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deep-searcher

deep-searcher

Route DeepSearcher workflows for provider setup, data ingestion, RAG querying, CLI and service operation, and evaluation.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: deep-searcher description: "Route DeepSearcher workflows for provider setup, data ingestion, RAG querying, CLI and service operation, and evaluation." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DeepSearcher

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.

Start here

User intentRead
Pick LLM, embedding, loader, crawler, or vector DB providers; diagnose missing SDKs or credentialssub-skills/provider-configuration/SKILL.md
Load local files/directories or websites into a collectionsub-skills/data-ingestion/SKILL.md
Ask questions over loaded data, choose DeepSearch vs ChainOfRAG vs NaiveRAG, or inspect retrieved referencessub-skills/rag-query/SKILL.md
Use the deepsearcher CLI or the bundled FastAPI service helpersub-skills/cli-and-service/SKILL.md
Validate or run the 2WikiMultiHopQA retrieval evaluation workflowsub-skills/evaluation/SKILL.md

Package facts to keep in mind

  • Distribution: deepsearcher.
  • Version inspected for this skill: 0.0.2.
  • Import module: deepsearcher.
  • Console script: deepsearcher = deepsearcher.cli:main.
  • Python requirement from package metadata: >=3.10.
  • The default configuration constructs providers during init_config(config), so provider and vector DB readiness matter even before query/load workflows run.
  • The inspected default config uses OpenAI, OpenAIEmbedding, PDFLoader, FireCrawlCrawler, and Milvus with a local ./milvus.db URI.

Install and sanity check

  1. Install the package in an environment with Python 3.10+:

    pip install deepsearcher
    
  2. 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.

  3. 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.

  4. 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.

How to route requests

  • Configuration, provider names, credentials, and optional extras: provider-configuration.
  • Loading files, crawling websites, chunking, and collection creation: data-ingestion.
  • Querying loaded data and interpreting references: rag-query.
  • CLI help, deepsearcher load/query, FastAPI endpoints, and deployment checks: cli-and-service.
  • 2WikiMultiHopQA recall evaluation and report outputs: evaluation.

Cross-cutting cautions

  • init_config(config) creates every configured provider, not just the one you care about. A bad default provider can fail before the workflow begins.
  • The default local Milvus URI is relative to the current working directory, so repeated runs in the same directory can lock ./milvus.db.
  • CLI help and service startup can expose provider initialization problems early; use the dedicated CLI/service sub-skill for those failure modes.
  • Do not point future agents back to the original repository checkout. Read the bundled references and scripts in this skill tree instead.

Skill metadata for future refreshes

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