$ python main.py search "what the 22G card can and cannot do" -k 3

[1] minimax-h3-turing/docs/en/01-hardware-limits.md > 01 · What a 2080Ti 22G Can and Cannot Do    (similarity 0.562)
[2] minimax-h3-turing/docs/en/02-w4a8-vs-w4a4.md > 02 · Quantization Measured > You Can Try Without 22G  (similarity 0.446)
[3] minimax-h3-turing/docs/en/01-hardware-limits.md > ... > 3. VRAM is just barely enough — manage it  (similarity 0.504)

$ python main.py ask "How should I choose between T8 aggressive mode and the final-render mode, and why?"

Answer:
* Drafts / preview / shot selection: use T8 aggressive mode — a 43% speedup
  (2.7 min/clip), and "a different picture of equal quality" is fine for picking shots.
* Final shots: use final-render mode (no T8). T8 makes the numerical trajectory
  fork, so re-running with the same seed produces a different clip — which breaks
  the reproducibility final outputs need.

[source: docs/en/08-t8-blockcache-4step.md > Practical Advice (4-step Turbo route)]
[source: docs/en/06-faq.md > 12. Cache-style accelerators break "same-seed re-runs"]

$ python scripts/eval_retrieval.py scripts/eval_cases.example.jsonl
vector-only: 9/10  →  hybrid: 10/10

python main.py search "T8 speedup" --rerank          # provider from config
python main.py search "T8 speedup" --rerank local    # cross-encoder (BAAI/bge-reranker-base)

%%{init: {'theme':'base','themeVariables':{'background':'#000000','primaryColor':'#000000','primaryTextColor':'#00FF41','primaryBorderColor':'#00FF41','lineColor':'#00FF41','secondaryColor':'#001a00','tertiaryColor':'#000000','clusterBkg':'#000000','clusterBorder':'#00FF41','edgeLabelBackground':'#000000','fontSize':'14px','fontFamily':'trebuchet ms, verdana, arial, sans-serif'},'themeCSS':'.nodeLabel { color: #00FF41 !important; } .edgeLabel { background: #000 !important; color: #00FF41 !important; } .cluster-label { color: #00FF41 !important; }'}}%%
flowchart LR
    subgraph sources["📥 Your machine"]
        notes["Obsidian / markdown notes"]
        docs["PDF tables · docx · project docs"]
        chats["ChatGPT / Claude exports"]
        mem["memories/ — agent-written notes"]
        wikidir["wiki/ — consolidated pages"]
    end

    subgraph loci["🧠 loci — local index, nothing leaves the machine"]
        ingest["ingest / watch<br>loaders → chunker → embedder"]
        store[("ChromaDB<br>hybrid index")]
        retrieve["hybrid retrieval<br>vector + BM25 → RRF"]
        mcp["loci-mcp<br>8 tools · resources · prompts"]
    end

    subgraph hosts["🖥️ Your AI hosts"]
        ide["Claude Code · Qoder · Trae<br>Cursor · Cline"]
        desktop["Claude Desktop"]
        term["Terminal<br>search / ask / chat / wiki"]
    end

    api["☁️ OpenAI-compatible API<br>Zhipu / DeepSeek / Kimi / OpenAI<br>or 100% offline via Ollama"]

    sources --> ingest --> store
    mem -. auto-indexed .-> store
    wikidir -. auto-indexed .-> store
    store --> retrieve
    retrieve --> term
    retrieve --> mcp
    mcp <--> ide
    mcp <-.-> desktop
    retrieve -. "embedding + chat calls only" .-> api

# option A: install from PyPI (adds `loci` and `loci-mcp` commands)
pip install "loci-rag[pdf,docx]"   # optional extras: PDF w/ tables, Word documents

# option B: zero-install quickstart
pip install -r requirements.txt

# 1. Configure: copy the example and fill in your values
cp config.example.toml config.toml
#    Any OpenAI-compatible endpoint works — see the provider table below.
#    Switching the embedding model later? Vectors from different models don't
#    mix: re-run `loci ingest --force` to rebuild the index.

# 2. Ingest (incremental — deduplicated by content hash, safe to re-run)
loci ingest            # or: python main.py ingest

# 3. Ask
loci ask "what did I write about X?"

%%{init: {'theme':'base','themeVariables':{'background':'#000000','primaryColor':'#000000','primaryTextColor':'#00FF41','primaryBorderColor':'#00FF41','lineColor':'#00FF41','secondaryColor':'#001a00','tertiaryColor':'#000000','clusterBkg':'#000000','clusterBorder':'#00FF41','edgeLabelBackground':'#000000','fontSize':'14px','fontFamily':'trebuchet ms, verdana, arial, sans-serif'},'themeCSS':'.nodeLabel { color: #00FF41 !important; } .edgeLabel { background: #000 !important; color: #00FF41 !important; } .cluster-label { color: #00FF41 !important; }'}}%%
flowchart TD
    A["pip install loci-rag"] --> B["cp config.example.toml config.toml<br>fill API keys + source dirs"]
    B --> C["loci ingest — hybrid index built"]
    C --> D["loci watch — index stays fresh (optional)"]
    C --> E{"What do you need?"}
    E -->|"a synthesized answer"| F["loci ask --verify<br>claim-by-claim audit"]
    E -->|"raw excerpts to quote"| G["loci search --tag memory"]
    E -->|"back-and-forth"| H["loci chat"]
    E -->|"scattered notes on a topic"| I["loci wiki topic<br>consolidate into a wiki page"]
    F --> J["loci remember —<br>keep what you learned"]
    I --> J

# Claude Code
claude mcp add loci -- loci-mcp

// Cursor / Cline / Qoder / Trae (mcpServers JSON — same shape everywhere)
{ "mcpServers": { "loci": { "command": "loci-mcp" } } }

%%{init: {'theme':'base','themeVariables':{'background':'#000000','primaryColor':'#000000','primaryTextColor':'#00FF41','primaryBorderColor':'#00FF41','lineColor':'#00FF41','actorBkg':'#000000','actorBorder':'#00FF41','actorTextColor':'#00FF41','signalColor':'#00FF41','signalTextColor':'#00FF41','noteBkgColor':'#001a00','noteBorderColor':'#00FF41','activationBkgColor':'#001a00','edgeLabelBackground':'#000000','fontSize':'14px','fontFamily':'trebuchet ms, verdana, arial, sans-serif'},'themeCSS':'.messageText { fill: #00FF41 !important; } .actor { fill: #000 !important; stroke: #00FF41 !important; } text.actor { fill: #00FF41 !important; }'}}%%
sequenceDiagram
    participant CC as Claude Code
    participant L as loci-mcp
    participant S as ChromaDB (local)
    participant T as Trae / Qoder / any IDE
    CC->>L: brain_remember("deploy rotates Mondays")
    L->>S: write memory.md + embed + index
    Note over S: persists across sessions and IDEs
    T->>L: brain_search("password rotation")
    L->>S: hybrid retrieval
    L-->>T: cited answer — the memory is recalled

{
  "mcpServers": {
    "loci": {
      "command": "python",
      "args": ["/path/to/loci/mcp_server.py"]
    }
  }
}

[llm]
base_url = "http://localhost:11434/v1"
api_key = "ollama"          # any non-empty placeholder
model = "qwen2.5:0.5b"

[embed]
base_url = "http://localhost:11434/v1"
api_key = "ollama"
model = "all-minilm"

git clone https://github.com/IvenKooLab/loci && cd loci
pip install -e ".[pdf,docx]"        # editable install for hacking on loci
pip install -r requirements-dev.txt
pytest                              # fully offline, no API keys needed