---
title: "FAQ"
description: "Common questions about Semantica — installation, features, integrations, and troubleshooting."
icon: "circle-question"
---
## 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.
```bash
pip install --upgrade semantica
```
## Installation
```bash
pip install semantica
```
See [Installation](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:
```bash
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:
```bash
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.
```python
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:
```bash
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:
```bash
pip list | grep semantica
pip install --upgrade semantica
```
```bash
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:
```python
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:
```bash
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:
```bash
pip install --upgrade semantica
# or for older versions:
set PYTHONIOENCODING=utf-8
```
## Support
Community chat and live support.
Bug reports and feature requests.
Help improve Semantica.