Ingest from files, web, databases, APIs, streams, email, Git repos, Parquet, Databricks, Snowflake, SAP, or MCP servers, all through a unified interface.

# WebIngestor needs the documents extra: pip install "semantica[documents]"
from semantica.ingest import FileIngestor, WebIngestor, ParquetIngestor, DBIngestor
Ingest an entire directory of contracts (PDF, DOCX, HTML, TXT)
docs = FileIngestor().ingest_directory("./contracts/", recursive=True)
Ingest live web content with robots.txt compliance
pages = WebIngestor().ingest_url("https://example.com/reports/annual-2024.html")
Ingest structured data from Parquet with Snappy compression
records = ParquetIngestor().ingest("./data/transactions.parquet")
Ingest from a SQL database - specify which tables to pull
rows = DBIngestor().ingest_database(
    connection_string="postgresql://user:pass@localhost/mydb",
    include_tables=["customer_events"],
    max_rows_per_table=50_000,
)

# Enterprise data platforms - pull tables straight out of your lakehouse
or warehouse, with lineage, instead of exporting to CSV first
from semantica.ingest import DatabricksIngestor, SnowflakeIngestor
pip install "semantica[db-databricks]"
databricks = DatabricksIngestor(
    host="https://adb-xxx.azuredatabricks.net",
    token="dapi-xxxxxxxx",              # or client_id/client_secret for OAuth M2M
    http_path="/sql/1.0/warehouses/xxxxxxxx",
    catalog="main",
)
customers    = databricks.ingest_table("customers", limit=10_000)
sales        = databricks.ingest_query("SELECT * FROM sales WHERE region = 'EMEA'")
table_lineage = databricks.get_table_lineage("customers", catalog="main", schema="default")  # Unity Catalog lineage
pip install semantica[db-snowflake]
snowflake = SnowflakeIngestor(
    account="myaccount",
    user="myuser",
    password="mypassword",              # or private_key=... for key-pair; use authenticator="oauth", token=... for OAuth
    warehouse="COMPUTE_WH",
    database="MYDB",
)
orders = snowflake.ingest_table("ORDERS", limit=10_000)

Security Note: Never hardcode credentials (token, password, private_key) in production code; pass them via environment variables (e.g., DATABRICKS_TOKEN, SNOWFLAKE_PASSWORD) or a secrets manager.

Supported sources: Local files (PDF, DOCX, PPTX, HTML, TXT, CSV, JSON, YAML, Excel, XML) · Web pages · RSS/Atom feeds · REST APIs · Databases (PostgreSQL, MySQL, SQLite, Oracle, SQL Server) · Parquet datasets · Databricks (Unity Catalog + Delta Lake) · Snowflake · SAP (OData v2/v4) · Git repositories · Email (IMAP/POP3) · Message streams (Kafka, RabbitMQ, Kinesis, Pulsar) · MCP resources · Apache Arrow/Feather/IPC (ArrowIngestor)

DuckDB, Elasticsearch, Google Drive, HuggingFace, MongoDB, and Pandas ingestion also ship (DuckDBIngestor, ElasticIngestor, GDriveIngestor, HuggingFaceIngestor, MongoIngestor, PandasIngestor) but aren't re-exported from the top-level semantica.ingest namespace yet — import them directly: from semantica.ingest.duckdb_ingestor import DuckDBIngestor.

