generate vector embeddings for the given text using TrustGraph's embedding models.
+
This tool converts text into high-dimensional vectors that capture semantic meaning,
enabling similarity searches, clustering, and other vector-based operations.
-
+
Args:
- texts: List of input texts to convert into embeddings. Each text can be a
- sentence, paragraph, or document.
+ text: The input text to convert into embeddings. Can be a sentence, paragraph,
+ or document. The text will be processed by the configured embedding model.
flow_id: Optional flow identifier to use for processing (default: "default").
Different flows may use different embedding models or configurations.
- workspace: Optional workspace to query. If omitted, uses the caller's
- default workspace.
-
+
Returns:
- EmbeddingsResponse containing a list of vectors, one per input text.
+ EmbeddingsResponse containing a list of vectors. Each vector is a list of floats
+ representing the text's semantic embedding in the model's vector space.
+
+ Example usage:
+ - Convert a query into embeddings for similarity search
+ - Generate embeddings for documents before storing them
+ - Create embeddings for comparison with existing knowledge
"""
- logger.info("Embeddings request")
+ logging.info("Embeddings request made")
if flow_id is None: flow_id = "default"
- manager = await self._get_manager(ctx)
+ manager = await get_socket_manager(ctx, "trustgraph")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Computing embeddings via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ if ctx is None:
+ raise RuntimeError("No context provided")
- request_data = {"texts": texts}
-
- gen = manager.request(
- "embeddings", request_data, flow_id, workspace=workspace,
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Computing embeddings via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
)
+ # Send websocket request
+ request_data = {"text": text}
+ logging.info("making request")
+
+ gen = manager.request("embeddings", request_data, flow_id)
+
async for response in gen:
+
+ # Extract vectors from response
vectors = response.get("vectors", [[]])
break
-
+
return EmbeddingsResponse(vectors=vectors)
async def text_completion(
@@ -465,47 +311,62 @@ class McpServer:
prompt: str,
system: str | None = None,
flow_id: str | None = None,
- workspace: str | None = None,
ctx: Context = None,
) -> TextCompletionResponse:
"""
Generate text completions using TrustGraph's language models.
-
+
+ This tool sends prompts to configured language models and returns generated text.
+ It supports both user prompts and system instructions for controlling generation.
+
Args:
prompt: The main prompt or question to send to the language model.
+ This is the primary input that guides the model's response.
system: Optional system prompt that sets the context, role, or behavior
- for the AI assistant.
- flow_id: Optional flow identifier (default: "default").
- workspace: Optional workspace to query. If omitted, uses the caller's
- default workspace.
-
+ for the AI assistant (e.g., "You are a helpful coding assistant").
+ System prompts influence how the model interprets and responds.
+ flow_id: Optional flow identifier (default: "default"). Different flows
+ may use different models, parameters, or processing pipelines.
+
Returns:
TextCompletionResponse containing the generated text response from the model.
+
+ Example usage:
+ - Ask questions and get AI-generated answers
+ - Generate code, documentation, or creative content
+ - Perform text analysis, summarization, or transformation tasks
+ - Use system prompts to control tone, style, or domain expertise
"""
if system is None: system = ""
if flow_id is None: flow_id = "default"
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Generating text completion via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ # Use websocket if context is available
+ logging.info("Text completion request made via websocket")
- request_data = {"system": system, "prompt": prompt}
+ manager = await get_socket_manager(ctx, "trustgraph")
- gen = manager.request(
- "text-completion", request_data, flow_id, workspace=workspace,
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Generating text completion via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
)
+ # Send websocket request
+ request_data = {"system": system, "prompt": prompt}
+
+ gen = manager.request("text-completion", request_data, flow_id)
+
async for response in gen:
+
+ # Extract vectors from response
text = response.get("response", "")
break
-
+
return TextCompletionResponse(response=text)
async def graph_rag(
@@ -517,43 +378,58 @@ class McpServer:
max_subgraph_size: int | None = None,
max_path_length: int | None = None,
flow_id: str | None = None,
- workspace: str | None = None,
ctx: Context = None,
) -> GraphRagResponse:
"""
Perform Graph-based Retrieval Augmented Generation (GraphRAG) queries.
-
+
GraphRAG combines knowledge graph traversal with language model generation to provide
- contextually rich answers.
-
+ contextually rich answers. It explores relationships between entities to build relevant
+ context before generating responses.
+
Args:
question: The question or query to answer using the knowledge graph.
+ The system will find relevant entities and relationships to inform the response.
collection: Knowledge collection to query (default: "default").
+ Different collections may contain domain-specific knowledge.
entity_limit: Maximum number of entities to retrieve during graph traversal.
+ Higher limits provide more context but increase processing time.
triple_limit: Maximum number of relationship triples to consider.
+ Controls the depth of relationship exploration.
max_subgraph_size: Maximum size of the subgraph to extract for context.
+ Larger subgraphs provide richer context but use more resources.
max_path_length: Maximum path length to traverse in the knowledge graph.
+ Longer paths can discover distant but relevant relationships.
flow_id: Processing flow to use (default: "default").
- workspace: Optional workspace to query. If omitted, uses the caller's
- default workspace.
-
+
Returns:
GraphRagResponse containing the generated answer informed by knowledge graph context.
+
+ Example usage:
+ - Answer complex questions requiring multi-hop reasoning
+ - Explore relationships between entities in your knowledge base
+ - Generate responses grounded in structured knowledge
+ - Perform research queries across connected information
"""
if collection is None: collection = "default"
if flow_id is None: flow_id = "default"
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Processing GraphRAG query via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("GraphRAG request made via websocket")
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Processing GraphRAG query via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
+
+ # Build request data with all parameters
request_data = {
"query": question
}
@@ -564,19 +440,20 @@ class McpServer:
if max_subgraph_size: request_data["max_subgraph_size"] = max_subgraph_size
if max_path_length: request_data["max_path_length"] = max_path_length
- gen = manager.request(
- "graph-rag", request_data, flow_id, workspace=workspace,
- )
+ gen = manager.request("graph-rag", request_data, flow_id)
text_chunks = []
async for response in gen:
+ # Handle new message format with message_type
message_type = response.get("message_type", "chunk")
+ # Only collect text from chunk messages
if message_type == "chunk":
chunk_text = response.get("response", "")
if chunk_text:
text_chunks.append(chunk_text)
+ # Check if session is complete
if response.get("end_of_session"):
break
@@ -587,447 +464,404 @@ class McpServer:
question: str,
collection: str | None = None,
flow_id: str | None = None,
- workspace: str | None = None,
ctx: Context = None,
) -> AgentResponse:
"""
Execute intelligent agent queries with reasoning and tool usage capabilities.
-
+
+ The agent can perform complex multi-step reasoning, use tools, and provide
+ detailed thought processes. It's designed for tasks requiring planning,
+ analysis, and iterative problem-solving.
+
Args:
- question: The question or task for the agent to solve.
+ question: The question or task for the agent to solve. Can be complex
+ queries requiring multiple steps, analysis, or tool usage.
collection: Knowledge collection the agent can access (default: "default").
- flow_id: Agent workflow to use (default: "default").
- workspace: Optional workspace to query. If omitted, uses the caller's
- default workspace.
-
+ Determines what information and tools are available.
