semantica/docs/reference/ingest.md

title, description, icon

title	description	icon
Ingest Module	Universal data ingestion from files, Parquet, XML, web, feeds, streams, repositories, email, and databases.	database

semantica.ingest is the entry point for loading data into Semantica. Every ingestor returns a list of DataSource objects with normalized content and metadata, regardless of the original format.

What You Get

PDF, DOCX, HTML, JSON, CSV, Excel, PPTX, and ZIP/TAR archives — type auto-detected from extension. PyArrow-based Parquet with Hive-style partition support and column selection (v0.5.0). XXE-safe lxml with XSD/DTD validation and directory scanning (v0.5.0). Real-time ingestion from Kafka, RabbitMQ, AWS Kinesis, and Apache Pulsar. `CloudStorageIngestor` — unified client for AWS S3, Google Cloud Storage, and Azure Blob Storage. `DBIngestor` (SQL via SQLAlchemy) and `SnowflakeIngestor` for data warehouse queries.

Quick Start

```python from semantica.ingest import FileIngestor

ingestor = FileIngestor()

# Single file — type auto-detected from extension
sources = ingestor.ingest("data/report.pdf")

# Recursive directory scan
sources = ingestor.ingest_directory("data/", recursive=True)

# Glob pattern
sources = ingestor.ingest("data/**/*.docx")
```

```python from semantica.ingest import DBIngestor

ingestor = DBIngestor(
    connection_string="postgresql://user:pass@localhost/db",
    query="SELECT id, content, created_at FROM documents WHERE status='active'"
)
sources = ingestor.ingest()
```

```python from semantica.pipeline import PipelineBuilder, ExecutionEngine from semantica.parse import DocumentParser from semantica.semantic_extract import NERExtractor from semantica.llms import Groq

llm       = Groq(model="llama-3.3-70b-versatile", api_key=os.getenv("GROQ_API_KEY"))
ingestor  = FileIngestor()
parser    = DocumentParser()
extractor = NERExtractor(method="llm", llm_provider=llm)

builder = PipelineBuilder()
builder.add_step("ingest",  "file_ingest",    handler=ingestor.ingest_file)
builder.add_step("parse",   "document_parse", handler=parser.parse)
builder.add_step("extract", "ner_extract",    handler=extractor.extract)
builder.connect_steps("ingest", "parse")
builder.connect_steps("parse",  "extract")

pipeline = builder.build("my_pipeline")
result   = ExecutionEngine().execute_pipeline(pipeline, data="data/")
```

Ingestors

### FileIngestor

```python
from semantica.ingest import FileIngestor

ingestor = FileIngestor()
sources  = ingestor.ingest("data/report.pdf")
sources  = ingestor.ingest_directory("data/", recursive=True)
sources  = ingestor.ingest("data/**/*.docx")
```

Supported formats: PDF, DOCX, TXT, HTML, JSON, CSV, Excel (XLSX/XLS), PPTX, ZIP/TAR archives.

### ParquetIngestor (v0.5.0)

PyArrow-based ingestion for Apache Parquet files, including Hive-style partitioned datasets:

```python
from semantica.ingest import ParquetIngestor

ingestor = ParquetIngestor()

# Single Parquet file
sources = ingestor.ingest("data/events.parquet")

# Partitioned directory (year=2024/month=01/...)
sources = ingestor.ingest("data/partitioned/")

# Load only specific columns
sources = ingestor.ingest("data/events.parquet", columns=["id", "text", "timestamp"])
```

### XMLIngestor (v0.5.0)

XXE-safe lxml-based ingestion with optional schema validation:

```python
from semantica.ingest import XMLIngestor

ingestor = XMLIngestor()
sources  = ingestor.ingest("data/records.xml")

# With XSD validation
ingestor = XMLIngestor(validate_xsd="schema.xsd")
sources  = ingestor.ingest("data/records/")

# With DTD validation
ingestor = XMLIngestor(validate_dtd=True)
sources  = ingestor.ingest("data/feed.xml")
```

<Note>
  `XMLIngestor` uses lxml with `resolve_entities=False` to prevent XML External Entity (XXE) injection attacks.
</Note>

### WebIngestor

```python
from semantica.ingest import WebIngestor

ingestor = WebIngestor(
    delay=1.0,            # seconds between requests
    respect_robots=True,  # honor robots.txt
    timeout=30,
)

sources = ingestor.ingest_url("https://example.com/about")
```

### FeedIngestor (RSS/Atom)

```python
from semantica.ingest import FeedIngestor

ingestor = FeedIngestor()
feed     = ingestor.ingest_feed("https://feeds.example.com/rss")

# Live monitoring — returns a FeedMonitor; callback fires on new items
monitor = ingestor.monitor_feeds(
    ["https://feeds.example.com/rss"],
    callback=process_new_items,
)
```

### RepoIngestor

Ingest Git repositories — source code, commit history, and dependency graphs:

```python
from semantica.ingest import RepoIngestor

ingestor = RepoIngestor(
    branch="main",
    file_types=[".py", ".md", ".yaml"],
    include_commits=True,
    commit_range="HEAD~100..HEAD",
)

sources = ingestor.ingest("https://github.com/org/repo")
sources = ingestor.ingest("/path/to/local/repo")
```