Extract structured knowledge from raw text in one pass.

from semantica.semantic_extract import (
    NamedEntityRecognizer,
    RelationExtractor,
    EventDetector,
    TripletExtractor,
)
text = """
Anthropic CEO Dario Amodei announced a $7.3B Series E funding round in partnership
with Google and Spark Capital, valuing the company at $61.5B as of Q4 2024.
"""
Named entity recognition with confidence thresholding
ner = NamedEntityRecognizer(confidence_threshold=0.7)
entities = ner.extract_entities(text)
→ [Entity(name="Dario Amodei", type="PERSON"), Entity(name="Anthropic", type="ORG"),
   Entity(name="Google", type="ORG"), Entity(name="$7.3B", type="MONEY"), ...]
Relationship extraction - bidirectional support
rel_extractor = RelationExtractor(confidence_threshold=0.6, bidirectional=True)
relations = rel_extractor.extract_relations(text, entities=entities)
→ [Relation(subject="Dario Amodei", predicate="ceo_of", object="Anthropic"),
   Relation(subject="Anthropic", predicate="raised", object="$7.3B Series E"), ...]
Event detection with temporal processing
events = EventDetector(extract_participants=True, extract_time=True).detect_events(text)
→ [Event(type="FUNDING", participants=["Anthropic","Google","Spark Capital"],
         amount="$7.3B", date="Q4 2024")]
RDF triplets with optional provenance metadata
triplets = TripletExtractor(include_temporal=True, include_provenance=True).extract_triplets(text)
→ [("Anthropic", "valuation", "$61.5B"), ("Dario Amodei", "is_ceo_of", "Anthropic"), ...]

Batch processing across many documents uses ner.process_batch([...]), not a per-call extract_entities_batch on the facade class.

Build a production knowledge graph from documents and run graph algorithms over it.

from semantica.ingest import FileIngestor
from semantica.kg import (
    GraphBuilder,
    GraphAnalyzer,
    CentralityCalculator,
    CommunityDetector,
    PathFinder,
    LinkPredictor,
    BiTemporalFact,
)
from datetime import datetime
Build KG - merge duplicate entities, track temporal edges
sources = FileIngestor().ingest_directory("./contracts/", recursive=True)
kg = GraphBuilder(merge_entities=True, enable_temporal=True).build(sources)
Graph analytics
analyzer    = GraphAnalyzer()
analysis    = analyzer.analyze_graph(kg)             # full graph metrics
centrality  = CentralityCalculator()
degree      = centrality.calculate_degree_centrality(kg)    # most-connected entities
betweenness = centrality.calculate_betweenness_centrality(kg)
communities = CommunityDetector().detect_communities(kg, method="louvain")  # natural clusters
path        = PathFinder().find_shortest_path(kg, "alice_chen", "contract_001")
predictions = LinkPredictor().predict_links(kg, top_k=10)   # relationship predictions
Bi-temporal facts - track valid time vs. recorded time independently
fact = BiTemporalFact(
    valid_from=datetime(2024, 3, 1),
    valid_until=datetime(2025, 1, 1),
    recorded_at=datetime(2024, 3, 5),
)

Run explainable rule-based inference, not a black box.

from semantica.reasoning import ReteEngine, Rule, Fact, RuleType
rete = ReteEngine()
rete.build_network([
    Rule(
        rule_id="aml_flag",
        name="Flag high-risk transactions",
        conditions=[
            {"field": "amount",  "operator": ">",  "value": 10_000},
            {"field": "country", "operator": "in", "value": ["IR", "KP", "SY"]},
        ],
        conclusion="flag_for_compliance_review",
        rule_type=RuleType.IMPLICATION,
    ),
    Rule(
        rule_id="velocity_check",
        name="Flag rapid sequential transfers",
        conditions=[
            {"field": "transfers_in_1h", "operator": ">", "value": 5},
            {"field": "total_amount",    "operator": ">", "value": 50_000},
        ],
        conclusion="flag_velocity_breach",
        rule_type=RuleType.IMPLICATION,
    ),
])
rete.add_fact(Fact("tx_001", "transaction", [{"amount": 15_000, "country": "IR"}]))
flagged = rete.match_patterns()
→ [{"rule": "aml_flag", "matched_facts": ["tx_001"], "conclusion": "flag_for_compliance_review"}]

Current limitation: ReteEngine's alpha-node condition matcher is intentionally simple in this release — validate match_patterns() output against your actual rule set before wiring it into a production compliance gate; more selective condition evaluation is on the roadmap.

