dbt-ml

dbt for unstructured data. Declarative YAML pipelines that turn folders of documents — PDFs, markdown, HTML, JSON, email, free-form text — into warehouse tables. Incremental processing, schema tests, dbt-style selectors, profiles, and a manifest artifact you can wire into other tools.

The current v0.2 preview is pure Python and supports DuckDB and BigQuery warehouses, local and GCS sources, document chunk models, and executable classic text-ML providers. Additional warehouse adapters, embeddings, and vector stores remain roadmap work; Rust and PyO3 are explicitly out of scope through v0.2.

Where dbt-ml fits

The 2026 landscape for unstructured document pipelines has two stable poles:

• Managed RAG-as-a-Service (Vectara, Bedrock Knowledge Bases, Vertex AI Search, Snowflake Cortex Search, Glean) — best when time-to-value matters and the team can't dedicate ML engineers.
• Compose best-of-breed Python components (LlamaParse → contextual chunking → Voyage embeddings → Qdrant → Cohere Rerank → Ragas) — best when retrieval quality, multi-tenant isolation, or unusual document types matter and you have ≥2 ML engineers.

dbt-ml is the opinionated, declarative path through the second lane. Where LlamaIndex is imperative Python, dbt-ml is YAML + a manifest + tests + lineage. Where Snowflake Cortex Search hides everything, dbt-ml makes every stage inspectable and reproducible. It's dbt-shaped: the same DAG + selectors + tests + artifacts pattern, applied to unstructured data.

---

You have a folder of files. Get them into your warehouse.

# Install from PyPI with the PDF parser used below
uv add 'dbt-ml[pdf]'

# 1. Scaffold a project for whatever shape your data is
uv run dbt-ml init my_project --template pdf      # or json, markdown, html

# 2. Drop your files into ./my_project/data/pdfs/  (or wherever the source points)

# 3. Run it
cd my_project
uv run dbt-ml run

# 4. Query the result
duckdb target/dbt_ml.duckdb -c "SELECT * FROM my_project.raw_pdf_text LIMIT 5"

That's the whole loop. Everything else (selectors, profiles, tests, LLM extraction, dbt handoff) is opt-in on top.

Optional dependencies

The core install stays lean. Add only the feature groups a project uses:

Extra	Features
pdf	PDF extraction and synthetic PDF generation (pypdf, fpdf2)
html	HTML extraction (beautifulsoup4)
text	Token counting, encoding cleanup, language detection, and near-duplicate detection
pii	Presidio PII detection and redaction; a spaCy language model is still installed separately
bigquery	BigQuery warehouse adapter
gcs	Google Cloud Storage document sources
all	Every optional feature above

For example, uv add 'dbt-ml[pdf,text]' installs PDF and text processing, while uv add 'dbt-ml[all]' provides the complete development/runtime feature set. Invoking a feature whose extra is absent raises an error with the exact installation command.

What dbt-ml actually does

Concept	What it means
Source	A glob over a folder. *.pdf, *.json, *.html, *.md — your choice.
Extraction model	One row per source file, produced by a backend (JSON, Markdown, PDF, HTML, email, or LLM).
Transform model	A Python module returning a Polars DataFrame, depends on other models via ref().
Classic ML model	An executable ml: model for deterministic features and classifiers, with persisted artifacts.
Materialization	full (always replace) or incremental (skip unchanged input on re-runs).
Tests	not_null, unique, min_rows, custom Python — with severity: warn if you want.
Profile	Warehouse + LLM config, swappable per `--target dev
Artifacts	target/manifest.json, target/run_results.json, target/sources.yml (for dbt).

Backends

Backend	Reads	Notes
json	*.json	Projects keys per options.fields. Deterministic, no API.
markdown	*.md	YAML frontmatter + body + optional word_count. Deterministic, no API.
pdf	*.pdf	Per-page text via pypdf. Warns on empty extracts (likely scanned). Deterministic, no API.
html	*.html/*.htm	Body text + CSS selectors + OpenGraph/meta via BeautifulSoup. Deterministic, no API.
email	*.eml	from/to/subject/date/body via stdlib email. Deterministic, no API.
llm	*.txt/*.md	Claude tool-use → structured fields. Uses the variable named by profile llm.api_key_env (default ANTHROPIC_API_KEY).