### EmailIngestor

Ingest emails via IMAP or POP3 with attachment extraction and thread analysis:

```python
from semantica.ingest import EmailIngestor
import os

ingestor = EmailIngestor(
    protocol="imap",
    host="imap.gmail.com",
    port=993,
    use_ssl=True,
    username=os.getenv("EMAIL_USER"),
    password=os.getenv("EMAIL_PASS"),
    folder="INBOX",
    attachment_types=[".pdf", ".docx", ".txt"],
    include_thread_analysis=True,
    max_emails=500,
)
sources = ingestor.ingest()
```

### CloudStorageIngestor

`CloudStorageIngestor` is a unified client for AWS S3, Google Cloud Storage, and Azure Blob Storage:

```python
from semantica.ingest import CloudStorageIngestor
import os

# AWS S3
ingestor = CloudStorageIngestor(
    provider="s3",
    bucket="my-documents-bucket",
    prefix="reports/2024/",
    region="us-east-1",
    aws_access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
    aws_secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
    # Omit credentials to use IAM instance profile / environment variables
)
sources = ingestor.ingest()

# Google Cloud Storage
ingestor = CloudStorageIngestor(
    provider="gcs",
    bucket="my-gcs-bucket",
    prefix="data/",
    credentials_file="gcp-credentials.json",  # or use ADC
)
sources = ingestor.ingest()

# Azure Blob Storage
ingestor = CloudStorageIngestor(
    provider="azure",
    container="documents",
    connection_string=os.getenv("AZURE_STORAGE_CONNECTION_STRING"),
)
sources = ingestor.ingest()
```

### DBIngestor (SQL)

```python
from semantica.ingest import DBIngestor

ingestor = DBIngestor()
result   = ingestor.ingest_database(
    connection_string="postgresql://user:pass@localhost/db",
    include_tables=["documents"],
)
```

### SnowflakeIngestor

```python
from semantica.ingest import SnowflakeIngestor
import os

ingestor = SnowflakeIngestor(
    account=os.getenv("SNOWFLAKE_ACCOUNT"),
    user=os.getenv("SNOWFLAKE_USER"),
    password=os.getenv("SNOWFLAKE_PASSWORD"),
    warehouse="COMPUTE_WH",
    database="ANALYTICS",
    schema="PUBLIC",
)
result = ingestor.ingest_query("SELECT * FROM documents")
result = ingestor.ingest_table("documents")
```

### StreamIngestor

Real-time ingestion from message brokers:

```python
from semantica.ingest import StreamIngestor

ingestor = StreamIngestor()

# Kafka — returns KafkaProcessor
processor = ingestor.ingest_kafka(
    topic="documents",
    bootstrap_servers=["localhost:9092"],
)

# RabbitMQ — returns RabbitMQProcessor
processor = ingestor.ingest_rabbitmq(
    queue="document_queue",
    connection_url="amqp://guest:guest@localhost/",
)

# AWS Kinesis — returns KinesisProcessor
processor = ingestor.ingest_kinesis(
    stream_name="documents-stream",
    region="us-east-1",
)

# Apache Pulsar — returns PulsarProcessor
processor = ingestor.ingest_pulsar(
    topic="persistent://public/default/documents",
    service_url="pulsar://localhost:6650",
)
```

OntologyIngestor

Ingest existing OWL or RDF ontology files as structured knowledge sources:

from semantica.ingest import OntologyIngestor

ingestor = OntologyIngestor()

ontology_data  = ingestor.ingest_ontology("domain_ontology.owl", format="turtle")
ontology_list  = ingestor.ingest_directory("ontologies/", recursive=True)

FileObject

FileIngestor returns FileObject instances:

@dataclass
class FileObject:
    content:     str             # raw text content
    source_id:   str             # unique identifier
    source_type: str             # "file" | "web" | "database" | "stream" | ...
    metadata:    Dict            # title, author, url, date, page_count, etc.
    raw_bytes:   Optional[bytes] # original binary content if available

Custom Ingestors

Register a custom ingestor and it participates in the full pipeline:

from semantica.ingest.registry import method_registry

def my_ingestor(source, **kwargs):
    return [{"content": "...", "metadata": {}, "source_id": source}]

method_registry.register("file", "my_format", my_ingestor)

Tips and Common Pitfalls

**`FileIngestor` is always the fastest path for local files.** It auto-detects format from extension, handles ZIP/TAR archives automatically, and supports glob patterns. Only reach for `DoclingParser` when `DocumentParser` can't handle your layout. **Use `ParquetIngestor` instead of `FileIngestor` for structured analytical data.** Parquet ingestion preserves column types (int, float, datetime) that CSV reading loses. Use `columns=["id", "text"]` to avoid loading unused columns — critical for wide tables with hundreds of columns. **`XMLIngestor` is XXE-safe by default.** Do not use standard `xml.etree.ElementTree` to pre-parse XML before passing to Semantica — it doesn't block XXE attacks. `XMLIngestor` uses lxml with `resolve_entities=False` to safely parse untrusted XML. **Rate-limit web crawling.** `WebIngestor(delay=1.0, respect_robots=True)` is the responsible default. Without rate limiting, you risk getting blocked by the target server or violating its terms of service. Parse raw sources into structured text and tables. Orchestrate ingest as the first pipeline step. Snowflake-specific setup and authentication guide. Track lineage from ingest through to inference.