> For the complete documentation index, see [llms.txt](https://docs.zingg.ai/zingg/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.zingg.ai/zingg/reference/cli-command-reference.md). # CLI Command Reference All Zingg CLI phase commands and flags in one place. ### Basic invocation pattern ```bash ./scripts/zingg.sh --phase --conf ``` ```bash ./scripts/zingg.sh --run ``` Use `--run` to execute a Python program through `zingg.sh`. Zingg Python programs are PySpark programs. The CLI executes both JSON config jobs and Python program jobs. It is not a replacement for the Python API; it is the runtime that executes both. ### All phases

Phase name	Edition	Description
`findTrainingData`	All	Scans the dataset and selects candidate pairs for labeling.
`label`	All	Opens interactive labeling session. Run after `findTrainingData`.
`generateDocs`	All	Generates HTML documentation of labeled training data for review.
`train`	All	Builds blocking and similarity models using labeled training data.
`match`	All	Applies trained models to the full dataset. Writes clusters to output.
`link`	All	Matches records across two separate datasets. Same model as `match`.
`recommend`	All	Generates stopword candidates for a specified field.
`verifyBlocking`	Enterprise	Checks blocking model coverage against known matching pairs.
`trainMatch`	Enterprise	Combined `train` and `match` in one phase call.
`findAndLabel`	Enterprise	Combined `findTrainingData` and label in one phase call.
`runIncremental`	Enterprise	Updates identity graph with new or changed records without a full re-match.
`explainOutput`	Enterprise	Shows pair-level evidence for how a specific cluster was formed.
`generateDocs`	All	Generates HTML documentation of model and data statistics.
`reassignZinggId`	Enterprise Plus	Reassigns Zingg IDs when switching to a new trained model.
`updateLabel`	All	Revisit and update previously marked training pairs. Run before `train` to correct labeling errors. Run `generateDocs` first to identify pairs to update.
`diff`	Enterprise	Compare two model outputs to understand exactly what changed between them before deploying. Identifies which clusters merged, split, or moved.
`runLookup`	Enterprise	Look up specific records in existing match output to find which entity cluster they belong to and get their Zingg ID

### All flags

Flag	Required/Optional	Description
`--phase`	Required	The Zingg phase is to run. See phases table above.
`--conf`	Required	Path to your JSON config file.
`--properties-file`	Optional	Path to Zingg runtime properties file (`zingg.conf`). This is required for Snowflake and some cloud connectors.
`--zinggDir`	Optional	Override the `zinggDir` from config. Useful for S3 and GCS paths.
`--showConcise`	Optional	`true` or `false`. When `true`, hides `DONT_USE` fields in the label phase terminal display.
`--column`	Required with `recommend`	The field name to generate stopword recommendations for.
`--originalZinggId`	Required with `reassignZinggId`	Path to the original production config file.
`--run`	Optional	Path to a Python program to execute through `zingg.sh`. Use instead of `--phase` and `--conf` when running Python-based Zingg programs.
`--zinggid`	Required with `explainOutput`	The Zingg ID of the cluster to explain. Find Zingg IDs in your match output `ZINGG_ID` column.

### Example commands #### **`findTrainingData`** ```bash ./scripts/zingg.sh --phase findTrainingData --conf config.json ``` #### **`label` (with concise display)** ```bash ./scripts/zingg.sh --phase label --conf config.json --showConcise=true ``` #### **`generateDocs`** ```bash ./scripts/zingg.sh --phase generateDocs --conf config.json --showConcise=true ``` #### **`train`** ```bash ./scripts/zingg.sh --phase train --conf config.json ``` #### **`match`** ```bash ./scripts/zingg.sh --phase match --conf config.json ``` #### **`link`** ```bash ./scripts/zingg.sh --phase link --conf config.json ``` #### **`recommend` (stopwords for `fname`)** ```bash ./scripts/zingg.sh --phase recommend --conf config.json --column fname ``` #### **`runIncremental`** ```bash ./scripts/zingg.sh --phase runIncremental --conf incrementalConf.json ``` #### **`reassignZinggId`** ```bash ./scripts/zingg.sh --phase reassignZinggId --conf configReassign.json --originalZinggId config.json --properties-file config/zingg.conf ``` #### **`updateLabel`** ```bash ./scripts/zingg.sh --phase updateLabel --conf config.json ``` #### `findAndLabel` (combined - smaller datasets) ```bash ./scripts/zingg.sh --phase findAndLabel --conf config.json ``` #### `verifyBlocking` (run after label) ```bash ./scripts/zingg.sh --phase verifyBlocking --conf config.json ``` #### **`diff` (compare two model outputs)** ```bash ./scripts/zingg.sh --phase diff --conf configNew.json --compareTo configBaseline.json --properties-file config/zingg.conf ``` #### **`explainOutput` (explain a specific cluster)** ```bash ./scripts/zingg.sh --phase explainOutput --zinggid ea67d79a-56a7-4431-ab55-d08bb3c10e2e --conf explainConfig.json ``` #### **trainMatch (combined - Enterprise)** ```bash ./scripts/zingg.sh --phase trainMatch --conf config.json ``` --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.zingg.ai/zingg/reference/cli-command-reference.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.