Add a new backend by inheriting from BaseBackend, defining a strict Pydantic option model, and decorating it with @register(options_model=...). Bare @register remains a pass-through compatibility path for existing third-party backends, but new backends should publish a typed option contract so compile and runtime enforce the same configuration.

Security Notes

dbt-ml projects are local code-and-data projects. Only run projects you trust: Python transforms and custom Python tests execute in your Python process, and project configuration controls source globs, generated paths, and executable modules. The discovered profile controls warehouse, cache, and credential environment-variable names.

Document parsers process local files with third-party libraries. Keep dependencies current before running dbt-ml over untrusted PDFs, HTML, email, or other documents, since malformed files can trigger parser CPU or memory bugs.

The llm backend sends document text to the configured model provider and stores cached structured responses in the configured cache database. Use deterministic local backends for sensitive documents unless remote processing is intended.

Trust model & filesystem boundaries

Paths declared in project YAML ship with a repo, so they are confined to the project directory — a path that resolves outside it (via .., an absolute path, or a symlink) is a configuration error (exit 2):

Path	Confined	Opt-out
source.path	yes	external: true on the source
source.file_pattern	relative only; absolute paths and .. are rejected	none
matched local source files	must stay below the resolved source root; symlinks are not followed	none
ml.artifact.path	yes	external: true on the artifact block
source-paths / model-paths / transform-paths / target-path	always	none
model-level llm cache_path	always	put it in profiles.yml instead
legacy inline duckdb.path	always	move external paths into profiles.yml

sources:
  - name: filings
    path: "D:/corpora/filings/"   # outside the repo — reviewable opt-in:
    external: true

external: true permits the declared source root outside the project. It does not permit pattern traversal or symlinked source files. Local discovery hashes through no-follow file descriptors where the platform supports them; fetches are verified snapshots in per-run scratch space, so a path swap after discovery does not change the bytes sent to a parser or remote model.

Project, source, and model YAML must be regular files under their configured roots. Configuration discovery does not follow symlinked files or directories.

profiles.yml paths (warehouse path:, llm cache_path:) are operator configuration, like dbt's, and are trusted as-is. An implicit project-local profiles file must be a regular file; pass --profiles-dir when intentionally using an operator-managed symlink.

dbt-ml clean removes only known local artifacts under target-path (manifest.json, run_results.json, generated sources.yml, docs/, and classic-ML artifacts/). It preserves configured warehouse/cache files and unknown files, never calls an adapter-level database/schema/dataset reset, rejects project-root or source/model/transform overlap, and refuses symlinked paths. There is no --force option.

Running a third-party project still executes its Python transforms and custom tests, and remote sources (gs://…) reach whatever your ambient credentials allow — review projects you didn't write before running them.

For scheduled/orchestrated runs, the llm backend can route uncached documents through the Anthropic Message Batches API — 50% token cost, at the price of minutes-scale latency (the run blocks until the batch completes). Cache hits still resolve locally, and the cost estimate in run results applies the batch discount automatically. Keep it off for dev loops:

extraction:
  backend: llm
  options:
    batch: true            # Message Batches API: 50% token cost, minutes-latency
    batch_poll_seconds: 30 # optional poll interval

LLM credentials

api_key_env stores an environment-variable name, never a secret. Runtime resolves the exact profile-owned variable and passes its value explicitly to synchronous and batch Anthropic clients; it never falls back to ANTHROPIC_API_KEY when another variable is configured. Model YAML cannot choose a credential variable. Missing credentials name only the variable and fail before a document is read or submitted, and compile uses the same resolution logic for its warning.

Reusable transform helpers are not profile-ambient. Pass api_key_env=ctx.llm.api_key_env when calling extract_fields_from_text() from a custom transform. LLM extraction models preflight credentials even if their response cache is warm.

The CLI

dbt-ml init <name> [--template {json,pdf,markdown,html}]   # scaffold a fresh project
dbt-ml seed [--count N] [--type {invoices,posts,...,tickets,emails}]
dbt-ml compile                                             # parse YAML, validate DAG, write manifest.json
dbt-ml graph                                               # Mermaid DAG to stdout
dbt-ml run [--select EXPR] [--exclude EXPR] [--full-refresh] [--threads N] [--watch] [--state DIR]
dbt-ml test [--select EXPR] [--exclude EXPR] [--store-failures] [--state DIR]
dbt-ml build [--select EXPR] [--exclude EXPR] [--full-refresh] [--threads N] [--store-failures] [--state DIR]
dbt-ml ls [--select EXPR] [--resource-type {model,source,all}] [--output {name,json}]
dbt-ml show <model> [--limit N]                            # peek at a materialized table
dbt-ml source freshness                                    # mtime vs warn_after/error_after
dbt-ml docs generate [--output DIR]                        # static HTML site from manifest.json
dbt-ml docs serve [--port N]                               # local http.server over target/docs/
dbt-ml emit-dbt-sources [--output PATH]                    # write dbt-compatible sources.yml
dbt-ml clean                                               # remove known target artifacts; preserve warehouses

