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KaifAhmad1
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Replace Hawksight-AI/semantica, semantica-dev/semantica, and semantica/semantica URLs across all docs files (17 files, ~100 links).
184 lines
5.1 KiB
Markdown
184 lines
5.1 KiB
Markdown
---
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title: "Getting Started"
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description: "The context and intelligence layer for AI — turning raw data into explainable, auditable knowledge graphs."
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icon: "rocket"
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---
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<Tip>
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Just here for code? Jump straight to the [Quickstart Tutorial](quickstart) or explore the [Cookbook](cookbook) for interactive notebooks.
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</Tip>
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---
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## What You Can Build
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<CardGroup cols={2}>
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<Card title="GraphRAG Systems" icon="diagram-project">
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Enhanced retrieval with semantic graph reasoning — ground LLM responses in traceable, structured knowledge.
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</Card>
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<Card title="Accountable AI Agents" icon="robot">
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Agents with structured decision history, causal chains, and precedent search. Every choice is recorded and auditable.
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</Card>
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<Card title="Production Knowledge Graphs" icon="sitemap">
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Build, validate, and maintain enterprise-grade semantic knowledge bases from multi-source data.
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</Card>
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<Card title="Compliance-Ready AI" icon="shield-check">
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W3C PROV-O provenance on every fact. HIPAA, SOX, GDPR, FDA 21 CFR Part 11 infrastructure built in.
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</Card>
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</CardGroup>
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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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<CodeGroup>
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```python Knowledge Graph
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from semantica.ingest import FileIngestor
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from semantica.parse import DocumentParser
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from semantica.semantic_extract import NERExtractor, RelationExtractor
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from semantica.kg import GraphBuilder
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# Ingest → Parse → Extract → Build
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ingestor = FileIngestor()
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sources = ingestor.ingest("data/sample.pdf")
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parser = DocumentParser()
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parsed = parser.parse(sources[0])
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ner = NERExtractor()
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entities = ner.extract(parsed)
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rel = RelationExtractor()
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relationships = rel.extract(parsed, entities=entities)
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graph = GraphBuilder(merge_entities=True).build(
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entities=entities, relationships=relationships
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)
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print(f"{len(graph.nodes)} nodes, {len(graph.edges)} edges")
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```
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```python Agent Context
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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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knowledge_graph=ContextGraph(advanced_analytics=True),
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decision_tracking=True,
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)
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# Store a memory with provenance
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context.store("GPT-4 outperforms GPT-3.5 on reasoning benchmarks by 40%")
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# Record a decision with causal chain
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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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```python GraphRAG
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from semantica.context import AgentContext, ContextGraph
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from semantica.vector_store import VectorStore
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from semantica.reasoning import ReasoningEngine
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context = AgentContext(
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vector_store=VectorStore(backend="faiss", dimension=768),
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knowledge_graph=ContextGraph(advanced_analytics=True),
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)
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# Load your knowledge graph
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context.load_graph("company_kg.json")
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# Multi-hop GraphRAG query
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result = context.query(
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"What companies were founded by people who worked at Apple?",
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mode="graphrag",
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reasoning=True,
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)
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# Every claim links back to a source node
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for claim in result.claims:
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print(f"{claim.text} → source: {claim.source_node}")
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```
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</CodeGroup>
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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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<CardGroup cols={2}>
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<Card title="Core Concepts" icon="book-open" href="concepts">
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Knowledge graphs, ontologies, and reasoning explained in depth.
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</Card>
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<Card title="Quickstart Tutorial" icon="play" href="quickstart">
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Full 6-step pipeline with `<Steps>` walkthrough.
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</Card>
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<Card title="Cookbook" icon="flask" href="cookbook">
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40+ domain-specific Jupyter notebook tutorials.
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</Card>
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<Card title="API Reference" icon="code" href="reference/context">
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Complete module documentation for every class and method.
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</Card>
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</CardGroup>
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---
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## Help
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<CardGroup cols={3}>
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<Card title="Discord" icon="discord" href="https://discord.gg/sV34vps5hH">
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Ask questions, share projects, get community support.
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</Card>
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<Card title="GitHub Issues" icon="github" href="https://github.com/semantica-agi/semantica/issues">
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Report bugs or request features.
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</Card>
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<Card title="FAQ" icon="circle-question" href="faq">
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Common questions answered.
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</Card>
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</CardGroup>
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