semantica/docs/getting-started.md

title, description, icon

title	description	icon
Getting Started	The context and intelligence layer for AI: turning raw data into explainable, auditable knowledge graphs.	rocket

Already installed? Jump straight to [Quickstart](quickstart). Need setup help first? See [Installation](installation).

What You Can Build

• GraphRAG Systems — Ground LLM responses in traceable, structured knowledge. Every claim links back to a source node.
• Accountable AI Agents — Agents with structured decision history, causal chains, and precedent search. Every choice is recorded and auditable.
• Production Knowledge Graphs — Build, validate, and maintain enterprise-grade semantic knowledge bases from multi-source data.
• Compliance-Ready AI — W3C PROV-O provenance on every fact. HIPAA, SOX, GDPR, FDA 21 CFR Part 11 infrastructure built in.

Setup in 3 Steps

```bash pip (recommended)
pip install semantica
```

```bash With all extras
pip install semantica[all]
```

```bash From source
git clone https://github.com/semantica-agi/semantica.git
cd semantica
pip install -e ".[dev]"
```

</CodeGroup>

<Check>
  Verify installation:
  ```python
  import semantica
  print(semantica.__version__)  # 0.5.0
  ```
</Check>

Pick the track that matches what you're building: each starts with a focused 5-minute example.

| Track | You want to... | Start with |
| :----- | :-------------- | :--------- |
| **Knowledge Graph** | Turn documents into structured, queryable graphs | [Quickstart → Step 1](quickstart) |
| **Agent Context** | Give your AI agent persistent memory and decision tracking | [Context reference](reference/context) |
| **GraphRAG** | Ground LLM answers in structured knowledge | [Concepts → GraphRAG](concepts#graphrag) |
| **MCP Integration** | Use Semantica from Claude Desktop or VS Code | [MCP Server](reference/mcp_server) |

The full 6-step pipeline: ingest, parse, extract, build, visualize, export: is in the [Quickstart](quickstart). Takes under 5 minutes with pattern-based extraction (no API key required).

<Note>
  An LLM API key is **optional** for the quickstart. Pattern-based extraction works out of the box: upgrade to LLM extraction for higher accuracy when you're ready.
</Note>

Choose Your Path

Build a structured knowledge graph from any document or data source.

```python
from semantica.ingest import FileIngestor
from semantica.parse import DocumentParser
from semantica.semantic_extract import NERExtractor, RelationExtractor
from semantica.kg import GraphBuilder

# 1. Ingest
sources = FileIngestor().ingest("data/report.pdf")

# 2. Parse
parsed = DocumentParser().parse(sources[0])

# 3. Extract
ner           = NERExtractor(method="pattern")  # no API key needed
entities      = ner.extract(parsed)
relationships = RelationExtractor().extract(parsed, entities=entities)

# 4. Build
graph = GraphBuilder(merge_entities=True).build(
    entities=entities, relationships=relationships
)
print(f"{len(graph['nodes'])} nodes, {len(graph['relationships'])} edges")
```

**Next:** [Full pipeline walkthrough →](quickstart)

Give your agent persistent memory, decision tracking, and precedent search.

```python
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 fact with provenance
context.store("GPT-4 outperforms GPT-3.5 on reasoning by 40%")

# Record a decision with full causal chain
decision_id = context.record_decision(
    category="model_selection",
    scenario="Choose LLM for production pipeline",
    reasoning="GPT-4 benchmark advantage justifies cost",
    outcome="selected_gpt4",
    confidence=0.91,
)

# Search past decisions before making a new one
precedents = context.find_precedents("model selection", limit=5)
```

**Next:** [Context module reference →](reference/context)

Ground every LLM response in your knowledge graph: no floating assertions.

```python
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),
)

# 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}")
```

**Next:** [GraphRAG concepts →](concepts#graphrag)

Use Semantica from Claude Desktop, VS Code, Cursor, or any MCP client: no Python code required after setup.

```bash
pip install semantica
```

Add to your MCP client config:

```json
{
  "mcpServers": {
    "semantica": {
      "command": "semantica-mcp"
    }
  }
}
```

12 tools available instantly: extract entities, query graph, record decisions, run reasoning, export results.

**Next:** [MCP Server reference →](reference/mcp_server)

Core Architecture

Semantica uses a modular, layered architecture: import only what you need.

• Input Layer — Load and prepare data from any source. Modules: ingest, parse, split, normalize
• Semantic Layer — Extract meaning from raw text. Modules: semantic_extract, kg, ontology, reasoning
• Storage Layer — Persist knowledge for retrieval. Modules: embeddings, vector_store, graph_store, triplet_store
• Quality Layer — Validate and deduplicate. Modules: deduplication, conflicts
• Context Layer — Track decisions and lineage. Modules: context, provenance, change_management
• Output Layer — Deliver results downstream. Modules: export, visualization, pipeline, explorer

"Which module do I need?" Quick Reference

I want to...	Module	Key class
Load a PDF / web page / database	ingest	FileIngestor, WebIngestor
Extract text and tables from a PDF	parse	DocumentParser, DoclingParser
Find entities in text	semantic_extract	NERExtractor
Build a knowledge graph	kg	GraphBuilder
Store and search vectors	vector_store	VectorStore
Give my agent persistent memory	context	AgentContext
Record AI decisions with audit trail	context	AgentContext.record_decision()
Query my graph with natural language	reasoning	GraphReasoner
Export to RDF / Neo4j / Parquet	export	RDFExporter, LPGExporter
Visualize a knowledge graph	visualization	KGVisualizer
Run a reproducible pipeline	pipeline	PipelineBuilder
Use Semantica from Claude Desktop	mcp_server	semantica-mcp

Next Steps

• Core Concepts — Knowledge graphs, ontologies, and reasoning explained in depth.
• Quickstart Tutorial — Full 6-step pipeline walkthrough with working code.
• Module Reference — Every module, class, and common chain explained.
• API Reference — Complete module documentation for every class and method.

Help

• Discord — Ask questions, share projects, get community support.
• GitHub Issues — Report bugs or request features.
• FAQ — Common questions answered.