Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
The English summary is being prepared. Showing the original description.
Routes DocsGPT deployment, source ingestion, retrieval, agent workflows, tools, integrations, and native or OpenAI-compatible API operations.
The English summary is being prepared. Showing the original description.
See how to installReview the instructions the agent will receive before you install.
Use this operating graph for DocsGPT 0.18-era administration, integration, and extension tasks. It is a task router, not a replacement for the focused references below.
| Task signal | Read |
|---|---|
| install, local development, Docker, Kubernetes, environment variables, model providers, OIDC/SCIM/RBAC, Postgres, Redis, S3, observability, upgrades | deploy-configure |
| upload, parse, OCR, audio, URL/sitemap/crawler, GitHub/Reddit/S3 source, Google Drive, SharePoint, Confluence, chunking, ingestion worker, re-ingestion | ingest-sources |
| embeddings, FAISS, pgvector, Qdrant, Milvus, MongoDB Atlas, Elasticsearch, classic/hybrid RAG, pre-screening, GraphRAG, retrieval quality | retrieval-vectorstores |
| classic/agentic/research/workflow agent, workflow nodes, CEL, schedules, webhooks, seeding, agent export/import | agents-workflows |
| built-in tools, generic API tool, MCP, artifacts, code execution, Read Document, sandbox, remote device, widget, Chatwoot | tools-integrations |
/api/answer, /stream, attachments, SSE, reconnect, /v1/chat/completions, structured output, multimodal clients, idempotency, /mcp | api-client-operations |
Use the ASGI composition for full behavior:
uvicorn application.asgi:asgi_app --host 0.0.0.0 --port 7091
The Flask development server exposes the WSGI app only. It omits /mcp and the native-async reconnect reader at GET /api/messages/<id>/events; use Flask only for a deliberately reduced inner loop.
A full deployment normally has:
docsgpt and parsing only when needed);Read architecture and terminology before changing boundaries across these components.
For a source-development checkout, use Python 3.12 and install the backend requirements into an isolated environment. This bundle includes the exact 0.18.0 backend requirements snapshot; use the target release's manifest only after checking provenance/version drift. Do not run bootstrap setup scripts during ordinary feature work when explicit environment configuration is sufficient.
From the runtime skill root:
python -m pip install -r references/backend-requirements-0.18.0.txt
python -c "from application.version import get_version; print(get_version())"
python -c "from application.asgi import asgi_app; print(type(asgi_app).__name__)"
For a running service, use the bundled read-only checker:
python scripts/check_deployment.py --base-url http://localhost:7091
Add --token only for a non-production test credential or pass --token-env so the credential is not written into shell history. Read the script help before probing a shared deployment.
.env, API keys, OAuth client secrets, JWT secrets, SCIM tokens, S3 credentials, and sandbox tokens as secrets. Never place them in logs or exported agent YAML.Read cross-cutting troubleshooting when failures span multiple components.
Read repository provenance before relying on exact routes, settings, schemas, or defaults in a different checkout. Refresh this graph when the commit, package version, public API, or major evidence paths change.
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
The English summary is being prepared. Showing the original description.
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
The English summary is being prepared. Showing the original description.
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
The English summary is being prepared. Showing the original description.
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
The English summary is being prepared. Showing the original description.
Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
The English summary is being prepared. Showing the original description.
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
The English summary is being prepared. Showing the original description.