Mohd Kaif
1f3cea5f0a
Replace plain markdown lists, tables, and numbered steps with interactive Mintlify v3 MDX components across all 50+ documentation files: - Tabs: provider/parser/method selection guides, citation formats, component details - Steps: setup flows, pipeline stages, connection initialization - CardGroup/Card: feature overviews, "what you get" sections, navigation footers - AccordionGroup: FAQ entries - Check/Warning/Tip/Note/Info: callouts replacing plain bold text and inline notes Files improved span the full docs surface: reference modules (context, llms, kg, reasoning, embeddings, deduplication, provenance, parse, ontology, core, semantic_extract), integrations (agno, docling, snowflake), graph/vector store backends (apache_age, pgvector), and top-level guides (contributing, governance, glossary, citation, community-projects, learning-more).
11 KiB
title, description, icon
| title | description | icon |
|---|---|---|
| FAQ | Common questions about Semantica — installation, features, integrations, and troubleshooting. | circle-question |
Quick Answers
| Question | Answer |
|---|---|
| License? | MIT — free forever, no paywalled features |
| Python version? | 3.8+ (3.11+ recommended) |
| API key required? | Optional — pattern extraction works with no keys |
| Works with LangChain / LlamaIndex? | Yes — Semantica is a layer on top, not a replacement |
| Production-ready? | Yes — 1,000+ tests, v0.5.0 ships with 12 security fixes |
| Latest version? | v0.5.0 (May 2026) |
| Local LLMs? | Yes — Ollama via LiteLLM, HuggingFaceLLM for air-gapped |
General
Semantica is an open-source framework for building context graphs and decision intelligence layers for AI. It transforms unstructured data — documents, APIs, databases — into structured knowledge graphs with full provenance tracking, making AI systems explainable and auditable.
It's not a replacement for LangChain or LlamaIndex. It's the accountability layer that goes on top — recording decisions, tracing facts to sources, and making reasoning transparent.
- Knowledge graphs from documents and multi-source data
- GraphRAG systems with graph-grounded retrieval and source attribution
- AI agents with structured decision history and semantic memory
- Compliance-ready pipelines with W3C PROV-O lineage (HIPAA, SOX, GDPR, FDA 21 CFR Part 11)
- Temporal graphs that track how facts change over time
- Ontology-driven knowledge bases with SHACL validation
Most frameworks stop at retrieval or generation. Semantica adds an accountability layer: every decision is recorded, every fact links to a source, and every reasoning step is explainable. It's designed for environments where you need to audit why an AI reached a conclusion — not just what it said.
Semantica works alongside these frameworks, not against them.
Yes — MIT licensed, no vendor lock-in, no paywalled features. Some capabilities require third-party API keys (e.g., OpenAI embeddings, Groq inference), but Semantica itself is always free and open source.
v0.5.0 — released May 2026.
Highlights: Ontology Hub, Distance Intelligence, Parquet/XML ingestion, 12 security fixes, Graph Explorer redesign, NER gateway fix.
pip install --upgrade semantica
Installation
pip install semantica
See Installation for virtual environment setup, optional extras ([gpu], [all], provider-specific), and platform-specific troubleshooting.
Python 3.8 or higher. Python 3.11+ is recommended for best performance and compatibility.
This was a known bug — fixed in v0.5.0. Upgrade:
pip install --upgrade semantica
If you're on an older version, install extras individually: pip install "semantica[core]", then add [llm-openai], [gpu], etc.
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.8 | 3.11+ |
| RAM | 4 GB | 16 GB+ |
| Storage | 2 GB | 20 GB+ |
| GPU | Optional | CUDA for embeddings and ML models |
Data & Features
| Category | Sources |
|---|---|
| Files | PDF, DOCX, HTML, JSON, CSV, Excel, PPTX, Parquet (v0.5.0), XML (v0.5.0), archives |
| Web | WebIngestor crawl, RSS feeds, sitemaps |
| Databases | PostgreSQL, MySQL, Snowflake via DBIngestor / SnowflakeIngestor |
| NoSQL | MongoDB via MongoIngestor, DuckDB via DuckDBIngestor |
| Streams | Kafka, real-time ingestion via StreamIngestor |
| Protocols | MCP (Model Context Protocol) via MCPIngestor |
| Cloud | Google Drive via GDriveIngestor, HuggingFace datasets |
Yes. Semantica supports:
- Custom NER and extraction models — register via
method_registry - Custom embedding models — any model with a
.encode()interface - Custom LLM providers — via LiteLLM (100+ models) or direct provider integration
- Custom pipeline processors — register via
PluginRegistry
Yes. When available, GPUs are used automatically for embedding generation, ML model inference, and vector operations. Install GPU support:
pip install "semantica[gpu]"
This includes PyTorch with CUDA, FAISS GPU, and CuPy.
