fidelis 0.1.1

Ingest messy tabular-ish feeds from any source and map them to your Pydantic model. An LLM writes a versioned spec once; every subsequent parse is deterministic, with zero LLM calls.

fidelis

Every partner sends the same data in a different shape. fidelis maps any of them onto one Pydantic model — an LLM writes the mapping once, then never runs again.

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A CSV from one partner, an Excel sheet from another, a JSON webhook from a third: the same data, with different column names, units, and quirks — and a field that's missing here and renamed there. Instead of a hand-written parser per source, you point fidelis at each one. The first time it meets a new shape, an LLM writes a versioned mapping spec — human-readable YAML you review and commit. Every run after that is pure, deterministic Python: zero LLM calls, every row validated, schema drift caught, and nothing dropped silently.

A file is just one adapter. Identity is bound to what the fields mean (their signature), not to a filename — so one spec covers a CSV today and the same vendor's JSON tomorrow.

Why fidelis

🧠 LLM once, then never	An LLM writes the spec the first time it sees a source shape. Every run after is pure, deterministic Python — no network, no surprises.
🔌 Any source, one parse()	CSV, Excel, JSON, or a list[dict] payload — matched by what fields mean, not by filename. One call, same result.
📝 The spec is the contract	A versioned, reviewable YAML — mapping, transforms, enrichment, and dedup all in one place. You diff it in PRs, not reverse-engineer a black box.
🛟 Never lose a row	Every input row becomes a validated record or a RowError with its row, field, and reason — then quarantine, fix, and re-ingest.
🔎 Drift-aware	A vendor adds or renames a column and fidelis catches it under an explicit policy — caught in CI, not silent corruption in prod.
🪶 Light & provider-agnostic	pydantic + pyyaml at the core; Anthropic / OpenAI / local LLMs are optional extras.

Contents

• Install
• The headline: one parse(), any source, same result
• Worked examples (examples/showcase/)
• How spec generation & caching work
• The YAML spec format
• Schema drift handling
• Quarantine round-trip
• Transforms
• Enrichment (post-mapping hooks)
• Shaping output (multi-record, multi-field, whole-column)
• Run context, coverage & LLM hints
• Provider strings
• Testing offline with FakeProvider
• API surface
• License

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Install

pip install fidelis            # core (pydantic + pyyaml)
# or pull in what you need:
pip install 'fidelis[all]'     # + Excel, HTTP providers, pandas & polars
pip install 'fidelis[excel]'   # + openpyxl for .xlsx sources
pip install 'fidelis[anthropic]' # + httpx for the Anthropic provider

Requires Python ≥ 3.11.

Working from a clone? Install in editable mode instead: pip install -e '.[dev]'.

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Don't have a model yet?

infer_model_source writes a draft Pydantic class from a sample — field names and types inferred from the data (deterministic, no LLM). Review it, then use it as your target:

from fidelis import infer_model_source
print(infer_model_source("feed.csv", class_name="User"))

Inference: textual booleans → bool, whole numbers → int, decimals → float, ISO/dd.mm.yyyy dates → date, else str; any empty/missing value in the sample makes the field Optional[...] = None.

---

The headline: one parse(), any source, same result

The same parser.parse() consumes a CSV file and a list[dict] payload. Because both carry the same fields by meaning, they resolve to the same cached spec and produce identical valid_rows.

from datetime import date
from pydantic import BaseModel
from fidelis import Parser

class User(BaseModel):
    email: str
    full_name: str
    signup_date: date

parser = Parser(
    target_model=User,
    spec_store="specs/",                # where specs live (a path, or a custom SpecStore)
    llm="anthropic:claude-opus-4-8",    # only used the first time a shape is seen
    on_unknown_column="error",          # default drift policy
)

