typical:   search -> fetch full page -> stuff into context -> answer
           ~5 sources, full token price per page, no span-level citation

here:      search -> fetch N pages -> parent/child chunk -> embed
           -> retrieve only relevant children -> expand parents on demand
           -> compress -> answer
           ~30 sources, lower token cost, structural citations

python -m venv .venv
.venv/Scripts/python -m pip install -e ".[dev,api,mcp]"
cp .env.example .env      # fill in LiteLLM and search keys
docker compose up -d qdrant
ares doctor

ares doctor                        # proxy, models and Qdrant reachable
ares fetch <url> --show            # raw HTML to clean markdown
ares chunk <url>                   # the parent/child tree
ares index <url>                   # embed and store, or report a cache hit
ares search "..." --no-rerank      # retrieval without the reranker
ares search "..." --rerank         # and with it
ares websearch "..."               # provider results
ares research "..."                # the whole loop, cited answer
ares eval                          # scorecard against the committed baseline