+ flow_id: Agent workflow to use (default: "default"). Different flows
+ may have different capabilities, tools, or reasoning strategies.
+
Returns:
AgentResponse containing the final answer after the agent's reasoning process.
+ During execution, you'll see intermediate thoughts and observations.
+
+ Example usage:
+ - Solve complex analytical problems requiring multiple steps
+ - Perform research tasks across multiple information sources
+ - Handle queries that need tool usage and decision-making
+ - Get detailed explanations of reasoning processes
+
+ Note: This tool provides real-time updates on the agent's thinking process
+ through log messages, so you can follow its reasoning steps.
"""
if collection is None: collection = "default"
if flow_id is None: flow_id = "default"
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Processing agent query via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Agent request made via websocket")
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Processing agent query via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
+
+ # Build request data with all parameters
request_data = {
"question": question
}
if collection: request_data["collection"] = collection
- gen = manager.request(
- "agent", request_data, flow_id, workspace=workspace,
- )
+ gen = manager.request("agent", request_data, flow_id)
async for response in gen:
- logger.debug("Agent response: %s", response)
+ logging.debug(f"Agent response: {response}")
- if ctx:
- if "thought" in response:
- await ctx.session.send_log_message(
- level="info",
- data=f"Thinking: {response['thought']}",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ if "thought" in response:
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Thinking: {response['thought']}",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
- if "observation" in response:
- await ctx.session.send_log_message(
- level="info",
- data=f"Observation: {response['observation']}",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ if "observation" in response:
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Observation: {response['observation']}",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
+ # Extract vectors from response
if "answer" in response:
answer = response.get("answer", "")
return AgentResponse(answer=answer)
async def triples_query(
self,
- s: str | None = None,
- s_type: str | None = None,
- p: str | None = None,
- p_type: str | None = None,
- o: str | None = None,
- o_type: str | None = None,
- collection: str | None = None,
- graph: str | None = None,
+ s_v: str | None = None,
+ s_e: bool | None = None,
+ p_v: str | None = None,
+ p_e: bool | None = None,
+ o_v: str | None = None,
+ o_e: bool | None = None,
limit: int | None = None,
flow_id: str | None = None,
- workspace: str | None = None,
ctx: Context = None,
) -> TriplesQueryResponse:
"""
Query knowledge graph triples using subject-predicate-object patterns.
-
- Each of s, p, o is an RDF term value. Use the corresponding _type
- parameter to specify the term kind:
- - "iri" (default for s and p): an IRI / entity reference
- - "literal" (default for o): a plain literal value
- - "blank": a blank node identifier
-
+
+ Knowledge graphs store information as triples (subject, predicate, object).
+ This tool allows flexible querying by specifying any combination of these
+ components, with wildcards for unspecified parts.
+
Args:
- s: Subject value to match. Leave None for wildcard.
- s_type: Subject term type: "iri" (default), "literal", or "blank".
- p: Predicate value to match. Leave None for wildcard.
- p_type: Predicate term type: "iri" (default), "literal", or "blank".
- o: Object value to match. Leave None for wildcard.
- o_type: Object term type: "iri", "literal" (default), or "blank".
- collection: Knowledge collection to query (default: "default").
- graph: Named graph IRI to restrict the query. None = default graph,
- "*" = all graphs.
+ s_v: Subject value to match (e.g., "John", "Apple Inc."). Leave None for wildcard.
+ s_e: Whether subject should be treated as an entity (True) or literal (False).
+ p_v: Predicate/relationship value (e.g., "works_for", "type_of"). Leave None for wildcard.
+ p_e: Whether predicate should be treated as an entity (True) or literal (False).
+ o_v: Object value to match (e.g., "Engineer", "Company"). Leave None for wildcard.
+ o_e: Whether object should be treated as an entity (True) or literal (False).
limit: Maximum number of triples to return (default: 20).
flow_id: Processing flow identifier (default: "default").
- workspace: Optional workspace to query. If omitted, uses the caller's
- default workspace.
-
+
Returns:
TriplesQueryResponse containing matching triples from the knowledge graph.
+
+ Example queries:
+ - Find all relationships for an entity: s_v="John", others None
+ - Find all instances of a relationship: p_v="works_for", others None
+ - Find specific facts: s_v="John", p_v="works_for", o_v=None
+ - Explore entity types: p_v="type_of", others None
+
+ Use this for:
+ - Exploring knowledge graph structure
+ - Finding specific facts or relationships
+ - Discovering connections between entities
+ - Validating or debugging knowledge content
"""
if flow_id is None: flow_id = "default"
if limit is None: limit = 20
- if collection is None: collection = "default"
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Processing triples query via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Triples query request made via websocket")
- request_data = {
- "limit": limit,
- "collection": collection,
- }
+ manager = await get_socket_manager(ctx, "trustgraph")
- if s is not None:
- request_data["s"] = _make_term(s, s_type or "iri")
-
- if p is not None:
- request_data["p"] = _make_term(p, p_type or "iri")
-
- if o is not None:
- request_data["o"] = _make_term(o, o_type or "literal")
-
- if graph is not None:
- request_data["g"] = graph
-
- gen = manager.request(
- "triples", request_data, flow_id, workspace=workspace,
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Processing triples query via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
)
+ # Build request data with Value objects
+ request_data = {
+ "limit": limit
+ }
+
+ # Add subject if provided
+ if s_v is not None:
+ request_data["s"] = {"v": s_v, "e": s_e }
+
+ # Add predicate if provided
+ if p_v is not None:
+ request_data["p"] = {"v": p_v, "e": p_e }
+
+ # Add object if provided
+ if o_v is not None:
+ request_data["o"] = {"v": o_v, "e": o_e }
+
+ gen = manager.request("triples", request_data, flow_id)
+
async for response in gen:
+ # Extract response data
triples = response.get("response", [])
break
-
+
return TriplesQueryResponse(triples=triples)
- async def sparql_query(
- self,
- query: str,
- collection: str | None = None,
- limit: int | None = None,
- flow_id: str | None = None,
- workspace: str | None = None,
- ctx: Context = None,
- ) -> SparqlQueryResponse:
- """
- Execute a SPARQL query against the knowledge graph.
-
- Supports SELECT, ASK, CONSTRUCT, and DESCRIBE query forms.
-
- Args:
- query: SPARQL query string (e.g. "SELECT ?s ?p ?o WHERE { ?s ?p ?o } LIMIT 10").
- collection: Knowledge collection to query (default: "default").
- limit: Safety limit on number of results (default: 10000).
- flow_id: Processing flow identifier (default: "default").
- workspace: Optional workspace to query. If omitted, uses the caller's
- default workspace.
