Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Routes DocsGPT deployment, source ingestion, retrieval, agent workflows, tools, integrations, and native or OpenAI-compatible API operations.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
日本語の概要は準備中です。原文の説明を表示しています。