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32
README.md
32
README.md
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@ -11,11 +11,11 @@
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<a href="https://trendshift.io/repositories/17291" target="_blank"><img src="https://trendshift.io/api/badge/repositories/17291" alt="trustgraph-ai%2Ftrustgraph | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
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# The semantic deployment platform
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# The agent runtime platform
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</div>
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TrustGraph is a comprehensive semantic infrastructure for agents built around context graphs — structured, queryable representations of your domain knowledge that ground every agent query in verified, explainable facts in private deployments with sovereign control. The platform is the full stack for agentic systems: context graphs, memory, retrieval, orchestration, and inference for deterministic agent workloads.
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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.
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The platform:
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- [x] Multi-model and multimodal database system
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@ -99,21 +99,23 @@ For a browser based configuration, try the [Configuration Terminal](https://conf
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- [**Developer APIs and CLI**](https://docs.trustgraph.ai/reference)
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- [**Deployment Guides**](https://docs.trustgraph.ai/deployment)
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## Context Graph UI
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## Workbench
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<img width="1389" height="961" alt="Image" src="https://github.com/user-attachments/assets/35c9250d-0f01-40cb-9294-1ee8fd9a1b56" />
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The **Workbench** provides tools for all major features of TrustGraph. The **Workbench** is on port `8888` by default.
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The UI provides tools for all major features of TrustGraph. The UI deploys on port `8888` by default.
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- **Agent Console** — Query your agents directly with streaming responses and live explainability event tracking, so you can watch reasoning unfold in real time
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- **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
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- **Context Explorer** — An interactive 3D context graph explorer with dynamic graph loading, BFS neighborhood extraction, edge pulse animation, and multiple navigation views
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- **Document Ingestion** — A complete upload and submission workflow with page and chunk inspection and document structure browsing
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- **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
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- **Schema Workbench** — Interactive schema management with list, create, edit, and delete operations including field and index management
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- **Flow Management** — Flow creation and detail views with configurable parameters, temperature controls, and grouped storage layout
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- **Workspace UX** — Workspace selection and management surfaced directly in the interface
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- **Prompt Editor** — A dedicated prompt editing workflow
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- **Vector Search**: Search the installed knowledge bases
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- **Agentic, GraphRAG and LLM Chat**: Chat interface for agents, GraphRAG queries, or direct to LLMs
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- **Relationships**: Analyze deep relationships in the installed knowledge bases
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- **Graph Visualizer**: 3D GraphViz of the installed knowledge bases
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- **Library**: Staging area for installing knowledge bases
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- **Flow Classes**: Workflow preset configurations
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- **Flows**: Create custom workflows and adjust LLM parameters during runtime
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- **Knowledge Cores**: Manage resuable knowledge bases
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- **Prompts**: Manage and adjust prompts during runtime
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- **Schemas**: Define custom schemas for structured data knowledge bases
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- **Ontologies**: Define custom ontologies for unstructured data knowledge bases
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- **Agent Tools**: Define tools with collections, knowledge cores, MCP connections, and tool groups
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- **MCP Tools**: Connect to MCP servers
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## TypeScript Library for UIs
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File diff suppressed because it is too large
Load diff
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@ -1,110 +1,49 @@
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from dataclasses import dataclass
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from websockets.asyncio.client import connect
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from urllib.parse import urlencode, urlparse, urlunparse, parse_qs
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import asyncio
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import logging
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import json
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import uuid
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import hashlib
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logger = logging.getLogger(__name__)
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def _token_key(token):
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"""Derive a dict key from a token without storing the raw secret."""
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return hashlib.sha256(token.encode()).hexdigest()[:16]
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import time
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class WebSocketManager:
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"""Manages an authenticated WebSocket connection to the TrustGraph
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gateway on behalf of a single caller.
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Each caller token gets its own WebSocketManager so that gateway-side
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identity, workspace, and capability scoping are preserved end-to-end.
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"""
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def __init__(self, url, token):
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def __init__(self, url, token=None):
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self.url = url
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# ── Security boundary: token storage ──
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# This is the MCP caller's Bearer token, forwarded verbatim to
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# the gateway. It MUST NOT be logged, persisted, or shared
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# across callers. It is held only for the lifetime of this
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# connection so that re-auth (e.g. after a reconnect) is
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# possible.
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self.token = token
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self.socket = None
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self.identity = None
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self.last_used = None
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# FIXME: authentication is broken. The /api/v1/socket endpoint uses
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# in-band auth (first-frame protocol via the Mux dispatcher), not
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# query-parameter tokens. This query-string token is silently ignored.
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# Fix: after connect(), send an auth frame with the bearer token as
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# the first message, matching the gateway's in-band auth protocol.
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def _build_url(self):
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if not self.token:
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return self.url
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parsed = urlparse(self.url)
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params = parse_qs(parsed.query)
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params["token"] = [self.token]
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new_query = urlencode(params, doseq=True)
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return urlunparse(parsed._replace(query=new_query))
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async def start(self):
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"""Connect and authenticate via the gateway's in-band auth
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protocol. Raises on auth failure."""