-
- Returns:
- SparqlQueryResponse containing the query results. The structure depends
- on query type:
- - SELECT: variables (column names) and bindings (rows of Term values)
- - ASK: ask_result (boolean)
- - CONSTRUCT/DESCRIBE: triples
- """
-
- if collection is None: collection = "default"
- if flow_id is None: flow_id = "default"
- if limit is None: limit = 10000
-
- manager = await self._get_manager(ctx)
-
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Processing SPARQL query via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
-
- request_data = {
- "query": query,
- "collection": collection,
- "limit": limit,
- }
-
- gen = manager.request(
- "sparql", request_data, flow_id, workspace=workspace,
- )
-
- async for response in gen:
- query_type = response.get("query-type", "")
- return SparqlQueryResponse(
- query_type=query_type,
- variables=response.get("variables", []),
- bindings=response.get("bindings", []),
- ask_result=response.get("ask-result", False),
- triples=response.get("triples", []),
- )
-
- async def graphql_query(
- self,
- query: str,
- collection: str | None = None,
- variables: Dict[str, Any] | None = None,
- operation_name: str | None = None,
- flow_id: str | None = None,
- workspace: str | None = None,
- ctx: Context = None,
- ) -> GraphQLQueryResponse:
- """
- Execute a GraphQL query against structured data (rows).
-
- Queries structured data schemas that have been loaded into TrustGraph.
- The available types and fields depend on the schemas configured in the
- target workspace.
-
- Args:
- query: GraphQL query string (e.g. '{ customers(where: {status: {eq: "active"}}) { id name } }').
- collection: Data collection to query (default: "default").
- variables: Optional GraphQL variables as a dict.
- operation_name: Optional operation name for multi-operation documents.
- flow_id: Processing flow identifier (default: "default").
- workspace: Optional workspace to query. If omitted, uses the caller's
- default workspace.
-
- Returns:
- GraphQLQueryResponse containing data (the query result) and errors
- (any GraphQL field-level errors).
- """
-
- if collection is None: collection = "default"
- if flow_id is None: flow_id = "default"
-
- manager = await self._get_manager(ctx)
-
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Processing GraphQL query via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
-
- request_data = {
- "query": query,
- "collection": collection,
- "variables": variables or {},
- }
-
- if operation_name is not None:
- request_data["operation_name"] = operation_name
-
- gen = manager.request(
- "rows", request_data, flow_id, workspace=workspace,
- )
-
- async for response in gen:
- return GraphQLQueryResponse(
- data=response.get("data"),
- errors=response.get("errors", []),
- )
-
async def graph_embeddings_query(
self,
vectors: List[List[float]],
limit: int | None = None,
flow_id: str | None = None,
- workspace: str | None = None,
ctx: Context = None,
) -> GraphEmbeddingsQueryResponse:
"""
Find entities in the knowledge graph using vector similarity search.
-
+
+ This tool performs semantic search by comparing embedding vectors to find
+ the most similar entities in the knowledge graph. It's useful for finding
+ conceptually related information even when exact text matches don't exist.
+
Args:
- vectors: List of embedding vectors to search with.
+ vectors: List of embedding vectors to search with. Each vector should be
+ a list of floats representing semantic embeddings (typically from
+ the embeddings tool). Multiple vectors can be provided for batch queries.
limit: Maximum number of similar entities to return (default: 20).
+ Higher limits provide more results but may include less relevant matches.
flow_id: Processing flow identifier (default: "default").
- workspace: Optional workspace to query. If omitted, uses the caller's
- default workspace.
-
+
Returns:
- GraphEmbeddingsQueryResponse containing entities ranked by similarity.
+ GraphEmbeddingsQueryResponse containing entities ranked by similarity to the
+ input vectors, along with similarity scores and entity metadata.
+
+ Example workflow:
+ 1. Use the 'embeddings' tool to convert text to vectors
+ 2. Use this tool to find similar entities in the knowledge graph
+ 3. Explore the returned entities for relevant information
+
+ Use this for:
+ - Semantic search across knowledge entities
+ - Finding conceptually similar content
+ - Discovering related entities without exact keyword matches
+ - Building recommendation systems based on entity similarity
"""
if flow_id is None: flow_id = "default"
if limit is None: limit = 20
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Processing graph embeddings query via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Graph embeddings query request made via websocket")
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Processing graph embeddings query via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
+
+ # Build request data
request_data = {
"vectors": vectors,
"limit": limit
}
- gen = manager.request(
- "graph-embeddings", request_data, flow_id, workspace=workspace,
- )
+ gen = manager.request("graph-embeddings", request_data, flow_id)
async for response in gen:
+ # Extract entities from response
entities = response.get("entities", [])
break
-
+
return GraphEmbeddingsQueryResponse(entities=entities)
async def get_config_all(
self,
- workspace: str | None = None,
ctx: Context = None,
) -> ConfigResponse:
"""
Retrieve the complete TrustGraph system configuration.
-
- Args:
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+
+ This tool returns all configuration settings for the TrustGraph system,
+ including model configurations, API keys, flow definitions, and system parameters.
+
Returns:
- ConfigResponse containing the full configuration as a nested dictionary.
+ ConfigResponse containing the full configuration as a nested dictionary
+ with all system settings, organized by category (e.g., models, flows, storage).
+
+ Use this for:
+ - Inspecting current system configuration
+ - Debugging configuration issues
+ - Understanding available models and settings
+ - Auditing system setup and parameters
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Retrieving all configuration via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get config all request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving all configuration via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "config"
}
- gen = manager.request("config", request_data, None, workspace=workspace)
+ gen = manager.request("config", request_data, None)
async for response in gen:
config = response.get("config", {})
break
-
+
return ConfigResponse(config=config)
async def get_config(
self,
keys: List[Dict[str, str]],
- workspace: str | None = None,
ctx: Context = None,
) -> ConfigGetResponse:
"""
Retrieve specific configuration values by key.
-
+
+ This tool allows you to fetch specific configuration settings without
+ retrieving the entire configuration. Useful for checking particular
+ settings or API keys.
+
Args:
- keys: List of configuration keys to retrieve. Each key should be a dict with
- 'type' and 'key' fields.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+ keys: List of configuration keys to retrieve. Each key should be a dict with:
+ - 'type': Configuration category (e.g., 'llm', 'embeddings', 'storage')
+ - 'key': Specific setting name within that category
+
Returns:
ConfigGetResponse containing the requested configuration values.
+
+ Example keys:
+ - {'type': 'llm', 'key': 'openai.model'}
+ - {'type': 'embeddings', 'key': 'default.model'}
+ - {'type': 'storage', 'key': 'database.url'}
+
+ Use this for:
+ - Checking specific model configurations
+ - Validating API key settings
+ - Inspecting individual system parameters
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Retrieving specific configuration via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get config request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving specific configuration via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "get",
"keys": keys
}
- gen = manager.request("config", request_data, None, workspace=workspace)
+ gen = manager.request("config", request_data, None)
async for response in gen:
values = response.get("values", [])
break
-
+
return ConfigGetResponse(values=values)
async def put_config(
self,
values: List[Dict[str, str]],
- workspace: str | None = None,
ctx: Context = None,
) -> PutConfigResponse:
"""
Update system configuration values.
-
+
+ This tool allows you to modify TrustGraph system settings, such as
+ model parameters, API keys, and system behavior configurations.
+
Args:
- values: List of configuration updates. Each should be a dict with
- 'type', 'key', and 'value' fields.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+ values: List of configuration updates. Each update should be a dict with:
+ - 'type': Configuration category (e.g., 'llm', 'embeddings')
+ - 'key': Specific setting name to update
+ - 'value': New value for the setting
+
Returns:
PutConfigResponse confirming the configuration update.