# Global flags (work on every command):
dbt-ml --project-dir <dir> --profiles-dir <dir> --target <name> <command>

Project, source, model, and profile models reject unknown keys; source/model YAML accepts schema version: 2. Before profile resolution, source discovery, or warehouse mutation, compile, run, and build validate registered backend names, source/model edge kinds, supported materializations, transform and custom-test modules/call signatures, built-in test option shapes, and relationship targets. Relationship tests add a DAG predecessor so their target relation is built first. Every shipped extraction backend has a strict, backend-specific option schema; unknown options, wrong types, invalid LLM field schemas, and out-of-range execution settings fail before source discovery. Executable classic-ML tasks, providers, provider options, metrics, and artifact paths are checked by the same preflight. YAML schema diagnostics include the file, one-based line and column, and full configuration path without echoing the rejected input value; duplicate mapping keys are rejected at their second declaration. Configuration failures exit 2.

Useful flags

• --watch on run listens to source paths and re-runs on file changes (debounced 500ms). Ctrl-C to stop.
• --threads N parallelizes per-document extraction within an extraction model. Most useful for PDF / LLM / HTML (I/O- or API-bound). The LLM cache is lock-serialized so threading is safe.
• --select / --exclude limit source discovery as well as model execution; an unrelated GCS branch is never listed or authenticated.

Selectors

dbt-shaped. Whitespace-separated tokens, optional + modifiers, tag: prefix.

dbt-ml run --select raw_pdf_text       # one model
dbt-ml run --select 'raw_pdf_text+'    # plus all downstream
dbt-ml run --select '+invoice_summary' # plus all upstream
dbt-ml run --select 'tag:raw+'         # all models tagged "raw" + their downstream
dbt-ml run --exclude tag:expensive
dbt-ml run --select 'state:modified+' --state ./main-manifest/
                                       # only models whose config or transform
                                       # code changed vs a previous manifest,
                                       # plus their downstream

state:modified compares each model's code_version (a hash of its extraction/transform/ml config and transform module source) against a manifest written by a previous compile or run. The CI recipe: store target/manifest.json from main, then on PRs run dbt-ml build --select 'state:modified+' --state path/to/main-manifest/.

Profiles

Warehouse and LLM config live in profiles.yml, not in dbt_ml_project.yml. Project YAML says profile: my_project; profile says where to write and which LLM to call. Swap --target prod to switch environments.

# profiles.yml — sits next to dbt_ml_project.yml, or in ~/.dbt_ml/profiles.yml
my_project:
  target: dev
  outputs:
    dev:
      warehouse:
        type: duckdb
        path: ./target/dbt_ml.duckdb
        schema: my_project
      source_paths:
        filings: ./data/dev/filings
      llm:
        provider: anthropic
        model: claude-haiku-4-5
        api_key_env: ANTHROPIC_API_KEY
        cache_path: ./target/llm_cache.duckdb
        pricing:                       # optional — enables estimated_cost_usd
          input_usd_per_mtok: 1.00     # in run summaries + run_results.json.
          output_usd_per_mtok: 5.00    # USD per million tokens; you own these
          cache_read_usd_per_mtok: 0.10   # numbers, dbt-ml ships no price table.
    prod:
      warehouse:
        type: duckdb
        path: "{{ env_var('DBT_ML_PROD_DB', '/data/prod/dbt_ml.duckdb') }}"
        schema: my_project_prod
      source_paths:
        filings: "{{ env_var('DBT_ML_FILINGS_ROOT', '/data/prod/filings') }}"
      llm:
        model: claude-sonnet-4-6
        cache_path: /data/prod/llm_cache.duckdb

Lookup order: --profiles-dir flag → $DBT_ML_PROFILES_DIR → <project>/profiles.yml → ~/.dbt_ml/profiles.yml.