# 1) A CSV file on disk → spec generated once and written to specs/.
from_file = parser.parse("incoming/acme_users.csv")

# 2) A list[dict] payload with the SAME fields → SAME cached spec, 0 LLM calls.
from_records = parser.parse([
    {"E-mail": " [e-mail] ", "Name": " Alice ", "Date": "01.02.2026"},
    {"E-mail": "[e-mail]", "Name": "Bob",     "Date": "15.03.2026"},
])

assert from_file.valid_rows == from_records.valid_rows   # identical Users

# Inspect the result of a parse:
from_file.valid_rows      # list[User]   — validated records
from_file.errors          # list[RowError] — what didn't fit, and why (never silent)
from_file.spec_used       # the Spec that was applied
from_file.spec_generated  # was the spec generated by the LLM in THIS run?
from_file.drift_report    # schema drift, if any
from_file.needs_review    # bool — any mappings still flagged needs_review?
print(from_file.summary())
# valid=2 errors=0 coverage=1.00 needs_review=True drift=False generated=True

Runnable, offline versions live in examples/ — no API key, no network. For a guided tour, examples/showcase/ walks one feature per folder (a CSV, a run.py, and the spec), simplest first:

python examples/showcase/01_basic/run.py        # rename + transforms + default
python examples/showcase/03_expand/run.py        # one row → many records
python examples/showcase/06_advanced/run.py      # the whole pipeline at once

Other sources & the kind hint

parse() infers the source kind from the type / file extension. Pass kind= to be explicit (e.g. a JSON string, or CSV text with no .csv extension):

parser.parse("incoming/users.xlsx")                  # Excel
parser.parse(json_payload, kind="json")              # JSON array
parser.parse("E-mail;Name;Date\n...", kind="csv")    # raw CSV text

Remote & compressed sources are handled transparently — pass an HTTP(S) URL or a .gz path and fidelis fetches/decompresses before dispatch:

parser.parse("https://example.com/daily/feed.csv")   # fetched over HTTP(S)
parser.parse("exports/users.json.gz")                # gunzipped, then parsed
parser.parse("https://example.com/feed.csv.gz")      # both

The adapter kind is inferred from the URL path / inner extension. (Excel is a binary container read from a path, so Excel-over-URL/gzip is not supported — download it first.)

Dedup / upsert keys

Declare the row key in the spec and fidelis collapses duplicate rows within a feed, keeping the first (default) or last occurrence. Dropped rows are never lost silently — they come back as result.duplicates, each pairing the kept row with the one it displaced.

# in the spec:
dedup:
  key: [email]        # one or more model fields; composite key supported
  keep: first         # first | last (last = upsert-within-feed)

result = parser.parse(feed)
print(len(result.valid_rows), "kept,", len(result.duplicates), "dropped")
for d in result.duplicates:
    print(d.key, "→ dropped", d.dropped)

The key is matched after validation/coercion, so "[e-mail]" and "[e-mail]" collide once strip_lower has run. validate_spec flags a key field that isn't on the model.

Global default. Parser(User, dedup_key="email", dedup_keep="last") still works for specs that don't declare their own dedup (pass a list or comma-separated string for a composite key). A spec's dedup takes precedence over the Parser default.

Typed output

ParseResult hands the validated rows straight into your stack:

result.to_dicts()              # list[dict] (mode="json" for JSON-native scalars)
result.to_pandas()             # pandas.DataFrame   — pip install 'fidelis[pandas]'
result.to_polars()             # polars.DataFrame   — pip install 'fidelis[polars]'
result.errors_to_dicts()       # rejected rows for a dead-letter file / report

---

How spec generation & caching work — LLM once, then deterministic

Identity is bound to the field signature, not to a filename. The signature is a short hash of the normalized set of source field names (trim, lower, collapsed whitespace/underscores). Field order and case do not matter, so one spec covers a CSV today and a JSON payload tomorrow if they mean the same thing.

data → fingerprint of field names
  ├─ spec found      → deterministic mapping (0 LLM calls)
  └─ no spec         → LLM generates a draft → saved to the spec store
                     → you review it (especially needs_review mappings)
                     → every subsequent run is LLM-free

What goes to the LLM is intentionally tiny and one-shot: only the inventory of field names, a small sample of rows (≤20 by default), and the JSON schema of your target model — never the whole dataset, and never per-row. Low-confidence mappings (below confidence_threshold, default 0.8) are auto-flagged needs_review so you know exactly what to check by hand.