# Recursive Datalog - natural language for graph queries
from semantica.reasoning import DatalogReasoner
engine = DatalogReasoner()
engine.add_fact("parent(tom, bob)")
engine.add_fact("parent(bob, ann)")
engine.add_fact("parent(ann, pat)")
engine.add_rule("ancestor(X, Y) :- parent(X, Y).")
engine.add_rule("ancestor(X, Z) :- parent(X, Y), ancestor(Y, Z).")
ancestors = engine.query("ancestor(tom, ?X)")
→ [{"X": "bob"}, {"X": "ann"}, {"X": "pat"}]

# Explainable reasoning - trace the path, not just the answer
from semantica.reasoning import ExplanationGenerator, Reasoner
reasoner = Reasoner()
reasoner.add_fact("parent(tom, bob)")
reasoner.add_rule("ancestor(X, Y) :- parent(X, Y)")
result = reasoner.forward_chain()
explainer = ExplanationGenerator()
explanation = explainer.generate_explanation(result)
→ Explanation(conclusion="...", steps=[ReasoningStep(...)], justification=Justification(...))

Drop-in vector store with multiple backends, hybrid search, and decision-aware retrieval.

from semantica.vector_store import VectorStore, HybridSearch
In-memory backend shown here: HybridSearch and explain_decision() work out of the box.
Swap backend="qdrant" / "weaviate" / "milvus" / "pinecone" / "pgvector" / "faiss" once you
scale past a single process — search() and store_decision() work identically on all of them.
vs = VectorStore(backend="inmemory", dimension=1536)
Store a decision with scenario description and outcome
vs.store_decision(
    scenario="Personal loan A-7291, $85k income, 31% DTI, 3yr employment",
    outcome="approved",
    confidence=0.94,
    category="loan_underwriting",
)
Semantic similarity search
results = vs.search(
    query="personal loan approval with low DTI",
    limit=10,
)
Hybrid search - dense + sparse retrieval in one pass with RRF fusion
hs   = HybridSearch(vector_store=vs)
hits = hs.search("high-risk transactions 2024")
Explain why a decision was retrieved
explanation = vs.explain_decision(results[0]["id"])

Backends: faiss · qdrant · weaviate · milvus · pinecone · pgvector · sqlite · inmemory

KG-aware splitting that preserves entity boundaries, relation triplets, and ontology concepts, essential for GraphRAG pipelines.

from semantica.split import TextSplitter, EntityAwareChunker, RelationAwareChunker
text = open("contracts/master_agreement.txt").read()
Standard recursive chunking
chunks = TextSplitter(method="recursive", chunk_size=1000, chunk_overlap=200).split(text)
Entity-aware chunking - never splits a named entity across chunks (GraphRAG)
chunks = TextSplitter(method="entity_aware", ner_method="llm", chunk_size=1000).split(text)
Relation-aware chunking - preserves (subject, predicate, object) triplets intact
chunks = RelationAwareChunker(chunk_size=1000, preserve_triplets=True).chunk(text)
Graph-based chunking - uses centrality to find natural community boundaries
chunks = TextSplitter(method="graph_based", chunk_size=1000).split(text)
Hierarchical chunking - multi-level (section → paragraph → sentence)
chunks = TextSplitter(method="hierarchical", levels=["section", "paragraph"]).split(text)

Supported methods: recursive · token · sentence · paragraph · semantic_transformer · entity_aware · relation_aware · graph_based · ontology_aware · hierarchical · community_detection · centrality_based · llm

Every fact is linked to its source. No black boxes, no mystery outputs.

