From a82b7cc9879a6f2df62c4602b22b2a9f52c930a8 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sat, 12 Sep 2026 09:40:27 -0600 Subject: [PATCH 01/30] py: quartodoc site scaffolding and its publish workflow Set up a Quarto project under pkg-py/docs that builds the API reference with quartodoc. The reference sections match the ones the R site groups its topics into, minus trajectories, which the Python package does not have. The site renders in place, and the workflow copies it to docs/py before the deploy, because Quarto warns and declines to clean its own output when the output directory sits outside the project. The deploy reuses the pkgdown workflow's action and its clean: false setting, so each site owns a subdirectory of gh-pages without erasing the other. griffe is held below 2 because quartodoc 0.11.1 passes it a docstring parser option that griffe 2 removed. Building the reference surfaced a docstring defect in commons.ui.server(): a closing paragraph sat inside the numpydoc Parameters block, so its words were parsed as parameter names. Moved it above the block. --- .github/workflows/quartodoc.yaml | 62 +++++ .gitignore | 1 + pkg-py/docs/.gitignore | 6 + pkg-py/docs/_quarto.yml | 84 +++++++ pkg-py/docs/styles.css | 32 +++ pkg-py/pyproject.toml | 4 + pkg-py/src/commons/_ui/_server.py | 8 +- pkg-py/uv.lock | 391 ++++++++++++++++++++++++++++++ 8 files changed, 584 insertions(+), 4 deletions(-) create mode 100644 .github/workflows/quartodoc.yaml create mode 100644 pkg-py/docs/.gitignore create mode 100644 pkg-py/docs/_quarto.yml create mode 100644 pkg-py/docs/styles.css diff --git a/.github/workflows/quartodoc.yaml b/.github/workflows/quartodoc.yaml new file mode 100644 index 00000000..01efc438 --- /dev/null +++ b/.github/workflows/quartodoc.yaml @@ -0,0 +1,62 @@ +on: + push: + branches: [main] + paths: [pkg-py/**, docs/**, .github/workflows/quartodoc.yaml] + pull_request: + paths: [pkg-py/**, docs/**, .github/workflows/quartodoc.yaml] + release: + types: [published] + workflow_dispatch: + +name: quartodoc.yaml + +permissions: read-all + +jobs: + quartodoc: + runs-on: ubuntu-latest + defaults: + run: + working-directory: pkg-py + # Only restrict concurrency for non-PR jobs + concurrency: + group: quartodoc-${{ github.event_name != 'pull_request' || github.run_id }} + permissions: + contents: write + steps: + - uses: actions/checkout@v6 + + - uses: quarto-dev/quarto-actions/setup@v2 + + - uses: astral-sh/setup-uv@v7 + with: + enable-cache: true + + - name: Install the package and its docs dependencies + run: uv sync --extra shiny --group docs + + - name: Build the reference pages + working-directory: pkg-py/docs + run: uv run quartodoc build + + - name: Render the site + working-directory: pkg-py/docs + run: uv run quarto render + + # The deploy publishes the whole of docs/, so the rendered site has to + # sit at the path it is served from. + - name: Move the site into place + working-directory: . + run: | + rm -rf docs/py + cp -r pkg-py/docs/_site docs/py + + # `clean: false` so this and the pkgdown deploy can each own a + # subdirectory of the same branch without erasing the other. + - name: Deploy to GitHub pages 🚀 + if: github.event_name != 'pull_request' + uses: JamesIves/github-pages-deploy-action@d92aa235d04922e8f08b40ce78cc5442fcfbfa2f # v4.8.0 + with: + clean: false + branch: gh-pages + folder: docs diff --git a/.gitignore b/.gitignore index 43134a46..56d938b2 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,7 @@ .Rproj.user .Rprofile /docs/r/ +/docs/py/ plans/ rsconnect/ CLAUDE.local.md diff --git a/pkg-py/docs/.gitignore b/pkg-py/docs/.gitignore new file mode 100644 index 00000000..83527807 --- /dev/null +++ b/pkg-py/docs/.gitignore @@ -0,0 +1,6 @@ +/reference/ +/_site/ +/.quarto/ +/objects.json + +**/*.quarto_ipynb diff --git a/pkg-py/docs/_quarto.yml b/pkg-py/docs/_quarto.yml new file mode 100644 index 00000000..99bde52e --- /dev/null +++ b/pkg-py/docs/_quarto.yml @@ -0,0 +1,84 @@ +project: + type: website + # Renders in place, because Quarto warns when its output directory sits + # outside the project. The deploy workflow copies _site into docs/py. + output-dir: _site + # Explicit, so the design notes under notes/ stay out of the site. + render: + - index.qmd + - governance.qmd + - reference/*.qmd + +website: + title: "commons for Python" + site-url: https://posit-dev.github.io/commons/py/ + repo-url: https://github.com/posit-dev/commons + repo-subdir: pkg-py + navbar: + left: + - href: index.qmd + text: Get started + - href: governance.qmd + text: Security and governance + - href: reference/index.qmd + text: Reference + right: + - text: R + href: ../r/ + - icon: github + href: https://github.com/posit-dev/commons + sidebar: + - id: reference + contents: reference/_sidebar.yml + +format: + html: + theme: cosmo + toc: true + css: styles.css + +metadata-files: + - reference/_sidebar.yml + +quartodoc: + package: commons + title: Reference + style: pkgdown + dir: reference + sidebar: reference/_sidebar.yml + render_interlinks: false + renderer: + style: markdown + table_style: description-list + sections: + - title: Main functions + desc: | + The main agent constructor, plus the building blocks for a commons agent. + contents: + - Commons + - data_source + - semantic_layer + - measure + - context_layer + - title: Supporting types + desc: | + The objects the constructors return, and the values they carry. + contents: + - DataSource + - SemanticLayer + - ContextLayer + - Measure + - Injected + - Tag + - list_tables + - title: Shiny UI and server + desc: | + Put a commons agent inside of an interactive chat app. Needs the + `shiny` extra. + package: commons.ui + contents: + - app + - server + - theme + - commons_chat_dependency + - asset_base_url diff --git a/pkg-py/docs/styles.css b/pkg-py/docs/styles.css new file mode 100644 index 00000000..b5c34852 --- /dev/null +++ b/pkg-py/docs/styles.css @@ -0,0 +1,32 @@ +/* Palette shared with the landing page at docs/styles.css. */ +:root { + --commons-background: #fbfbfb; + --commons-foreground: #212529; + --commons-muted: #5f6368; + --commons-link: #256b58; + --commons-border: #dee2e6; +} + +body { + color: var(--commons-foreground); + background: var(--commons-background); +} + +a { + color: var(--commons-link); +} + +.navbar { + border-bottom: 1px solid var(--commons-border); +} + +h1, +h2, +h3 { + letter-spacing: -0.02em; +} + +/* Keep the reference tables readable when a signature is long. */ +.doc-signature code { + overflow-wrap: anywhere; +} diff --git a/pkg-py/pyproject.toml b/pkg-py/pyproject.toml index 42d388c6..0d2fa5ef 100644 --- a/pkg-py/pyproject.toml +++ b/pkg-py/pyproject.toml @@ -43,6 +43,10 @@ shiny = [ [dependency-groups] dev = ["pytest>=8", "pytest-asyncio", "ruff", "pyrefly", "pandas>=2", "polars>=1", "pins>=0.9", "commons[shiny]"] +docs = [ + "griffe<2", + "quartodoc>=0.11.1", +] [build-system] requires = ["hatchling"] diff --git a/pkg-py/src/commons/_ui/_server.py b/pkg-py/src/commons/_ui/_server.py index 6ff632df..7672611b 100644 --- a/pkg-py/src/commons/_ui/_server.py +++ b/pkg-py/src/commons/_ui/_server.py @@ -24,6 +24,10 @@ def server(id: str, client: Commons, **kwargs: Any) -> shinychat.Chat: inside the server function and pass it here, so each session gets its own agent state. + A `client` that is not a commons agent raises `TypeError`: a plain + chatlas chat has none of the citation or provenance handling the chat + surface renders. + Parameters ---------- id @@ -32,10 +36,6 @@ def server(id: str, client: Commons, **kwargs: Any) -> shinychat.Chat: A commons agent. **kwargs Passed to `shinychat.Chat()`. - - A `client` that is not a commons agent raises `TypeError`: a plain - chatlas chat has none of the citation or provenance handling the chat - surface renders. 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2001 From: Josh Taillon Date: Sat, 12 Sep 2026 09:41:49 -0600 Subject: [PATCH 02/30] py: introduction and governance pages for the Python site The introduction covers the ground the R vignette covers: the trust flow and where each provenance outcome comes from, the semantic and context layers, data sources and dictionaries, and the chat UI. It leaves out what the Python package does not have, rather than describing the R behaviour: there is no code-execution path in the outcome table, no warehouse semantic layer section, and no agent skill or trajectory review. The governance page answers the same questions for Python. Two differences from the R page carry weight. The SQL check parses the statement with sqlglot and allowlists read-only shapes, so it catches writes hidden inside a read that a first-word test admits. And no tool runs model-written code, so the sandboxing, network, and subprocess material has no counterpart; the page says so plainly instead of leaving the question open. The landing page's Python button and get-started paragraph now point at the site rather than at the package README on GitHub. --- docs/index.html | 4 +- pkg-py/docs/governance.qmd | 55 ++++++++ pkg-py/docs/index.qmd | 261 +++++++++++++++++++++++++++++++++++++ 3 files changed, 318 insertions(+), 2 deletions(-) create mode 100644 pkg-py/docs/governance.qmd create mode 100644 pkg-py/docs/index.qmd diff --git a/docs/index.html b/docs/index.html index 28f3a560..935ce000 100644 --- a/docs/index.html +++ b/docs/index.html @@ -25,7 +25,7 @@

commons

@@ -70,7 +70,7 @@

Get started

To get started with the R package, check out the introductory vignette. The package ships with an agent skill to help you hook your trusted code and context up to the agent.

-

The Python package is based on the same ideas, so the linked vignette from the R package will give you a sense of where the package is going. (The resulting apps will look very similar regardless of whether you use R or Python—they literally share the same CSS!) Check out the package README to learn more.

+

To get started with the Python package, check out the introduction. (The resulting apps will look very similar regardless of whether you use R or Python—they literally share the same CSS!)

