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KaifAhmad1andClaude Sonnet 4.6
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- Rewrote index.md to match README (tagline, badges, Problem/Solution text) - Improved getting-started, concepts, quickstart, installation, faq, use-cases, contributing, glossary, learning-more, examples, modules, architecture, cookbook, deep-dive pages: tighter prose, fixed headings/bullets, removed inconsistencies and duplicate sections - Removed overuse of emojis from headings in integration pages (docling, snowflake) - Fixed change_management reference page: closed unclosed JSON code block that broke the right TOC, demoted noisy sub-headings to bold text - CSS layout: widened content area (max-width 1440px grid, left sidebar 11rem, right TOC narrowed to 11rem for broader content), tightened TOC spacing and font size, fixed word-wrap/overflow on TOC links - Added mkdocs_local.yml for local serving without mkdocs-jupyter plugin Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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2.8 KiB
Getting Started
Semantica is the context and intelligence layer for AI — turning raw data into explainable, auditable knowledge graphs for high-stakes domains.
!!! tip "Just here for code?" Jump straight to the Quick Start or explore the Cookbook for interactive notebooks.
What You Can Build
- GraphRAG Systems — enhanced retrieval with semantic graph reasoning
- AI Agents — accountable agents with structured decision history and memory
- Knowledge Graphs — production-ready semantic knowledge bases
- Compliance-Ready AI — auditable systems with full W3C PROV-O provenance
Installation
pip install semantica
With all optional dependencies:
pip install semantica[all]
Verify:
import semantica
print(semantica.__version__)
Quick Start
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
context.store("GPT-4 outperforms GPT-3.5 on reasoning benchmarks by 40%")
# Record a decision
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)
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
- Core Concepts — knowledge graphs, ontologies, reasoning explained
- Quickstart Tutorial — build a full pipeline step by step
- Cookbook — 14 domain-specific Jupyter notebook tutorials
- API Reference — complete module documentation
Help
- Discord Community — ask questions, share projects
- GitHub Issues — report bugs or request features
- FAQ — common questions answered