Docket

A self-hosted research assistant for SEC filings. Drop in 10-Ks and 10-Qs, ask questions in plain English, and get answers that cite the exact filing and page they came from. Everything runs on your own machine with a small local model, so no filing or question ever leaves it.

What it does

• Answers with receipts. Every answer points back to the filing and page it was drawn from, so you can check it in seconds.
• Works across your whole coverage list. Ask about one company or compare several; Docket searches every filing you have added.
• Shows its work. Each answer keeps a full trace: what was searched, which passages were picked, and the exact prompt the model saw.
• Runs locally. Retrieval and generation run on mettle, a local inference engine that fits on a 6 GB GPU.

How it works

flowchart LR
    A[Filing PDFs] -->|extract, chunk| B[Docket]
    B -->|index| C[(mettle engine<br/>search + rerank)]
    Q[Your question] --> B
    B -->|find best passages| C
    C -->|top passages| B
    B -->|grounded prompt| D[Local model]
    D --> E[Answer with<br/>filing + page citations]

Loading

Docket handles the app side: the dashboard, ingesting filings, the question-answering agent and the citations. The mettle engine does the heavy lifting: embeddings, search, reranking and the language model itself.

Getting started

You need Python 3.11+, uv, an NVIDIA GPU with about 6 GB of memory, and a built mettle engine.

git clone https://github.com/adityam23/docket && cd docket
uv sync --extra ingest --extra agent

# Let Docket start and manage the engine for you
export DK_ENGINE_BINARY=/path/to/mettled
export DK_METTLE_CONFIG=/path/to/mettle-config.toml

uv run dk serve

Open http://127.0.0.1:8760, go to filings and add some PDFs, then ask away on the ask page. If you already run mettle yourself, set DK_BACKEND_URL to its /v1 address instead of the two engine variables.

Settings can also go in a .env file in the project folder.

From the terminal

These talk to the engine directly, so keep dk serve (or your own mettle) running.

uv run dk ingest ~/filings          # add every PDF in a folder
uv run dk ask "What was AMD's revenue in 2022?"
uv run dk docs                      # list what's indexed

A quick tour

Home: what's indexed and whether the engine is up.

Filings: add PDFs and watch each one get indexed.

Observability: a trace for every answer, step by step.

Status

Docket is an early, actively developed project. Next on the list are a per-answer confidence signal and an interpretability "lab mode" that shows which parts of the model's internals drove an answer.

License

GPL-3.0. See LICENSE.