- Batching — process documents in configurable chunks to control memory usage
- Parallel processing —
Pipeline(workers=N)runs extraction steps concurrently - Delta processing — update graphs incrementally without full recompute on new data
- Persistent backends — swap in-memory NetworkX for Neo4j, FalkorDB, or Apache AGE for large-scale production graphs
TemporalKnowledgeGraph attaches valid_from / valid_until windows to nodes and edges, enabling point-in-time queries and historical analysis. Supports all 13 Allen interval algebra relations and OWL-Time export.
from semantica.kg import TemporalKnowledgeGraph
tkg = TemporalKnowledgeGraph()
tkg.add_temporal_triple("A", "caused", "B", valid_from="2024-01", valid_until="2024-06")
snapshot = tkg.query_at_time("2024-03")
Available since v0.4.0.
A visual browser UI for the full ontology lifecycle — launched via semantica.explorer. Includes:
- Visual editor — create and edit classes, properties, and relationships
- SHACL Studio — author, validate, and export SHACL shapes
- Alignment authoring — map concepts across ontologies
- Health dashboard — coverage, consistency, and constraint violation metrics
- Version control — diff and history for ontology changes
Available since v0.5.0.
Semantic neighborhood exploration for any entity in the graph. Returns structured proximity data with distance band classification.
- N×N distance matrices across a set of entities
- Ego-mode visualization centered on a single node
- Distance bands:
near/mid/farbased on embedding thresholds - Embedding cache optimization for repeated queries
Available since v0.5.0.
Fixed in v0.5.0. The response_format=json_object parameter is now conditionally omitted for incompatible gateways, with a plain generate() plus JSON parsing fallback applied automatically. Upgrade to fix:
pip install --upgrade semantica
Technical
- Neo4j — industry standard, Cypher query language
- FalkorDB — Redis-protocol, ultra-low latency
- Apache AGE — PostgreSQL extension, OpenCypher
- Amazon Neptune — managed AWS, SPARQL and Gremlin
- NetworkX — in-memory, for development and small graphs
RDF (Turtle, JSON-LD, N-Triples, XML), Apache Parquet, ArangoDB AQL, Apache Arrow, LPG, CSV, YAML, OWL ontologies, and distance matrices.
FAISS, Pinecone, Weaviate, Qdrant, Milvus, PgVector, and in-memory. All backends share the same VectorStore API — swap with one line change.
Groq, OpenAI, Anthropic, Google Gemini, Ollama (fully local), DeepSeek, Novita AI, LiteLLM (100+ models via a single interface), and any OpenAI-compatible gateway.
Yes. v0.5.0 ships with:
- 1,000+ passing tests across Python 3.8–3.12
PipelineValidatorandFailureHandlerwith exponential backoff and configurable retry policies- W3C PROV-O provenance tracking across all modules
- Change management with SHA-256 checksums and full audit trails
- 12 security vulnerability fixes: eval injection, pickle deserialization, SQL injection, XXE, SSRF, ReDoS, path traversal, and more
Troubleshooting
Ensure the correct Python environment is active:
pip list | grep semantica
pip install --upgrade semantica
pip install --upgrade pip wheel
pip install semantica
If [all] fails on Windows, install extras individually instead.
Reduce batch sizes, enable streaming ingestion, or switch to a persistent graph backend:
from semantica.graph_store import FalkorDBStore
store = FalkorDBStore(host="localhost", port=6379)
builder = GraphBuilder(merge_entities=True, graph_store=store)
Install GPU support and confirm CUDA is available:
pip install "semantica[gpu]"
nvidia-smi # confirm GPU is visible
Fixed in v0.5.0. Upgrade, or set the encoding environment variable for older versions:
pip install --upgrade semantica
# or for older versions:
set PYTHONIOENCODING=utf-8