Set api_key_env to the name of the credential variable itself, as above; do not wrap it in env_var(). dbt-ml deliberately rejects secret-value interpolation in this field so validation errors and resolved configuration cannot contain the key.

BigQuery

Install the extra, then point a target at a GCP project. Profile fields mirror dbt-bigquery, so an existing dbt profile ports over: auth via ADC (method: oauth, the default), keyfile: (service-account), keyfile_json: (inline/base64 JSON — CI-friendly with env_var()), or token/refresh_token + client secrets (oauth-secrets), plus impersonate_service_account, scopes, execution_project, quota_project, priority, maximum_bytes_billed, and the job_retries / job_retry_deadline_seconds / job_creation_timeout_seconds / job_execution_timeout_seconds knobs. method: may be omitted — it's inferred from which credential fields are set. (dbt's dataproc_* fields don't apply: dbt-ml transforms run in-process, not on Dataproc.)

pip install 'dbt-ml[bigquery]'

my_project:
  target: prod
  outputs:
    prod:
      warehouse:
        type: bigquery
        project: my-gcp-project
        dataset: dbt_ml                # `schema:` works too
        location: US                   # optional
        # keyfile: "{{ env_var('DBT_ML_BQ_KEYFILE') }}"   # optional; omit for ADC

Materialized tables, --store-failures tables, and incremental state all live in the configured dataset — no DuckDB involved. dbt-ml clean does not drop or mutate the BigQuery dataset; it only removes known local target artifacts. emit-dbt-sources emits database: <project> / schema: <dataset> so a dbt-bigquery project can consume the tables directly.

Partitioning & clustering (warehouse_options)

Models may declare adapter-specific physical layout under warehouse_options: (issue #91), mirroring dbt-bigquery's partition_by / cluster_by resource configs:

- name: filings_chunks
  materialization: incremental
  warehouse_options:
    partition_by:
      field: filing_date        # omit for ingestion-time partitioning
      data_type: date           # timestamp | date (default) | datetime | int64
      granularity: day          # hour | day (default) | month | year
      # int64 instead takes: range: {start: 0, end: 100, interval: 10}
    cluster_by: [cik, form_type] # up to 4 columns; a single string works too
    require_partition_filter: true
    partition_expiration_days: 365
    hours_to_expiration: 72      # whole-table TTL
    labels: {team: econ, env: prod}   # table labels + job labels
    kms_key_name: projects/p/locations/us/keyRings/r/cryptoKeys/k
    incremental_strategy: merge  # or insert_overwrite (see below)

The block is validated by the active adapter: BigQuery rejects unknown or malformed keys at run time, while adapters with no layout knobs (DuckDB today) ignore it entirely — so one project can run DuckDB in dev and BigQuery in prod. Layout applies when the table is created or fully rebuilt (full models rebuild every run); an existing incremental table keeps its layout, so adding or changing partition_by on an incremental model needs one --full-refresh. Rebuilds are staged and swapped: the replacement table is built and validated first, so a bad layout declaration fails the run without touching the last good table. warehouse_options never changes code_version — declaring it does not reprocess documents. labels are applied to the table and to the load / query jobs the run issues for that model (for cost attribution).

incremental_strategy: insert_overwrite replaces every partition present in the incoming batch instead of merging by document_id — dbt-bigquery semantics, with partition pruning instead of a full-table key scan. Two contracts come with it: documents sharing a partition must always re-extract together (unchanged documents in a touched partition are dropped, because incremental batches contain only changed documents), and one run's changed documents must fit in a single flush (flush_every, default 5000) so a partition is never split across flushes. Time partitioning with a field is required. When in doubt, stay on merge — it is always correct.

String values support {{ env_var('NAME') }} and {{ env_var('NAME', 'default') }} — the one piece of dbt's Jinja grammar profiles need, so credentials and per-environment paths stay out of the file. api_key_env is the deliberate exception: it must be a literal variable name. An unset interpolated variable with no default is a load-time error. Each warehouse: block is validated against the config schema of the adapter named by type:; unknown types and typo'd fields fail at resolve time with the adapter named. Use target-level source_paths: when the same source should read from different local roots or gs:// prefixes in dev/staging/prod. Keys are source names from project YAML; values replace only source.path, leaving document_id and incremental identity based on the source-relative object path and content/generation hash.