diff --git a/pkg-py/docs/governance.qmd b/pkg-py/docs/governance.qmd new file mode 100644 index 00000000..0bf3d271 --- /dev/null +++ b/pkg-py/docs/governance.qmd @@ -0,0 +1,55 @@ +--- +title: "Security and governance for commons agents" +--- + +commons lets you build and deploy agents that run SQL queries against live databases. This raises security and governance questions. How do you make sure that an agent cannot delete production data? Can an agent show users tables, rows, or business context that they are not allowed to see? + +This page explains the boundaries that commons provides and the responsibilities that remain with the application author and server administrator. + +## commons' harness + +A commons agent is a chatlas chat with a system prompt and a set of tools. The system prompt lists the available data sources and business context. Tools let the model retrieve more context, call a trusted calculation, or run a SQL query. + +We assume that a model can make any request that its tools allow. Application security must therefore not depend on the model obeying an instruction such as "never reveal sensitive data." Instead, give an agent access only to data that the current user of the application is allowed to see. The system prompt, tool arguments, and tool results also go to the model provider. The agent must not have access to data that you [do not trust that provider to process](https://posit.co/blog/trust-llm-tools). + +## SQL code execution + +### Destructive actions + +The agent can run one read-only statement at a time. commons parses each statement with [sqlglot](https://github.com/tobymao/sqlglot) and checks its structure, not its text. It accepts a single `SELECT` or set operation (`UNION`, `INTERSECT`, `EXCEPT`), with or without a leading `WITH`. commons rejects everything else: stacked statements, `INSERT`, `UPDATE`, `DELETE`, `DROP`, `GRANT`, `SET`, `USE`, `PRAGMA`, and statements that sqlglot cannot parse. The check is an allowlist, so a statement form that nobody thought of fails closed. + +Because it reads the parse tree, the check also catches a write hidden inside a read. `WITH t AS (SELECT 1) DELETE FROM sales` is a `DELETE`, `WITH d AS (DELETE ... RETURNING *) SELECT * FROM d` deletes rows through its CTE, and `SELECT ... INTO t` creates a table. A first-word test lets all three through. commons also rejects locking reads such as `SELECT ... FOR UPDATE`, because they block other writers. + +For data frames and pins, which commons loads into its own DuckDB database, it also hardens the connection. It disables community and unsigned extensions, automatic extension install and load, external access, and the local filesystem. Then it sets `lock_configuration`, so a query cannot turn those settings back on. The lock freezes settings only, not tables, which is how commons can still load a pin's rows on first use. + +These checks provide defense in depth, but they are not a database sandbox. When you supply a SQLAlchemy engine, commons queries it as-is, and a statement that the parser accepts runs with all the permissions of that connection. The primary safeguard against destructive SQL is therefore database-enforced read-only access. Open the connection as a read-only user or role. + +### Data access + +The `tables` argument to `data_source()` controls which tables commons describes to the model. It is not an authorization boundary. SQL written by the agent can query any object that the connection can reach. + +On Posit Connect, [viewer OAuth integrations](https://docs.posit.co/connect/admin/access-controls/) can give an interactive application the current viewer's Snowflake or Databricks credentials. If the application creates its engine from those credentials, the warehouse continues to enforce that viewer's existing access policies, including row-level and column-level security. + +For a Snowflake or Databricks source, commons records the connection identity when it builds the data source. It records the current user, catalog (database), and schema. On Snowflake it also records the active role and the secondary roles. Before every query and every table description, commons reads the identity again and compares it with the record. If any field changed, commons raises an error, and the application must rebuild the data source. For any other backend, commons records no identity. + +Viewer credentials are not automatic. commons uses the engine that the application supplies. When you use viewer credentials, create the engine and the commons agent inside the Shiny server function. Then each session has the correct database identity. + +When viewer credentials are not available, use a service account with access only to the data that the application needs. Every viewer then has the same database permissions, so share the application only with users who are allowed that access. + +## No code execution + +The Python agent has no tool that runs code written by the model. Its tools search context, describe tables, call trusted calculations, and run SQL. commons creates no subprocess, and no Python code from the model runs in the application process or anywhere else. The questions about sandboxing a model's code, such as filesystem, network, and system-call limits, do not arise for this package. The SQL checks above, and the permissions of the connection, are the whole boundary. + +## Permissioning facts + +The rows returned by SQL are not the only sensitive information available to an agent. The description and details of each data dictionary, and its glossary entries up to a size cap, go into the system prompt. The full dictionary entry for a table, with a summary of sampled rows, goes to the model when a table is first described or queried. Context documents and the rest of the dictionary prose are available to the model through search. + +Only include facts that you can share with both the application's viewers and its model provider. If one audience must not see a fact, use separate applications with separate context, or put the facts behind viewer credentials. Do not ask the model to hide the fact from that audience. + +## Telemetry + +The Python package has no option to log conversations. The `Commons` constructor has no `log` argument, and there is no API to read or review trajectories. Conversation logging and review are features of the R package today. + +commons emits [OpenTelemetry](https://opentelemetry.io/) spans under the tracer name `co.posit.python-package.commons`. Today the only span is `commons_server_start`, which `commons.ui.server()` opens when it wires an agent to a chat element. The span carries the id of the chat element and no message content. The OpenTelemetry API records nothing until the application configures an SDK provider and an exporter. + +Other libraries in the process, such as chatlas, can emit spans of their own. Their content is governed by those libraries and by your OpenTelemetry configuration, not by commons. diff --git a/pkg-py/docs/index.qmd b/pkg-py/docs/index.qmd new file mode 100644 index 00000000..0bf53c72 --- /dev/null +++ b/pkg-py/docs/index.qmd @@ -0,0 +1,261 @@ +--- +title: "Introduction to commons" +--- + +This page explains the structure of a commons project and how to start building a commons agent in Python. + +```python +import commons +``` + +commons requires Python 3.11 or later. Install it with `pip install commons`. If you want the chat UI, install the `shiny` extra with `pip install "commons[shiny]"`. + +## Design philosophy + +AI agents for data analysis range from cautious and narrowly correct to wildly untrustworthy. commons increases the chance of a correct answer by giving the agent access to trusted code that you already have. The agent keeps enough freedom to answer new, realistic questions. commons also derives a provenance outcome from the path the agent took, so a user can decide how much to trust each answer. + +If you are a data analyst, data scientist, or other data practitioner, you probably have a collection of trusted code. This is the code you depend on for your analyses, apps, and reports. The core idea of commons is that an agent gives more correct answers when it can run this code and read its documentation. + +The high-trust path occurs when a question matches one of these **trusted calculations**. For example, a commons agent can analyze biodiversity data, and a user asks: + +> How many total animals were observed at Oak Bluff? + +The agent searches for a trusted calculation that answers the question. If it finds one, it runs that calculation and reports the result with the green check-shield provenance marker for the `Verified answer` outcome. + +> At Oak Bluff, 59 individual animals were observed across 5 species, based on 28 hours of survey effort. Note this reflects observed individuals during surveys, not necessarily a full census of every animal present at the site. + +The agent had to decide which trusted calculation to run. It did not have to decide what code to write. This removes degrees of freedom and lets the agent use code that you already vetted. + +Users also ask questions that leave the high-trust path. For those, the agent searches for context and writes custom SQL. Answers from this path show blue quote-mark citation markers, or the yellow exclamation provenance marker for the `Untrusted` outcome. + +## Trust flow + +A commons agent uses trusted calculations when it can. When the user asks a question, the agent first searches the semantic layer for a trusted calculation. If it finds one, it calls that calculation, and the answer shows the green check-shield provenance marker for the `Verified answer` outcome. + +If no relevant trusted calculation exists, the agent takes the lower-trust path. It searches the context layer for information, inspects the tables it needs, and writes a read-only SQL query. These answers show blue quote-mark citation markers that open the details of a verified source, or the yellow exclamation provenance marker for the `Untrusted` outcome. + +The lower-trust path has two possible provenance outcomes. When the agent writes custom SQL, it can also quote supporting text from a trusted source. If commons finds the quoted text in that source, the provenance outcome is `Cited`, and the answer shows blue quote-mark citation markers. If no citation verifies, the provenance outcome is `Untrusted`, and the answer shows the yellow exclamation provenance marker. + +The table below lists how each provenance outcome can occur: + +| How the answer is produced | Provenance outcome | +|---|---| +| A trusted Python [measure](#semantic-layer) | `Verified answer` | +| A [data dictionary metric](#definitions), possibly grouped or filtered with definitions | `Verified answer` | +| Custom SQL, including SQL that uses [data dictionary definitions](#definitions) | `Cited` or `Untrusted` | +| No data tool used (for example, the agent already had the information, or no accessible information answered the question) | No provenance outcome | + +The agent does not decide the provenance outcome. commons derives it from the tools the agent called and the citations it made. + +## Information layers + +commons has two primary layers of information: the **semantic layer** and the **context layer**. The semantic layer holds trusted calculations, ideally lifted from reliable code that you already use. The context layer holds background information. The agent uses this information to decide what custom SQL to write and how to interpret results. Data dictionaries span the two layers. + +| Layer | Sources | Role | +|---|---|---| +| Semantic layer | Measures in `.py` files and [`definitions`](#definitions) in `data-dict.yaml` | Provides trusted calculations. | +| Context layer | Markdown files and descriptive fields in [`data-dict.yaml`](#data-dictionaries) | Informs custom SQL and guides interpretation. | + +### Semantic layer + +The most direct way to add a trusted calculation is a **measure**. A measure is a Python function decorated with `@commons.measure`. The decorator needs a description, from its `description` argument or from the docstring of the function. When measures exist, the agent searches for a measure that matches the question. If it finds one, it calls that measure, with arguments if the measure takes any. + +A measure can take two kinds of parameter. A parameter annotated `Annotated[T, Field(description=...)]` is supplied by the model, and the model reads the description to decide how to call it. A parameter annotated `commons.Injected[T]` is supplied by commons and never reaches the model. commons injects the connection of the data source that has the same name as the parameter. A parameter with no description and no `Injected` annotation is a construction error. + +`data-dict.yaml` files also add to the semantic layer through [`definitions`](#definitions). + +### Context layer + +The context layer holds unstructured text from Markdown files and the descriptive fields of `data-dict.yaml`. commons indexes this text and retrieves the parts that match a question. Facts that every conversation needs belong in `instructions`, not in the context layer. + +### Examples + +Here are short examples of a data dictionary, a measure file, and a context document for the biodiversity example: + +#### Data dictionary + +`dictionaries/biodiversity.yaml` + +```yaml +tables: + - name: observations + description: Species observations by nature preserve. + columns: + - name: count + description: Individuals observed during surveys. +``` + +#### Measure file + +`measures/biodiversity.py` + +```python +from typing import Annotated, Any + +from pydantic import Field + +import commons + + +@commons.measure(description="Species richness by site.") +def biodiversity_by_site( + biodiversity: commons.Injected[Any], + site: Annotated[str, Field(description="Site name.")], +) -> Any: + return biodiversity.execute( + "SELECT COUNT(DISTINCT species) AS species_richness " + "FROM observations WHERE obs_site = ?", + [site], + ).fetchdf() +``` + +The `biodiversity` parameter receives the connection of the data source named `biodiversity`. The model supplies `site`. + +#### Context document + +`context/biodiversity.md` + +```markdown +# Interpreting survey results + +Observed individuals reflect organisms recorded during surveys. They are not a complete population census of a nature preserve. +``` + +## Data sources + +One of the first decisions for a commons agent is which data the agent can reach. A data source joins the data with the tables that the agent can see. It can also carry a data dictionary that describes those tables and their trusted definitions. + +Create a data source with `commons.data_source()`. The data can be named pandas or polars data frames, a [pins](https://rstudio.github.io/pins-python/) board, or a SQLAlchemy `Engine`. commons loads data frames and pins into an in-process DuckDB database. commons queries a database engine directly and copies nothing. + +For example, the following code creates a data source from two data frames. Each name becomes a table that the agent can query. `dictionary` is an optional path to a `data-dict.yaml` file. + +```python +biodiversity = commons.data_source( + observations=observations, + site_area=site_area, + dictionary="dictionaries/biodiversity.yaml", +) +``` + +For an engine or a pins board, the `tables` argument selects the tables to expose. A Snowflake or Databricks engine also imports its catalog. If that catalog is too large for the system prompt, the agent gets a `search_catalog` tool. + +### Data dictionaries + +A data dictionary provides structured documentation for one data source. Use it to state what each table represents, the meaning and type of each column, relationships between tables, and glossary terms. It also holds trusted `definitions`. commons reads the [`data-dict.yaml` specification](https://data-dict.tidyverse.org/). + +A commons agent uses a data dictionary in three ways: + +- Dataset-level descriptions give broad context that is always available. Glossary terms go in the system prompt as space allows. +- When the agent first uses a documented table in a conversation, it receives the description, columns, relationships, and glossary terms of that table. +- Descriptive fields, such as `description` and `details`, are part of the context layer. + +#### Definitions + +**Definitions** are named, governed expressions attached to tables in `data-dict.yaml`. They let an agent reuse trusted metrics, filters, and derived values, so they add to the semantic layer. + +Each definition is an expression in the [data-dict expression language](https://data-dict.tidyverse.org/expressions.html), not in the SQL dialect of your database: + +```yaml +tables: + - name: observations + columns: + - name: count + type: number + definitions: + - name: total_individuals + label: Total individuals observed + description: Sum of the individuals recorded in surveys. + expr: SUM(count) +``` + +There are three kinds of definition. A definition can take part in a trusted metric calculation, or the agent can use it in custom SQL:[^definition-sql] + +| Kind | Example | Use in a trusted metric calculation | +|---|---|---| +| Metric | `SUM(n)` | Computes the metric | +| Filter | `status = 'active'` | Restricts rows or provides a grouping dimension | +| Derived value | `price * quantity` | Provides a grouping dimension | + +commons infers the kind from the expression. An aggregate or constant expression is a metric. A row-level Boolean expression is a filter. Every other row-level expression is a derived value. + +[^definition-sql]: In custom SQL, the agent writes a definition as a `{{name}}` token, and commons expands it to the SQL compiled for the data source. This is still custom SQL, so the provenance outcome is `Cited` or `Untrusted`, not `Verified answer`. + +See the [DevRel Agent `data-dict.yaml`](https://github.com/posit-dev/devrel-agent/blob/main/dictionaries/devrel.data-dict.yaml) for examples of definitions. + +When you construct a data source, commons validates each definition and compiles it to the SQL dialect of the source. A definition on a table that the source does not expose is a construction error. + +## Project directory organization + +A commons agent is easiest to maintain when each piece has its own file: + +```text +. +|-- app.py +|-- agent.py +|-- pyproject.toml +|-- AGENTS.md # or the file your coding agent reads (for example, CLAUDE.md) +|-- instructions.md +|-- dictionaries/ +| `-- biodiversity.yaml +|-- measures/ +| `-- biodiversity.py +`-- context/ + `-- context.md +``` + +## Constructing the agent + +Use `commons.Commons()` to construct an agent. Pass it a chatlas `Chat` and one or more data sources, plus any semantic and context layers. The `instructions` argument adds text to the end of the commons system prompt. Pass a string, or the path to a text or Markdown file. + +```python +import chatlas +import commons + +biodiversity = commons.data_source( + observations=observations, + site_area=site_area, + dictionary="dictionaries/biodiversity.yaml", +) + +agent = commons.Commons( + chatlas.ChatAnthropic(model="claude-sonnet-5"), + {"biodiversity": biodiversity}, + semantic_layer=commons.semantic_layer("measures"), + context_layer=commons.context_layer(files=["context/context.md"]), + instructions="instructions.md", +) + +agent.chat("How many species were observed at Oak Bluff?") +``` + +`data_sources` is one `DataSource` or a mapping of name to `DataSource`. Name the sources when a measure takes an injected connection, because commons injects by name. `semantic_layer()` accepts measures, modules, or paths to `.py` files and directories. `context_layer()` accepts a list of file paths. + +A `Commons` agent is a chatlas `Chat`. It builds its own system prompt and tools, so it does not carry over the system prompt or the turns on `client`. `chat()` and `stream_async()` are the two ways to ask a question. The other chatlas entry points, such as `chat_async()`, `stream()`, and `chat_structured()`, raise `NotImplementedError`, because an answer through them carries no citations and no provenance marker. + +### Chat UI + +`commons.ui` puts the agent behind a py-shiny chat. It needs the `shiny` extra. For local development, `commons.ui.app(agent)` returns a complete Shiny app: + +```python +app = commons.ui.app(agent) +``` + +`commons.ui.app()` shares one agent across every session, so it suits one visitor at a time. For a deployed app, build the page with `commons.ui.theme()`, and construct the agent inside the server function. Then pass it to `commons.ui.server()`, so each session gets its own agent: + +```python +import shinychat +from shiny import App + +app_ui = shinychat.page_chat("Biodiversity", id="chat", theme=commons.ui.theme()) + + +def app_server(input, output, session): + commons.ui.server("chat", make_agent()) + + +app = App(app_ui, app_server) +``` + +`commons.ui.server()` warms the agent once the session is idle after its first page. `agent.prewarm()` builds the context index and reads pending pins before a deployment. This example assumes that `observations` and `site_area` are data frames loaded when the app starts. + +The commons agent skill for coding agents, and the tools that review logged conversations, ship with the R package today. From b6700a0c0ecceb3292f04375b75e0d995730f11a Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sat, 12 Sep 2026 09:42:32 -0600 Subject: [PATCH 03/30] ci: serialize the two GitHub Pages deploys Both site workflows push to gh-pages, and a release or a change under docs/ triggers both. With a concurrency group each, the two runs could push at the same time and one site's update would be lost. One shared group for non-PR runs makes the second wait instead. --- .github/workflows/pkgdown.yaml | 5 +++-- .github/workflows/quartodoc.yaml | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml index 630c359c..232fe25d 100644 --- a/.github/workflows/pkgdown.yaml +++ b/.github/workflows/pkgdown.yaml @@ -20,9 +20,10 @@ jobs: defaults: run: working-directory: pkg-r - # Only restrict concurrency for non-PR jobs + # Shared with the sibling site's workflow: both deploy to gh-pages, and + # two pushes to one branch race. concurrency: - group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} + group: gh-pages-${{ github.event_name != 'pull_request' || github.run_id }} env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} permissions: diff --git a/.github/workflows/quartodoc.yaml b/.github/workflows/quartodoc.yaml index 01efc438..105fab85 100644 --- a/.github/workflows/quartodoc.yaml +++ b/.github/workflows/quartodoc.yaml @@ -18,9 +18,10 @@ jobs: defaults: run: working-directory: pkg-py - # Only restrict concurrency for non-PR jobs + # Shared with the sibling site's workflow: both deploy to gh-pages, and + # two pushes to one branch race. concurrency: - group: quartodoc-${{ github.event_name != 'pull_request' || github.run_id }} + group: gh-pages-${{ github.event_name != 'pull_request' || github.run_id }} permissions: contents: write steps: From b038a696924758d9d4e0093a6382d4c4fc43e982 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sat, 12 Sep 2026 09:46:13 -0600 Subject: [PATCH 04/30] py: correct two claims the docs pages overstated The introduction said definitions compile to the dialect of the source, with no limit. Only DuckDB, Snowflake, and Databricks have an emitter, so a PostgreSQL engine carrying a dictionary with definitions fails at construction. The page now states that alongside the other construction errors. The governance page enumerated the SQL guard's allowlist and got the enumeration short: _READ_ONLY also holds Subquery, Values, and Pivot. It now gives SELECT and the set operations as examples rather than as the whole set, and describes the rejected forms as the tree search the guard performs instead of as a flat keyword list. --- pkg-py/docs/governance.qmd | 4 ++-- pkg-py/docs/index.qmd | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pkg-py/docs/governance.qmd b/pkg-py/docs/governance.qmd index 0bf3d271..8008e592 100644 --- a/pkg-py/docs/governance.qmd +++ b/pkg-py/docs/governance.qmd @@ -16,9 +16,9 @@ We assume that a model can make any request that its tools allow. Application se ### Destructive actions -The agent can run one read-only statement at a time. commons parses each statement with [sqlglot](https://github.com/tobymao/sqlglot) and checks its structure, not its text. It accepts a single `SELECT` or set operation (`UNION`, `INTERSECT`, `EXCEPT`), with or without a leading `WITH`. commons rejects everything else: stacked statements, `INSERT`, `UPDATE`, `DELETE`, `DROP`, `GRANT`, `SET`, `USE`, `PRAGMA`, and statements that sqlglot cannot parse. The check is an allowlist, so a statement form that nobody thought of fails closed. +The agent can run one read-only statement at a time. commons parses each statement with [sqlglot](https://github.com/tobymao/sqlglot) and checks its structure, not its text. The check is an allowlist of statement forms that only read, such as `SELECT` and the set operations. commons refuses every other form, stacked statements, and statements that sqlglot cannot parse. A form that nobody thought of therefore fails closed. -Because it reads the parse tree, the check also catches a write hidden inside a read. `WITH t AS (SELECT 1) DELETE FROM sales` is a `DELETE`, `WITH d AS (DELETE ... RETURNING *) SELECT * FROM d` deletes rows through its CTE, and `SELECT ... INTO t` creates a table. A first-word test lets all three through. commons also rejects locking reads such as `SELECT ... FOR UPDATE`, because they block other writers. +The check also catches a write hidden inside a read. `WITH t AS (SELECT 1) DELETE FROM sales` arrives as a `DELETE`, `WITH d AS (DELETE ... RETURNING *) SELECT * FROM d` deletes rows through its CTE, and `SELECT ... INTO t` creates a table. The guard refuses a statement with a write, DDL, or session-changing node anywhere in its tree, such as `DROP`, `ATTACH`, `GRANT`, `SET`, or `USE`. It also refuses locking reads such as `SELECT ... FOR UPDATE`, because they block other writers. For data frames and pins, which commons loads into its own DuckDB database, it also hardens the connection. It disables community and unsigned extensions, automatic extension install and load, external access, and the local filesystem. Then it sets `lock_configuration`, so a query cannot turn those settings back on. The lock freezes settings only, not tables, which is how commons can still load a pin's rows on first use. diff --git a/pkg-py/docs/index.qmd b/pkg-py/docs/index.qmd index 0bf53c72..e58682b4 100644 --- a/pkg-py/docs/index.qmd +++ b/pkg-py/docs/index.qmd @@ -182,7 +182,7 @@ commons infers the kind from the expression. An aggregate or constant expression See the [DevRel Agent `data-dict.yaml`](https://github.com/posit-dev/devrel-agent/blob/main/dictionaries/devrel.data-dict.yaml) for examples of definitions. -When you construct a data source, commons validates each definition and compiles it to the SQL dialect of the source. A definition on a table that the source does not expose is a construction error. +When you construct a data source, commons validates each definition and compiles it for DuckDB, Snowflake, or Databricks, whichever the source uses. Construction fails for another dialect, a definition on a table the source does not expose, or a metric of mixed row and aggregate grain. ## Project directory organization From 255e0ebd84e9ab504b34e2bc5dbb2a0a81dc4a85 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 11:53:33 -0600 Subject: [PATCH 05/30] Add Python logo to documentation navbar and simplify site title --- pkg-py/docs/_quarto.yml | 3 ++- pkg-py/docs/python-logo.svg | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) create mode 100644 pkg-py/docs/python-logo.svg diff --git a/pkg-py/docs/_quarto.yml b/pkg-py/docs/_quarto.yml index 99bde52e..0789621d 100644 --- a/pkg-py/docs/_quarto.yml +++ b/pkg-py/docs/_quarto.yml @@ -10,11 +10,12 @@ project: - reference/*.qmd website: - title: "commons for Python" + title: "commons" site-url: https://posit-dev.github.io/commons/py/ repo-url: https://github.com/posit-dev/commons repo-subdir: pkg-py navbar: + logo: python-logo.svg left: - href: index.qmd text: Get started diff --git a/pkg-py/docs/python-logo.svg b/pkg-py/docs/python-logo.svg new file mode 100644 index 00000000..05155f37 --- /dev/null +++ b/pkg-py/docs/python-logo.svg @@ -0,0 +1 @@ + \ No newline at end of file From ac4387c350a723b516ac16f23090a56ee43d7fcf Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 12:01:38 -0600 Subject: [PATCH 06/30] Add R logo SVG and style navbar R link to display logo --- pkg-py/docs/r-logo.svg | 14 ++++++++++++++ pkg-py/docs/styles.css | 12 ++++++++++++ 2 files changed, 26 insertions(+) create mode 100644 pkg-py/docs/r-logo.svg diff --git a/pkg-py/docs/r-logo.svg b/pkg-py/docs/r-logo.svg new file mode 100644 index 00000000..78281f78 --- /dev/null +++ b/pkg-py/docs/r-logo.svg @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/pkg-py/docs/styles.css b/pkg-py/docs/styles.css index b5c34852..2bcba722 100644 --- a/pkg-py/docs/styles.css +++ b/pkg-py/docs/styles.css @@ -30,3 +30,15 @@ h3 { .doc-signature code { overflow-wrap: anywhere; } + +/* Show the R logo in place of the "R" navbar link text. The url() resolves + relative to this stylesheet at the site root, so it works from any page + depth (unlike an img src written into the navbar HTML). */ +.navbar a[href$="/r/"] .menu-text { + font-size: 0; + display: inline-block; + background: url("r-logo.svg") no-repeat center / contain; + width: 1.42rem; + height: 1.1rem; + vertical-align: -0.2em; +} From 524c42af7ef917986009e981235f79c5cc393b33 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 12:07:52 -0600 Subject: [PATCH 07/30] Update R logo navbar styling with flexbox layout and refined dimensions --- pkg-py/docs/styles.css | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/pkg-py/docs/styles.css b/pkg-py/docs/styles.css index 2bcba722..11b26c1b 100644 --- a/pkg-py/docs/styles.css +++ b/pkg-py/docs/styles.css @@ -34,11 +34,17 @@ h3 { /* Show the R logo in place of the "R" navbar link text. The url() resolves relative to this stylesheet at the site root, so it works from any page depth (unlike an img src written into the navbar HTML). */ +.navbar a[href$="/r/"] { + display: flex; + align-items: center; + /* Fill the stretched nav item, so the centered logo matches the line-box + center of the neighboring icon links. */ + height: 100%; +} + .navbar a[href$="/r/"] .menu-text { font-size: 0; - display: inline-block; background: url("r-logo.svg") no-repeat center / contain; - width: 1.42rem; - height: 1.1rem; - vertical-align: -0.2em; + width: 1.29rem; + height: 1rem; } From a9d398366058b851898e1e22fa21fd9ab7880182 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 12:13:07 -0600 Subject: [PATCH 08/30] Improve R documentation link styling with hover tooltip and proper logo sizing --- pkg-py/docs/_quarto.yml | 2 +- pkg-py/docs/styles.css | 6 ++++++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/pkg-py/docs/_quarto.yml b/pkg-py/docs/_quarto.yml index 0789621d..f2f1e3d8 100644 --- a/pkg-py/docs/_quarto.yml +++ b/pkg-py/docs/_quarto.yml @@ -24,7 +24,7 @@ website: - href: reference/index.qmd text: Reference right: - - text: R + - text: '' href: ../r/ - icon: github href: https://github.com/posit-dev/commons diff --git a/pkg-py/docs/styles.css b/pkg-py/docs/styles.css index 11b26c1b..50911737 100644 --- a/pkg-py/docs/styles.css +++ b/pkg-py/docs/styles.css @@ -42,7 +42,13 @@ h3 { height: 100%; } +/* The span carrying the hover title is sized to the logo, so the tooltip + appears anywhere over the image. */ .navbar a[href$="/r/"] .menu-text { + display: flex; +} + +.navbar a[href$="/r/"] .r-logo { font-size: 0; background: url("r-logo.svg") no-repeat center / contain; width: 1.29rem; From acd91c66379930e7ca121628b21d65df54974a9c Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 12:19:58 -0600 Subject: [PATCH 09/30] Organize Quarto docs assets into subdirectory and update gitignore --- pkg-py/.gitignore | 3 +++ pkg-py/docs/_quarto.yml | 2 +- pkg-py/docs/{ => assets}/r-logo.svg | 0 pkg-py/docs/{ => assets}/styles.css | 9 +++++++-- 4 files changed, 11 insertions(+), 3 deletions(-) rename pkg-py/docs/{ => assets}/r-logo.svg (100%) rename pkg-py/docs/{ => assets}/styles.css (71%) diff --git a/pkg-py/.gitignore b/pkg-py/.gitignore index 6ad91405..21a416cc 100644 --- a/pkg-py/.gitignore +++ b/pkg-py/.gitignore @@ -14,3 +14,6 @@ dist/ # Jupyter writes these beside a notebook; they carry outputs and local paths. .ipynb_checkpoints/ + +/.quarto/ +**/*.quarto_ipynb diff --git a/pkg-py/docs/_quarto.yml b/pkg-py/docs/_quarto.yml index f2f1e3d8..23438aac 100644 --- a/pkg-py/docs/_quarto.yml +++ b/pkg-py/docs/_quarto.yml @@ -36,7 +36,7 @@ format: html: theme: cosmo toc: true - css: styles.css + css: assets/styles.css metadata-files: - reference/_sidebar.yml diff --git a/pkg-py/docs/r-logo.svg b/pkg-py/docs/assets/r-logo.svg similarity index 100% rename from pkg-py/docs/r-logo.svg rename to pkg-py/docs/assets/r-logo.svg diff --git a/pkg-py/docs/styles.css b/pkg-py/docs/assets/styles.css similarity index 71% rename from pkg-py/docs/styles.css rename to pkg-py/docs/assets/styles.css index 50911737..8a473305 100644 --- a/pkg-py/docs/styles.css +++ b/pkg-py/docs/assets/styles.css @@ -32,8 +32,10 @@ h3 { } /* Show the R logo in place of the "R" navbar link text. The url() resolves - relative to this stylesheet at the site root, so it works from any page - depth (unlike an img src written into the navbar HTML). */ + relative to this stylesheet, so it works from any page depth (unlike an + img src written into the navbar HTML). Both files live under assets/ + because quarto preview (1.10) fails to copy a CSS url() resource located + at the project root: "Cannot copy ... to a subdirectory of itself". */ .navbar a[href$="/r/"] { display: flex; align-items: center; @@ -53,4 +55,7 @@ h3 { background: url("r-logo.svg") no-repeat center / contain; width: 1.29rem; height: 1rem; + /* Optical alignment: the logo's visual weight sits low (the gray oval is + lighter than the blue R), so nudge it up from the geometric center. */ + margin-top: -2px; } From 2b38886b0ec756e1a6923d5bc8b2d3b271999a75 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 12:42:16 -0600 Subject: [PATCH 10/30] docs: manage the site logos from a single source Move the two navbar language marks to logos/ at the repository root and sync them into both documentation sites, like the other shared sources. The copies are served as they stand, so destinations marked :bare skip the generated README. --- .github/workflows/verify-shared-synced.yaml | 6 ++++++ logos/README.md | 10 ++++++++++ {pkg-py/docs => logos}/python-logo.svg | 0 {pkg-py/docs/assets => logos}/r-logo.svg | 0 pkg-py/docs/_quarto.yml | 2 +- pkg-py/docs/assets/logos/python-logo.svg | 1 + pkg-py/docs/assets/logos/r-logo.svg | 14 ++++++++++++++ pkg-py/docs/assets/styles.css | 2 +- pkg-r/pkgdown/assets/logos/python-logo.svg | 1 + pkg-r/pkgdown/assets/logos/r-logo.svg | 14 ++++++++++++++ scripts/sync-shared.sh | 18 +++++++++++++++--- 11 files changed, 63 insertions(+), 5 deletions(-) create mode 100644 logos/README.md rename {pkg-py/docs => logos}/python-logo.svg (100%) rename {pkg-py/docs/assets => logos}/r-logo.svg (100%) create mode 100644 pkg-py/docs/assets/logos/python-logo.svg create mode 100644 pkg-py/docs/assets/logos/r-logo.svg create mode 100644 pkg-r/pkgdown/assets/logos/python-logo.svg create mode 100644 pkg-r/pkgdown/assets/logos/r-logo.svg diff --git a/.github/workflows/verify-shared-synced.yaml b/.github/workflows/verify-shared-synced.yaml index fd98da04..1bc73113 100644 --- a/.github/workflows/verify-shared-synced.yaml +++ b/.github/workflows/verify-shared-synced.yaml @@ -10,11 +10,14 @@ on: - tests/shared/** - prompts/** - www/** + - logos/** - pkg-r/tests/testthat/fixtures/shared/** - pkg-r/inst/prompts/** - pkg-r/inst/www/** - pkg-py/src/commons/prompts/** - pkg-py/src/commons/www/** + - pkg-py/docs/assets/logos/** + - pkg-r/pkgdown/assets/logos/** - scripts/sync-shared.sh - .github/workflows/verify-shared-synced.yaml pull_request: @@ -22,11 +25,14 @@ on: - tests/shared/** - prompts/** - www/** + - logos/** - pkg-r/tests/testthat/fixtures/shared/** - pkg-r/inst/prompts/** - pkg-r/inst/www/** - pkg-py/src/commons/prompts/** - pkg-py/src/commons/www/** + - pkg-py/docs/assets/logos/** + - pkg-r/pkgdown/assets/logos/** - scripts/sync-shared.sh - .github/workflows/verify-shared-synced.yaml workflow_dispatch: diff --git a/logos/README.md b/logos/README.md new file mode 100644 index 00000000..a7c28893 --- /dev/null +++ b/logos/README.md @@ -0,0 +1,10 @@ +# Site logos + +The language marks the documentation site navbars show: `python-logo.svg` +beside the Python site's title, and `r-logo.svg` both beside the R site's +title and as the Python site's link to the R site. The Python mark is the +PSF's (via Wikimedia); the R mark is from r-project.org (CC BY-SA 4.0). + +`scripts/sync-shared.sh` copies this directory into `pkg-py/docs/assets/logos/` +and `pkg-r/pkgdown/assets/logos/`, and CI fails when a copy is stale. Edit the +files here, never a copy. diff --git a/pkg-py/docs/python-logo.svg b/logos/python-logo.svg similarity index 100% rename from pkg-py/docs/python-logo.svg rename to logos/python-logo.svg diff --git a/pkg-py/docs/assets/r-logo.svg b/logos/r-logo.svg similarity index 100% rename from pkg-py/docs/assets/r-logo.svg rename to logos/r-logo.svg diff --git a/pkg-py/docs/_quarto.yml b/pkg-py/docs/_quarto.yml index 23438aac..2d6c0e5d 100644 --- a/pkg-py/docs/_quarto.yml +++ b/pkg-py/docs/_quarto.yml @@ -15,7 +15,7 @@ website: repo-url: https://github.com/posit-dev/commons repo-subdir: pkg-py navbar: - logo: python-logo.svg + logo: assets/logos/python-logo.svg left: - href: index.qmd text: Get started diff --git a/pkg-py/docs/assets/logos/python-logo.svg b/pkg-py/docs/assets/logos/python-logo.svg new file mode 100644 index 00000000..05155f37 --- /dev/null +++ b/pkg-py/docs/assets/logos/python-logo.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/pkg-py/docs/assets/logos/r-logo.svg b/pkg-py/docs/assets/logos/r-logo.svg new file mode 100644 index 00000000..78281f78 --- /dev/null +++ b/pkg-py/docs/assets/logos/r-logo.svg @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/pkg-py/docs/assets/styles.css b/pkg-py/docs/assets/styles.css index 8a473305..4a802c3d 100644 --- a/pkg-py/docs/assets/styles.css +++ b/pkg-py/docs/assets/styles.css @@ -52,7 +52,7 @@ h3 { .navbar a[href$="/r/"] .r-logo { font-size: 0; - background: url("r-logo.svg") no-repeat center / contain; + background: url("logos/r-logo.svg") no-repeat center / contain; width: 1.29rem; height: 1rem; /* Optical alignment: the logo's visual weight sits low (the gray oval is diff --git a/pkg-r/pkgdown/assets/logos/python-logo.svg b/pkg-r/pkgdown/assets/logos/python-logo.svg new file mode 100644 index 00000000..05155f37 --- /dev/null +++ b/pkg-r/pkgdown/assets/logos/python-logo.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/pkg-r/pkgdown/assets/logos/r-logo.svg b/pkg-r/pkgdown/assets/logos/r-logo.svg new file mode 100644 index 00000000..78281f78 --- /dev/null +++ b/pkg-r/pkgdown/assets/logos/r-logo.svg @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/scripts/sync-shared.sh b/scripts/sync-shared.sh index 611d87dc..c92a0e3e 100755 --- a/scripts/sync-shared.sh +++ b/scripts/sync-shared.sh @@ -15,11 +15,13 @@ root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" # The Python suite reads tests/shared/ in place, so only the R package needs # that copy; both packages ship the prompts and the browser assets. Nothing # in pkg-py reads www/ until its UI layer lands, but the destination belongs -# with the move rather than with the first reader. +# with the move rather than with the first reader. The logos go to the two +# documentation sites, which serve them as they stand. sources=( "tests/shared pkg-r/tests/testthat/fixtures/shared" "prompts pkg-r/inst/prompts pkg-py/src/commons/prompts" "www pkg-r/inst/www pkg-py/src/commons/www" + "logos pkg-py/docs/assets/logos:bare pkg-r/pkgdown/assets/logos:bare" ) for entry in "${sources[@]}"; do @@ -28,7 +30,7 @@ for entry in "${sources[@]}"; do # Every file, whatever its extension: a fixture may use a format other than # JSON, and globbing would drop such a file silently while leaving the # staleness check green. READMEs document the source, so they are the one - # exclusion; each destination gets a generated one instead. + # exclusion; most destinations get a generated one instead. files="$(cd "$root/$src" && find . -type f ! -name README.md | sed 's|^\./||' | sort)" if [ -z "$files" ]; then echo "No files found in $src" >&2 @@ -36,6 +38,14 @@ for entry in "${sources[@]}"; do fi for dest in $dests; do + # A destination marked :bare is copied onto a website as it stands, so a + # generated README would be published alongside the files. Those keep the + # warning in the source directory only. + readme=true + case "$dest" in + *:bare) dest="${dest%:bare}"; readme=false ;; + esac + # Regenerate rather than overwrite, so a file deleted from the source also # disappears from the copy. rm -rf "${root:?}/$dest" @@ -45,9 +55,11 @@ for entry in "${sources[@]}"; do cp "$root/$src/$rel" "$root/$dest/$rel" done - cat >"$root/$dest/README.md" <"$root/$dest/README.md" < Date: Sun, 13 Sep 2026 12:42:31 -0600 Subject: [PATCH 11/30] r: mirror the logo treatment on the pkgdown site Show the R logo beside the package name and a Python-logo link to the Quarto site in the navbar, mirroring the Python site. The link is absolute because the dev-mode site builds under /r/dev/. The R logo is the synced copy under pkgdown/assets/logos/. --- pkg-r/_pkgdown.yml | 12 ++++++++++++ pkg-r/pkgdown/extra.css | 13 +++++++++++++ 2 files changed, 25 insertions(+) diff --git a/pkg-r/_pkgdown.yml b/pkg-r/_pkgdown.yml index 47f170e3..6db3f288 100644 --- a/pkg-r/_pkgdown.yml +++ b/pkg-r/_pkgdown.yml @@ -5,6 +5,18 @@ development: template: bootstrap: 5 +navbar: + structure: + right: [search, github, python] + components: + python: + icon: fab fa-python fa-lg + # Absolute, so it works from both the release site (/r/) and the dev + # site (/r/dev/), unlike the unversioned Quarto site's ../r/ link. + href: https://posit-dev.github.io/commons/py/ + title: Python Documentation + aria-label: Python documentation + reference: - title: Main functions description: The main agent constructor, plus the building blocks for a commons agent. diff --git a/pkg-r/pkgdown/extra.css b/pkg-r/pkgdown/extra.css index 4b915bf8..90b15aa3 100644 --- a/pkg-r/pkgdown/extra.css +++ b/pkg-r/pkgdown/extra.css @@ -1 +1,14 @@ @import url("articles/commons.css"); + +/* Show the R logo ahead of the package name in the navbar, mirroring the + Python logo on the Quarto site (pkg-py/docs). The url() resolves relative + to this stylesheet at the site root, so it works from any page depth. */ +.navbar-brand::before { + content: ""; + display: inline-block; + background: url("logos/r-logo.svg") no-repeat center / contain; + width: 1.42rem; + height: 1.1rem; + margin-right: 0.4rem; + vertical-align: -0.2em; +} From b23ca6e844495dae47886808b80b72d81debca27 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 13:21:12 -0600 Subject: [PATCH 12/30] docs: give the Python site a landing page separate from Get started Mirror the R docs structure: the root page is a general introduction with installation instructions, and Introduction to commons moves to its own Get started page. --- pkg-py/docs/_quarto.yml | 3 +- pkg-py/docs/get-started.qmd | 259 +++++++++++++++++++++++++++++++++++ pkg-py/docs/index.qmd | 260 ++++-------------------------------- 3 files changed, 284 insertions(+), 238 deletions(-) create mode 100644 pkg-py/docs/get-started.qmd diff --git a/pkg-py/docs/_quarto.yml b/pkg-py/docs/_quarto.yml index 2d6c0e5d..6130b269 100644 --- a/pkg-py/docs/_quarto.yml +++ b/pkg-py/docs/_quarto.yml @@ -6,6 +6,7 @@ project: # Explicit, so the design notes under notes/ stay out of the site. render: - index.qmd + - get-started.qmd - governance.qmd - reference/*.qmd @@ -17,7 +18,7 @@ website: navbar: logo: assets/logos/python-logo.svg left: - - href: index.qmd + - href: get-started.qmd text: Get started - href: governance.qmd text: Security and governance diff --git a/pkg-py/docs/get-started.qmd b/pkg-py/docs/get-started.qmd new file mode 100644 index 00000000..a8aa46b6 --- /dev/null +++ b/pkg-py/docs/get-started.qmd @@ -0,0 +1,259 @@ +--- +title: "Introduction to commons" +--- + +This page explains the structure of a commons project and how to start building a commons agent in Python. + +```python +import commons +``` + +## Design philosophy + +AI agents for data analysis range from cautious and narrowly correct to wildly untrustworthy. commons increases the chance of a correct answer by giving the agent access to trusted code that you already have. The agent keeps enough freedom to answer new, realistic questions. commons also derives a provenance outcome from the path the agent took, so a user can decide how much to trust each answer. + +If you are a data analyst, data scientist, or other data practitioner, you probably have a collection of trusted code. This is the code you depend on for your analyses, apps, and reports. The core idea of commons is that an agent gives more correct answers when it can run this code and read its documentation. + +The high-trust path occurs when a question matches one of these **trusted calculations**. For example, a