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- Rewrote all 26 reference module pages: removed blockquote taglines and horizontal rule separators, added "What You Get" bullet summaries, added constructor/method parameter tables, expanded thin files (graph_store, triplet_store, visualization, provenance) with full API coverage, added backend comparison tables and real-world usage patterns - Renamed Modules tab from "API Reference" and group from "Context & Knowledge" to "Context & Intelligence" in docs.json - Fixed logo: copied "Semantica Logo.png" to web-safe semantica-logo.png and updated all 4 references in docs.json - Improved core docs (index, modules, concepts, quickstart, installation, getting-started) with better fonts, bullet points, and complete module listings (mcp_server, evals, core, utils previously missing) - Rewrote community pages (community, community-projects, contributing-guide, use-cases, architecture, faq, learning-more, glossary) with heading hierarchy fixes, expanded definitions, and better structure - Fixed markdown linter warnings: MD036 bold-as-heading, MD001 heading skips, MD040 missing code fence language, MD032 blank lines around lists
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title, description, icon
| title | description | icon |
|---|---|---|
| Community Projects | Projects, extensions, and integrations built by the Semantica community. | people-group |
Semantica is used across academia, enterprise, and independent research. Below is a snapshot of the ecosystem being built by the community.
Projects Using Semantica
Research & Academia
Teams in academia are using Semantica to build structured, auditable knowledge from unstructured scientific literature.
- Academic literature mapping — citation graph construction across multi-year corpora with temporal provenance
- Biomedical knowledge graphs — connecting genes, proteins, drugs, and diseases from PubMed and preprint feeds
- Social network analysis — community detection and influence analysis over entity-linked interaction graphs
- Computational linguistics — coreference resolution pipelines with entity-linked output for downstream NLP tasks
Enterprise & Industry
Production deployments span regulated and high-stakes industries where AI accountability is not optional.
- Business intelligence — corporate knowledge bases built from filings, reports, and internal documentation
- Cybersecurity & threat intelligence — adversary attribution graphs, CVE-linked threat feeds, incident timelines
- Healthcare & clinical AI — patient safety graphs, drug interaction knowledge bases, HIPAA-compliant audit trails
- Financial services — fraud detection graphs, regulatory compliance pipelines (SOX/GDPR/MiFID II), risk lineage
- Legal & compliance — contract analysis pipelines, regulatory change tracking, evidence-backed research graphs
- Critical infrastructure — supply chain risk graphs, energy grid event graphs, logistics provenance
Independent & Open Source
- GraphRAG toolkits — custom retrieval layers built on top of Semantica's
context+vector_storemodules - Domain-specific extractors — NER and relation extractors for clinical, legal, and scientific text
- Temporal graph dashboards — visual timelines built with Semantica's
TemporalKnowledgeGraph+ custom visualization adapters
Supported Integrations
Vector Databases
- FAISS — in-process, CPU/GPU
- Pinecone — managed vector cloud
- Weaviate — schema-first hybrid search
- Qdrant — high-performance Rust-native
- Milvus — enterprise-scale distributed
- PgVector — Postgres-native for SQL stacks
Graph Databases
- Neo4j — industry standard, Cypher query
- FalkorDB — Redis-protocol, low-latency
- Apache AGE — PostgreSQL extension, OpenCypher
- Amazon Neptune — managed AWS, SPARQL + Gremlin
LLM Providers
- OpenAI (GPT-4o, GPT-4, GPT-3.5)
- Anthropic (Claude Opus, Sonnet, Haiku)
- Google Gemini
- Groq (LLaMA, Mixtral — fast inference)
- Ollama (fully local, air-gapped)
- HuggingFace
- DeepSeek
- Novita AI
- LiteLLM (100+ model gateway)
NLP Libraries
- spaCy — production NER and dependency parsing
- NLTK — tokenization and feature extraction
- Sentence Transformers — semantic embeddings
- FastEmbed — lightweight, fast inference
Community Extensions
The plugin system (PluginRegistry) makes it easy to add new capabilities without touching core code. The community has built:
- Custom entity extractors — domain-specific NER for clinical entities, legal clause types, and financial instruments
- Export adapters — specialized serialization formats for proprietary industry systems
- Ingestor plugins — adapters for SharePoint, Notion, Confluence, and custom databases
- Visualization plugins — enhanced dashboards with Plotly, D3.js, and custom graph renderers
- Evaluation harnesses — domain-specific precision/recall benchmarks using
semantica.evals
Build Your Own Extension
Any Semantica component can be extended via the registry pattern:
from semantica.ingest.registry import method_registry
def my_ingestor(source):
return [{"text": "...", "metadata": {}, "source": source}]
method_registry.register("file", "my_format", my_ingestor)
See Architecture for the full extension guide.