GCS sources

Sources can point at Google Cloud Storage instead of local directories — raw documents stay in the bucket, dbt-ml materializes into the warehouse:

pip install 'dbt-ml[gcs]'

# sources/documents.yml
version: 2
sources:
  - name: report_html
    path: gs://my-raw-bucket/reports   # bucket + prefix
    project: my-gcp-project             # optional when ADC cannot infer it
    file_pattern: "*.html"             # basename match; "2026/*.html" matches paths
    max_objects: 20000                 # listing bound (default 5000)

  - name: meeting_transcripts
    path: gs://my-raw-bucket/transcripts
    file_pattern: "*.pdf"
    freshness:
      warn_after: { count: 45, period: day }

Incremental identity comes from the object listing (md5 → crc32c → generation), so unchanged objects are skipped without downloading anything; changed objects are fetched generation-pinned into a per-run scratch directory. Extraction rows gain source_uri (gs://bucket/name#generation — exact lineage to the raw object version) and a source_metadata JSON column (size, updated, content type, hashes). source freshness uses object updated timestamps.

Auth is Application Default Credentials: gcloud auth application-default login locally, or GOOGLE_APPLICATION_CREDENTIALS pointing at a service-account JSON in CI. User ADC may not carry a default Google Cloud project; set GOOGLE_CLOUD_PROJECT or add project: to the GCS source when project inference is unavailable.

Document extraction contract

Every extraction row carries identity, lineage, and parser provenance: document_id, source_path, source_uri (local file:// URI, or gs://bucket/name#generation for GCS), content_hash, code_version, backend_name, backend_version (the parsing library's version, e.g. pypdf/6.1), and extracted_at (one UTC timestamp per run). Remote sources populate the nullable source_metadata JSON column.

Upgrading note: these columns are new — existing incremental extraction models will report a schema change on their next reprocess; run once with --full-refresh (or set on_schema_change: append_new_columns).

Declared extraction schema

Top-level model fields: is the warehouse output contract for extraction payload columns. Lineage columns above are automatic; when fields: is non-empty, undeclared backend payload fields are dropped before materialization.

fields:
  - name: invoice_id
    data_type: string
  - name: total
    data_type: float
  - name: paid
    data_type: boolean

Supported types are string, integer, float, boolean, date, timestamp, and json (type: and dtype: are accepted input aliases for data_type:). A successful zero-document run materializes a typed, zero-row relation from this contract, so downstream tests and models see a real table. Type changes participate in code_version; invalid casts fail without publishing a full-model staging table. A declared field without data_type defaults to string. Omitting fields: retains legacy dynamic backend output, but cannot type payload columns for an initially empty corpus.

Structure-preserving options for document parsing:

# Sectioned HTML (reports, filings): headings/tables as JSON with char
# offsets into `text`, so a downstream parser slices sections without
# touching HTML.
- name: raw_reports
  source: ref('report_html')
  extraction:
    backend: html
    options:
      include_structure: true   # emits `sections` and `tables`
  materialization: incremental

# Multi-page PDF (transcripts, reports): per-page char offsets into
# `text`, so e.g. speaker-turn parsing can attribute any match to a page.
- name: raw_transcripts
  source: ref('meeting_transcripts')
  extraction:
    backend: pdf
    options:
      include_pages: true       # emits `pages` [{page, char_start, char_end}]
  materialization: incremental

sections entries are {level, heading, char_start, source, anchor?}; tables are {index, char_start, n_rows, n_cols, cells}. Domain-specific logic (section taxonomy, speaker parsing) belongs in a transform layered after extraction — the backends stay generic.

By default sections only sees semantic <h1>–<h6> tags (source: "tag"). Corpora that style their headings instead — SEC inline-XBRL filings render headings as bold <div>/<span> blocks — need one of the opt-in detectors:

- name: raw_filings
  source: ref('filing_html')
  extraction:
    backend: html
    options:
      include_structure: true
      styled_headings: true      # heuristic: short, fully-bold leaf blocks
      heading_selectors:         # and/or explicit CSS selectors
        - "div.doc-title"        # matches become level 1
        - "div[id^='item']"      # matches become level 2, and so on
  materialization: incremental

styled_headings treats a leaf block element whose text is short and entirely bold as a heading, ranking levels by font size (largest = level 1); entries carry source: "style". heading_selectors names headings explicitly (source: "selector"), with selector order setting the level; its matches win over the heuristic, and semantic heading tags always work. A selector that matches nothing logs a warning on the run.