Helper methods for review flows / CI:

spec = parser.generate_spec(source)      # generate a draft Spec, don't parse
problems = parser.validate_spec(spec)    # [] means the spec is well-formed

validate_spec checks that every target (and targets) exists on the model, isn't mapped twice, every required model field is covered, transforms are known, confidences are in [0, 1], and that everything the spec references by name — enrich / batch_enrich / expand expanders / column_steps / dedup keys, plus transforms inside rules — is registered and valid.

Where specs live (spec_store)

By default specs are YAML files next to your project (spec_store="specs/"). In production you may want them in S3, a database, or a config service — implement SpecStore and pass it in. The same spec_store argument takes either a path or a store:

Parser(User, spec_store="specs/")          # files (default)
Parser(User, spec_store=S3SpecStore(...))  # your backend

A spec's identity is its field signature (the hash of the source field names), which maps cleanly onto a storage key — get(signature) is one object GET / one SELECT by primary key, no scanning. Only drift detection looks across specs (via all()); a backend where listing everything is impractical can override find_drift_candidate or skip it.

from fidelis import SpecStore, Spec

class S3SpecStore(SpecStore):
    def __init__(self, bucket, prefix=""):
        self.s3, self.bucket, self.prefix = boto3.client("s3"), bucket, prefix

    def get(self, signature):                       # one GET, keyed by signature
        try:
            obj = self.s3.get_object(Bucket=self.bucket,
                                     Key=f"{self.prefix}spec_{signature}.yaml")
        except self.s3.exceptions.NoSuchKey:
            return None
        return Spec.from_yaml(obj["Body"].read().decode())

    def save(self, spec):
        self.s3.put_object(Bucket=self.bucket,
                           Key=f"{self.prefix}spec_{spec.signature}.yaml",
                           Body=spec.dump_yaml().encode())

    # implement all() too if you want schema-drift detection

FileSpecStore and an in-memory MemorySpecStore (handy for tests) ship built-in.

---

The YAML spec format

A spec is a human-readable, version-controlled contract for ingesting one source format. A real, hand-written example lives at specs/partner_acme_users.yaml and maps onto the User model above:

version: 1
generated_by: claude-opus-4-8        # or "human" after a manual edit
generated_at: "2026-06-28"
signature: 6a93a9                     # hash of the normalized source field names

# Stage 1 — structural dialect. Only for text/file sources (CSV/TSV);
# skipped entirely for already-structured inputs (list[dict] / JSON).
parsing:
  delimiter: ";"
  encoding: utf-8
  quote_char: '"'

# Stage 2 — universal field mapping (source column -> target model field).
mappings:
  - target: email
    source: "E-mail Address"
    transform: strip_lower
    confidence: 0.98
    status: ok

  - target: full_name
    source: "Customer Full Name"
    transform: strip
    confidence: 0.91
    status: ok

  - target: signup_date
    source: "Registration Date"
    transform: "parse_date:%d.%m.%Y"
    confidence: 0.62
    status: needs_review     # LLM unsure — a human verifies, then sets ok

  - target: source_system    # a CONSTANT — no source column
    value: "acme"

  - target: country          # source if present, else this DEFAULT
    source: "Country"
    value: "US"

# Stage 3 — record-level steps, referenced by registered name (you add these by
# hand; the LLM never writes them). See "Enrichment", "Row expansion", "Dedup",
# and "Shaping output" for rules / unpivot / column_steps / skip_when.
expand:                      # one row -> many records
  - field: airport_code      # split this column...
    delimiter: "|"           # ...on this separator (declarative, no code)
enrich:                      # per-row (runs on each fanned-out row)
  - fill_domain
batch_enrich:               # whole-batch, register_batch_enrichment
  - attach_scores
dedup:                      # collapse duplicate rows after validation
  key: [email]
  keep: first               # first | last