+
+ Example updates:
+ - {'type': 'llm', 'key': 'openai.model', 'value': 'gpt-4'}
+ - {'type': 'embeddings', 'key': 'batch_size', 'value': '100'}
+
+ Use this for:
+ - Switching between different models
+ - Updating API credentials
+ - Modifying system behavior parameters
+ - Configuring processing settings
+
+ Note: Configuration changes may require system restart to take effect.
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Updating configuration via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Put config request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Updating configuration via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "put",
"values": values
}
- gen = manager.request("config", request_data, None, workspace=workspace)
+ gen = manager.request("config", request_data, None)
async for response in gen:
return PutConfigResponse()
@@ -1035,73 +869,97 @@ class McpServer:
async def delete_config(
self,
keys: List[Dict[str, str]],
- workspace: str | None = None,
ctx: Context = None,
) -> DeleteConfigResponse:
"""
Delete specific configuration entries from the system.
-
+
+ This tool removes configuration settings, reverting them to system defaults
+ or disabling specific features.
+
Args:
- keys: List of configuration keys to delete. Each should be a dict with
- 'type' and 'key' fields.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+ keys: List of configuration keys to delete. Each key should be a dict with:
+ - 'type': Configuration category (e.g., 'llm', 'embeddings')
+ - 'key': Specific setting name to remove
+
Returns:
DeleteConfigResponse confirming the deletion.
+
+ Use this for:
+ - Removing custom model configurations
+ - Clearing API credentials
+ - Resetting settings to defaults
+ - Cleaning up obsolete configurations
+
+ Warning: Deleting essential configuration may cause system functionality
+ to be disabled until properly reconfigured.
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Deleting configuration via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Delete config request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Deleting configuration via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "delete",
"keys": keys
}
- gen = manager.request("config", request_data, None, workspace=workspace)
+ gen = manager.request("config", request_data, None)
async for response in gen:
return DeleteConfigResponse()
async def get_prompts(
self,
- workspace: str | None = None,
ctx: Context = None,
) -> GetPromptsResponse:
"""
List all available prompt templates in the system.
-
- Args:
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+
+ Prompt templates are reusable prompts that can be used with language models
+ for consistent behavior across different queries and use cases.
+
Returns:
GetPromptsResponse containing a list of available prompt template IDs.
+ Each ID can be used with get_prompt to retrieve the full template.
+
+ Use this for:
+ - Discovering available prompt templates
+ - Exploring pre-configured prompts for different tasks
+ - Finding templates for specific use cases
+ - Understanding what prompt options are available
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Retrieving prompt templates via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get prompts request made via websocket")
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving prompt templates via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
+
+ # First get all config
request_data = {
"operation": "config"
}
- gen = manager.request("config", request_data, None, workspace=workspace)
+ gen = manager.request("config", request_data, None)
async for response in gen:
config = response.get("config", {})
@@ -1113,36 +971,49 @@ class McpServer:
async def get_prompt(
self,
prompt_id: str,
- workspace: str | None = None,
ctx: Context = None,
) -> GetPromptResponse:
"""
Retrieve a specific prompt template by ID.
-
+
+ Prompt templates contain structured prompts with placeholders, instructions,
+ and metadata for specific tasks or domains.
+
Args:
prompt_id: The unique identifier of the prompt template to retrieve.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+ Use get_prompts to see available template IDs.
+
Returns:
- GetPromptResponse containing the complete prompt template.
+ GetPromptResponse containing the complete prompt template with its
+ structure, placeholders, and usage instructions.
+
+ Use this for:
+ - Examining prompt template structure
+ - Understanding how to use specific templates
+ - Copying or modifying existing prompts
+ - Learning prompt engineering patterns
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Retrieving prompt template '{prompt_id}' via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get prompt request made via websocket")
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving prompt template '{prompt_id}' via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
+
+ # First get all config
request_data = {
"operation": "config"
}
- gen = manager.request("config", request_data, None, workspace=workspace)
+ gen = manager.request("config", request_data, None)
async for response in gen:
config = response.get("config", {})
@@ -1154,35 +1025,44 @@ class McpServer:
async def get_system_prompt(
self,
- workspace: str | None = None,
ctx: Context = None,
) -> GetSystemPromptResponse:
"""
Retrieve the current system prompt configuration.
-
- Args:
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+
+ The system prompt defines the default behavior, personality, and instructions
+ for language models across the TrustGraph system.
+
Returns:
- GetSystemPromptResponse containing the system prompt text.
+ GetSystemPromptResponse containing the system prompt text and configuration.
+
+ Use this for:
+ - Understanding default AI behavior settings
+ - Checking current system-wide prompt configuration
+ - Auditing AI personality and instruction settings
+ - Debugging unexpected AI responses
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Retrieving system prompt via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get system prompt request made via websocket")
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving system prompt via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
+
+ # First get all config
request_data = {
"operation": "config"
}
- gen = manager.request("config", request_data, None, workspace=workspace)
+ gen = manager.request("config", request_data, None)
async for response in gen:
config = response.get("config", {})
@@ -1193,39 +1073,51 @@ class McpServer:
async def get_token_costs(
self,
- workspace: str | None = None,
ctx: Context = None,
) -> ConfigTokenCostsResponse:
"""
Retrieve token pricing information for all configured AI models.
-
- Args:
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+
+ This tool provides cost information for input and output tokens across
+ different language models, helping with budget planning and cost optimization.
+
Returns:
- ConfigTokenCostsResponse containing pricing data for each model.
+ ConfigTokenCostsResponse containing pricing data for each model including:
+ - Model name/identifier
+ - Input token cost (per token)
+ - Output token cost (per token)
+
+ Use this for:
+ - Estimating costs for different models
+ - Choosing cost-effective models for tasks
+ - Budget planning and cost analysis
+ - Monitoring and optimizing AI spending
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Retrieving token costs via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get token costs request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving token costs via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "getvalues",
"type": "token-costs"
}
- gen = manager.request("config", request_data, None, workspace=workspace)
+ gen = manager.request("config", request_data, None)
async for response in gen:
values = response.get("values", [])
+ # Transform to match TypeScript API format
costs = []
for item in values:
try:
@@ -1238,89 +1130,106 @@ class McpServer:
except (json.JSONDecodeError, AttributeError):
continue
break
-
+
return ConfigTokenCostsResponse(costs=costs)
async def get_knowledge_cores(
self,
- workspace: str | None = None,
ctx: Context = None,
) -> KnowledgeCoresResponse:
"""
List all available knowledge graph cores in the current workspace.
- Args:
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
+ Knowledge cores are packaged collections of structured knowledge that can
+ be loaded into the system for querying and reasoning. They contain entities,
+ relationships, and facts organized as knowledge graphs.
Returns:
KnowledgeCoresResponse containing a list of available knowledge core IDs.
+
+ Use this for:
+ - Discovering available knowledge collections
+ - Understanding what knowledge domains are accessible
+ - Planning which cores to load for specific tasks
+ - Managing knowledge resources
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Retrieving knowledge graph cores via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get knowledge cores request made via websocket")
+
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving knowledge graph cores via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "list-kg-cores",
}
- gen = manager.request(
- "knowledge", request_data, None, workspace=workspace,
- )
+ gen = manager.request("knowledge", request_data, None)
async for response in gen:
ids = response.get("ids", [])
break
-
+
return KnowledgeCoresResponse(ids=ids)
async def delete_kg_core(
self,
core_id: str,
- workspace: str | None = None,
ctx: Context = None,
) -> DeleteKgCoreResponse:
"""
Permanently delete a knowledge graph core.