Streaming large corpora

Extraction streams rows to the warehouse every flush_every documents (default 5000), so corpus size is bounded by the flush size, not memory:

- name: raw_filings
  source: ref('filing_html')
  extraction:
    backend: html
    flush_every: 1000   # smaller = lower memory, finer crash recovery
  materialization: incremental

Incremental writes are atomic per flush: DuckDB uses a transaction and BigQuery loads a unique staging table then executes one MERGE. Missing, NULL, or duplicate incremental keys are rejected before mutation. A killed run keeps successful earlier flushes and their state, and the re-run picks up the remainder. With BigQuery append_new_columns, schema addition happens before the MERGE; a failed merge preserves all rows but can leave the new, nullable column in place.

Full models publish a unique staging table only after every document succeeds. A parser/backend error preserves the previous target and state. Backend warnings and zero-source-match warnings appear in the CLI and run_results.json. Changing flush_every never invalidates incremental state. One edge: with on_schema_change: fail and more than one flush, the first flush is compared against the existing table — heterogeneous corpora whose early documents lack a column can fail where a whole-run union carried it; use append_new_columns there.

Chunking (RAG)

A chunk: model splits an upstream document's text into one row per chunk — the grain RAG and agent retrieval need. Chunk IDs are deterministic and content-addressed, so an unchanged document re-runs to identical IDs (safe for incremental MERGE into a warehouse/vector store).

- name: document_chunks
  depends_on: [ref('document_registry')]   # an extraction model
  chunk:
    strategy: recursive        # recursive (char splitter) | tokens (tiktoken)
    text_field: text           # upstream column to split
    chunk_size: 800            # chars (recursive) or tokens (tokens)
    chunk_overlap: 100
  materialization: incremental

Each chunk row carries chunk_id, document_id, chunk_index, chunk_count, text, chunk_strategy, chunked_at, plus every upstream column except the split text field — so document lineage (source_uri, content_hash, parser provenance) flows onto every chunk for free. Incremental chunk models skip unchanged documents, re-chunk changed ones without leaving orphan chunks, and prune chunks of deleted documents.

The recommended document-layer shape (GCS raw files → BigQuery tables):

model	grain	kind
document_registry	one row per document/version	extraction (include_structure)
document_chunks	one row per chunk	chunk
document_extractions	one row per structured field set	extraction (llm) or transform

See examples/rag_chunks_pipeline/ for a runnable registry → chunks project. Domain keys (symbol, filing date, …) and embeddings belong in transforms / downstream dbt models layered on top — the chunk grain stays generic.

Built-in text preprocessing

Reference any of these as a Python transform module — no project-local code needed. Users can override by writing their own transforms/<name>.py (project-local files win over installed packages).

- name: post_text_stats
  depends_on: [ref('raw_posts')]
  transform:
    type: python
    module: dbt_ml.text.transforms.text_stats   # built-in, ships with dbt-ml
    options:
      text_field: body
      emit: [word_count, sentence_count]

Module	What it does
dbt_ml.text.transforms.text_stats	Adds word_count / char_count / sentence_count / paragraph_count
dbt_ml.text.transforms.clean_encoding	Fixes mojibake (UTF-8-as-Latin-1 confusion) via ftfy
dbt_ml.text.transforms.detect_language	Adds a 2-letter ISO language code per row via langdetect
dbt_ml.text.transforms.count_tokens	Adds token_count for an OpenAI / Claude-style tokenizer (tiktoken)
dbt_ml.text.transforms.find_duplicates	Flags near-duplicate rows via MinHash + LSH (Jaccard threshold configurable)
dbt_ml.text.transforms.redact_pii	Detects + redacts PII via Microsoft Presidio (requires en_core_web_sm spaCy model)

All are pure functions importable via from dbt_ml.text import … if you'd rather wire them into your own transforms.

PII setup — redact_pii uses spaCy under the hood. First-time install:

python -m spacy download en_core_web_sm

Without the model, calls into redact_pii raise a clear PIIError pointing at this command.

For a customer-facing relation, use an allow-list projection:

- name: redacted_tickets
  depends_on: [ref('raw_tickets')]
  transform:
    type: python
    module: dbt_ml.text.transforms.redact_pii
    options:
      text_field: summary
      output_field: summary_redacted
      entities_field: pii_entities
      keep_fields: [ticket_id, summary_redacted, pii_entities]

entities_field stores type, offsets, and confidence by default; it does not store the matched substring. include_raw_text: true opts back into raw PII evidence and makes that output sensitive. When output_field differs from text_field, the original text is dropped unless retain_input_text: true is set. keep_fields and drop_fields are mutually exclusive, and unknown projection fields fail loudly. Other upstream columns are otherwise retained, so use keep_fields for a relation that must exclude names, email addresses, or other sensitive source columns.

