Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Route DeepSearcher workflows for provider setup, data ingestion, RAG querying, CLI and service operation, and evaluation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this repo skill when a task mentions the DeepSearcher package, its deepsearcher console command, document loading, website crawling, retrieval-augmented querying, the HTTP service, or the evaluation workflow. The skill is split into focused sub-skills so future agents can jump straight to the right workflow without reopening the original checkout.
| User intent | Read |
|---|---|
| Pick LLM, embedding, loader, crawler, or vector DB providers; diagnose missing SDKs or credentials | sub-skills/provider-configuration/SKILL.md |
| Load local files/directories or websites into a collection | sub-skills/data-ingestion/SKILL.md |
| Ask questions over loaded data, choose DeepSearch vs ChainOfRAG vs NaiveRAG, or inspect retrieved references | sub-skills/rag-query/SKILL.md |
Use the deepsearcher CLI or the bundled FastAPI service helper | sub-skills/cli-and-service/SKILL.md |
| Validate or run the 2WikiMultiHopQA retrieval evaluation workflow | sub-skills/evaluation/SKILL.md |
deepsearcher.0.0.2.deepsearcher.deepsearcher = deepsearcher.cli:main.>=3.10.init_config(config), so provider and vector DB readiness matter even before query/load workflows run../milvus.db URI.Install the package in an environment with Python 3.10+:
pip install deepsearcher
For this checkout's inspected default local stack, also ensure a Milvus Lite-compatible set and a FireCrawl version that exports ScrapeOptions:
pip install "pymilvus==2.5.8" "milvus-lite==2.5.1" "setuptools<81" "firecrawl-py==2.16.5"
These pins were needed in the inspected environment to keep the default Milvus/FireCrawl paths usable.
Run the bundled environment check before deeper work:
python scripts/check_deepsearcher_environment.py
Add --check-cli-help if you want a temp-cwd CLI probe with a dummy OpenAI key.
If you only need to verify the installed package identity, the helper's version/import output is enough. If the installed package is missing optional provider SDKs, route to the provider sub-skill before assuming the repo skill is incomplete.
provider-configuration.data-ingestion.rag-query.deepsearcher load/query, FastAPI endpoints, and deployment checks: cli-and-service.evaluation.init_config(config) creates every configured provider, not just the one you care about. A bad default provider can fail before the workflow begins../milvus.db.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.