# How to Customize Semantica AGI for Specific Tasks: A Complete Guide to Modular Pipeline Engineering

> Customize Semantica AGI for specific tasks using modular pipeline engineering. Our guide explains declarative DAG composition, subclassing, and rule injection for deep customization without core framework edits.

- Repository: [Semantica /semantica](https://github.com/semantica-agi/semantica)
- Tags: how-to-guide
- Published: 2026-08-23

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**Semantica AGI is architected as a modular, end-to-end knowledge pipeline that enables deep customization through component subclassing, declarative DAG composition, and domain-specific rule injection without requiring modifications to the core framework.**

The open-source `semantica-agi/semantica` repository provides a decision-intelligence platform designed for adaptation across finance, healthcare, legal, and risk-management domains. When you need to customize Semantica AGI for specific tasks, you leverage its layered architecture to swap ingestion sources, plug in fine-tuned models, and encode business policies as deterministic rules. Each layer exposes explicit extension points that allow you to rewire data flow while maintaining provenance and auditability.

## Understanding the Modular Architecture Layers

The architecture decomposes into nine distinct layers, each exposing standard interfaces for customization. In [`semantica/pipeline/pipeline_builder.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/pipeline/pipeline_builder.py), the `PipelineBuilder` class orchestrates these layers into a reproducible directed acyclic graph (DAG).

**Key extension layers include:**

- **Sources → Ingest**: Add custom `Ingestor` subclasses in `semantica.ingest` for proprietary APIs or encrypted file formats
- **Parse → Normalize → Split**: Swap `DocumentParser` implementations or configure entity-aware chunkers from `semantica.split`
- **Extract**: Replace `NamedEntityRecognizer` or `RelationExtractor` with domain-fine-tuned models
- **Conflict Detection → Deduplication**: Inject custom `DuplicateDetector` strategies or domain-specific conflict resolution rules
- **Knowledge Graph Construction**: Configure `GraphBuilder` parameters like `merge_entities=False` to preserve provenance
- **Reasoning**: Extend `OntologyGenerator` or add forward-chaining rules via `semantica.reasoning`
- **Storage**: Select polyglot backends—FAISS, Qdrant, Neo4j, or Neptune—through the unified vector store interface
- **Export & Visualization**: Build custom exporters in `semantica/export` or visualizer plugins in `semantica/visualization`

## Composing Custom Pipelines with the Declarative DSL

The `PipelineBuilder` DSL in [`semantica/pipeline/pipeline_builder.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/pipeline/pipeline_builder.py) enables you to wire components into task-specific workflows without imperative boilerplate. The `ExecutionEngine` in [`semantica/pipeline/execution_engine.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/pipeline/execution_engine.py) handles parallelism, delta-mode processing, and progress tracking, while [`semantica/pipeline/pipeline_validator.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/pipeline/pipeline_validator.py) validates DAG integrity before runtime.

```python
from semantica.pipeline import PipelineBuilder, ExecutionEngine
from semantica.ingest import FileIngestor
from semantica.semantic_extract import NamedEntityRecognizer
from semantica.kg import GraphBuilder

# 1️⃣ Ingest PDFs

ingestor = FileIngestor()

# 2️⃣ Extract domain-specific entities

ner = NamedEntityRecognizer(confidence_threshold=0.75, model="my-fin-ner")

# 3️⃣ Build the knowledge graph

kg_builder = GraphBuilder(merge_entities=True, enable_temporal=True)

# Wire the DAG

builder = PipelineBuilder()
builder.add_step("ingest", "file_ingest", handler=ingestor.ingest_directory)
builder.add_step("extract", "ner_extract", handler=ner.extract_entities)
builder.add_step("kg", "graph_build", handler=kg_builder.build)

builder.connect_steps("ingest", "extract").connect_steps("extract", "kg")

pipeline = builder.build("financial_contracts")
result = ExecutionEngine().execute_pipeline(pipeline, data="./contracts/")

```

## Swapping Core Components for Domain Requirements

You customize Semantica AGI by subclassing base components registered in the package [`__init__.py`](https://github.com/semantica-agi/semantica/blob/main/__init__.py) files. This approach preserves upstream compatibility while injecting domain logic.

### Custom Ingestors for Proprietary Data Sources

To handle encrypted PDFs or proprietary APIs, subclass `FileIngestor` from [`semantica/ingest/__init__.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/ingest/__init__.py):

```python
from semantica.ingest import FileIngestor

class EncryptedPdfIngestor(FileIngestor):
    def ingest_file(self, path, **kwargs):
        # decrypt → parse → return document dict

        ...