Classic text and document ML

Classic ML is a first-class dbt-ml lane alongside LLM/RAG work. The ml: model block executes deterministic text/document workflows and persists their artifacts; shipped providers cover Count/TF-IDF/hashing features and Naive Bayes classification. Additional regression, clustering, topic-model, and NLP providers remain roadmap work.

- name: ticket_tfidf
  depends_on: [ref('raw_tickets')]
  ml:
    task: features
    mode: fit_transform
    provider: builtin.tfidf
    text_field: body
    artifact:
      path: target/artifacts/ticket_tfidf
    metrics: [vocabulary_size]
    options:
      ngram_range: [1, 2]
      max_features: 50000

Executable feature providers are builtin.count, builtin.tfidf, and builtin.hashing. They write long-form sparse feature tables with stable row_id, term, term_index, count, tf, idf, tfidf, and value columns where applicable. Fitted vocabulary providers persist target/artifacts/<model>/metadata.json plus vocabulary.json; hashing is stateless and persists metadata only.

Common options include analyzer: word | char | char_wb, ngram_range, min_df, max_df, max_features, stop_words, binary, n_features, and alternate_sign. See docs/classic-ml.md for the full design contract.

The first supervised provider is builtin.naive_bayes, which trains a deterministic text classifier from text_field and label_field, persists a model artifact, and materializes prediction rows with scores/probabilities.

Tests

Structural:

tests:
  - not_null: [vendor, total]            # column-level, fails the run
  - unique: invoice_id                   # single-column
  - unique: [a, b]                       # composite (compiled to dbt_utils on emit)
  - min_rows: 100
  - not_empty                            # bare-string form of min_rows: 1
  - not_null: total                      # warn doesn't fail the run
    severity: warn
  - relationships: { column: vendor_id, to: ref('vendors'), field: id }  # referential integrity
  - python: tests.my_check               # custom: tests/my_check.py defines run(con, table_ref) -> str | None

Traditional ML / statistical data-quality checks (deterministic, no LLM, no sampling — see issue #10 for the full design including the optional LLM-judge tier):

tests:
  - matches_regex: { column: arxiv_id, pattern: '^\d{4}\.\d{4,5}$' }
  - accepted_values: { column: primary_category, values: [cs.LG, cs.CL, stat.ML] }
  - accepted_range: { column: n_authors, min: 1, max: 30 }
  - null_rate: { column: title, max: 0.0 }       # silent-extraction-failure guard
  # deterministic faithfulness — extracted value must appear in the source text,
  # catching hallucinated values with zero LLM calls:
  - grounded_in: { value: title, source: abstract, method: exact }

grounded_in also supports method: fuzzy with a min_score. These run as full-table aggregates, so they stay cheap and reproducible.

Inspecting failures. Pass --store-failures to dbt-ml test or dbt-ml build to persist the offending rows of each failing test to a dbt_ml_test_failures__<model>__<test>[__<column>] table (replaced each run). The test output reports the table name and row count. These tables are inspection artifacts and are kept out of the model namespace (they don't show up in dbt-ml ls or emit-dbt-sources).

dbt-ml build runs and tests each model in dependency order, skipping a model's descendants when it errors or fails a test — so a bad upstream extraction stops before it pollutes everything downstream.

Examples in this repo

Path	What it shows
examples/invoice_pipeline/	JSON extraction → per-vendor + monthly aggregations
examples/blog_pipeline/	Markdown frontmatter → per-author word counts
examples/pdf_invoice_pipeline/	PDFs → text via pypdf → LLM-extracted structured fields
examples/llm_invoice_pipeline/	Free-form invoice text → LLM extraction (no PDF stage)
examples/support_tickets_pipeline/	JSON tickets → open queue + SLA breaches + per-team workload (no LLM)
examples/arxiv_papers/	arXiv metadata → deterministic data-quality checks (incl. grounded_in)
examples/dbt_consumer/	dbt-duckdb project consuming dbt-ml-materialized tables
examples/classic_text_ml/	deterministic sparse text features + Naive Bayes classification
examples/rag_chunks_pipeline/	document registry → deterministic RAG chunks

Each example is runnable end-to-end with uv run dbt-ml --project-dir examples/<name> ....