commons agent can analyze biodiversity data, and a user asks: + +> How many total animals were observed at Oak Bluff? + +The agent searches for a trusted calculation that answers the question. If it finds one, it runs that calculation and reports the result with the green check-shield provenance marker for the `Verified answer` outcome. + +> At Oak Bluff, 59 individual animals were observed across 5 species, based on 28 hours of survey effort. Note this reflects observed individuals during surveys, not necessarily a full census of every animal present at the site. + +The agent had to decide which trusted calculation to run. It did not have to decide what code to write. This removes degrees of freedom and lets the agent use code that you already vetted. + +Users also ask questions that leave the high-trust path. For those, the agent searches for context and writes custom SQL. Answers from this path show blue quote-mark citation markers, or the yellow exclamation provenance marker for the `Untrusted` outcome. + +## Trust flow + +A commons agent uses trusted calculations when it can. When the user asks a question, the agent first searches the semantic layer for a trusted calculation. If it finds one, it calls that calculation, and the answer shows the green check-shield provenance marker for the `Verified answer` outcome. + +If no relevant trusted calculation exists, the agent takes the lower-trust path. It searches the context layer for information, inspects the tables it needs, and writes a read-only SQL query. These answers show blue quote-mark citation markers that open the details of a verified source, or the yellow exclamation provenance marker for the `Untrusted` outcome. + +The lower-trust path has two possible provenance outcomes. When the agent writes custom SQL, it can also quote supporting text from a trusted source. If commons finds the quoted text in that source, the provenance outcome is `Cited`, and the answer shows blue quote-mark citation markers. If no citation verifies, the provenance outcome is `Untrusted`, and the answer shows the yellow exclamation provenance marker. + +The table below lists how each provenance outcome can occur: + +| How the answer is produced | Provenance outcome | +|---|---| +| A trusted Python [measure](#semantic-layer) | `Verified answer` | +| A [data dictionary metric](#definitions), possibly grouped or filtered with definitions | `Verified answer` | +| Custom SQL, including SQL that uses [data dictionary definitions](#definitions) | `Cited` or `Untrusted` | +| No data tool used (for example, the agent already had the information, or no accessible information answered the question) | No provenance outcome | + +The agent does not decide the provenance outcome. commons derives it from the tools the agent called and the citations it made. + +## Information layers + +commons has two primary layers of information: the **semantic layer** and the **context layer**. The semantic layer holds trusted calculations, ideally lifted from reliable code that you already use. The context layer holds background information. The agent uses this information to decide what custom SQL to write and how to interpret results. Data dictionaries span the two layers. + +| Layer | Sources | Role | +|---|---|---| +| Semantic layer | Measures in `.py` files and [`definitions`](#definitions) in `data-dict.yaml` | Provides trusted calculations. | +| Context layer | Markdown files and descriptive fields in [`data-dict.yaml`](#data-dictionaries) | Informs custom SQL and guides interpretation. | + +### Semantic layer + +The most direct way to add a trusted calculation is a **measure**. A measure is a Python function decorated with `@commons.measure`. The decorator needs a description, from its `description` argument or from the docstring of the function. When measures exist, the agent searches for a measure that matches the question. If it finds one, it calls that measure, with arguments if the measure takes any. + +A measure can take two kinds of parameter. A parameter annotated `Annotated[T, Field(description=...)]` is supplied by the model, and the model reads the description to decide how to call it. A parameter annotated `commons.Injected[T]` is supplied by commons and never reaches the model. commons injects the connection of the data source that has the same name as the parameter. A parameter with no description and no `Injected` annotation is a construction error. + +`data-dict.yaml` files also add to the semantic layer through [`definitions`](#definitions). + +### Context layer + +The context layer holds unstructured text from Markdown files and the descriptive fields of `data-dict.yaml`. commons indexes this text and retrieves the parts that match a question. Facts that every conversation needs belong in `instructions`, not in the context layer. + +### Examples + +Here are short examples of a data dictionary, a measure file, and a context document for the biodiversity example: + +#### Data dictionary + +`dictionaries/biodiversity.yaml` + +```yaml +tables: + - name: observations + description: Species observations by nature preserve. + columns: + - name: count + description: Individuals observed during surveys. +``` + +#### Measure file + +`measures/biodiversity.py` + +```python +from typing import Annotated, Any + +from pydantic import Field + +import commons + + +@commons.measure(description="Species richness by site.") +def biodiversity_by_site( + biodiversity: commons.Injected[Any], + site: Annotated[str, Field(description="Site name.")], +) -> Any: + return biodiversity.execute( + "SELECT COUNT(DISTINCT species) AS species_richness " + "FROM observations WHERE obs_site = ?", + [site], + ).fetchdf() +``` + +The `biodiversity` parameter receives the connection of the data source named `biodiversity`. The model supplies `site`. + +#### Context document + +`context/biodiversity.md` + +```markdown +# Interpreting survey results + +Observed individuals reflect organisms recorded during surveys. They are not a complete population census of a nature preserve. +``` + +## Data sources + +One of the first decisions for a commons agent is which data the agent can reach. A data source joins the data with the tables that the agent can see. It can also carry a data dictionary that describes those tables and their trusted definitions. + +Create a data source with `commons.data_source()`. The data can be named pandas or polars data frames, a [pins](https://rstudio.github.io/pins-python/) board, or a SQLAlchemy `Engine`. commons loads data frames and pins into an in-process DuckDB database. commons queries a database engine directly and copies nothing. + +For example, the following code creates a data source from two data frames. Each name becomes a table that the agent can query. `dictionary` is an optional path to a `data-dict.yaml` file. + +```python +biodiversity = commons.data_source( + observations=observations, + site_area=site_area, + dictionary="dictionaries/biodiversity.yaml", +) +``` + +For an engine or a pins board, the `tables` argument selects the tables to expose. A Snowflake or Databricks engine also imports its catalog. If that catalog is too large for the system prompt, the agent gets a `search_catalog` tool. + +### Data dictionaries + +A data dictionary provides structured documentation for one data source. Use it to state what each table represents, the meaning and type of each column, relationships between tables, and glossary terms. It also holds trusted `definitions`. commons reads the [`data-dict.yaml` specification](https://data-dict.tidyverse.org/). + +A commons agent uses a data dictionary in three ways: + +- Dataset-level descriptions give broad context that is always available. Glossary terms go in the system prompt as space allows. +- When the agent first uses a documented table in a conversation, it receives the description, columns, relationships, and glossary terms of that table. +- Descriptive fields, such as `description` and `details`, are part of the context layer. + +#### Definitions + +**Definitions** are named, governed expressions attached to tables in `data-dict.yaml`. They let an agent reuse trusted metrics, filters, and derived values, so they add to the semantic layer. + +Each definition is an expression in the [data-dict expression language](https://data-dict.tidyverse.org/expressions.html), not in the SQL dialect of your database: + +```yaml +tables: + - name: observations + columns: + - name: count + type: number + definitions: + - name: total_individuals + label: Total individuals observed + description: Sum of the individuals recorded in surveys. + expr: SUM(count) +``` + +There are three kinds of definition. A definition can take part in a trusted metric calculation, or the agent can use it in custom SQL:[^definition-sql] + +| Kind | Example | Use in a trusted metric calculation | +|---|---|---| +| Metric | `SUM(n)` | Computes the metric | +| Filter | `status = 'active'` | Restricts rows or provides a grouping dimension | +| Derived value | `price * quantity` | Provides a grouping dimension | + +commons infers the kind from the expression. An aggregate or constant expression is a metric. A row-level Boolean expression is a filter. Every other row-level expression is a derived value. + +[^definition-sql]: In custom SQL, the agent writes a definition as a `{{name}}` token, and commons expands it to the SQL compiled for the data source. This is still custom SQL, so the provenance outcome is `Cited` or `Untrusted`, not `Verified answer`. + +See the [DevRel Agent `data-dict.yaml`](https://github.com/posit-dev/devrel-agent/blob/main/dictionaries/devrel.data-dict.yaml) for examples of definitions. + +When you construct a data source, commons validates each definition and compiles it for DuckDB, Snowflake, or Databricks, whichever the source uses. Construction fails for another dialect, a definition on a table the source does not expose, or a metric of mixed row and aggregate grain. + +## Project directory organization + +A commons agent is easiest to maintain when each piece has its own file: + +```text +. +|-- app.py +|-- agent.py +|-- pyproject.toml +|-- AGENTS.md # or the file your coding agent reads (for example, CLAUDE.md) +|-- instructions.md +|-- dictionaries/ +| `-- biodiversity.yaml +|-- measures/ +| `-- biodiversity.py +`-- context/ + `-- context.md +``` + +## Constructing the agent + +Use `commons.Commons()` to construct an agent. Pass it a chatlas `Chat` and one or more data sources, plus any semantic and context layers. The `instructions` argument adds text to the end of the commons system prompt. Pass a string, or the path to a text or Markdown file. + +```python +import chatlas +import commons + +biodiversity = commons.data_source( + observations=observations, + site_area=site_area, + dictionary="dictionaries/biodiversity.yaml", +) + +agent = commons.Commons( + chatlas.ChatAnthropic(model="claude-sonnet-5"), + {"biodiversity": biodiversity}, + semantic_layer=commons.semantic_layer("measures"), + context_layer=commons.context_layer(files=["context/context.md"]), + instructions="instructions.md", +) + +agent.chat("How many species were observed at Oak Bluff?") +``` + +`data_sources` is one `DataSource` or a mapping of name to `DataSource`. Name the sources when a measure takes an injected connection, because commons injects by name. `semantic_layer()` accepts measures, modules, or paths to `.py` files and directories. `context_layer()` accepts a list of file paths. + +A `Commons` agent is a chatlas `Chat`. It builds its own system prompt and tools, so it does not carry over the system prompt or the turns on `client`. `chat()` and `stream_async()` are the two ways to ask a question. The other chatlas entry points, such as `chat_async()`, `stream()`, and `chat_structured()`, raise `NotImplementedError`, because an answer through them carries no citations and no provenance marker. + +### Chat UI + +`commons.ui` puts the agent behind a py-shiny chat. It needs the `shiny` extra. For local development, `commons.ui.app(agent)` returns a complete Shiny app: + +```python +app = commons.ui.app(agent) +``` + +`commons.ui.app()` shares one agent across every session, so it suits one visitor at a time. For a deployed app, build the page with `commons.ui.theme()`, and construct the agent inside the server function. Then pass it to `commons.ui.server()`, so each session gets its own agent: + +```python +import shinychat +from shiny import App + +app_ui = shinychat.page_chat("Biodiversity", id="chat", theme=commons.ui.theme()) + + +def app_server(input, output, session): + commons.ui.server("chat", make_agent()) + + +app = App(app_ui, app_server) +``` + +`commons.ui.server()` warms the agent once the session is idle after its first page. `agent.prewarm()` builds the context index and reads pending pins before a deployment. This example assumes that `observations` and `site_area` are data frames loaded when the app starts. + +The commons agent skill for coding agents, and the tools that review logged conversations, ship with the R package today. diff --git a/pkg-py/docs/index.qmd b/pkg-py/docs/index.qmd index e58682b4..1496c857 100644 --- a/pkg-py/docs/index.qmd +++ b/pkg-py/docs/index.qmd @@ -1,261 +1,47 @@ --- -title: "Introduction to commons" +title: "commons" --- -This page explains the structure of a commons project and how to start building a commons agent in Python. +commons helps data scientists build trustworthy data agents. -```python -import commons -``` - -commons requires Python 3.11 or later. Install it with `pip install commons`. If you want the chat UI, install the `shiny` extra with `pip install "commons[shiny]"`. - -## Design philosophy - -AI agents for data analysis range from cautious and narrowly correct to wildly untrustworthy. commons increases the chance of a correct answer by giving the agent access to trusted code that you already have. The agent keeps enough freedom to answer new, realistic questions. commons also derives a provenance outcome from the path the agent took, so a user can decide how much to trust each answer. - -If you are a data analyst, data scientist, or other data practitioner, you probably have a collection of trusted code. This is the code you depend on for your analyses, apps, and reports. The core idea of commons is that an agent gives more correct answers when it can run this code and read its documentation. - -The high-trust path occurs when a question matches one of these **trusted calculations**. For example, a commons agent can analyze biodiversity data, and a user asks: - -> How many total animals were observed at Oak Bluff? - -The agent searches for a trusted calculation that answers the question. If it finds one, it runs that calculation and reports the result with the green check-shield provenance marker for the `Verified answer` outcome. - -> At Oak Bluff, 59 individual animals were observed across 5 species, based on 28 hours of survey effort. Note this reflects observed individuals during surveys, not necessarily a full census of every animal present at the site. - -The agent had to decide which trusted calculation to run. It did not have to decide what code to write. This removes degrees of freedom and lets the agent use code that you already vetted. - -Users also ask questions that leave the high-trust path. For those, the agent searches for context and writes custom SQL. Answers from this path show blue quote-mark citation markers, or the yellow exclamation provenance marker for the `Untrusted` outcome. - -## Trust flow - -A commons agent uses trusted calculations when it can. When the user asks a question, the agent first searches the semantic layer for a trusted calculation. If it finds one, it calls that calculation, and the answer shows the green check-shield provenance marker for the `Verified answer` outcome. - -If no relevant trusted calculation exists, the agent takes the lower-trust path. It searches the context layer for information, inspects the tables it needs, and writes a read-only SQL query. These answers show blue quote-mark citation markers that open the details of a verified source, or the yellow exclamation provenance marker for the `Untrusted` outcome. - -The lower-trust path has two possible provenance outcomes. When the agent writes custom SQL, it can also quote supporting text from a trusted source. If commons finds the quoted text in that source, the provenance outcome is `Cited`, and the answer shows blue quote-mark citation markers. If no citation verifies, the provenance outcome is `Untrusted`, and the answer shows the yellow exclamation provenance marker. - -The table below lists how each provenance outcome can occur: - -| How the answer is produced | Provenance outcome | -|---|---| -| A trusted Python [measure](#semantic-layer) | `Verified answer` | -| A [data dictionary metric](#definitions), possibly grouped or filtered with definitions | `Verified answer` | -| Custom SQL, including SQL that uses [data dictionary definitions](#definitions) | `Cited` or `Untrusted` | -| No data tool used (for example, the agent already had the information, or no accessible information answered the question) | No provenance outcome | - -The agent does not decide the provenance outcome. commons derives it from the tools the agent called and the citations it made. - -## Information layers - -commons has two primary layers of information: the **semantic layer** and the **context layer**. The semantic layer holds trusted calculations, ideally lifted from reliable code that you already use. The context layer holds background information. The agent uses this information to decide what custom SQL to write and how to interpret results. Data dictionaries span the two layers. - -| Layer | Sources | Role | -|---|---|---| -| Semantic layer | Measures in `.py` files and [`definitions`](#definitions) in `data-dict.yaml` | Provides trusted calculations. | -| Context layer | Markdown files and descriptive fields in [`data-dict.yaml`](#data-dictionaries) | Informs custom SQL and guides interpretation. | - -### Semantic layer - -The most direct way to add a trusted calculation is a **measure**. A measure is a Python function decorated with `@commons.measure`. The decorator needs a description, from its `description` argument or from the docstring of the function. When measures exist, the agent searches for a measure that matches the question. If it finds one, it calls that measure, with arguments if the measure takes any. - -A measure can take two kinds of parameter. A parameter annotated `Annotated[T, Field(description=...)]` is supplied by the model, and the model reads the description to decide how to call it. A parameter annotated `commons.Injected[T]` is supplied by commons and never reaches the model. commons injects the connection of the data source that has the same name as the parameter. A parameter with no description and no `Injected` annotation is a construction error. - -`data-dict.yaml` files also add to the semantic layer through [`definitions`](#definitions). - -### Context layer - -The context layer holds unstructured text from Markdown files and the descriptive fields of `data-dict.yaml`. commons indexes this text and retrieves the parts that match a question. Facts that every conversation needs belong in `instructions`, not in the context layer. - -### Examples - -Here are short examples of a data dictionary, a measure file, and a context document for the biodiversity example: - -#### Data dictionary - -`dictionaries/biodiversity.yaml` - -```yaml -tables: - - name: observations - description: Species observations by nature preserve. - columns: - - name: count - description: Individuals observed during surveys. -``` - -#### Measure file - -`measures/biodiversity.py` - -```python -from typing import Annotated, Any - -from pydantic import Field - -import commons +Data teams typically have trusted code that they use to analyze their data and build reports and apps. commons leverages that expertise, situating the information in a series of prompts and tools designed to create an accurate, fast, and cost-effective agent. +Trusted calculations can come from Python functions (as _measures_) or [data dictionary](https://data-dict.tidyverse.org/) definitions. -@commons.measure(description="Species richness by site.") -def biodiversity_by_site( - biodiversity: commons.Injected[Any], - site: Annotated[str, Field(description="Site name.")], -) -> Any: - return biodiversity.execute( - "SELECT COUNT(DISTINCT species) AS species_richness " - "FROM observations WHERE obs_site = ?", - [site], - ).fetchdf() -``` - -The `biodiversity` parameter receives the connection of the data source named `biodiversity`. The model supplies `site`. - -#### Context document - -`context/biodiversity.md` - -```markdown -# Interpreting survey results - -Observed individuals reflect organisms recorded during surveys. They are not a complete population census of a nature preserve. -``` - -## Data sources - -One of the first decisions for a commons agent is which data the agent can reach. A data source joins the data with the tables that the agent can see. It can also carry a data dictionary that describes those tables and their trusted definitions. - -Create a data source with `commons.data_source()`. The data can be named pandas or polars data frames, a [pins](https://rstudio.github.io/pins-python/) board, or a SQLAlchemy `Engine`. commons loads data frames and pins into an in-process DuckDB database. commons queries a database engine directly and copies nothing. - -For example, the following code creates a data source from two data frames. Each name becomes a table that the agent can query. `dictionary` is an optional path to a `data-dict.yaml` file. - -```python -biodiversity = commons.data_source( - observations=observations, - site_area=site_area, - dictionary="dictionaries/biodiversity.yaml", -) -``` - -For an engine or a pins board, the `tables` argument selects the tables to expose. A Snowflake or Databricks engine also imports its catalog. If that catalog is too large for the system prompt, the agent gets a `search_catalog` tool. - -### Data dictionaries - -A data dictionary provides structured documentation for one data source. Use it to state what each table represents, the meaning and type of each column, relationships between tables, and glossary terms. It also holds trusted `definitions`. commons reads the [`data-dict.yaml` specification](https://data-dict.tidyverse.org/). - -A commons agent uses a data dictionary in three ways: - -- Dataset-level descriptions give broad context that is always available. Glossary terms go in the system prompt as space allows. -- When the agent first uses a documented table in a conversation, it receives the description, columns, relationships, and