# Per-source drift policy (overrides the Parser's global setting).
on_unknown_column: error     # ignore | error | regenerate

Field notes:

• signature is the primary key binding the spec to data; it is what parse() looks up. Editing source field names changes it. The target model is not in the spec — you bind it in code (Parser(target_model=...)), so a spec maps source fields onto field names and stays model-agnostic.
• status is ok or needs_review; a needs_review mapping is surfaced via ParseResult.needs_review.
• transform is a built-in name or name:arg (see below).
• value sets a target without (or as a fallback to) a source: with no source it's a constant; with a source it's the default used when the source cell is empty/missing. A mapping needs at least one of source / value.
• expand / enrich / batch_enrich / dedup describe the record-level steps in the spec, by registered name — so the YAML is the whole contract and varies per source. The functions are registered in code; the spec just references them, exactly like transform. (The matching Parser(...) arguments still work as a global default for specs that don't declare a step.)
• parsing is only meaningful for text sources and is ignored for structured inputs.

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Schema drift handling (on_unknown_column)

When a familiar source gains or loses a column, its field signature changes and no spec matches exactly. fidelis recognizes this as drift of a known source (by field-set similarity) rather than a brand-new format, and applies the policy from the spec's on_unknown_column (falling back to the Parser default). In every case ParseResult.drift_report describes exactly what changed.

Policy	Behavior
ignore	Silently ignore unknown columns and keep mapping the known ones.
error	(default) Raise DriftError with a precise message; lose nothing silently.
regenerate	Ask the LLM to fill in only the missing part of the mapping, then continue.

parser = Parser(target_model=User, llm="anthropic:claude-opus-4-8",
                on_unknown_column="regenerate")
result = parser.parse(source_with_a_new_column)
print(result.drift_report.describe())
# Schema drift (regenerate): new fields: Phone

Note on detection limits. Since specs are keyed by a hash of the source field names, a drifted source is recognized as "the same source, changed" only when its field set still overlaps a known spec's by ≥ 50% (Jaccard). If a source mutates so heavily that most columns are renamed at once, it is indistinguishable from a brand-new format and a fresh spec is generated instead of raising drift. Heavy renames therefore need a fresh review of the new spec.

Note on the error policy. Because error raises DriftError, no ParseResult is returned — the drift details live on exc.report instead of result.drift_report. Use ignore or regenerate if you need the drift reported inside the result object.

---

Quarantine round-trip

No bad row is ever dropped silently — every failure is a RowError carrying the original source record. The quarantine helpers turn those into a file a human can fix, then read it back into clean records to re-ingest:

result = parser.parse("feed.csv")
result.write_quarantine("bad_rows.csv")      # original data + _error_reason column
# …a human opens bad_rows.csv and fixes the offending cells…

from fidelis import read_quarantine
fixed = read_quarantine("bad_rows.csv")      # diagnostic columns stripped
again = parser.parse(fixed)                  # same field signature → same spec

Each exported row is the source record plus three _-prefixed diagnostic columns (_row_index, _error_field, _error_reason) that are stripped on the way back in — so the re-ingested rows keep the original field signature and reuse the same spec (no spurious drift). .json paths round-trip as JSON, anything else as CSV.

---

Transforms

A transform normalizes a raw cell before validation. Reference it in the spec by name, or name:arg. Built-ins:

Transform	Example	Effect
strip	strip	Trim surrounding whitespace.
strip_lower	strip_lower	Trim and lowercase (great for emails).
to_int	to_int	Parse to int (tolerant of "12.0", spaces).
to_float	to_float	Parse to float (accepts , decimal separator).
to_bool	to_bool	yes/1/true/y → True, no/0/false/n → False.
parse_date	parse_date:%d.%m.%Y	strptime with the given format (ISO if omitted). Pipe-separate several formats to try in order: parse_date:%Y-%m-%d|%d/%m/%Y.
clip	clip:0:100	Clamp a number into a range (clip:0: / clip::100 for one-sided).