+ This operation removes a knowledge core from storage. Use with caution
+ as this action cannot be undone.
+
Args:
core_id: Unique identifier of the knowledge core to delete.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
Returns:
DeleteKgCoreResponse confirming the deletion.
+
+ Use this for:
+ - Cleaning up obsolete knowledge cores
+ - Removing test or experimental data
+ - Managing storage space
+ - Maintaining organized knowledge collections
+
+ Warning: This permanently deletes the knowledge core and all its data.
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Deleting knowledge graph core '{core_id}' via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Delete KG core request made via websocket")
+
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Deleting knowledge graph core '{core_id}' via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "delete-kg-core",
"id": core_id,
}
- gen = manager.request(
- "knowledge", request_data, None, workspace=workspace,
- )
+ gen = manager.request("knowledge", request_data, None)
async for response in gen:
break
-
+
return DeleteKgCoreResponse()
async def load_kg_core(
@@ -1328,34 +1237,46 @@ class McpServer:
core_id: str,
flow: str,
collection: str | None = None,
- workspace: str | None = None,
ctx: Context = None,
) -> LoadKgCoreResponse:
"""
Load a knowledge graph core into the active system for querying.
+ This operation makes a knowledge core available for GraphRAG queries,
+ triple searches, and other knowledge-based operations.
+
Args:
core_id: Unique identifier of the knowledge core to load.
- flow: Processing flow to use for loading the core.
- collection: Target collection name (default: "default").
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
+ flow: Processing flow to use for loading the core. Different flows
+ may apply different processing, indexing, or optimization steps.
+ collection: Target collection name (default: "default"). The loaded
+ knowledge will be available under this collection name.
Returns:
LoadKgCoreResponse confirming the core has been loaded.
+
+ Use this for:
+ - Making knowledge cores available for queries
+ - Switching between different knowledge domains
+ - Loading domain-specific knowledge for tasks
+ - Preparing knowledge for GraphRAG operations
"""
if collection is None: collection = "default"
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Loading knowledge graph core '{core_id}' via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Load KG core request made via websocket")
+
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Loading knowledge graph core '{core_id}' via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "load-kg-core",
@@ -1364,241 +1285,292 @@ class McpServer:
"collection": collection
}
- gen = manager.request(
- "knowledge", request_data, None, workspace=workspace,
- )
+ gen = manager.request("knowledge", request_data, None)
async for response in gen:
break
-
+
return LoadKgCoreResponse()
async def get_kg_core(
self,
core_id: str,
- workspace: str | None = None,
ctx: Context = None,
) -> GetKgCoreResponse:
"""
Download and retrieve the complete content of a knowledge graph core.
+ This tool streams the entire content of a knowledge core, returning all
+ entities, relationships, and metadata. Due to potentially large data sizes,
+ the content is streamed in chunks.
+
Args:
core_id: Unique identifier of the knowledge core to retrieve.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
Returns:
GetKgCoreResponse containing all chunks of the knowledge core data.
+ Each chunk contains part of the knowledge graph structure.
+
+ Use this for:
+ - Examining knowledge core content and structure
+ - Debugging knowledge graph data
+ - Exporting knowledge for backup or analysis
+ - Understanding the scope and quality of knowledge
+
+ Note: Large knowledge cores may take significant time to download.
+ Progress updates are provided through log messages during streaming.
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Retrieving knowledge graph core '{core_id}' via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get KG core request made via websocket")
+
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving knowledge graph core '{core_id}' via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "get-kg-core",
"id": core_id,
}
+ # Collect all streaming responses
chunks = []
- gen = manager.request(
- "knowledge", request_data, None, workspace=workspace,
- )
+ gen = manager.request("knowledge", request_data, None)
async for response in gen:
+ # Check for end of stream
if response.get("eos", False):
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Completed streaming KG core data",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Completed streaming KG core data",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
break
else:
chunks.append(response)
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Received KG core chunk ({len(chunks)} chunks so far)",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
-
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Received KG core chunk ({len(chunks)} chunks so far)",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
+
return GetKgCoreResponse(chunks=chunks)
async def get_flows(
self,
- workspace: str | None = None,
ctx: Context = None,
) -> FlowsResponse:
"""
List all available processing flows in the system.
-
- Args:
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+
+ Flows define processing pipelines for different types of operations
+ (e.g., document processing, knowledge extraction, query handling).
+ Each flow encapsulates a specific workflow with configured steps.
+
Returns:
FlowsResponse containing a list of available flow identifiers.
+
+ Use this for:
+ - Discovering available processing workflows
+ - Understanding what processing options are available
+ - Choosing appropriate flows for specific tasks
+ - Planning workflow-based operations
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Retrieving available flows via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get flows request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving available flows via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "list-flows"
}
- gen = manager.request(
- "flow", request_data, None, workspace=workspace,
- )
+ gen = manager.request("flow", request_data, None)
async for response in gen:
flow_ids = response.get("flow-ids", [])
break
-
+
return FlowsResponse(flow_ids=flow_ids)
async def get_flow(
self,
flow_id: str,
- workspace: str | None = None,
ctx: Context = None,
) -> FlowResponse:
"""
Retrieve the complete definition of a specific processing flow.
-
+
+ This tool returns the detailed configuration, steps, and parameters
+ of a processing flow, showing how it processes data and what operations it performs.
+
Args:
flow_id: Unique identifier of the flow to retrieve.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+
Returns:
- FlowResponse containing the complete flow definition.
+ FlowResponse containing the complete flow definition including:
+ - Flow configuration and parameters
+ - Processing steps and their order
+ - Input/output specifications
+ - Dependencies and requirements
+
+ Use this for:
+ - Understanding how specific flows work
+ - Debugging flow processing issues
+ - Learning flow configuration patterns
+ - Customizing or duplicating flows
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Retrieving flow definition for '{flow_id}' via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get flow request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving flow definition for '{flow_id}' via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "get-flow",
"flow-id": flow_id,
}
- gen = manager.request(
- "flow", request_data, None, workspace=workspace,
- )
+ gen = manager.request("flow", request_data, None)
async for response in gen:
flow_data = response.get("flow", "{}")
+ # Parse JSON flow definition as done in TypeScript
flow = json.loads(flow_data) if isinstance(flow_data, str) else flow_data
break
-
+
return FlowResponse(flow=flow)
async def get_flow_classes(
self,
- workspace: str | None = None,
ctx: Context = None,
) -> FlowClassesResponse:
"""
List all available flow class templates.
-
- Args:
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+
+ Flow classes are templates that define types of processing workflows.
+ They serve as blueprints for creating specific flow instances with
+ customized parameters.
+
Returns:
FlowClassesResponse containing a list of available flow class names.
+
+ Use this for:
+ - Discovering available flow templates
+ - Understanding what types of processing are supported
+ - Planning new flow creation
+ - Exploring system capabilities
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Retrieving flow classes via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get flow classes request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving flow classes via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "list-classes"
}
- gen = manager.request(
- "flow", request_data, None, workspace=workspace,
- )
+ gen = manager.request("flow", request_data, None)
async for response in gen:
class_names = response.get("class-names", [])
break
-
+
return FlowClassesResponse(class_names=class_names)
async def get_flow_class(
self,
class_name: str,
- workspace: str | None = None,
ctx: Context = None,
) -> FlowClassResponse:
"""
Retrieve the definition of a specific flow class template.