glossary terms of that table. -- Descriptive fields, such as `description` and `details`, are part of the context layer. - -#### Definitions +## Installation -**Definitions** are named, governed expressions attached to tables in `data-dict.yaml`. They let an agent reuse trusted metrics, filters, and derived values, so they add to the semantic layer. +commons requires Python 3.11 or later. To install the package, run: -Each definition is an expression in the [data-dict expression language](https://data-dict.tidyverse.org/expressions.html), not in the SQL dialect of your database: - -```yaml -tables: - - name: observations - columns: - - name: count - type: number - definitions: - - name: total_individuals - label: Total individuals observed - description: Sum of the individuals recorded in surveys. - expr: SUM(count) +```bash +pip install commons ``` -There are three kinds of definition. A definition can take part in a trusted metric calculation, or the agent can use it in custom SQL:[^definition-sql] - -| Kind | Example | Use in a trusted metric calculation | -|---|---|---| -| Metric | `SUM(n)` | Computes the metric | -| Filter | `status = 'active'` | Restricts rows or provides a grouping dimension | -| Derived value | `price * quantity` | Provides a grouping dimension | - -commons infers the kind from the expression. An aggregate or constant expression is a metric. A row-level Boolean expression is a filter. Every other row-level expression is a derived value. - -[^definition-sql]: In custom SQL, the agent writes a definition as a `{{name}}` token, and commons expands it to the SQL compiled for the data source. This is still custom SQL, so the provenance outcome is `Cited` or `Untrusted`, not `Verified answer`. - -See the [DevRel Agent `data-dict.yaml`](https://github.com/posit-dev/devrel-agent/blob/main/dictionaries/devrel.data-dict.yaml) for examples of definitions. +If you want the chat UI, install the `shiny` extra: -When you construct a data source, commons validates each definition and compiles it for DuckDB, Snowflake, or Databricks, whichever the source uses. Construction fails for another dialect, a definition on a table the source does not expose, or a metric of mixed row and aggregate grain. - -## Project directory organization - -A commons agent is easiest to maintain when each piece has its own file: - -```text -. -|-- app.py -|-- agent.py -|-- pyproject.toml -|-- AGENTS.md # or the file your coding agent reads (for example, CLAUDE.md) -|-- instructions.md -|-- dictionaries/ -| `-- biodiversity.yaml -|-- measures/ -| `-- biodiversity.py -`-- context/ - `-- context.md +```bash +pip install "commons[shiny]" ``` -## Constructing the agent - -Use `commons.Commons()` to construct an agent. Pass it a chatlas `Chat` and one or more data sources, plus any semantic and context layers. The `instructions` argument adds text to the end of the commons system prompt. Pass a string, or the path to a text or Markdown file. - -```python -import chatlas -import commons +Or, for the development version: -biodiversity = commons.data_source( - observations=observations, - site_area=site_area, - dictionary="dictionaries/biodiversity.yaml", -) - -agent = commons.Commons( - chatlas.ChatAnthropic(model="claude-sonnet-5"), - {"biodiversity": biodiversity}, - semantic_layer=commons.semantic_layer("measures"), - context_layer=commons.context_layer(files=["context/context.md"]), - instructions="instructions.md", -) - -agent.chat("How many species were observed at Oak Bluff?") +```bash +pip install "git+https://github.com/posit-dev/commons#subdirectory=pkg-py" ``` -`data_sources` is one `DataSource` or a mapping of name to `DataSource`. Name the sources when a measure takes an injected connection, because commons injects by name. `semantic_layer()` accepts measures, modules, or paths to `.py` files and directories. `context_layer()` accepts a list of file paths. - -A `Commons` agent is a chatlas `Chat`. It builds its own system prompt and tools, so it does not carry over the system prompt or the turns on `client`. `chat()` and `stream_async()` are the two ways to ask a question. The other chatlas entry points, such as `chat_async()`, `stream()`, and `chat_structured()`, raise `NotImplementedError`, because an answer through them carries no citations and no provenance marker. +## Get started -### Chat UI +commons uses [chatlas](https://posit-dev.github.io/chatlas/) to access LLMs, so you will need access to one of chatlas's supported providers. -`commons.ui` puts the agent behind a py-shiny chat. It needs the `shiny` extra. For local development, `commons.ui.app(agent)` returns a complete Shiny app: - -```python -app = commons.ui.app(agent) -``` +[Introduction to commons](get-started.qmd) explains the structure of a commons agent and the creation process. -`commons.ui.app()` shares one agent across every session, so it suits one visitor at a time. For a deployed app, build the page with `commons.ui.theme()`, and construct the agent inside the server function. Then pass it to `commons.ui.server()`, so each session gets its own agent: +## Trusted answers -```python -import shinychat -from shiny import App +There are two provenance paths available to a commons agent. When a user asks a question for which there is trusted code, the agent runs that code and reports the result. When no trusted code covers the question, the agent writes custom SQL, leaning on additional context provided to the agent. -app_ui = shinychat.page_chat("Biodiversity", id="chat", theme=commons.ui.theme()) +Answers display provenance according to the analysis path followed, so users can determine how much trust to put in a given answer. +For more information, see [Introduction to commons](get-started.qmd). -def app_server(input, output, session): - commons.ui.server("chat", make_agent()) - - -app = App(app_ui, app_server) -``` - -`commons.ui.server()` warms the agent once the session is idle after its first page. `agent.prewarm()` builds the context index and reads pending pins before a deployment. This example assumes that `observations` and `site_area` are data frames loaded when the app starts. +## Evaluation -The commons agent skill for coding agents, and the tools that review logged conversations, ship with the R package today. +The [DevRel agent](https://github.com/posit-dev/devrel-agent) is an example commons agent that answers questions about adoption, engagement, and growth across Posit's open-source projects. Its repository contains an [evaluation](https://github.com/posit-dev/devrel-agent/tree/main/evals) comparing the commons agent with Claude Code on the same data; the [R documentation](../r/#evaluation) presents the results. From 525cff797af2be5b225cab1bf34c35cf75b3c6b6 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 13:35:25 -0600 Subject: [PATCH 13/30] docs: show the trusted marker in the Python Get started page Sync the shared provenance-marker figs into the docs assets, port the vignette's inline-marker CSS rule, and mark the example verified answer in the blockquote, as the R vignette does. --- .github/workflows/verify-shared-synced.yaml | 2 ++ pkg-py/docs/assets/figs/citation-definition.svg | 4 ++++ pkg-py/docs/assets/figs/citation-mark.svg | 5 +++++ pkg-py/docs/assets/figs/citation-prose.svg | 4 ++++ pkg-py/docs/assets/figs/citation-schema.svg | 4 ++++ pkg-py/docs/assets/figs/trusted-icon.svg | 6 ++++++ pkg-py/docs/assets/figs/warning-icon.svg | 7 +++++++ pkg-py/docs/assets/styles.css | 13 +++++++++++++ pkg-py/docs/get-started.qmd | 8 ++++---- scripts/sync-shared.sh | 4 +++- 10 files changed, 52 insertions(+), 5 deletions(-) create mode 100644 pkg-py/docs/assets/figs/citation-definition.svg create mode 100644 pkg-py/docs/assets/figs/citation-mark.svg create mode 100644 pkg-py/docs/assets/figs/citation-prose.svg create mode 100644 pkg-py/docs/assets/figs/citation-schema.svg create mode 100644 pkg-py/docs/assets/figs/trusted-icon.svg create mode 100644 pkg-py/docs/assets/figs/warning-icon.svg diff --git a/.github/workflows/verify-shared-synced.yaml b/.github/workflows/verify-shared-synced.yaml index 1bc73113..c6851da7 100644 --- a/.github/workflows/verify-shared-synced.yaml +++ b/.github/workflows/verify-shared-synced.yaml @@ -17,6 +17,7 @@ on: - pkg-py/src/commons/prompts/** - pkg-py/src/commons/www/** - pkg-py/docs/assets/logos/** + - pkg-py/docs/assets/figs/** - pkg-r/pkgdown/assets/logos/** - scripts/sync-shared.sh - .github/workflows/verify-shared-synced.yaml @@ -32,6 +33,7 @@ on: - pkg-py/src/commons/prompts/** - pkg-py/src/commons/www/** - pkg-py/docs/assets/logos/** + - pkg-py/docs/assets/figs/** - pkg-r/pkgdown/assets/logos/** - scripts/sync-shared.sh - .github/workflows/verify-shared-synced.yaml diff --git a/pkg-py/docs/assets/figs/citation-definition.svg b/pkg-py/docs/assets/figs/citation-definition.svg new file mode 100644 index 00000000..8e321d7b --- /dev/null +++ b/pkg-py/docs/assets/figs/citation-definition.svg @@ -0,0 +1,4 @@ + + + + diff --git a/pkg-py/docs/assets/figs/citation-mark.svg b/pkg-py/docs/assets/figs/citation-mark.svg new file mode 100644 index 00000000..0ffe1a42 --- /dev/null +++ b/pkg-py/docs/assets/figs/citation-mark.svg @@ -0,0 +1,5 @@ + + + + + diff --git a/pkg-py/docs/assets/figs/citation-prose.svg b/pkg-py/docs/assets/figs/citation-prose.svg new file mode 100644 index 00000000..1c154982 --- /dev/null +++ b/pkg-py/docs/assets/figs/citation-prose.svg @@ -0,0 +1,4 @@ + + + + diff --git a/pkg-py/docs/assets/figs/citation-schema.svg b/pkg-py/docs/assets/figs/citation-schema.svg new file mode 100644 index 00000000..41ebf3a7 --- /dev/null +++ b/pkg-py/docs/assets/figs/citation-schema.svg @@ -0,0 +1,4 @@ + + + + diff --git a/pkg-py/docs/assets/figs/trusted-icon.svg b/pkg-py/docs/assets/figs/trusted-icon.svg new file mode 100644 index 00000000..1aff24a7 --- /dev/null +++ b/pkg-py/docs/assets/figs/trusted-icon.svg @@ -0,0 +1,6 @@ + + + + + diff --git a/pkg-py/docs/assets/figs/warning-icon.svg b/pkg-py/docs/assets/figs/warning-icon.svg new file mode 100644 index 00000000..bb3d1924 --- /dev/null +++ b/pkg-py/docs/assets/figs/warning-icon.svg @@ -0,0 +1,7 @@ + + + + + diff --git a/pkg-py/docs/assets/styles.css b/pkg-py/docs/assets/styles.css index 4a802c3d..7944cd8a 100644 --- a/pkg-py/docs/assets/styles.css +++ b/pkg-py/docs/assets/styles.css @@ -26,6 +26,19 @@ h3 { letter-spacing: -0.02em; } +/* Inline provenance markers, sized to sit in running text. The same rule + styles the markers in the R package's vignettes. */ +.commons-documentation-marker { + background: transparent; + border: 0; + border-radius: 0; + height: 1.5em; + margin: 0; + padding: 0; + vertical-align: text-bottom; + width: 1.5em; +} + /* Keep the reference tables readable when a signature is long. */ .doc-signature code { overflow-wrap: anywhere; diff --git a/pkg-py/docs/get-started.qmd b/pkg-py/docs/get-started.qmd index a8aa46b6..552f517b 100644 --- a/pkg-py/docs/get-started.qmd +++ b/pkg-py/docs/get-started.qmd @@ -10,17 +10,17 @@ import commons ## Design philosophy -AI agents for data analysis range from cautious and narrowly correct to wildly untrustworthy. commons increases the chance of a correct answer by giving the agent access to trusted code that you already have. The agent keeps enough freedom to answer new, realistic questions. commons also derives a provenance outcome from the path the agent took, so a user can decide how much to trust each answer. +AI agents for data analysis range from cautious and narrowly correct to wildly untrustworthy. `commons` increases the chance of a correct answer by giving the agent access to trusted code that you already have, while the agent keeps enough freedom to answer new, realistic questions. `commons` also derives a provenance outcome from the path the agent took, so a user can decide how much to trust each answer. -If you are a data analyst, data scientist, or other data practitioner, you probably have a collection of trusted code. This is the code you depend on for your analyses, apps, and reports. The core idea of commons is that an agent gives more correct answers when it can run this code and read its documentation. +If you are a data analyst, data scientist, or other data practitioner, you probably have a collection of trusted code. This is the code you depend on for your analyses, apps, and reports. The core idea of `commons` is that an agent gives more correct answers when it can run this code and read its documentation. -The high-trust path occurs when a question matches one of these **trusted calculations**. For example, a commons agent can analyze biodiversity data, and a user asks: +The high-trust path occurs when a question matches one of these **trusted calculations**. For example, a `commons` agent can analyze biodiversity data, and a user asks: > How many total animals were observed at Oak Bluff? The agent searches for a trusted calculation that answers the question. If it finds one, it runs that calculation and reports the result with the green check-shield provenance marker for the `Verified answer` outcome. -> At Oak Bluff, 59 individual animals were observed across 5 species, based on 28 hours of survey effort. Note this reflects observed individuals during surveys, not necessarily a full census of every animal present at the site. +> At Oak Bluff, 59 individual animals were observed across 5 species, based on 28 hours of survey effort. Note this reflects observed individuals during surveys, not necessarily a full census of every animal present at the site. Verified answer The agent had to decide which trusted calculation to run. It did not have to decide what code to write. This removes degrees of freedom and lets the agent use code that you already vetted. diff --git a/scripts/sync-shared.sh b/scripts/sync-shared.sh index c92a0e3e..8bd04e2c 100755 --- a/scripts/sync-shared.sh +++ b/scripts/sync-shared.sh @@ -16,12 +16,14 @@ root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" # that copy; both packages ship the prompts and the browser assets. Nothing # in pkg-py reads www/ until its UI layer lands, but the destination belongs # with the move rather than with the first reader. The logos go to the two -# documentation sites, which serve them as they stand. +# documentation sites, and the provenance-marker figs to the Python one, which +# serve them as they stand. sources=( "tests/shared pkg-r/tests/testthat/fixtures/shared" "prompts pkg-r/inst/prompts pkg-py/src/commons/prompts" "www pkg-r/inst/www pkg-py/src/commons/www" "logos pkg-py/docs/assets/logos:bare pkg-r/pkgdown/assets/logos:bare" + "www/commons-chat/figs pkg-py/docs/assets/figs:bare" ) for entry in "${sources[@]}"; do From b868a520de95d5e43afc0c8e4667beb6586af266 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 13:35:26 -0600 Subject: [PATCH 14/30] docs: use uv pip in the Python install instructions --- pkg-py/docs/index.qmd | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/pkg-py/docs/index.qmd b/pkg-py/docs/index.qmd index 1496c857..197240da 100644 --- a/pkg-py/docs/index.qmd +++ b/pkg-py/docs/index.qmd @@ -13,26 +13,30 @@ Trusted calculations can come from Python functions (as _measures_) or [data dic commons requires Python 3.11 or later. To install the package, run: ```bash -pip install commons +uv pip install commons ``` -If you want the chat UI, install the `shiny` extra: +If you want the chat UI, install the `shiny` extra, as well: ```bash -pip install "commons[shiny]" +uv pip install "commons[shiny]" ``` Or, for the development version: ```bash -pip install "git+https://github.com/posit-dev/commons#subdirectory=pkg-py" +# just the core package: +uv pip install "commons @ git+https://github.com/posit-dev/commons#subdirectory=pkg-py" + +# with the shiny UI dependencies: +uv pip install "commons[shiny] @ git+https://github.com/posit-dev/commons#subdirectory=pkg-py" ``` ## Get started commons uses [chatlas](https://posit-dev.github.io/chatlas/) to access LLMs, so you will need access to one of chatlas's supported providers. -[Introduction to commons](get-started.qmd) explains the structure of a commons agent and the creation process. +The [Introduction to commons](get-started.qmd) page explains the structure of a commons agent and the creation process. ## Trusted answers @@ -40,8 +44,8 @@ There are two provenance paths available to a commons agent. When a user asks a Answers display provenance according to the analysis path followed, so users can determine how much trust to put in a given answer. -For more information, see [Introduction to commons](get-started.qmd). +For more information, see the [Introduction to commons](get-started.qmd) page. ## Evaluation -The [DevRel agent](https://github.com/posit-dev/devrel-agent) is an example commons agent that answers questions about adoption, engagement, and growth across Posit's open-source projects. Its repository contains an [evaluation](https://github.com/posit-dev/devrel-agent/tree/main/evals) comparing the commons agent with Claude Code on the same data; the [R documentation](../r/#evaluation) presents the results. +The [DevRel agent](https://github.com/posit-dev/devrel-agent) is an example commons agent (using the R implementation) that answers questions about adoption, engagement, and growth across Posit's open-source projects. Its repository contains an [evaluation](https://github.com/posit-dev/devrel-agent/tree/main/evals) comparing the commons agent with Claude Code on the same data; the [R documentation](../r/#evaluation) presents the results. From 13eaba4c7cab5c38fe5ba75d8e85eb475a6c4450 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 13:38:58 -0600 Subject: [PATCH 15/30] Add note about creating virtual environment in installation instructions --- pkg-py/docs/index.qmd | 1 + 1 file changed, 1 insertion(+) diff --git a/pkg-py/docs/index.qmd b/pkg-py/docs/index.qmd index 197240da..adda4244 100644 --- a/pkg-py/docs/index.qmd +++ b/pkg-py/docs/index.qmd @@ -13,6 +13,7 @@ Trusted calculations can come from Python functions (as _measures_) or [data dic commons requires Python 3.11 or later. To install the package, run: ```bash +# create a virtual environment with `uv venv` if you need first, then run: uv pip install commons ``` From 8f38239e7f9ccfe83922dbb98dbc5e54d43f84de Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 14:20:46 -0600 Subject: [PATCH 16/30] Update documentation with clearer explanations of trust flow and add visual provenance markers to outcomes table --- pkg-py/docs/get-started.qmd | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/pkg-py/docs/get-started.qmd b/pkg-py/docs/get-started.qmd index 552f517b..21604607 100644 --- a/pkg-py/docs/get-started.qmd +++ b/pkg-py/docs/get-started.qmd @@ -22,25 +22,25 @@ The agent searches for a trusted calculation that answers the question. If it fi > At Oak Bluff, 59 individual animals were observed across 5 species, based on 28 hours of survey effort. Note this reflects observed individuals during surveys, not necessarily a full census of every animal present at the site. Verified answer -The agent had to decide which trusted calculation to run. It did not have to decide what code to write. This removes degrees of freedom and lets the agent use code that you already vetted. +Although the agent had to decide *which* trusted calculation to run. It did not have to decide *what code to write*, reducing degrees of freedom and allowing the agent to use code that you already vetted. -Users also ask questions that leave the high-trust path. For those, the agent searches for context and writes custom SQL. Answers from this path show blue quote-mark citation markers, or the yellow exclamation provenance marker for the `Untrusted` outcome. +However, we also expect users to ask questions that stray from the “happy path.” For those, the agent searches for additional context and writes custom code (currently SQL, but soon custom Python code as well). Answers from this path either include a blue quote-mark citation marker if a citation was found in the added context, or display the yellow exclamation provenance marker for an `Untrusted` outcome. ## Trust flow -A commons agent uses trusted calculations when it can. When the user asks a question, the agent first searches the semantic layer for a trusted calculation. If it finds one, it calls that calculation, and the answer shows the green check-shield provenance marker for the `Verified answer` outcome. +A `commons` agent uses trusted calculations when it can. When the user asks a question, the agent first searches the semantic layer for a trusted calculation. If it finds one, it calls that calculation, and the answer shows the green check-shield provenance marker for the `Verified answer` outcome. -If no relevant trusted calculation exists, the agent takes the lower-trust path. It searches the context layer for information, inspects the tables it needs, and writes a read-only SQL query. These answers show blue quote-mark citation markers that open the details of a verified source, or the yellow exclamation provenance marker for the `Untrusted` outcome. +If a relevant trusted calculation is not found, the agent proceeds down the lower-trust path. It searches through the context for additional information, then uses that information to write custom SQL (or soon Python) code to answer the user’s question. These answers either include blue quote-mark citation markers that open details about verified sources or display the yellow exclamation provenance marker for an `Untrusted` outcome. -The lower-trust path has two possible provenance outcomes. When the agent writes custom SQL, it can also quote supporting text from a trusted source. If commons finds the quoted text in that source, the provenance outcome is `Cited`, and the answer shows blue quote-mark citation markers. If no citation verifies, the provenance outcome is `Untrusted`, and the answer shows the yellow exclamation provenance marker. +The lower-trust path has two possible provenance outcomes. When the agent writes custom SQL or Python, it can also include supporting text quoted from a trusted source. If commons verifies that the quoted text appears in that source, the provenance outcome is `Cited` and the answer displays blue quote-mark citation markers that open the source details. If no citation verifies, the provenance outcome is `Untrusted` and the answer displays the yellow exclamation provenance marker The table below lists how each provenance outcome can occur: | How the answer is produced | Provenance outcome | |---|---| -| A trusted Python [measure](#semantic-layer) | `Verified answer` | -| A [data dictionary metric](#definitions), possibly grouped or filtered with definitions | `Verified answer` | -| Custom SQL, including SQL that uses [data dictionary definitions](#definitions) | `Cited` or `Untrusted` | +| A trusted Python [measure](#semantic-layer) | `Verified answer` | +| A [data dictionary metric](#definitions), possibly grouped or filtered with definitions | `Verified answer` | +| Custom SQL, including SQL that uses [data dictionary definitions](#definitions) | `Cited` or `Untrusted` | | No data tool used (for example, the agent already had the information, or no accessible information answered the question) | No provenance outcome | The agent does not decide the provenance outcome. commons derives it from the tools the agent called and the citations it made. From a3fb9ddae6cb487bfd706dcc4464544dbf4a8556 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 14:43:43 -0600 Subject: [PATCH 17/30] Update documentation: clarify parameter types in measures and add example --- pkg-py/docs/get-started.qmd | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/pkg-py/docs/get-started.qmd b/pkg-py/docs/get-started.qmd index 21604607..2b52e447 100644 --- a/pkg-py/docs/get-started.qmd +++ b/pkg-py/docs/get-started.qmd @@ -32,7 +32,7 @@ A `commons` agent uses trusted calculations when it can. When the user asks a qu If a relevant trusted calculation is not found, the agent proceeds down the lower-trust path. It searches through the context for additional information, then uses that information to write custom SQL (or soon Python) code to answer the user’s question. These answers either include blue quote-mark citation markers that open details about verified sources or display the yellow exclamation provenance marker for an `Untrusted` outcome. -The lower-trust path has two possible provenance outcomes. When the agent writes custom SQL or Python, it can also include supporting text quoted from a trusted source. If commons verifies that the quoted text appears in that source, the provenance outcome is `Cited` and the answer displays blue quote-mark citation markers that open the source details. If no citation verifies, the provenance outcome is `Untrusted` and the answer displays the yellow exclamation provenance marker +The lower-trust path has two possible provenance outcomes. When the agent writes custom SQL or Python, it can also include supporting text quoted from a trusted source. If `commons` verifies that the quoted text appears in that source, the provenance outcome is `Cited` and the answer displays blue quote-mark citation markers that open the source details. If no citation verifies, the provenance outcome is `Untrusted` and the answer displays the yellow exclamation provenance marker The table below lists how each provenance outcome can occur: @@ -43,11 +43,11 @@ The table below lists how each provenance outcome can occur: | Custom SQL, including SQL that uses [data dictionary definitions](#definitions) | `Cited` or `Untrusted` | | No data tool used (for example, the agent already had the information, or no accessible information answered the question) | No provenance outcome | -The agent does not decide the provenance outcome. commons derives it from the tools the agent called and the citations it made. +The agent does not decide the provenance outcome. `commons` derives it from the tools the agent called and the citations it made. ## Information layers -commons has two primary layers of information: the **semantic layer** and the **context layer**. The semantic layer holds trusted calculations, ideally lifted from reliable code that you already use. The context layer holds background information. The agent uses this information to decide what custom SQL to write and how to interpret results. Data dictionaries span the two layers. +`commons` has two primary layers of information: the **semantic layer** and the **context layer**. The semantic layer holds trusted calculations, ideally lifted from reliable code that you already use. The context layer holds background information. The agent uses this information to decide what custom SQL to write and how to interpret results. Data dictionaries span the two layers. | Layer | Sources | Role | |---|---|---| @@ -58,7 +58,20 @@ commons has two primary layers of information: the **semantic layer** and the ** The most direct way to add a trusted calculation is a **measure**. A measure is a Python function decorated with `@commons.measure`. The decorator needs a description, from its `description` argument or from the docstring of the function. When measures exist, the agent searches for a measure that matches the question. If it finds one, it calls that measure, with arguments if the measure takes any. -A measure can take two kinds of parameter. A parameter annotated `Annotated[T, Field(description=...)]` is supplied by the model, and the model reads the description to decide how to call it. A parameter annotated `commons.Injected[T]` is supplied by commons and never reaches the model. commons injects the connection of the data source that has the same name as the parameter. A parameter with no description and no `Injected` annotation is a construction error. +A measure can take two kinds of parameter in its signature. A parameter declared as `Annotated[T, Field(description=...)]` gets its value from the model, which reads the description to decide what value to pass. A parameter declared as `commons.Injected[T]` gets its value from `commons`. The model never sees this parameter: `commons` passes the connection of the data source that has the same name as the parameter. A parameter with no description and no `Injected` annotation will result in an error. + +As an example, in this measure, `commons` passes `biodiversity` and the model passes `site`: + +```python +@commons.measure(description="Species observed at one site.") +def species_at_site( + biodiversity: commons.Injected[Any], # commons passes the connection + site: Annotated[str, Field(description="Site name.")], # the model passes a value +) -> Any: + ... +``` + +When a user asks "Which species were observed at Oak Bluff?", the model reads the description of `site` and passes `"Oak Bluff"`. `commons` passes the connection of the data source named `biodiversity`, and the measure runs with both. `data-dict.yaml` files also add to the semantic layer through [`definitions`](#definitions). From 92d6494ae57366124852bd4d3401a7513bb2f3a3 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 14:52:18 -0600 Subject: [PATCH 18/30] Improve data sources documentation with clearer explanations and code examples --- pkg-py/docs/get-started.qmd | 46 ++++++++++++++++++++++++++++--------- 1 file changed, 35 insertions(+), 11 deletions(-) diff --git a/pkg-py/docs/get-started.qmd b/pkg-py/docs/get-started.qmd index 2b52e447..229ae5c6 100644 --- a/pkg-py/docs/get-started.qmd +++ b/pkg-py/docs/get-started.qmd @@ -134,9 +134,9 @@ Observed individuals reflect organisms recorded during surveys. They are not a c ## Data sources -One of the first decisions for a commons agent is which data the agent can reach. A data source joins the data with the tables that the agent can see. It can also carry a data dictionary that describes those tables and their trusted definitions. +One of the primary decisions you’ll need to make when building a `commons` agent is which data sources to grant the agent access to. Each data source combines the underlying data with the tables to expose to the agent. It can also include a data dictionary describing those tables and trusted calculations on them. -Create a data source with `commons.data_source()`. The data can be named pandas or polars data frames, a [pins](https://rstudio.github.io/pins-python/) board, or a SQLAlchemy `Engine`. commons loads data frames and pins into an in-process DuckDB database. commons queries a database engine directly and copies nothing. +Data sources are created with `commons.data_source()`. The data can be named `pandas` or `polars` data frames, a [pins](https://rstudio.github.io/pins-python/) board, or a SQLAlchemy `Engine`. `commons` loads data frames and pins into an in-process DuckDB database. `commons` queries a database engine directly without copying data into a local database. For example, the following code creates a data source from two data frames. Each name becomes a table that the agent can query. `dictionary` is an optional path to a `data-dict.yaml` file. @@ -148,13 +148,37 @@ biodiversity = commons.data_source( ) ``` -For an engine or a pins board, the `tables` argument selects the tables to expose. A Snowflake or Databricks engine also imports its catalog. If that catalog is too large for the system prompt, the agent gets a `search_catalog` tool. +For an engine or a pins board, the `tables` argument selects the tables to expose. An engine takes a list of table names: + +```python +import sqlalchemy + +engine = sqlalchemy.create_engine("duckdb:///surveys.duckdb") +surveys = commons.data_source(engine, tables=["observations", "sites"]) +``` + +With a pins board, `tables` maps each table name the agent sees to the pin that supplies the data: + +```python +surveys = commons.data_source( + board, + tables={"observations": "survey-observations", "sites": "survey-sites"}, +) +``` + +A Snowflake or Databricks engine also imports its catalog, so commons can resolve the selection against the warehouse and check access. `exclude` drops objects from the catalog listing by glob: + +```python +warehouse = commons.data_source(engine, exclude=["TMP_*"]) +``` + +If the catalog is too large for the system prompt, the agent gets a `search_catalog` tool instead of a table listing. The system prompt reports only the number of selected catalog objects and tells the agent to call `search_catalog` before `describe_table`. The agent then searches the catalog for objects that match the question and describes only those before it writes SQL. ### Data dictionaries -A data dictionary provides structured documentation for one data source. Use it to state what each table represents, the meaning and type of each column, relationships between tables, and glossary terms. It also holds trusted `definitions`. commons reads the [`data-dict.yaml` specification](https://data-dict.tidyverse.org/). +A data dictionary provides structured documentation for one data source. Use it to state what each table represents, the meaning and type of each column, relationships between tables, and glossary terms. It also holds trusted `definitions`. `commons` reads the [`data-dict.yaml` specification](https://data-dict.tidyverse.org/). -A commons agent uses a data dictionary in three ways: +A `commons` agent uses a data dictionary in three ways: - Dataset-level descriptions give broad context that is always available. Glossary terms go in the system prompt as space allows. - When the agent first uses a documented table in a conversation, it receives the description, columns, relationships, and glossary terms of that table. @@ -187,17 +211,17 @@ There are three kinds of definition. A definition can take part in a trusted met | Filter | `status = 'active'` | Restricts rows or provides a grouping dimension | | Derived value | `price * quantity` | Provides a grouping dimension | -commons infers the kind from the expression. An aggregate or constant expression is a metric. A row-level Boolean expression is a filter. Every other row-level expression is a derived value. +`commons` infers the kind from the expression. An aggregate or constant expression is a metric. A row-level Boolean expression is a filter. Every other row-level expression is a derived value. -[^definition-sql]: In custom SQL, the agent writes a definition as a `{{name}}` token, and commons expands it to the SQL compiled for the data source. This is still custom SQL, so the provenance outcome is `Cited` or `Untrusted`, not `Verified answer`. +[^definition-sql]: In custom SQL, the agent writes a definition as a `{{name}}` token, and `commons` expands it to the SQL compiled for the data source. This is still custom SQL, so the provenance outcome is `Cited` or `Untrusted`, not `Verified answer`. -See the [DevRel Agent `data-dict.yaml`](https://github.com/posit-dev/devrel-agent/blob/main/dictionaries/devrel.data-dict.yaml) for examples of definitions. +See the [DevRel Agent's `data-dict.yaml`](https://github.com/posit-dev/devrel-agent/blob/main/dictionaries/devrel.data-dict.yaml) for examples of definitions. -When you construct a data source, commons validates each definition and compiles it for DuckDB, Snowflake, or Databricks, whichever the source uses. Construction fails for another dialect, a definition on a table the source does not expose, or a metric of mixed row and aggregate grain. +When a data source is constructed, `commons` validates each definition and compiles it to the source’s SQL dialect. ## Project directory organization -A commons agent is easiest to maintain when each piece has its own file: +A `commons` agent is easiest to maintain when each piece has its own file: ```text . @@ -216,7 +240,7 @@ A commons agent is easiest to maintain when each piece has its own file: ## Constructing the agent -Use `commons.Commons()` to construct an agent. Pass it a chatlas `Chat` and one or more data sources, plus any semantic and context layers. The `instructions` argument adds text to the end of the commons system prompt. Pass a string, or the path to a text or Markdown file. +Use `commons.Commons()` to construct an agent. Pass it a ``chatlas.Chat` and one or more data sources, plus any semantic and context layers. You can also optionally append information to the `commons` agent system prompt using the `instructions` argument. ```python import chatlas From 9ddf7aca4123f40ef3dc775e236f2644337896f3 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 15:10:16 -0600 Subject: [PATCH 19/30] docs: illustrate and mark up the Python Get started page Add the provenance markers to the outcomes table, rewrite the measure-parameter paragraph in plainer English with an example, give examples for table selection and warehouse catalogs, and use named client=/data_sources= arguments in every user-facing Commons() call, as the constructor names them. --- pkg-py/README.md | 4 ++-- pkg-py/demo.ipynb | 4 ++-- pkg-py/demo.py | 4 ++-- pkg-py/docs/get-started.qmd | 6 +++--- 4 files changed, 9 insertions(+), 9 deletions(-) diff --git a/pkg-py/README.md b/pkg-py/README.md index 57d7622a..ae47cd99 100644 --- a/pkg-py/README.md +++ b/pkg-py/README.md @@ -33,8 +33,8 @@ def region_revenue( agent = commons.Commons( - chatlas.ChatAnthropic(model="claude-sonnet-5"), - commons.data_source(sales=sales, dictionary="data-dict.yaml"), + client=chatlas.ChatAnthropic(model="claude-sonnet-5"), + data_sources=commons.data_source(sales=sales, dictionary="data-dict.yaml"), semantic_layer=commons.semantic_layer(region_revenue), context_layer=commons.context_layer(files=["reporting-policy.md"]), ) diff --git a/pkg-py/demo.ipynb b/pkg-py/demo.ipynb index 0e49c9c8..13678b5c 100644 --- a/pkg-py/demo.ipynb +++ b/pkg-py/demo.ipynb @@ -318,8 +318,8 @@ ")\n", "\n", "mine = commons.Commons(\n", - " demo.client(),\n", - " {'warehouse': commons.data_source(stands=demo.stands, surveys=demo.surveys)},\n", + " client=demo.client(),\n", + " data_sources={'warehouse': commons.data_source(stands=demo.stands, surveys=demo.surveys)},\n", " semantic_layer=layer,\n", " context_layer=commons.context_layer(files=[demo.notes_file()]),\n", ")\n", diff --git a/pkg-py/demo.py b/pkg-py/demo.py index ae458491..01ba41a7 100644 --- a/pkg-py/demo.py +++ b/pkg-py/demo.py @@ -221,9 +221,9 @@ def client() -> chatlas.Chat: def agent() -> commons.Commons: """The canopy agent, over all three layers.""" return commons.Commons( - client(), + client=client(), # Named, because a measure's `warehouse` argument is injected by name. - {"warehouse": commons.data_source(stands=stands, surveys=surveys)}, + data_sources={"warehouse": commons.data_source(stands=stands, surveys=surveys)}, semantic_layer=commons.semantic_layer( canopy_by_county, low_canopy_stands, canopy_headline ), diff --git a/pkg-py/docs/get-started.qmd b/pkg-py/docs/get-started.qmd index 229ae5c6..90a0c10d 100644 --- a/pkg-py/docs/get-started.qmd +++ b/pkg-py/docs/get-started.qmd @@ -240,7 +240,7 @@ A `commons` agent is easiest to maintain when each piece has its own file: ## Constructing the agent -Use `commons.Commons()` to construct an agent. Pass it a ``chatlas.Chat` and one or more data sources, plus any semantic and context layers. You can also optionally append information to the `commons` agent system prompt using the `instructions` argument. +Use `commons.Commons()` to construct an agent. Pass it a `chatlas.Chat` and one or more data sources, plus any semantic and context layers. You can also optionally append information to the `commons` agent system prompt using the `instructions` argument. ```python import chatlas @@ -253,8 +253,8 @@ biodiversity = commons.data_source( ) agent = commons.Commons( - chatlas.ChatAnthropic(model="claude-sonnet-5"), - {"biodiversity": biodiversity}, + client=chatlas.ChatAnthropic(model="claude-sonnet-5"), + data_sources={"biodiversity": biodiversity}, semantic_layer=commons.semantic_layer("measures"), context_layer=commons.context_layer(files=["context/context.md"]), instructions="instructions.md", From 85adaf1f337b60dc892bde658d1611c6a6128c2b Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 15:18:38 -0600 Subject: [PATCH 20/30] Improve documentation clarity and update Commons agent API information --- pkg-py/docs/get-started.qmd | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/pkg-py/docs/get-started.qmd b/pkg-py/docs/get-started.qmd index 90a0c10d..42d68194 100644 --- a/pkg-py/docs/get-started.qmd +++ b/pkg-py/docs/get-started.qmd @@ -265,17 +265,24 @@ agent.chat("How many species were observed at Oak Bluff?") `data_sources` is one `DataSource` or a mapping of name to `DataSource`. Name the sources when a measure takes an injected connection, because commons injects by name. `semantic_layer()` accepts measures, modules, or paths to `.py` files and directories. `context_layer()` accepts a list of file paths. -A `Commons` agent is a chatlas `Chat`. It builds its own system prompt and tools, so it does not carry over the system prompt or the turns on `client`. `chat()` and `stream_async()` are the two ways to ask a question. The other chatlas entry points, such as `chat_async()`, `stream()`, and `chat_structured()`, raise `NotImplementedError`, because an answer through them carries no citations and no provenance marker. +Underneath, a `Commons` agent inherits from a `chatlas.Chat` object, but builds its own system prompt and tools. As such, it follows the `Chat` API and provides the `chat()` and `stream_async()` methods as ways to ask a question. The other `chatlas` entry points, such as `chat_async()`, `stream()`, and `chat_structured()`, are not supported and will raise `NotImplementedError` at this moment. ### Chat UI -`commons.ui` puts the agent behind a py-shiny chat. It needs the `shiny` extra. For local development, `commons.ui.app(agent)` returns a complete Shiny app: +The above examples will build a terminal-based agent without any UI attached. If you want to +use the agent in an interactive web UI, the `commons.ui` module will put the agent behind a +[Shiny](https://shiny.posit.co/py/) chat. This requires installing the `shiny` extra group. + +When developing locally, you can use `commons.ui.app(agent)` to return a complete Shiny app: ```python app = commons.ui.app(agent) ``` -`commons.ui.app()` shares one agent across every session, so it suits one visitor at a time. For a deployed app, build the page with `commons.ui.theme()`, and construct the agent inside the server function. Then pass it to `commons.ui.server()`, so each session gets its own agent: +`commons.ui.app()` shares one agent across every session, so it suits one visitor at a time. +For a deployed app, you should instead build the page with `commons.ui.theme()`, and construct +the agent inside the Shiny server function, then pass it to `commons.ui.server()`, +so each session gets its own agent: ```python import shinychat @@ -290,7 +297,3 @@ def app_server(input, output, session): app = App(app_ui, app_server) ``` - -`commons.ui.server()` warms the agent once the session is idle after its first page. `agent.prewarm()` builds the context index and reads pending pins before a deployment. This example assumes that `observations` and `site_area` are data frames loaded when the app starts. - -The commons agent skill for coding agents, and the tools that review logged conversations, ship with the R package today. From 953aa56f9ff4bf8d70c0f7aa060944185a7fd315 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 15:23:02 -0600 Subject: [PATCH 21/30] Update documentation: expand deployed app example with full Commons initialization --- pkg-py/docs/get-started.qmd | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/pkg-py/docs/get-started.qmd b/pkg-py/docs/get-started.qmd index 42d68194..bb4b4f6b 100644 --- a/pkg-py/docs/get-started.qmd +++ b/pkg-py/docs/get-started.qmd @@ -280,9 +280,8 @@ app = commons.ui.app(agent) ``` `commons.ui.app()` shares one agent across every session, so it suits one visitor at a time. -For a deployed app, you should instead build the page with `commons.ui.theme()`, and construct -the agent inside the Shiny server function, then pass it to `commons.ui.server()`, -so each session gets its own agent: +For a deployed app, you should instead build the page with `commons.ui.theme()` and construct +the agent inside the Shiny server function, so each session gets its own agent: ```python import shinychat @@ -292,7 +291,18 @@ app_ui = shinychat.page_chat("Biodiversity", id="chat", theme=commons.ui.theme() def app_server(input, output, session): - commons.ui.server("chat", make_agent()) + # The same construction as above; building it per session keeps each + # session's citation and provenance state separate. + commons.ui.server( + "chat", + commons.Commons( + client=chatlas.ChatAnthropic(model="claude-sonnet-5"), + data_sources={"biodiversity": biodiversity}, + semantic_layer=commons.semantic_layer("measures"), + context_layer=commons.context_layer(files=["context/context.md"]), + instructions="instructions.md", + ), + ) app = App(app_ui, app_server) From a97648e5e994770a3761c5ee67fc8dc514413152 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 15:27:11 -0600 Subject: [PATCH 22/30] docs: add a page footer to the Python site Mirror the pkgdown footer: developer credit on the left, a Built with Quarto link on the right. --- pkg-py/docs/_quarto.