from semantica.provenance import ProvenanceManager
prov = ProvenanceManager(storage_path="./provenance.db")
Track where every entity came from
prov.track_entity(
    entity_id="acme_corp",
    source="contracts/acme_master_agreement_2024.pdf",
    metadata={"page": 1, "confidence": 0.97, "extractor": "NamedEntityRecognizer"},
)
Track a relationship's provenance - entity linkage travels in metadata
prov.track_relationship(
    relationship_id="alice_works_for_acme",
    source="hr_records/employees_q1_2024.csv",
    metadata={"source_entity_id": "alice_chen", "target_entity_id": "acme_corp"},
)
Answer "where did this come from?"
lineage = prov.get_lineage("acme_corp")
trail   = prov.trace_lineage("alice_chen")   # full ancestor chain
entry   = prov.get_provenance("acme_corp")

Generate ontologies from data, validate shapes, and manage your vocabulary.

from semantica.ontology import OntologyGenerator, OntologyValidator
data = {
    "entities": [
        {"id": "acme_corp",  "type": "Organization", "industry": "SaaS", "founded": 2012},
        {"id": "alice_chen", "type": "Person",        "role": "CTO",     "since": 2019},
    ],
    "relationships": [
        {"source": "alice_chen", "target": "acme_corp", "type": "works_for"},
    ],
}
gen       = OntologyGenerator(base_uri="https://semantica.dev/ontology/")
ontology  = gen.generate_ontology(data)
classes   = gen.infer_classes(data)
props     = gen.infer_properties(data, classes)
optimized = gen.optimize_ontology(ontology)
Validate against SHACL shapes
validator = OntologyValidator()
report    = validator.validate(ontology)
→ ValidationResult(valid=True, consistent=True, satisfiable=True, errors=[], warnings=[])

Detect and resolve conflicting facts from multiple sources before they corrupt your knowledge base.

from semantica.conflicts import ConflictDetector, ConflictResolver, SourceTracker
entities_from_source_a = [
    {"id": "alice_chen", "role": "CTO",   "salary": 250_000, "start_date": "2019-03-01"},
]
entities_from_source_b = [
    {"id": "alice_chen", "role": "VP Eng", "salary": 275_000, "start_date": "2019-03-01"},
]
Detect all conflict types: value, type, relationship, temporal, logical
detector   = ConflictDetector()
conflicts  = detector.detect_conflicts(entities_from_source_a + entities_from_source_b)
→ [Conflict(entity="alice_chen", field="role",   values=["CTO","VP Eng"], severity="HIGH"),
   Conflict(entity="alice_chen", field="salary",  values=[250000,275000],   severity="MEDIUM")]
Resolve using multiple strategies
resolver = ConflictResolver()
resolved = resolver.resolve_conflicts(conflicts, strategy="credibility_weighted")  # weighted by source trust
resolved = resolver.resolve_conflicts(conflicts, strategy="most_recent")          # prefer most recent
resolved = resolver.resolve_conflicts(conflicts, strategy="voting")               # majority wins
Track source credibility over time
tracker = SourceTracker()
tracker.register_source("source_a", source_type="document", credibility_score=0.85)
tracker.register_source("source_b", source_type="document", credibility_score=0.72)

Block, cluster, and merge duplicates with semantic similarity.

from semantica.deduplication import DuplicateDetector, EntityMerger
entities = [
    {"id": "e1", "name": "Acme Corporation",  "domain": "acme.com"},
    {"id": "e2", "name": "Acme Corp.",         "domain": "acme.com"},
    {"id": "e3", "name": "ACME Corp",          "domain": "acme.co"},
    {"id": "e4", "name": "Globex Industries",  "domain": "globex.com"},
]
detector   = DuplicateDetector(similarity_threshold=0.75, use_clustering=True)
candidates = detector.detect_duplicates(entities)
groups     = detector.detect_duplicate_groups(entities)
→ DuplicateGroup(entities=["e1","e2","e3"], confidence=0.91, strategy="semantic+blocking")

merger  = EntityMerger(preserve_provenance=True)
ops     = merger.merge_duplicates(entities, strategy="keep_most_complete")
history = merger.get_merge_history()

