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100 lines
2.8 KiB
Markdown
100 lines
2.8 KiB
Markdown
# Getting Started
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**Semantica** is the context and intelligence layer for AI — turning raw data into explainable, auditable knowledge graphs for high-stakes domains.
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!!! tip "Just here for code?"
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Jump straight to the [Quick Start](#quick-start) or explore the [Cookbook](cookbook.md) for interactive notebooks.
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---
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## What You Can Build
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- **GraphRAG Systems** — enhanced retrieval with semantic graph reasoning
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- **AI Agents** — accountable agents with structured decision history and memory
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- **Knowledge Graphs** — production-ready semantic knowledge bases
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- **Compliance-Ready AI** — auditable systems with full W3C PROV-O provenance
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---
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## Installation
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```bash
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pip install semantica
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```
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With all optional dependencies:
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```bash
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pip install semantica[all]
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```
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Verify:
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```python
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import semantica
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print(semantica.__version__)
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```
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---
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## Quick Start
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```python
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from semantica.context import AgentContext, ContextGraph
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from semantica.vector_store import VectorStore
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context = AgentContext(
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vector_store=VectorStore(backend="faiss", dimension=768),
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vector_store=VectorStore(backend="inmemory"),
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knowledge_graph=ContextGraph(advanced_analytics=True),
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decision_tracking=True,
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)
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# Store a memory
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context.store("GPT-4 outperforms GPT-3.5 on reasoning benchmarks by 40%")
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# Record a decision
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decision_id = context.record_decision(
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category="model_selection",
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scenario="Choose LLM for production pipeline",
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reasoning="GPT-4 benchmark advantage justifies cost increase",
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outcome="selected_gpt4",
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confidence=0.91,
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)
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# Find similar past decisions
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precedents = context.find_precedents("model selection", limit=5)
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```
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---
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## Core Architecture
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Semantica uses a modular, layered architecture — import only what you need.
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| Layer | Modules | Purpose |
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|-------|---------|---------|
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| **Input** | `ingest`, `parse`, `split`, `normalize` | Load and prepare data |
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| **Semantic** | `semantic_extract`, `kg`, `ontology`, `reasoning` | Extract meaning |
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| **Storage** | `embeddings`, `vector_store`, `graph_store` | Persist knowledge |
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| **Quality** | `deduplication`, `conflicts` | Validate and clean |
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| **Context** | `context`, `provenance`, `change_management` | Track decisions and lineage |
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| **Output** | `export`, `visualization`, `pipeline` | Deliver results |
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---
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## Next Steps
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- [Core Concepts](concepts.md) — knowledge graphs, ontologies, reasoning explained
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- [Quickstart Tutorial](quickstart.md) — build a full pipeline step by step
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- [Cookbook](cookbook.md) — 14 domain-specific Jupyter notebook tutorials
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- [API Reference](reference/core.md) — complete module documentation
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---
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## Help
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- [Discord Community](https://discord.gg/sV34vps5hH) — ask questions, share projects
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- [GitHub Issues](https://github.com/Hawksight-AI/semantica/issues) — report bugs or request features
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- [FAQ](faq.md) — common questions answered
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