-
+
+ Flow classes define the structure, parameters, and capabilities of
+ flow types. This tool returns the class specification including
+ configurable parameters and processing logic.
+
Args:
class_name: Name of the flow class to retrieve.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+
Returns:
- FlowClassResponse containing the flow class definition.
+ FlowClassResponse containing the flow class definition with:
+ - Class parameters and configuration options
+ - Processing capabilities and requirements
+ - Usage instructions and examples
+
+ Use this for:
+ - Understanding flow class capabilities
+ - Learning how to configure new flows
+ - Troubleshooting flow creation issues
+ - Exploring advanced flow features
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Retrieving flow class definition for '{class_name}' via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get flow class request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving flow class definition for '{class_name}' via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "get-class",
"class-name": class_name
}
- gen = manager.request(
- "flow", request_data, None, workspace=workspace,
- )
+ gen = manager.request("flow", request_data, None)
async for response in gen:
class_def_data = response.get("class-definition", "{}")
+ # Parse JSON class definition as done in TypeScript
class_definition = json.loads(class_def_data) if isinstance(class_def_data, str) else class_def_data
break
-
+
return FlowClassResponse(class_definition=class_definition)
async def start_flow(
@@ -1606,32 +1578,43 @@ class McpServer:
flow_id: str,
class_name: str,
description: str,
- workspace: str | None = None,
ctx: Context = None,
) -> StartFlowResponse:
"""
Create and start a new processing flow instance.
-
+
+ This tool creates a new flow based on a flow class template and starts
+ it running. The flow will begin processing according to its configuration.
+
Args:
flow_id: Unique identifier for the new flow instance.
class_name: Flow class template to use for creating the flow.
+ Use get_flow_classes to see available classes.
description: Human-readable description of the flow's purpose.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+
Returns:
StartFlowResponse confirming the flow has been started.
+
+ Use this for:
+ - Creating new processing workflows
+ - Starting automated processing tasks
+ - Launching background operations
+ - Initiating data processing pipelines
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Starting flow '{flow_id}' with class '{class_name}' via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Start flow request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Starting flow '{flow_id}' with class '{class_name}' via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "start-flow",
@@ -1640,135 +1623,162 @@ class McpServer:
"description": description
}
- gen = manager.request(
- "flow", request_data, None, workspace=workspace,
- )
+ gen = manager.request("flow", request_data, None)
async for response in gen:
break
-
+
return StartFlowResponse()
async def stop_flow(
self,
flow_id: str,
- workspace: str | None = None,
ctx: Context = None,
) -> StopFlowResponse:
"""
Stop a running flow instance.
-
+
+ This tool gracefully stops a running flow, allowing it to complete
+ current operations before shutting down.
+
Args:
flow_id: Unique identifier of the flow instance to stop.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
-
+
Returns:
StopFlowResponse confirming the flow has been stopped.
+
+ Use this for:
+ - Stopping unwanted or completed flows
+ - Managing system resources
+ - Interrupting long-running processes
+ - Maintaining flow lifecycle
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Stopping flow '{flow_id}' via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Stop flow request made via websocket")
+
+ manager = await get_socket_manager(ctx, "trustgraph")
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Stopping flow '{flow_id}' via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "stop-flow",
"flow-id": flow_id
}
- gen = manager.request(
- "flow", request_data, None, workspace=workspace,
- )
+ gen = manager.request("flow", request_data, None)
async for response in gen:
break
-
+
return StopFlowResponse()
async def get_documents(
self,
- workspace: str | None = None,
ctx: Context = None,
) -> DocumentsResponse:
"""
List all documents stored in the TrustGraph document library.
- Args:
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
+ This tool returns metadata for all documents that have been uploaded
+ to the system, including their processing status and properties.
Returns:
- DocumentsResponse containing metadata for each document.
+ DocumentsResponse containing metadata for each document including:
+ - Document ID and title
+ - Upload timestamp
+ - MIME type and size information
+ - Tags and custom metadata
+ - Processing status
+
+ Use this for:
+ - Browsing available documents
+ - Managing document collections
+ - Finding documents by metadata
+ - Auditing document storage
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Retrieving documents list via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get documents request made via websocket")
+
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving documents list via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "list-documents",
}
- gen = manager.request(
- "librarian", request_data, None, workspace=workspace,
- )
+ gen = manager.request("librarian", request_data, None)
async for response in gen:
document_metadatas = response.get("document-metadatas", [])
break
-
+
return DocumentsResponse(document_metadatas=document_metadatas)
async def get_processing(
self,
- workspace: str | None = None,
ctx: Context = None,
) -> ProcessingResponse:
"""
List all documents currently in the processing queue.
- Args:
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
+ This tool shows documents that are being processed or waiting to be
+ processed, along with their processing status and configuration.
Returns:
- ProcessingResponse containing processing metadata.
+ ProcessingResponse containing processing metadata including:
+ - Processing job ID and document ID
+ - Processing flow and status
+ - Target collection
+ - Timestamp and progress information
+
+ Use this for:
+ - Monitoring document processing progress
+ - Debugging processing issues
+ - Managing processing queues
+ - Understanding system workload
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Retrieving processing list via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Get processing request made via websocket")
+
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Retrieving processing list via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "list-processing",
}
- gen = manager.request(
- "librarian", request_data, None, workspace=workspace,
- )
+ gen = manager.request("librarian", request_data, None)
async for response in gen:
processing_metadatas = response.get("processing-metadatas", [])
break
-
+
return ProcessingResponse(processing_metadatas=processing_metadatas)
async def load_document(
@@ -1780,39 +1790,50 @@ class McpServer:
title: str = "",
comments: str = "",
tags: List[str] | None = None,
- workspace: str | None = None,
ctx: Context = None,
) -> LoadDocumentResponse:
"""
Upload a document to the TrustGraph document library.
+ This tool stores documents with rich metadata for later processing,
+ search, and knowledge extraction. Documents can be text files, PDFs,
+ or other supported formats.
+
Args:
document: The document content as a string. For binary files,
this should be base64-encoded content.
document_id: Optional unique identifier. If not provided, one will be generated.
metadata: Optional list of custom metadata key-value pairs.
- mime_type: MIME type of the document.
+ mime_type: MIME type of the document (e.g., 'text/plain', 'application/pdf').
title: Human-readable title for the document.
comments: Optional description or notes about the document.
- tags: List of tags for categorizing the document.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
+ tags: List of tags for categorizing and finding the document.
Returns:
LoadDocumentResponse confirming the document has been stored.
+
+ Use this for:
+ - Adding new documents to the knowledge base
+ - Storing reference materials and data sources
+ - Building document collections for processing
+ - Importing external content for analysis
"""
if tags is None: tags = []
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data="Loading document to library via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Load document request made via websocket")
+
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Loading document to library via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
import time
timestamp = int(time.time())
@@ -1831,55 +1852,63 @@ class McpServer:
"content": document
}
- gen = manager.request(
- "librarian", request_data, None, workspace=workspace,
- )
+ gen = manager.request("librarian", request_data, None)
async for response in gen:
break
-
+
return LoadDocumentResponse()
async def remove_document(
self,
document_id: str,
- workspace: str | None = None,
ctx: Context = None,
) -> RemoveDocumentResponse:
"""
Permanently remove a document from the library.