Standardize text, entities, dates, numbers, and encodings before building your knowledge graph.

from semantica.normalize import (
    TextNormalizer,
    EntityNormalizer,
    DateNormalizer,
    NumberNormalizer,
    DataCleaner,
)
Unicode, whitespace, casing, HTML tags, smart quotes
text  = TextNormalizer().normalize("  Acme Corp.'s Q4 report...  ")
→ "Acme Corp.'s Q4 report..."
Alias resolution + entity disambiguation with confidence scores
canonical = EntityNormalizer().normalize_entity("ACME Corp.")
→ NormalizedEntity(canonical="Acme Corporation", type="Organization", confidence=0.91)
Natural language date parsing with timezone conversion
dt    = DateNormalizer().normalize_date("3 weeks ago")
→ datetime(2026, 7, 1, tzinfo=UTC)
Numbers with currency symbols and magnitude suffixes
price = NumberNormalizer().normalize_number("$1.25M")
→ 1250000.0
Deduplicate, validate, and impute missing values across a dataset
clean = DataCleaner().clean_data(records, remove_duplicates=True, handle_missing=True)

Compose ingestion, extraction, and graph-building into a declarative, parallel pipeline.

from semantica.pipeline import PipelineBuilder, ExecutionEngine
builder = PipelineBuilder()
add_step() returns the created PipelineStep, not the builder, so these don't chain
builder.add_step("ingest",      step_type="ingest",           source="./contracts/", recursive=True)
builder.add_step("extract",     step_type="ner_extract")
builder.add_step("relations",   step_type="relation_extract")
builder.add_step("build_kg",    step_type="kg_build",         merge_entities=True)
builder.add_step("deduplicate", step_type="deduplicate",      threshold=0.75)
builder.add_step("export",      step_type="export",           format="turtle", output="kg.ttl")
connect_steps() and set_parallelism() return the builder, so these do chain
pipeline = (
    builder
    .connect_steps("ingest",      "extract")
    .connect_steps("extract",     "relations")
    .connect_steps("relations",   "build_kg")
    .connect_steps("build_kg",    "deduplicate")
    .connect_steps("deduplicate", "export")
    .set_parallelism(4)
    .build(name="contracts_pipeline")
)
engine   = ExecutionEngine()
result   = engine.execute_pipeline(pipeline)
status   = engine.get_pipeline_status(pipeline.name)
progress = engine.get_progress(pipeline.name)

Track when facts were true in the world vs. when they were recorded, and query either axis.

from semantica.context import ContextGraph
from semantica.kg import (
    BiTemporalFact,
    TemporalGraphQuery,
    TemporalNormalizer,
)
from datetime import datetime
graph = ContextGraph(advanced_analytics=True)
graph.add_node("alice_chen", "Person",       role="VP Engineering")
graph.add_node("acme_corp",  "Organization", valuation=1_200_000_000)
A temporally-bounded edge - valid_from/valid_until define when it held true
graph.add_edge(
    "alice_chen", "acme_corp", edge_type="works_for",
    valid_from="2024-03-01T00:00:00", valid_until="2025-01-01T00:00:00",
)
Point-in-time snapshots - replay history without reprocessing
snapshot_2023 = graph.state_at("2023-06-01")
snapshot_2024 = graph.state_at("2024-01-01")
Bi-temporal facts - valid_time is when true in the world;
recorded_at is when you learned about it
fact = BiTemporalFact(
    valid_from=datetime(2024, 3, 1),
    valid_until=datetime(2025, 1, 1),
    recorded_at=datetime(2024, 3, 5),
)
Query facts valid within a time window - to_kg_dict() is the official
adapter that emits {"entities", "relationships"} with source_id/target_id
keys, the shape query_time_range() expects (no manual mapping required)
kg = graph.to_kg_dict()
tq = TemporalGraphQuery()
facts_in_window = tq.query_time_range(
    kg, query="valid_facts", start_time="2024-01-01", end_time="2024-12-31"
)
Normalize natural language temporal expressions - returns a (start, end) range
norm = TemporalNormalizer()
start, end = norm.normalize("last quarter")