Empty values pass through untouched — requiredness is enforced by Pydantic validation, not by transforms. Register your own from code:

from fidelis import register_transform, available_transforms

# Plain call…
register_transform("upper", lambda value, arg: str(value).upper())

# …or as a decorator (like register_enrichment / register_expander):
@register_transform("shout")
def shout(value, arg):
    return str(value).upper() + "!"

print(available_transforms())   # [..., 'shout', 'to_int', 'upper', ...]

Then use transform: upper in any spec.

---

Enrichment (post-mapping hooks)

A transform sees a single cell. An enrichment sees the whole mapped record and the original source row — so it can derive a field with no source column, combine several columns (mapped or raw), mask a value, or look something up in an external source. Enrichers run after mapping and before validation, so any field they add is validated like the rest.

Like a transform, you register the function in code and reference it by name in the spec — so the YAML stays the full contract and enrichment can vary per source. The signature is (record, source): record is the mapped target dict, source is the raw input row.

import fidelis

@fidelis.register_enrichment("fill_domain")
def fill_domain(record, source):
    record["domain"] = record["email"].split("@", 1)[1]
    return record            # or mutate in place and return None

# Reach RAW columns that aren't mapped to any target — e.g. combine two:
@fidelis.register_enrichment("full_name_from_parts")
def full_name_from_parts(record, source):
    record["full_name"] = f"{source['First Name']} {source['Last Name']}".strip()
    return record

# in the spec for this source:
enrich:
  - fill_domain
  - full_name_from_parts

• Enrichers are applied in spec order — each sees the previous one's output.
• validate_spec flags an enrich name that isn't registered; at parse time an unknown name raises loudly.
• If an enricher raises, that record becomes a RowError (never silently dropped), with the failing enricher's name in the reason.
• available_enrichments() lists what's registered.

Global default. Parser(User, enrich=["fill_domain"]) still works — it applies to every spec that doesn't declare its own enrich. The constructor arg also accepts callables directly (handy for quick scripts); specs reference names only. Spec-declared steps take precedence over the Parser default.

How it differs from a transform:

	Transform	Enrichment
Sees	one cell value	the mapped record + the source row
Configured	transform: in the spec	enrich: in the spec
Can add fields with no source column	no	yes
Runs when	during mapping	after mapping, before validation

Combine (many → one), split (one → many), coalesce, and conditional mapping are all just enrichers that read source — declared in the spec, registered in code. Plain 1→1 column mapping and constants stay in mappings.

Batch enrichment — one bulk lookup instead of N

A per-row enricher that calls a database or an API runs once per row. When the lookup can be batched, register a batch enricher instead: it receives every clean record at once and returns a list of the same length, in order — so you make a single bulk call for the whole feed.

@fidelis.register_batch_enrichment("attach_scores")
def attach_scores(records):
    ids = [r["user_id"] for r in records]
    scores = score_service.bulk_fetch(ids)      # ONE call, not len(records)
    for r, s in zip(records, scores):
        r["score"] = s
    return records                               # same length, same order

# in the spec:
batch_enrich:
  - attach_scores

• Batch enrichers run after the per-row enrich, over the clean rows only — rows that failed mapping are excluded and preserved as errors.
• The one-to-one contract is enforced: returning a different length (or a non-dict) raises BatchEnrichmentError. Batch enrichment is all-or-nothing — use it to fill/derive fields across the feed, not to filter rows.
• Configuring batch_enrich materializes the feed (it needs the whole set); without it the pipeline stays lazy.
• As with enrich, Parser(batch_enrich=[...]) is a global default for specs that don't declare their own.