+ This operation deletes a document and all its associated metadata.
+ Use with caution as this action cannot be undone.
+
Args:
document_id: Unique identifier of the document to remove.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
Returns:
RemoveDocumentResponse confirming the document has been deleted.
+
+ Use this for:
+ - Cleaning up obsolete or incorrect documents
+ - Managing storage space
+ - Removing sensitive or inappropriate content
+ - Maintaining organized document collections
+
+ Warning: This permanently deletes the document and all its metadata.
"""
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Removing document '{document_id}' from library via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Remove document request made via websocket")
+
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Removing document '{document_id}' from library via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
request_data = {
"operation": "remove-document",
"document-id": document_id,
}
- gen = manager.request(
- "librarian", request_data, None, workspace=workspace,
- )
+ gen = manager.request("librarian", request_data, None)
async for response in gen:
break
-
+
return RemoveDocumentResponse()
async def add_processing(
@@ -1889,37 +1918,53 @@ class McpServer:
flow: str,
collection: str | None = None,
tags: List[str] | None = None,
- workspace: str | None = None,
ctx: Context = None,
) -> AddProcessingResponse:
"""
Queue a document for processing through a specific workflow.
+ This tool adds a document to the processing queue where it will be
+ processed by the specified flow to extract knowledge, create embeddings,
+ or perform other analysis operations.
+
Args:
processing_id: Unique identifier for this processing job.
document_id: ID of the document to process (must exist in library).
- flow: Processing flow to use.
+ flow: Processing flow to use. Different flows perform different
+ types of analysis (e.g., knowledge extraction, summarization).
collection: Target collection for processed knowledge (default: "default").
+ Results will be stored under this collection name.
tags: Optional tags for categorizing this processing job.
- workspace: Optional workspace. If omitted, uses the caller's
- default workspace.
Returns:
AddProcessingResponse confirming the document has been queued.
+
+ Use this for:
+ - Processing uploaded documents into knowledge
+ - Extracting entities and relationships from text
+ - Creating searchable embeddings
+ - Converting documents into structured knowledge
+
+ Note: Processing may take time depending on document size and flow complexity.
+ Use get_processing to monitor progress.
"""
if collection is None: collection = "default"
if tags is None: tags = []
- manager = await self._get_manager(ctx)
+ if ctx is None:
+ raise RuntimeError("No context provided")
- if ctx:
- await ctx.session.send_log_message(
- level="info",
- data=f"Adding document '{document_id}' to processing queue via websocket...",
- logger="notification_stream",
- related_request_id=ctx.request_id,
- )
+ logging.info("Add processing request made via websocket")
+
+ manager = await get_socket_manager(ctx)
+
+ await ctx.session.send_log_message(
+ level="info",
+ data=f"Adding document '{document_id}' to processing queue via websocket...",
+ logger="notification_stream",
+ related_request_id=ctx.request_id,
+ )
import time
timestamp = int(time.time())
@@ -1936,61 +1981,38 @@ class McpServer:
}
}
- gen = manager.request(
- "librarian", request_data, None, workspace=workspace,
- )
+ gen = manager.request("librarian", request_data, None)
async for response in gen:
break
-
+
return AddProcessingResponse()
-
def main():
parser = argparse.ArgumentParser(description='TrustGraph MCP Server')
- parser.add_argument(
- '--host', default='0.0.0.0',
- help='Host to bind to (default: 0.0.0.0)',
- )
- parser.add_argument(
- '--port', type=int, default=8000,
- help='Port to bind to (default: 8000)',
- )
- parser.add_argument(
- '--websocket-url',
- default='ws://api-gateway:8088/api/v1/socket',
- help='WebSocket URL for the TrustGraph gateway',
- )
- parser.add_argument(
- '--auth-issuer',
- default=os.environ.get("AUTH_ISSUER", ""),
- help='OAuth issuer URL for MCP auth metadata discovery',
- )
- parser.add_argument(
- '--auth-resource-url',
- default=os.environ.get("AUTH_RESOURCE_URL", ""),
- help='Resource server URL for OAuth protected resource metadata',
- )
+ parser.add_argument('--host', default='0.0.0.0', help='Host to bind to (default: 0.0.0.0)')
+ parser.add_argument('--port', type=int, default=8000, help='Port to bind to (default: 8000)')
+ parser.add_argument('--websocket-url', default='ws://api-gateway:8088/api/v1/socket', help='WebSocket URL to connect to (default: ws://api-gateway:8088/api/v1/socket)')
+ # Add logging arguments
add_logging_args(parser)
args = parser.parse_args()
+ # Setup logging before creating server
setup_logging(vars(args))
- server = McpServer(
- host=args.host,
- port=args.port,
- websocket_url=args.websocket_url,
- auth_issuer=args.auth_issuer,
- auth_resource_url=args.auth_resource_url,
- )
+ # Read gateway auth token from environment
+ gateway_token = os.environ.get("GATEWAY_SECRET", "")
+
+ # Create and run the MCP server
+ server = McpServer(host=args.host, port=args.port, websocket_url=args.websocket_url, gateway_token=gateway_token)
server.run()
-
def run():
+ """Legacy function for backward compatibility"""
main()
-
if __name__ == "__main__":
main()
+
diff --git a/trustgraph-mcp/trustgraph/mcp_server/tg_socket.py b/trustgraph-mcp/trustgraph/mcp_server/tg_socket.py
index 9fbf7459..bff8ae75 100644
--- a/trustgraph-mcp/trustgraph/mcp_server/tg_socket.py
+++ b/trustgraph-mcp/trustgraph/mcp_server/tg_socket.py
@@ -1,110 +1,49 @@
+from dataclasses import dataclass
from websockets.asyncio.client import connect
+from urllib.parse import urlencode, urlparse, urlunparse, parse_qs
import asyncio
import logging
import json
import uuid
-import hashlib
-
-logger = logging.getLogger(__name__)
-
-
-def _token_key(token):
- """Derive a dict key from a token without storing the raw secret."""
- return hashlib.sha256(token.encode()).hexdigest()[:16]
-
+import time
class WebSocketManager:
- """Manages an authenticated WebSocket connection to the TrustGraph
- gateway on behalf of a single caller.
- Each caller token gets its own WebSocketManager so that gateway-side
- identity, workspace, and capability scoping are preserved end-to-end.
- """
-
- def __init__(self, url, token):
+ def __init__(self, url, token=None):
self.url = url
- # ── Security boundary: token storage ──
- # This is the MCP caller's Bearer token, forwarded verbatim to
- # the gateway. It MUST NOT be logged, persisted, or shared
- # across callers. It is held only for the lifetime of this
- # connection so that re-auth (e.g. after a reconnect) is
- # possible.
self.token = token
self.socket = None
- self.identity = None
- self.last_used = None
+
+ # FIXME: authentication is broken. The /api/v1/socket endpoint uses
+ # in-band auth (first-frame protocol via the Mux dispatcher), not
+ # query-parameter tokens. This query-string token is silently ignored.
