Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Route DeepSearcher workflows for provider setup, data ingestion, RAG querying, CLI and service operation, and evaluation.
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Review the instructions the agent will receive before you install.
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.No reviews yet. Tell others what you thought.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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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.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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