Row expansion (one row → many)

Sometimes a single source row stands for several entities — one line whose Airports column lists "JFK;LAX;ORD" should become three rows. An expander returns a list of records; the row fans out, and then enrich / batch_enrich / validation / dedup all run per fanned-out row — so each airport gets its own lookup and its own derived fields.

The common case — split one column — is fully declarative, no code: name the column and the delimiter right in the spec.

expand:
  - field: airport_code      # the (target) column holding the list
    delimiter: "|"           # the list separator — each vendor sets its own
enrich:
  - resolve_airport          # runs once per airport, not per source line

For arbitrary fan-out, register a custom expander — (record, source) → list — and reference it with expander::

@fidelis.register_expander("by_region")
def by_region(record, source):
    return [{**record, "region": r} for r in lookup_regions(source["Airports"])]

expand:
  - expander: by_region

Expanders run before enrich, so enrichment is per fanned-out row (per airport).

• Pipeline order: map → expand → enrich → batch_enrich → validate → dedup.
• A step may return zero, one, or many rows. If one fanned-out row fails, it becomes a RowError pointing back at the source line; its siblings still pass.
• Parser(expand=[...]) is a global default (registered names or callables) for specs that don't declare their own.

---

Shaping output (multi-record, multi-field, whole-column)

Beyond 1 row → 1 record, the spec can reshape the output. Full pipeline: skip_when → unpivot → rules → map → column_steps → expand → enrich → batch_enrich → validate → dedup.

Conditional multi-record (rules) — one row → a record per firing rule (base mappings + the rule's). E.g. a row with retail and wholesale prices becomes two records:

mappings:
  - {target: sku, source: SKU, transform: strip}     # shared
rules:
  - when: {field: RETAIL_PRICE, op: not_empty}        # ops: not_empty/empty/eq/ne/in/gt/lt/ge/le
    mappings:
      - {target: kind, value: retail}
      - {target: amount, source: RETAIL_PRICE, transform: to_float}
  - when: {field: WHOLESALE_PRICE, op: not_empty}
    mappings:
      - {target: kind, value: wholesale}
      - {target: amount, source: WHOLESALE_PRICE, transform: to_float}

Column-group fan-out (unpivot) — repeating groups → one record each:

unpivot:
  count: 3                       # indices 1..3 (or list them in `index`)
  index_field: quarter
  columns: {PRICE: "Q{i}_PRICE", QTY: "Q{i}_QTY"}

Multi-target transform (targets) — one transform fills several fields (keep the converted and the original). The transform returns a dict:

mappings:
  - {source: WEIGHT, transform: lbs_to_kg,
     targets: {kg: weight_kg, original: weight_original, unit: weight_unit}}

Whole-column stage (column_steps) — decisions over a column's distribution, applied before validation (median→/100, 2→4-digit year):

@fidelis.register_column_step("cents_if_huge")
def cents_if_huge(values, context=None):       # whole column as a list, same length out
    import statistics
    nums = [v for v in values if isinstance(v, (int, float))]
    if nums and statistics.median(nums) > 10_000:
        return [v / 100 if isinstance(v, (int, float)) else v for v in values]
    return values

column_steps: {price: cents_if_huge}

Skip & clip — drop rows declaratively, clamp numbers:

skip_when:
  - {field: STATUS, op: in, value: [CANCELLED, VOID]}   # drop these rows
mappings:
  - {target: pct, source: PCT, transform: "clip:0:100"}  # clamp into [0, 100]

Skipped / no-rule-fired rows produce no record but still count in the coverage denominator (below).