Composing with dbt

dbt-ml does the unstructured→structured "E" and dbt does the SQL "T". emit-dbt-sources targets the matching adapter: dbt-duckdb can share the DuckDB file, and dbt-bigquery can read the configured BigQuery dataset. The DuckDB bridge:

uv run dbt-ml --project-dir examples/invoice_pipeline run
uv run dbt-ml --project-dir examples/invoice_pipeline emit-dbt-sources \
  --output examples/dbt_consumer/models/sources/_dbt_ml_sources.yml

cd examples/dbt_consumer && uv sync && uv run dbt build --profiles-dir .

emit-dbt-sources translates dbt-ml tables into a dbt-compatible sources.yml. Column tests carry over (not_null, single-column unique); composite unique becomes a dbt_utils.unique_combination_of_columns macro test.

Artifacts

dbt-ml compile writes the manifest; run and build write the manifest and run results under target-path:

• manifest.json — project, sources, models, refs, tags, code_version per model, DAG nodes+edges+execution order. Re-generated each run.
• run_results.json — run-level metadata (warehouse target, status, counts, elapsed, and sources_considered) plus per-model documents processed/skipped, rows written, duration, warnings, errors, status, and the fully-qualified output relation. LLM extraction models also carry token accounting in metrics (API calls, cache hits, input/output/cache tokens, and estimated_cost_usd when the profile sets pricing:). run/build also accept --json to print this payload to stdout.
• sources.yml — only when you call emit-dbt-sources. dbt-shaped.
• docs/ — static HTML site (dbt-ml docs generate) with project overview, Mermaid DAG, per-model pages. Serve locally with dbt-ml docs serve.

External tools (lineage viewers, CI dashboards, the dbt-consumer above) consume these. run/build exit 0 on success, 1 on run failure, and 2 on a configuration error, so an orchestrator can branch on the cause. Because dbt-ml tables are dbt sources, they wire natively into the dagster-dbt integration — see docs/orchestration-dagster.md (use emit-dbt-sources --dagster-meta to pin the Dagster asset keys).

Benchmarks

uv run python scripts/benchmark.py --count 5000

5000-doc benchmark on the JSON backend:

seed 5000 invoices                          0.8s    →   6.3k docs/sec
first run (cold)                            4.8s    →   1.0k docs/sec
second run (all skipped)                    0.3s    →  19.9k docs/sec
third run (1 changed)                       0.3s    →  18.2k docs/sec
full-refresh                                4.3s    →   1.2k docs/sec

These historical v0.1 numbers are a local baseline, not a service-level guarantee. Current runs support --threads for per-document extraction and parallel independent model batches; benchmark your own parser, warehouse, and source mix.

Layout

src/dbt_ml/
├── cli.py                 # click: init/seed/compile/graph/run/test/show/clean/source freshness/emit-dbt-sources
├── config/                # pydantic models for project/source/model/profile + loader
├── profile.py             # profile discovery + resolution (warehouse + llm)
├── dag.py                 # graphlib-based DAG, selectors (+ name +, tag:foo), Mermaid render
├── adapters/              # warehouse adapters + adapter-owned incremental state
├── runner.py              # extract → materialize orchestration
├── manifest.py            # target/manifest.json + run_results.json
├── dbt_export.py          # target/sources.yml (dbt-shaped)
├── freshness.py           # source mtime check
├── backends/              # json, markdown, pdf, html, email, llm
├── transforms/runner.py   # loads user Python transform modules + TransformContext
├── checks/                # schema tests + custom Python tests + severity
├── synth/                 # synthetic data generators per shape
└── templates/             # init scaffolds for {json,pdf,markdown,html}

Roadmap

The live plan is maintained in GitHub issues tagged roadmap. Already shipped in the v0.2 preview: the warehouse adapter seam, DuckDB and BigQuery, GCS sources, recursive/token chunk models, layout-preserving HTML/PDF metadata, PII redaction, and the first classic-ML providers.

Next adapter work follows dbt-core's warehouse set over time: Postgres first, then Snowflake, Databricks, and Redshift. Embeddings/vector storage, more parser providers, and evaluation/reranking remain roadmap items. Incremental state stays adapter-owned. Rust, PyO3, and Metaxy remain explicitly deferred.