```

Register and use this in any pipeline by instantiating `EncryptedPdfIngestor()` in place of the base class.

### Domain-Specific Entity Recognition Models

Swap the default extractor with a medically fine-tuned NER by configuring the `NamedEntityRecognizer` exposed in [`semantica/semantic_extract/__init__.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/semantic_extract/__init__.py):

```python
from semantica.semantic_extract import NamedEntityRecognizer

medical_ner = NamedEntityRecognizer(
    confidence_threshold=0.85,
    model="biobert-ner-v2",
    entity_types=["DIAGNOSIS", "MEDICATION", "DOSAGE"]
)

```

## Implementing Business Logic with Custom Reasoning Rules

Encode deterministic policies using the `ReteEngine` class defined in [`semantica/reasoning/rete_engine.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/reasoning/rete_engine.py). This forward-chaining rule engine evaluates conditions against the knowledge graph and triggers conclusions.

Example AML (Anti-Money Laundering) rule set:

```python
from semantica.reasoning import ReteEngine, Rule, RuleType

engine = ReteEngine()
engine.build_network([
    Rule(
        rule_id="high_risk_tx",
        name="High-risk transaction flag",
        conditions=[
            {"field": "amount", "operator": ">", "value": 100_000},
            {"field": "country", "operator": "in", "value": ["IR", "KP"]},
        ],
        conclusion="flag_for_review",
        rule_type=RuleType.IMPLICATION,
    )
])

```

Rules compile into a Rete network for efficient pattern matching across large fact bases.

## Configuring Storage and Export Backends

The `VectorStore` abstraction in [`semantica/vector_store/vector_store.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/vector_store/vector_store.py) supports pluggable backends. For GPU-accelerated semantic search:

```python
from semantica.vector_store import VectorStore

vs = VectorStore(backend="faiss", dimension=1536, use_gpu=True)
vs.store_decision(...)

```

For regulatory compliance, export provenance trails using `RDFExporter` from [`semantica/export/rdf_exporter.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/export/rdf_exporter.py):

```python
from semantica.export import RDFExporter

kg = graph.to_kg_dict()
RDFExporter().export(kg, "audit.ttl", format="turtle")

```

This generates PROV-O compliant audit trails suitable for regulatory submission.

## Summary

- **Semantica AGI** exposes nine modular layers, each customizable through subclassing and configuration
- Use **PipelineBuilder** ([`semantica/pipeline/pipeline_builder.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/pipeline/pipeline_builder.py)) to compose task-specific DAGs declaratively
- Add **custom ingestors** by subclassing `FileIngestor` and registering them in the pipeline
- Inject **domain-specific models** by configuring extractor classes with fine-tuned model paths
- Encode **business policies** as Rete rules in [`semantica/reasoning/rete_engine.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/reasoning/rete_engine.py) for deterministic reasoning
- Select **storage backends** (FAISS, Qdrant, Neo4j) via the unified `VectorStore` interface
- Generate **compliance artefacts** using `RDFExporter` for PROV-O formatted audit trails

## Frequently Asked Questions

### How do I add a new data source to Semantica AGI?

Create a subclass of the base `Ingestor` class (or `FileIngestor` for file-based sources) in [`semantica/ingest/__init__.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/ingest/__init__.py), implement the `ingest_file` or `ingest_stream` method, and instantiate it in your `PipelineBuilder` chain. The system automatically handles provenance tracking for custom sources.

### Can I use a fine-tuned transformer model for entity extraction?

Yes. Pass your model identifier or local path to the `model` parameter when initializing `NamedEntityRecognizer` from [`semantica/semantic_extract/__init__.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/semantic_extract/__init__.py). Set `confidence_threshold` to filter low-probability predictions specific to your domain.

### What is the best way to implement complex business rules?

Use the `ReteEngine` in [`semantica/reasoning/rete_engine.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/reasoning/rete_engine.py) to build forward-chaining rule networks. Define conditions as dictionaries with field operators, and specify conclusions using `RuleType.IMPLICATION`. This executes deterministically across the knowledge graph without manual iteration.

### How do I switch from CPU to GPU vector search?

Initialize `VectorStore` with `backend="faiss"` (or another GPU-supported backend) and set `use_gpu=True` as shown in [`semantica/vector_store/vector_store.py`](https://github.com/semantica-agi/semantica/blob/main/semantica/vector_store/vector_store.py). Ensure CUDA dependencies are installed; the abstraction handles device allocation automatically.