+ # Fix: after connect(), send an auth frame with the bearer token as
+ # the first message, matching the gateway's in-band auth protocol.
+ def _build_url(self):
+ if not self.token:
+ return self.url
+ parsed = urlparse(self.url)
+ params = parse_qs(parsed.query)
+ params["token"] = [self.token]
+ new_query = urlencode(params, doseq=True)
+ return urlunparse(parsed._replace(query=new_query))
async def start(self):
- """Connect and authenticate via the gateway's in-band auth
- protocol. Raises on auth failure."""
-
- # ── Security boundary: MCP server → gateway ──
- # The WebSocket connects to the gateway and authenticates using
- # the caller's Bearer token via the in-band first-frame auth
- # protocol. The token belongs to the MCP client — we forward
- # it as-is and never interpret its contents.
- self.socket = await connect(self.url)
+ self.socket = await connect(self._build_url())
self.pending_requests = {}
self.running = True
-
- await self._authenticate()
-
self.reader_task = asyncio.create_task(self.reader())
- async def _authenticate(self):
- """Send in-band auth frame and wait for auth-ok / auth-failed.
-
- The gateway expects ``{"type": "auth", "token": "..."}`` as the
- first frame on a new WebSocket. Any service frame sent before
- auth-ok is rejected.
- """
- await self.socket.send(json.dumps({
- "type": "auth",
- "token": self.token,
- }))
-
- response_text = await asyncio.wait_for(self.socket.recv(), 10)
- response = json.loads(response_text)
-
- if response.get("type") == "auth-ok":
- logger.info(
- "WebSocket authenticated, default workspace: %s",
- response.get("workspace"),
- )
- return
-
- # Auth failed — close immediately, do not leave an
- # unauthenticated socket open.
- await self.socket.close()
- self.socket = None
-
- if response.get("type") == "auth-failed":
- raise RuntimeError(
- "Gateway rejected the authentication token"
- )
-
- raise RuntimeError(
- f"Unexpected auth response type: {response.get('type')}"
- )
-
- async def whoami(self):
- """Verify the token by calling the gateway's whoami endpoint.
- Returns the identity dict and caches it on ``self.identity``.
- """
- gen = self.request("iam", {"operation": "whoami"}, flow_id=None)
- async for response in gen:
- self.identity = response
- return response
-
async def stop(self):
self.running = False
- if hasattr(self, "reader_task"):
- await self.reader_task
+ await self.reader_task
async def reader(self):
- """Background task: read WebSocket frames and route them to the
- correct pending-request queue by ``id``."""
+ """
+ Background task to read websocket responses and route to correct
+ request
+ """
while self.running:
try:
@@ -120,21 +59,23 @@ class WebSocketManager:
request_id = response.get("id")
if request_id and request_id in self.pending_requests:
+ # Put the response in the queue
queue = self.pending_requests[request_id]
await queue.put(response)
else:
- logger.warning(
- "Response for unknown request ID: %s", request_id
+ logging.warning(
+ f"Response for unknown request ID: {request_id}"
)
except Exception as e:
- logger.error("Error in websocket reader: %s", e)
+ logging.error(f"Error in websocket reader: {e}")
+ # Put error in all pending queues
for queue in self.pending_requests.values():
try:
await queue.put({"error": str(e)})
- except Exception:
+ except:
pass
self.pending_requests.clear()
@@ -145,29 +86,25 @@ class WebSocketManager:
async def request(
self, service, request_data, flow_id="default",
- workspace=None,
):
- """Send a request via WebSocket and yield responses.
-
- Args:
- service: Gateway service name (e.g. "graph-rag", "config").
- request_data: Inner request payload.
- flow_id: Optional flow identifier. ``None`` omits the field
- (workspace-level services don't use flows).
- workspace: Optional workspace override. When ``None`` the
- gateway uses the caller's default workspace.
+ """
+ Send a request via websocket and handle single or streaming responses
"""
- import time
- self.last_used = time.monotonic()
-
+ # Generate unique request ID
request_id = f"{uuid.uuid4()}"
+ # Determine if this service streams responses
+ streaming_services = {"agent"}
+ is_streaming = service in streaming_services
+
+ # Create a queue for all responses (streaming and single)
response_queue = asyncio.Queue()
self.pending_requests[request_id] = response_queue
try:
+ # Build request message
message = {
"id": request_id,
"service": service,
@@ -177,16 +114,7 @@ class WebSocketManager:
if flow_id is not None:
message["flow"] = flow_id
- # ── Security boundary: workspace scoping ──
- # When the caller supplies a workspace, we set it on the
- # message envelope. The gateway's enforce_workspace()
- # validates that the authenticated identity is permitted
- # to access the target workspace — we MUST NOT skip or
- # override that check. When workspace is None, the
- # gateway default-fills from the identity's bound workspace.
- if workspace is not None:
- message["workspace"] = workspace
-
+ # Send request
await self.socket.send(json.dumps(message))
while self.running:
@@ -199,17 +127,19 @@ class WebSocketManager:
continue
if "error" in response:
- if isinstance(response["error"], dict):
- raise RuntimeError(
- response["error"].get("message", str(response["error"]))
- )
+ if "message" in response["error"]:
+ raise RuntimeError(response["error"]["text"])
else:
raise RuntimeError(str(response["error"]))
yield response["response"]
- if response.get("complete"):
- break
+ if "complete" in response:
+ if response["complete"]:
+ break
- finally:
+ except Exception as e:
+ # Clean up on error
self.pending_requests.pop(request_id, None)
+ raise e
+
diff --git a/trustgraph-ocr/trustgraph/decoding/ocr/pdf_decoder.py b/trustgraph-ocr/trustgraph/decoding/ocr/pdf_decoder.py
index 0d5101df..1b4815c6 100755
--- a/trustgraph-ocr/trustgraph/decoding/ocr/pdf_decoder.py
+++ b/trustgraph-ocr/trustgraph/decoding/ocr/pdf_decoder.py
@@ -107,14 +107,7 @@ class Processor(FlowProcessor):
# Get the source document ID
source_doc_id = v.document_id or v.metadata.id
- try:
- pages = convert_from_bytes(blob)
- except Exception as e:
- logger.error(
- f"Failed to decode PDF {source_doc_id}: "
- f"{type(e).__name__}: {e}"
- )
- return
+ pages = convert_from_bytes(blob)
for ix, page in enumerate(pages):
diff --git a/trustgraph-unstructured/trustgraph/decoding/universal/processor.py b/trustgraph-unstructured/trustgraph/decoding/universal/processor.py
index deedb7b4..b4936786 100644
--- a/trustgraph-unstructured/trustgraph/decoding/universal/processor.py
+++ b/trustgraph-unstructured/trustgraph/decoding/universal/processor.py
@@ -418,14 +418,7 @@ class Processor(FlowProcessor):
doc_uri_str = document_uri(source_doc_id)
# Extract elements using unstructured
- try:
- elements = self.extract_elements(blob, mime_type)
- except Exception as e:
- logger.error(
- f"Failed to extract elements from {source_doc_id}: "
- f"{type(e).__name__}: {e}"
- )
- return
+ elements = self.extract_elements(blob, mime_type)
if not elements:
logger.warning("No elements extracted from document")