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Mohd KaifandClaude Sonnet 4.6
b282487b17
- 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>
151 lines
3.3 KiB
Markdown
151 lines
3.3 KiB
Markdown
# Quickstart
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Build your first knowledge graph in 5 minutes.
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!!! tip "Prerequisites"
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Semantica installed (`pip install semantica`). If not, see the [Installation Guide](installation.md).
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---
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## Pipeline Overview
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```mermaid
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flowchart LR
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A[Ingest] --> B[Parse]
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B --> C[Extract]
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C --> D[Build Graph]
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D --> E[Visualize / Export]
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```
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---
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## Step 1 — Ingest
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Load documents from files, directories, or the web.
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```python
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from semantica.ingest import FileIngestor
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ingestor = FileIngestor()
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sources = ingestor.ingest("data/sample.pdf")
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```
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Supported formats: PDF, DOCX, HTML, JSON, CSV, Excel, PPTX, archives. For web content, use `WebIngestor`.
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---
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## Step 2 — Parse
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Extract structured text from raw documents.
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```python
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from semantica.parse import DocumentParser
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parser = DocumentParser()
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parsed = parser.parse(sources[0])
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```
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For complex layouts (tables, columns): use `DoclingParser` instead — it handles PDF tables and structured DOCX/PPTX better.
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---
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## Step 3 — Extract Entities and Relationships
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```python
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from semantica.semantic_extract import NERExtractor, RelationExtractor
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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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```
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Each entity gets a type, confidence score, and source reference. Relationships are extracted as typed triplets: `(subject, predicate, object)`.
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---
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## Step 4 — Build the Knowledge Graph
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```python
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from semantica.kg import GraphBuilder
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builder = GraphBuilder(merge_entities=True)
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graph = builder.build(entities=entities, relationships=relationships)
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print(f"{len(graph.nodes)} nodes, {len(graph.edges)} edges")
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```
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`merge_entities=True` resolves duplicates across sources automatically.
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---
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## Step 5 — Visualize
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```python
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from semantica.visualization import GraphVisualizer
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viz = GraphVisualizer()
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viz.visualize(graph, output="graph.html") # interactive HTML
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```
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---
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## Step 6 — Export
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```python
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from semantica.export import RDFExporter
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exporter = RDFExporter()
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rdf = exporter.export_to_rdf(graph, format="turtle")
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```
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Other formats: `"json-ld"`, `"nt"`, `"xml"`, Parquet, ArangoDB AQL. See [Export Reference](reference/export.md).
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---
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## Common Patterns
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### Process text directly (no file)
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```python
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from semantica.semantic_extract import NERExtractor
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ner = NERExtractor()
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entities = ner.extract("Apple Inc. was founded by Steve Jobs in 1976.")
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```
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### Incremental build from multiple sources
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```python
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from semantica.kg import GraphBuilder
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all_entities, all_rels = [], []
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for doc in parsed_docs:
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all_entities.extend(ner.extract(doc))
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all_rels.extend(rel.extract(doc, entities=all_entities))
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graph = GraphBuilder(merge_entities=True).build(
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entities=all_entities, relationships=all_rels
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)
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```
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---
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## Troubleshooting
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| Problem | Fix |
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| No entities extracted | Check the document has machine-readable text (not just scanned images) |
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| Slow processing | Process in chunks; use GPU acceleration (`pip install semantica[gpu]`) |
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| Memory errors | Reduce batch size or switch to a persistent graph backend |
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---
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## Next Steps
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- [Core Concepts](concepts.md) — understand how knowledge graphs and reasoning work
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- [Modules Guide](modules.md) — every module explained
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- [Use Cases](use-cases.md) — domain-specific examples
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- [Cookbook](cookbook.md) — interactive Jupyter notebooks for each step
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