Export to any format required by regulators, graph databases, or downstream systems.

from semantica.export import (
    RDFExporter,
    JSONExporter,
    ParquetExporter,
    LPGExporter,
    ReportGenerator,
)
kg = {"entities": [...], "relationships": [...]}
rdf = RDFExporter()
turtle_str = rdf.export_to_rdf(kg, format="turtle")     # returns string
jsonld_str = rdf.export_to_rdf(kg, format="json-ld")
rdf.export(kg, "kg_audit.ttl",    format="turtle")
rdf.export(kg, "kg_audit.jsonld", format="json-ld")
rdf.export(kg, "kg_audit.nt",     format="ntriples")
Columnar analytics - Snappy-compressed Parquet (writes kg_snapshot_entities.parquet
and kg_snapshot_relationships.parquet)
ParquetExporter(compression="snappy").export_knowledge_graph(kg, "kg_snapshot")
JSON knowledge graph
JSONExporter().export_knowledge_graph(kg, "kg.json")
Neo4j / Memgraph Cypher statements for graph database import
LPGExporter().export(kg, "kg_import.cypher")
Human-readable HTML report
ReportGenerator().generate_report(
    {"title": "KG Audit Report", "summary": "Weekly ingestion summary", "metrics": {"entities": len(kg["entities"])}},
    file_path="audit_report.html",
    format="html",
)

Render force-directed graphs, community maps, ontology hierarchies, and temporal dashboards.

from semantica.visualization import (
    KGVisualizer,
    OntologyVisualizer,
    EmbeddingVisualizer,
    TemporalVisualizer,
)
import numpy as np
kg = {"entities": [...], "relationships": [...]}
Interactive force-directed graph (opens in browser)
viz = KGVisualizer(layout="force", color_scheme="default")
viz.visualize_network(kg, output="interactive", file_path="kg.html")
viz.visualize_communities(kg, communities, output="interactive")
viz.visualize_centrality(kg, centrality, centrality_type="degree")
viz.visualize_entity_types(kg, output="html", file_path="entity_types.html")
Ontology class hierarchy
OntologyVisualizer().visualize_hierarchy(ontology, output="interactive")
2D embedding projection (UMAP / t-SNE / PCA)
EmbeddingVisualizer().visualize_2d_projection(
    embeddings=np.array([...]),
    labels=["entity_a", "entity_b"],
    method="umap",
)
Timeline scrubber - watch the graph evolve
TemporalVisualizer().visualize_timeline(kg, output="interactive")

One shared intelligence layer. All agents read and write to the same context graph.

# pip install semantica[agno]
from agno.agent import Agent
from agno.team import Team
from agno.models.anthropic import Claude
from semantica.context import ContextGraph
from semantica.vector_store import VectorStore
from integrations.agno import AgnoSharedContext, AgnoDecisionKit, AgnoKGToolkit
shared = AgnoSharedContext(
    vector_store=VectorStore(backend="faiss"),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    decision_tracking=True,
)
researcher = Agent(
    name="Researcher",
    model=Claude(id="claude-sonnet-4-5"),
    memory=shared.bind_agent("researcher"),
    tools=[AgnoKGToolkit(context=shared)],
)
analyst = Agent(
    name="Analyst",
    model=Claude(id="claude-sonnet-4-5"),
    memory=shared.bind_agent("analyst"),
    tools=[AgnoDecisionKit(context=shared)],
)
team = Team(agents=[researcher, analyst], mode="coordinate")
Researcher's findings are instantly available to the Analyst - no copy, no sync

→ runnable notebooks in the cookbook, each self-contained and runnable in under 5 minutes