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/pkg-py/docs/_quarto.yml b/pkg-py/docs/_quarto.yml index 6130b269..ddb68e9b 100644 --- a/pkg-py/docs/_quarto.yml +++ b/pkg-py/docs/_quarto.yml @@ -32,6 +32,9 @@ website: sidebar: - id: reference contents: reference/_sidebar.yml + page-footer: + left: "Developed by Josh Taillon, Posit Software, PBC." + right: "Site built with [Quarto](https://quarto.org/)." format: html: From 6bc22e0d5bd1a0bf07242f33bfdfca875a723c7b Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 15:48:19 -0600 Subject: [PATCH 23/30] Add feature parity documentation and update governance page --- pkg-py/docs/_quarto.yml | 3 +++ pkg-py/docs/feature-parity.qmd | 15 ++++++++++++++ pkg-py/docs/governance.qmd | 38 ++++++++++++++-------------------- 3 files changed, 34 insertions(+), 22 deletions(-) create mode 100644 pkg-py/docs/feature-parity.qmd diff --git a/pkg-py/docs/_quarto.yml b/pkg-py/docs/_quarto.yml index ddb68e9b..35e10fe3 100644 --- a/pkg-py/docs/_quarto.yml +++ b/pkg-py/docs/_quarto.yml @@ -8,6 +8,7 @@ project: - index.qmd - get-started.qmd - governance.qmd + - feature-parity.qmd - reference/*.qmd website: @@ -22,6 +23,8 @@ website: text: Get started - href: governance.qmd text: Security and governance + - href: feature-parity.qmd + text: Feature parity - href: reference/index.qmd text: Reference right: diff --git a/pkg-py/docs/feature-parity.qmd b/pkg-py/docs/feature-parity.qmd new file mode 100644 index 00000000..e9c44418 --- /dev/null +++ b/pkg-py/docs/feature-parity.qmd @@ -0,0 +1,15 @@ +--- +title: "Feature parity with the R package" +--- + +While both packages are first-class implementations of `commons`, the R implementation was developed first, and the Python package does not yet implement everything the R package does. This page lists the gaps that remain as of the current version and what a Python agent does instead. + +| R-only feature | What a Python agent does instead | +|---|---| +| Snowflake semantic views and Databricks metric views as trusted calculations, answered `Verified answer` | Imports the catalog of a Snowflake or Databricks engine, but answers these questions with custom SQL, so `Cited` or `Untrusted` | +| Custom R code from the agent, run in a sandboxed worker process | Writes custom SQL only | +| gt tables and ggplot figures returned by measures, rendered in the chat | Renders data-frame results as tables | +| `trajectory_read()` and `trajectory_review()`, for reading and annotating logged conversations | No equivalent yet | +| The commons agent skill for coding agents | Ships with the R package only | + +This page will shrink (and eventually disappear) as the Python package matures. diff --git a/pkg-py/docs/governance.qmd b/pkg-py/docs/governance.qmd index 8008e592..ff010930 100644 --- a/pkg-py/docs/governance.qmd +++ b/pkg-py/docs/governance.qmd @@ -2,54 +2,48 @@ title: "Security and governance for commons agents" --- -commons lets you build and deploy agents that run SQL queries against live databases. This raises security and governance questions. How do you make sure that an agent cannot delete production data? Can an agent show users tables, rows, or business context that they are not allowed to see? +`commons` allows users to build and deploy agents that can run SQL queries against live databases and (soon) execute arbitrary Python code. This raises several security and governance questions: How do you make sure that an agent cannot delete production data? Can model-generated Python code interfere with a [Posit Connect](https://posit.co/products/enterprise/connect) deployment? Can an agent show users tables, rows, or business context that they shouldn't be able to see? -This page explains the boundaries that commons provides and the responsibilities that remain with the application author and server administrator. +This page explains the boundaries that `commons` provides and the responsibilities that remain with the application author and server administrator. -## commons' harness +## The `commons` harness -A commons agent is a chatlas chat with a system prompt and a set of tools. The system prompt lists the available data sources and business context. Tools let the model retrieve more context, call a trusted calculation, or run a SQL query. +A `commons` agent is a `chatlas.Chat` object that carries a system prompt and a set of tools. The system prompt lists the available data sources and business context. Tools let the model retrieve more context, call a trusted calculation, or run a SQL query. -We assume that a model can make any request that its tools allow. Application security must therefore not depend on the model obeying an instruction such as "never reveal sensitive data." Instead, give an agent access only to data that the current user of the application is allowed to see. The system prompt, tool arguments, and tool results also go to the model provider. The agent must not have access to data that you [do not trust that provider to process](https://posit.co/blog/trust-llm-tools). +We assume that a model might make any request allowed by its tools. Application security should therefore not depend on the model following an instruction like “never reveal sensitive data.” Instead, only give an agent access to data that the current user of the application is allowed to see. The system prompt, tool arguments, and tool results are also sent to the model provider. The agent must therefore not have access to data that you [do not trust that provider to process](https://posit.co/blog/trust-llm-tools). ## SQL code execution ### Destructive actions -The agent can run one read-only statement at a time. commons parses each statement with [sqlglot](https://github.com/tobymao/sqlglot) and checks its structure, not its text. The check is an allowlist of statement forms that only read, such as `SELECT` and the set operations. commons refuses every other form, stacked statements, and statements that sqlglot cannot parse. A form that nobody thought of therefore fails closed. +The agent can run one read-only statement at a time. `commons` parses each statement with [sqlglot](https://github.com/tobymao/sqlglot) and checks its structure against an allowlist of statement forms that only read, such as `SELECT` and the set operations. `commons` refuses every other form, stacked statements, and statements that `sqlglot` cannot parse, so it fails safe/closed. -The check also catches a write hidden inside a read. `WITH t AS (SELECT 1) DELETE FROM sales` arrives as a `DELETE`, `WITH d AS (DELETE ... RETURNING *) SELECT * FROM d` deletes rows through its CTE, and `SELECT ... INTO t` creates a table. The guard refuses a statement with a write, DDL, or session-changing node anywhere in its tree, such as `DROP`, `ATTACH`, `GRANT`, `SET`, or `USE`. It also refuses locking reads such as `SELECT ... FOR UPDATE`, because they block other writers. +For data frames and pins, which `commons` loads into its own DuckDB database, it also hardens the connection. It disables community and unsigned extensions, automatic extension install and load, external access, and the local filesystem. Then it sets `lock_configuration`, so a query cannot turn those settings back on. -For data frames and pins, which commons loads into its own DuckDB database, it also hardens the connection. It disables community and unsigned extensions, automatic extension install and load, external access, and the local filesystem. Then it sets `lock_configuration`, so a query cannot turn those settings back on. The lock freezes settings only, not tables, which is how commons can still load a pin's rows on first use. - -These checks provide defense in depth, but they are not a database sandbox. When you supply a SQLAlchemy engine, commons queries it as-is, and a statement that the parser accepts runs with all the permissions of that connection. The primary safeguard against destructive SQL is therefore database-enforced read-only access. Open the connection as a read-only user or role. +These checks provide defense in depth, but they are not a database sandbox. When you supply a database connection, `commons` queries it as-is, and a statement that the parser accepts runs with all the permissions of that connection. The primary safeguard against destructive SQL is therefore database-enforced read-only access, and you should open the connection as a read-only user or role. ### Data access -The `tables` argument to `data_source()` controls which tables commons describes to the model. It is not an authorization boundary. SQL written by the agent can query any object that the connection can reach. +The `tables` argument to `data_source()` controls which tables `commons` describes to the model, but it is not an authorization boundary. SQL written by the agent can query any object that the connection can reach. On Posit Connect, [viewer OAuth integrations](https://docs.posit.co/connect/admin/access-controls/) can give an interactive application the current viewer's Snowflake or Databricks credentials. If the application creates its engine from those credentials, the warehouse continues to enforce that viewer's existing access policies, including row-level and column-level security. -For a Snowflake or Databricks source, commons records the connection identity when it builds the data source. It records the current user, catalog (database), and schema. On Snowflake it also records the active role and the secondary roles. Before every query and every table description, commons reads the identity again and compares it with the record. If any field changed, commons raises an error, and the application must rebuild the data source. For any other backend, commons records no identity. +For a Snowflake or Databricks source, `commons` snapshots the connection’s principal and namespace when it creates a Snowflake or Databricks data source, and its active and secondary roles as well on Snowflake, and rejects subsequent operations if that identity changes. -Viewer credentials are not automatic. commons uses the engine that the application supplies. When you use viewer credentials, create the engine and the commons agent inside the Shiny server function. Then each session has the correct database identity. +Viewer credentials are not automatic. `commons` uses the engine that the application supplies. When using viewer credentials, you should create the engine and the `commons` agent inside the Shiny server function so that each session has the correct database identity. -When viewer credentials are not available, use a service account with access only to the data that the application needs. Every viewer then has the same database permissions, so share the application only with users who are allowed that access. +When viewer credentials are not available, it is recommended to use a service account with access only to the data that the application needs. Every viewer then has the same database permissions, so share the application only with users who are allowed that access. -## No code execution +## Python code execution -The Python agent has no tool that runs code written by the model. Its tools search context, describe tables, call trusted calculations, and run SQL. commons creates no subprocess, and no Python code from the model runs in the application process or anywhere else. The questions about sandboxing a model's code, such as filesystem, network, and system-call limits, do not arise for this package. The SQL checks above, and the permissions of the connection, are the whole boundary. +The Python agent currently has no tool that runs code written by the model, but this feature is expected in the near future. Currently, there is no concern for Python `commons` agents executing arbitrary Python code. When the feature is added, it will generally follow the R package's [R code execution](../r/articles/governance.html#r-code-execution) design. ## Permissioning facts The rows returned by SQL are not the only sensitive information available to an agent. The description and details of each data dictionary, and its glossary entries up to a size cap, go into the system prompt. The full dictionary entry for a table, with a summary of sampled rows, goes to the model when a table is first described or queried. Context documents and the rest of the dictionary prose are available to the model through search. -Only include facts that you can share with both the application's viewers and its model provider. If one audience must not see a fact, use separate applications with separate context, or put the facts behind viewer credentials. Do not ask the model to hide the fact from that audience. +Because of this, you should only include facts that you can share with both the application's viewers and its model provider. If one audience must not see a fact, use separate applications with separate context, or put the facts behind viewer credentials. Asking the model to hide the fact from that audience is not a reliable means of preventing unauthorized access to the information. ## Telemetry -The Python package has no option to log conversations. The `Commons` constructor has no `log` argument, and there is no API to read or review trajectories. Conversation logging and review are features of the R package today. - -commons emits [OpenTelemetry](https://opentelemetry.io/) spans under the tracer name `co.posit.python-package.commons`. Today the only span is `commons_server_start`, which `commons.ui.server()` opens when it wires an agent to a chat element. The span carries the id of the chat element and no message content. The OpenTelemetry API records nothing until the application configures an SDK provider and an exporter. - -Other libraries in the process, such as chatlas, can emit spans of their own. Their content is governed by those libraries and by your OpenTelemetry configuration, not by commons. +The Python implementation of `commons` currently does not support telemetry or conversation logging, though this feature is planned to be added in the near future. When it is added, it will generally follow the R package's [Logging trajectories](../r/articles/governance.html#logging-trajectories) design. From d3a3874f20db434482215ebbb7b40d981bc4cb9d Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 15:51:37 -0600 Subject: [PATCH 24/30] Update feature parity documentation with clarifications and future roadmap notes --- pkg-py/docs/feature-parity.qmd | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/pkg-py/docs/feature-parity.qmd b/pkg-py/docs/feature-parity.qmd index e9c44418..07cdf556 100644 --- a/pkg-py/docs/feature-parity.qmd +++ b/pkg-py/docs/feature-parity.qmd @@ -2,13 +2,13 @@ title: "Feature parity with the R package" --- -While both packages are first-class implementations of `commons`, the R implementation was developed first, and the Python package does not yet implement everything the R package does. This page lists the gaps that remain as of the current version and what a Python agent does instead. +While both Python and R packages are first-class implementations of `commons`, the R implementation was developed first, and the Python package does not yet implement everything the R package does. This page lists the gaps that remain as of the current version and what a Python agent does instead. | R-only feature | What a Python agent does instead | |---|---| | Snowflake semantic views and Databricks metric views as trusted calculations, answered `Verified answer` | Imports the catalog of a Snowflake or Databricks engine, but answers these questions with custom SQL, so `Cited` or `Untrusted` | -| Custom R code from the agent, run in a sandboxed worker process | Writes custom SQL only | -| gt tables and ggplot figures returned by measures, rendered in the chat | Renders data-frame results as tables | +| Custom R code from the agent, run in a sandboxed worker process | Writes custom SQL only, with Python execution coming soon | +| `gt` tables and `ggplot` figures returned by measures, rendered in the chat | Renders data-frame results as tables, with no current plot support | | `trajectory_read()` and `trajectory_review()`, for reading and annotating logged conversations | No equivalent yet | | The commons agent skill for coding agents | Ships with the R package only | From 10ae648ef0d385f5707d1ee3ff1d7beecc6af985 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 15:56:06 -0600 Subject: [PATCH 25/30] Update documentation: rename "Reference" to "API Reference" and add demo application links --- pkg-py/docs/_quarto.yml | 2 +- pkg-py/docs/get-started.qmd | 4 ++++ pkg-py/docs/index.qmd | 2 +- 3 files changed, 6 insertions(+), 2 deletions(-) diff --git a/pkg-py/docs/_quarto.yml b/pkg-py/docs/_quarto.yml index 35e10fe3..7928a6a2 100644 --- a/pkg-py/docs/_quarto.yml +++ b/pkg-py/docs/_quarto.yml @@ -26,7 +26,7 @@ website: - href: feature-parity.qmd text: Feature parity - href: reference/index.qmd - text: Reference + text: API Reference right: - text: '' href: ../r/ diff --git a/pkg-py/docs/get-started.qmd b/pkg-py/docs/get-started.qmd index bb4b4f6b..6b22c14a 100644 --- a/pkg-py/docs/get-started.qmd +++ b/pkg-py/docs/get-started.qmd @@ -307,3 +307,7 @@ def app_server(input, output, session): app = App(app_ui, app_server) ``` + +## Example application + +[`demo.py`](https://github.com/posit-dev/commons/blob/main/pkg-py/demo.py), in the Python package's directory, is a fuller worked example: an agent over made-up forest canopy data with a semantic layer of measures and a context layer. Run it with `uv run shiny run demo.py` for the chat UI, or `uv run python demo.py` to ask the same questions from the terminal. [`demo.ipynb`](https://github.com/posit-dev/commons/blob/main/pkg-py/demo.ipynb) is the same agent in a notebook, with cells for reading what the agent registered and adding a measure of your own. diff --git a/pkg-py/docs/index.qmd b/pkg-py/docs/index.qmd index adda4244..667e2c09 100644 --- a/pkg-py/docs/index.qmd +++ b/pkg-py/docs/index.qmd @@ -37,7 +37,7 @@ uv pip install "commons[shiny] @ git+https://github.com/posit-dev/commons#subdir commons uses [chatlas](https://posit-dev.github.io/chatlas/) to access LLMs, so you will need access to one of chatlas's supported providers. -The [Introduction to commons](get-started.qmd) page explains the structure of a commons agent and the creation process. +The [Introduction to commons](get-started.qmd) page explains the structure of a commons agent and the creation process. For a complete example application, see [`demo.py`](https://github.com/posit-dev/commons/blob/main/pkg-py/demo.py) in the repository. ## Trusted answers From a3c12bfef371a11ae2bd140046fd057cf1189b1a Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 15:59:59 -0600 Subject: [PATCH 26/30] Format package name with backticks for consistency in documentation --- pkg-py/docs/index.qmd | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/pkg-py/docs/index.qmd b/pkg-py/docs/index.qmd index 667e2c09..989c5a7c 100644 --- a/pkg-py/docs/index.qmd +++ b/pkg-py/docs/index.qmd @@ -2,15 +2,15 @@ title: "commons" --- -commons helps data scientists build trustworthy data agents. +`commons` helps data scientists build trustworthy data agents. -Data teams typically have trusted code that they use to analyze their data and build reports and apps. commons leverages that expertise, situating the information in a series of prompts and tools designed to create an accurate, fast, and cost-effective agent. +Data teams typically have trusted code that they use to analyze their data and build reports and apps. `commons` leverages that expertise, situating the information in a series of prompts and tools designed to create an accurate, fast, and cost-effective agent. Trusted calculations can come from Python functions (as _measures_) or [data dictionary](https://data-dict.tidyverse.org/) definitions. ## Installation -commons requires Python 3.11 or later. To install the package, run: +`commons` requires Python 3.11 or later. To install the package, run: ```bash # create a virtual environment with `uv venv` if you need first, then run: @@ -35,18 +35,18 @@ uv pip install "commons[shiny] @ git+https://github.com/posit-dev/commons#subdir ## Get started -commons uses [chatlas](https://posit-dev.github.io/chatlas/) to access LLMs, so you will need access to one of chatlas's supported providers. +`commons` uses [`chatlas`](https://posit-dev.github.io/chatlas/) to access LLMs, so you will need access to one of chatlas's supported providers. -The [Introduction to commons](get-started.qmd) page explains the structure of a commons agent and the creation process. For a complete example application, see [`demo.py`](https://github.com/posit-dev/commons/blob/main/pkg-py/demo.py) in the repository. +The [Introduction to commons](get-started.qmd) page explains the structure of a `commons` agent and the creation process. For a complete example application, see [`demo.py`](https://github.com/posit-dev/commons/blob/main/pkg-py/demo.py) in the repository. ## Trusted answers -There are two provenance paths available to a commons agent. When a user asks a question for which there is trusted code, the agent runs that code and reports the result. When no trusted code covers the question, the agent writes custom SQL, leaning on additional context provided to the agent. +There are two provenance paths available to a `commons` agent. When a user asks a question for which there is trusted code, the agent runs that code and reports the result. When no trusted code covers the question, the agent writes custom SQL, leaning on additional context provided to the agent. Answers display provenance according to the analysis path followed, so users can determine how much trust to put in a given answer. -For more information, see the [Introduction to commons](get-started.qmd) page. +For more information, see the [Introduction to `commons`](get-started.qmd) page. ## Evaluation -The [DevRel agent](https://github.com/posit-dev/devrel-agent) is an example commons agent (using the R implementation) that answers questions about adoption, engagement, and growth across Posit's open-source projects. Its repository contains an [evaluation](https://github.com/posit-dev/devrel-agent/tree/main/evals) comparing the commons agent with Claude Code on the same data; the [R documentation](../r/#evaluation) presents the results. +The [DevRel agent](https://github.com/posit-dev/devrel-agent) is an example `commons` agent (using the R implementation) that answers questions about adoption, engagement, and growth across Posit's open-source projects. Its repository contains an [evaluation](https://github.com/posit-dev/devrel-agent/tree/main/evals) comparing the `commons` agent with Claude Code on the same data; the [R documentation](../r/#evaluation) presents the results. From 4ca7918c7df71d90d1ea6b8def0eccde7a493883 Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 16:11:52 -0600 Subject: [PATCH 27/30] Update navigation link labels to include "Documentation" --- docs/index.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/index.html b/docs/index.html index 935ce000..ce3af2a7 100644 --- a/docs/index.html +++ b/docs/index.html @@ -24,8 +24,8 @@