---

Run context, coverage & LLM hints

Run context (context=) — hand lookups / thresholds / config to hooks instead of hardcoding globals. Any transform / enricher / batch / expander / column-step that declares a context parameter receives it (others are unaffected); it's isolated per parse():

@fidelis.register_transform("canon")
def canon(value, arg, context):
    return context["synonyms"].get(str(value).strip(), value)

Parser(Supplier, context={"synonyms": {...}, "threshold": 0.85})

Coverage (result.coverage) — one quality number for the whole run: the fraction of source rows that produced a record.

result.coverage          # Coverage(rows_in=1000, rows_with_output=980, rows_with_error=12, …)
result.coverage.score    # 0.98  — also in result.summary()

Domain hints (domain_hints=) — give the LLM typed context when it generates a spec (only affects generation, never the deterministic path):

Parser(Item, llm="anthropic:claude-opus-4-8",
       domain_hints={"currencies": ["USD", "EUR"], "categories": ["A", "B", "C"]})

---

Provider strings (anthropic / openai / local)

Pick a provider with a "provider:model" string (or pass a provider instance). The provider is only ever used for one-shot schema inference — never per row.

Parser(target_model=User, llm="anthropic:claude-opus-4-8")     # Anthropic Messages API
Parser(target_model=User, llm="openai:gpt-4o-mini")            # OpenAI
Parser(target_model=User, llm="local:llama3")                  # any OpenAI-compatible endpoint

• anthropic — reads ANTHROPIC_API_KEY from the environment (or pass llm_options={"api_key": ...}). Needs httpx (pip install 'fidelis[anthropic]').
• openai — reads OPENAI_API_KEY. Needs httpx.
• local — any OpenAI-compatible server (Ollama, vLLM, LM Studio…); defaults to http://localhost:11434/v1/chat/completions, no API key required. Override with llm_options={"base_url": ...} or LOCAL_LLM_BASE_URL.

Extra constructor arguments go through llm_options:

Parser(target_model=User, llm="anthropic:claude-opus-4-8",
       llm_options={"api_key": "sk-...", "timeout": 30})

If no spec matches and no llm is configured, parse() raises SpecNotFoundError — it will never reach the network behind your back.

---

Testing offline with FakeProvider

FakeProvider is a deterministic, network-free LLMProvider for tests and offline flows. Hand it the mappings (or a canned JSON response, or a responder callable) you want it to "infer":

from fidelis import Parser, FakeProvider

provider = FakeProvider(mappings=[
    {"source": "E-mail", "target": "email", "transform": "strip_lower", "confidence": 0.98},
    {"source": "Name",   "target": "full_name", "transform": "strip", "confidence": 0.91},
    {"source": "Date",   "target": "signup_date", "transform": "parse_date:%d.%m.%Y", "confidence": 0.62},
])

parser = Parser(target_model=User, spec_store=tmp_dir, llm=provider)
result = parser.parse([{"E-mail": "[e-mail]", "Name": "Alice", "Date": "01.02.2026"}])
assert provider.call_count == 1          # generated once
parser.parse(...)                        # subsequent calls: still 1 — cached, deterministic

This is exactly how the examples in examples/ run with no API key.

---

API surface

from fidelis import (
    Parser, ParseResult, RowError, DriftReport, DuplicateRow, Coverage, SpecNotFoundError,
    Spec, Mapping, ParsingSpec, DedupSpec, UnpivotSpec, Condition, Rule, ExpandStep,
    compute_signature, find_spec_by_signature,
    normalize_field_name, detect_drift, DriftError,
    SpecStore, FileSpecStore, MemorySpecStore,
    quarantine_rows, write_quarantine, read_quarantine, infer_model_source,
    register_transform, apply_transform, available_transforms, TransformError,
    register_enrichment, available_enrichments, EnrichmentError,
    register_batch_enrichment, available_batch_enrichments, BatchEnrichmentError,
    register_expander, available_expanders, split_rows, ExpansionError,
    register_column_step, available_column_steps, ColumnStepError,
    LLMProvider, resolve_provider,
    AnthropicProvider, OpenAIProvider, LocalProvider, FakeProvider,
    from_csv, from_records, from_json, from_excel, SourceData,
)

---

License

MIT.