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# ── Security boundary: MCP server → gateway ──
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# The WebSocket connects to the gateway and authenticates using
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# the caller's Bearer token via the in-band first-frame auth
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# protocol. The token belongs to the MCP client — we forward
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# it as-is and never interpret its contents.
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self.socket = await connect(self.url)
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self.socket = await connect(self._build_url())
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self.pending_requests = {}
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self.running = True
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await self._authenticate()
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self.reader_task = asyncio.create_task(self.reader())
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async def _authenticate(self):
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"""Send in-band auth frame and wait for auth-ok / auth-failed.
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The gateway expects ``{"type": "auth", "token": "..."}`` as the
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first frame on a new WebSocket. Any service frame sent before
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auth-ok is rejected.
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"""
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await self.socket.send(json.dumps({
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"type": "auth",
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"token": self.token,
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}))
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response_text = await asyncio.wait_for(self.socket.recv(), 10)
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response = json.loads(response_text)
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if response.get("type") == "auth-ok":
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logger.info(
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"WebSocket authenticated, default workspace: %s",
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response.get("workspace"),
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)
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return
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# Auth failed — close immediately, do not leave an
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# unauthenticated socket open.
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await self.socket.close()
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self.socket = None
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if response.get("type") == "auth-failed":
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raise RuntimeError(
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"Gateway rejected the authentication token"
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)
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raise RuntimeError(
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f"Unexpected auth response type: {response.get('type')}"
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)
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async def whoami(self):
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"""Verify the token by calling the gateway's whoami endpoint.
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Returns the identity dict and caches it on ``self.identity``.
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"""
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gen = self.request("iam", {"operation": "whoami"}, flow_id=None)
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async for response in gen:
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self.identity = response
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return response
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async def stop(self):
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self.running = False
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if hasattr(self, "reader_task"):
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await self.reader_task
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await self.reader_task
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async def reader(self):
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"""Background task: read WebSocket frames and route them to the
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correct pending-request queue by ``id``."""
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"""
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Background task to read websocket responses and route to correct
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request
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"""
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while self.running:
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try:
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request_id = response.get("id")
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if request_id and request_id in self.pending_requests:
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# Put the response in the queue
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queue = self.pending_requests[request_id]
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await queue.put(response)
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else:
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logger.warning(
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"Response for unknown request ID: %s", request_id
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logging.warning(
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f"Response for unknown request ID: {request_id}"
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)
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except Exception as e:
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logger.error("Error in websocket reader: %s", e)
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logging.error(f"Error in websocket reader: {e}")
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# Put error in all pending queues
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for queue in self.pending_requests.values():
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try:
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await queue.put({"error": str(e)})
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except Exception:
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except:
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pass
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self.pending_requests.clear()
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async def request(
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self, service, request_data, flow_id="default",
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workspace=None,
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):
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"""Send a request via WebSocket and yield responses.
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Args:
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service: Gateway service name (e.g. "graph-rag", "config").
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request_data: Inner request payload.
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flow_id: Optional flow identifier. ``None`` omits the field
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(workspace-level services don't use flows).
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workspace: Optional workspace override. When ``None`` the
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gateway uses the caller's default workspace.
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"""
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Send a request via websocket and handle single or streaming responses
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"""
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import time
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self.last_used = time.monotonic()
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# Generate unique request ID
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request_id = f"{uuid.uuid4()}"
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# Determine if this service streams responses
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streaming_services = {"agent"}
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is_streaming = service in streaming_services
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# Create a queue for all responses (streaming and single)
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response_queue = asyncio.Queue()
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self.pending_requests[request_id] = response_queue
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try:
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# Build request message
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message = {
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"id": request_id,
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"service": service,
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if flow_id is not None:
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message["flow"] = flow_id
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# ── Security boundary: workspace scoping ──
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# When the caller supplies a workspace, we set it on the
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# message envelope. The gateway's enforce_workspace()
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# validates that the authenticated identity is permitted
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# to access the target workspace — we MUST NOT skip or
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# override that check. When workspace is None, the
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# gateway default-fills from the identity's bound workspace.
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if workspace is not None:
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message["workspace"] = workspace
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# Send request
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await self.socket.send(json.dumps(message))
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while self.running:
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continue
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if "error" in response:
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if isinstance(response["error"], dict):
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raise RuntimeError(
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response["error"].get("message", str(response["error"]))
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)
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if "message" in response["error"]:
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raise RuntimeError(response["error"]["text"])
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else:
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raise RuntimeError(str(response["error"]))
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yield response["response"]
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if response.get("complete"):
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break
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if "complete" in response:
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if response["complete"]:
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break
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finally:
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except Exception as e:
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# Clean up on error
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self.pending_requests.pop(request_id, None)
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raise e
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