commons

From 843bf59929434fc34ee83376307cfb55d84121af Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 17:03:40 -0600 Subject: [PATCH 28/30] docs: show the hex sticker on the Python home page The sticker becomes a synced root artifact so the landing page and the Python site share one source, rather than each carrying its own copy. The R site keeps taking it from man/figures/logo.png, pkgdown's convention. --- .github/workflows/verify-shared-synced.yaml | 6 ++++++ .../{commons-logo.png => hex/commons-hex.png} | Bin docs/index.html | 6 +++--- hex/README.md | 7 +++++++ hex/commons-hex.png | Bin 0 -> 365371 bytes pkg-py/docs/assets/hex/commons-hex.png | Bin 0 -> 365371 bytes pkg-py/docs/assets/styles.css | 19 ++++++++++++++++++ pkg-py/docs/index.qmd | 4 ++++ scripts/sync-shared.sh | 5 +++-- 9 files changed, 42 insertions(+), 5 deletions(-) rename docs/assets/{commons-logo.png => hex/commons-hex.png} (100%) create mode 100644 hex/README.md create mode 100644 hex/commons-hex.png create mode 100644 pkg-py/docs/assets/hex/commons-hex.png diff --git a/.github/workflows/verify-shared-synced.yaml b/.github/workflows/verify-shared-synced.yaml index c6851da7..4261bf06 100644 --- a/.github/workflows/verify-shared-synced.yaml +++ b/.github/workflows/verify-shared-synced.yaml @@ -11,6 +11,7 @@ on: - prompts/** - www/** - logos/** + - hex/** - pkg-r/tests/testthat/fixtures/shared/** - pkg-r/inst/prompts/** - pkg-r/inst/www/** @@ -18,6 +19,8 @@ on: - pkg-py/src/commons/www/** - pkg-py/docs/assets/logos/** - pkg-py/docs/assets/figs/** + - pkg-py/docs/assets/hex/** + - docs/assets/hex/** - pkg-r/pkgdown/assets/logos/** - scripts/sync-shared.sh - .github/workflows/verify-shared-synced.yaml @@ -27,6 +30,7 @@ on: - prompts/** - www/** - logos/** + - hex/** - pkg-r/tests/testthat/fixtures/shared/** - pkg-r/inst/prompts/** - pkg-r/inst/www/** @@ -34,6 +38,8 @@ on: - pkg-py/src/commons/www/** - pkg-py/docs/assets/logos/** - pkg-py/docs/assets/figs/** + - pkg-py/docs/assets/hex/** + - docs/assets/hex/** - pkg-r/pkgdown/assets/logos/** - scripts/sync-shared.sh - .github/workflows/verify-shared-synced.yaml diff --git a/docs/assets/commons-logo.png b/docs/assets/hex/commons-hex.png similarity index 100% rename from docs/assets/commons-logo.png rename to docs/assets/hex/commons-hex.png diff --git a/docs/index.html b/docs/index.html index ce3af2a7..a0dad6e0 100644 --- a/docs/index.html +++ b/docs/index.html @@ -9,8 +9,8 @@ - - + + @@ -18,7 +18,7 @@
- +

commons

An R and Python package that helps data scientists build trustworthy data analysis agents.

diff --git a/hex/README.md b/hex/README.md new file mode 100644 index 00000000..f4be17eb --- /dev/null +++ b/hex/README.md @@ -0,0 +1,7 @@ +# Hex sticker + +`commons-hex.png` is the package's hex sticker, shown on the landing page and at the top of the Python site's home page. It is a web-sized reduction of `pkg-r/inst/hex/commons.png`, which `pkg-r/inst/hex/make_hex.py` generates from the source artwork. + +`scripts/sync-shared.sh` copies this directory into `docs/assets/hex/` and `pkg-py/docs/assets/hex/`, and CI fails when a copy is stale. Edit the file here, never a copy. + +The R site does not get a copy: pkgdown picks the sticker up from `pkg-r/man/figures/logo.png` by convention, which is also what the R README embeds. diff --git a/hex/commons-hex.png b/hex/commons-hex.png new file mode 100644 index 0000000000000000000000000000000000000000..8c630933a18a2d54ad641bfac540f3e900e8516b GIT binary patch literal 365371 zcmX_obyOVP6D17p?(Xiv9fCU{xCJM;ySonV8X%D1?l8E!2X}Y(o$t42_m4i^)6?fo zcXhq0Tet3;C{<+{6huNq2nYxiIoYr35D)-3@TLt93qJGUB}D+;63Dj=L!J-?^^&|0o4CL*8cB8 zDg>o|2naCYJA{tT&yx^XFN2C#*M_yc)-=Z-_~=-MzohH z+I|puQPv>_(J$iHqRmNcvk+HDHv7m>9So>a^&Xrcf9O(PGT%Ls)@a`qw9Nx%n=dz|V zHDxKKj~(XPwCdLDd@8$CJPeptBY&LFyk78e`LW>Wk+QH0|2M|5*ScwWX*rDhx%}e& zqfsi8M*t54uIRG!bC+g$PawI%5sq_f@L21?AMeVCxW(n^^anE0kAl_u(maX*{RXiH zP>F32KC%9&7H<=aZoveA$)UqeskQwSPoLx2wJX7BFN^-SF^%^AYI{JO<%J zNR{TNgQ;ZS=QYSE;QlyDyB3=ToyCzsmmC%@edl|eT{Ny$J?QV_ra&`$`Aw}v;hjyZ z#1AT8e^eKwf9!^$F|F|MlvkTpXEt93KMR~p?gE5-mX=VvMZQ|#3MeV%7w_ZCB^Lh>0Q>9NlJ 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zI;w(*z0T5V$wNA#B_Hg~o`xuRel2mPw+W(+h$fRA;1PTOQ9NuHf_S z`rLaub5kFG^v>JkQObkrHsVJO45)!z{>KNDA)-eOL=AWiT>iS(tsJf{d!wsWelJPN zKZ9t(B;Tn94ipsJYQ!MC0;353q|$UN5Szd$2-b6AfujYJ-NbzdaRuLM)Z9czdkcgZ z#azBCzcjJi>$Jb$ZM8m9uTS6p_+58R=0ZFYi5iF+7+3?*IT%=lkwDZy{u;RQwKrZ- z8yo&DS1P@=SWK?eSq33;Fb8%WKQ(X}Rx|wEP~E)*Qw}5W4CWA5@MFXkd=2{*96($F zrzCP;nuvJ>9i`lAf2z@%{nY;3Z{MFomm-O%fvABTH4vSH9F-YKMGfSvf#Owfxbdfx zk&$|YOH4rs0pa!CIFrWe>dep$#*1-B#ykcl*{T08IB$an1mD10{NvQR@`5hw= zf);t_10mP_3P_W21+LrZv^!tzv>G3p+c*Bj!w)_YVpPR|z3NRtk>FmI9${*{@oY*pc&ppS^O=+W4 zQ3Fu}{ThhQLBBR)K5AeIXyB?%Z+c;=TzXrvR(T5!bh<3>c!WV!pq&=sx$r3EcB}Pw zoqFpNk8ayMJ}BLYA2kp)@Z4!2ItS04t|OBfiy%1M0gSdh(w43b@jz7NpoAD^+fBZGXny7)LtbynpEah)6${scF z3>w(@s#h&fR;_t$DXIKE4t4rjY+^9b(a?-5z|P9;Zm0d-cG~<gST}8bwpnj}Z0{YLcRVdZY=|0&8aQt>5SKEZH(zD!NYubH zYhdz`okvG6T6I5GGdzeXf>qdMkw-)09OlzJ1ULoZDCKUN-kZ+P{fFl9C;xii_PY;1 zGl$q3H4rs$zGxth9GoxTWb8-Oz!_*@{i|QSYGnD!O~q369feZqXE1S4~8B zuZ^RW+s%*8O-hcT*ju{K +``` + `commons` helps data scientists build trustworthy data agents. Data teams typically have trusted code that they use to analyze their data and build reports and apps. `commons` leverages that expertise, situating the information in a series of prompts and tools designed to create an accurate, fast, and cost-effective agent. diff --git a/scripts/sync-shared.sh b/scripts/sync-shared.sh index 8bd04e2c..283b2678 100755 --- a/scripts/sync-shared.sh +++ b/scripts/sync-shared.sh @@ -16,14 +16,15 @@ root="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" # that copy; both packages ship the prompts and the browser assets. Nothing # in pkg-py reads www/ until its UI layer lands, but the destination belongs # with the move rather than with the first reader. The logos go to the two -# documentation sites, and the provenance-marker figs to the Python one, which -# serve them as they stand. +# documentation sites, the provenance-marker figs to the Python one, and the +# hex to the Python site and the landing page, which serve them as they stand. sources=( "tests/shared pkg-r/tests/testthat/fixtures/shared" "prompts pkg-r/inst/prompts pkg-py/src/commons/prompts" "www pkg-r/inst/www pkg-py/src/commons/www" "logos pkg-py/docs/assets/logos:bare pkg-r/pkgdown/assets/logos:bare" "www/commons-chat/figs pkg-py/docs/assets/figs:bare" + "hex docs/assets/hex:bare pkg-py/docs/assets/hex:bare" ) for entry in "${sources[@]}"; do From 94fd4cc08ff2cc316575804c85a9958c5f4277df Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 17:20:29 -0600 Subject: [PATCH 29/30] docs: link PyPI from the install instructions The root README already linked it; the landing page and the Python site named PyPI without linking it. --- docs/index.html | 2 +- pkg-py/docs/index.qmd | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/index.html b/docs/index.html index a0dad6e0..4eaa3c27 100644 --- a/docs/index.html +++ b/docs/index.html @@ -42,7 +42,7 @@

commons

install.packages("commons")
-

To install the Python package from PyPI, run:

+

To install the Python package from PyPI, run:

pip install commons
diff --git a/pkg-py/docs/index.qmd b/pkg-py/docs/index.qmd index c844f5c9..671b16dd 100644 --- a/pkg-py/docs/index.qmd +++ b/pkg-py/docs/index.qmd @@ -14,7 +14,7 @@ Trusted calculations can come from Python functions (as _measures_) or [data dic ## Installation -`commons` requires Python 3.11 or later. To install the package, run: +`commons` requires Python 3.11 or later. To install [the package from PyPI](https://pypi.org/project/commons/), run: ```bash # create a virtual environment with `uv venv` if you need first, then run: From a29f4495c4da0724023d67851917897e4512e4ef Mon Sep 17 00:00:00 2001 From: Josh Taillon Date: Sun, 13 Sep 2026 17:22:08 -0600 Subject: [PATCH 30/30] docs: link PyPI from the Python site navbar and the package metadata Documentation in [project.urls] puts the site in PyPI's sidebar, which had only the repository and the issue tracker. --- pkg-py/docs/_quarto.yml | 3 +++ pkg-py/pyproject.toml | 1 + 2 files changed, 4 insertions(+) diff --git a/pkg-py/docs/_quarto.yml b/pkg-py/docs/_quarto.yml index 7928a6a2..8cfd0b39 100644 --- a/pkg-py/docs/_quarto.yml +++ b/pkg-py/docs/_quarto.yml @@ -30,6 +30,9 @@ website: right: - text: '' href: ../r/ + - icon: box-seam + href: https://pypi.org/project/commons/ + aria-label: commons on PyPI - icon: github href: https://github.com/posit-dev/commons sidebar: diff --git a/pkg-py/pyproject.toml b/pkg-py/pyproject.toml index 0d2fa5ef..8e37f953 100644 --- a/pkg-py/pyproject.toml +++ b/pkg-py/pyproject.toml @@ -26,6 +26,7 @@ dependencies = [ [project.urls] Homepage = "https://github.com/posit-dev/commons" +Documentation = "https://posit-dev.github.io/commons/py/" Issues = "https://github.com/posit-dev/commons/issues" [project.optional-dependencies]