semantica/docs/faq.md

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FAQ	Common questions about Semantica: installation, features, integrations, and troubleshooting.	circle-question

Use **Ctrl+F** / **Cmd+F** to search this page. Common jumps: [Installation](#installation) · [Data & Features](#data--features) · [Troubleshooting](#troubleshooting)

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?	v0.6.0 (July 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, Databricks via DBIngestor / SnowflakeIngestor / DatabricksIngestor
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 / far based 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
• PipelineValidator and FailureHandler with 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

Support

• Discord — Community chat and live support.
• GitHub Issues — Bug reports and feature requests.
• Contributing — Help improve Semantica.