---
title: "Getting Started"
description: "The context and intelligence layer for AI — turning raw data into explainable, auditable knowledge graphs."
icon: "rocket"
---
Just here for code? Jump straight to the [Quickstart Tutorial](quickstart) or explore the [Cookbook](cookbook) for interactive notebooks.
---
## What You Can Build
Enhanced retrieval with semantic graph reasoning — ground LLM responses in traceable, structured knowledge.
Agents with structured decision history, causal chains, and precedent search. Every choice is recorded and auditable.
Build, validate, and maintain enterprise-grade semantic knowledge bases from multi-source data.
W3C PROV-O provenance on every fact. HIPAA, SOX, GDPR, FDA 21 CFR Part 11 infrastructure built in.
---
## Installation
```bash
pip install semantica
```
With all optional dependencies:
```bash
pip install semantica[all]
```
Verify:
```python
import semantica
print(semantica.__version__)
```
---
## Quick Start
```python Knowledge Graph
from semantica.ingest import FileIngestor
from semantica.parse import DocumentParser
from semantica.semantic_extract import NERExtractor, RelationExtractor
from semantica.kg import GraphBuilder
# Ingest → Parse → Extract → Build
ingestor = FileIngestor()
sources = ingestor.ingest("data/sample.pdf")
parser = DocumentParser()
parsed = parser.parse(sources[0])
ner = NERExtractor()
entities = ner.extract(parsed)
rel = RelationExtractor()
relationships = rel.extract(parsed, entities=entities)
graph = GraphBuilder(merge_entities=True).build(
entities=entities, relationships=relationships
)
print(f"{len(graph.nodes)} nodes, {len(graph.edges)} edges")
```
```python Agent Context
from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore
context = AgentContext(
vector_store=VectorStore(backend="faiss", dimension=768),
knowledge_graph=ContextGraph(advanced_analytics=True),
decision_tracking=True,
)
# Store a memory with provenance
context.store("GPT-4 outperforms GPT-3.5 on reasoning benchmarks by 40%")
# Record a decision with causal chain
decision_id = context.record_decision(
category="model_selection",
scenario="Choose LLM for production pipeline",
reasoning="GPT-4 benchmark advantage justifies cost increase",
outcome="selected_gpt4",
confidence=0.91,
)
# Find similar past decisions
precedents = context.find_precedents("model selection", limit=5)
```
```python GraphRAG
from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore
from semantica.reasoning import ReasoningEngine
context = AgentContext(
vector_store=VectorStore(backend="faiss", dimension=768),
knowledge_graph=ContextGraph(advanced_analytics=True),
)
# Load your knowledge graph
context.load_graph("company_kg.json")
# Multi-hop GraphRAG query
result = context.query(
"What companies were founded by people who worked at Apple?",
mode="graphrag",
reasoning=True,
)
# Every claim links back to a source node
for claim in result.claims:
print(f"{claim.text} → source: {claim.source_node}")
```
---
## Core Architecture
Semantica uses a modular, layered architecture — import only what you need.
| Layer | Modules | Purpose |
|-------|---------|---------|
| **Input** | `ingest`, `parse`, `split`, `normalize` | Load and prepare data |
| **Semantic** | `semantic_extract`, `kg`, `ontology`, `reasoning` | Extract meaning |
| **Storage** | `embeddings`, `vector_store`, `graph_store` | Persist knowledge |
| **Quality** | `deduplication`, `conflicts` | Validate and clean |
| **Context** | `context`, `provenance`, `change_management` | Track decisions and lineage |
| **Output** | `export`, `visualization`, `pipeline` | Deliver results |
---
## Next Steps
Knowledge graphs, ontologies, and reasoning explained in depth.
Full 6-step pipeline with `` walkthrough.
40+ domain-specific Jupyter notebook tutorials.
Complete module documentation for every class and method.
---
## Help
Ask questions, share projects, get community support.
Report bugs or request features.
Common questions answered.