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

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, 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 enables you to wire components into task-specific workflows without imperative boilerplate. The ExecutionEngine in semantica/pipeline/execution_engine.py handles parallelism, delta-mode processing, and progress tracking, while semantica/pipeline/pipeline_validator.py validates DAG integrity before runtime.

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 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:

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:

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. This forward-chaining rule engine evaluates conditions against the knowledge graph and triggers conclusions.

Example AML (Anti-Money Laundering) rule set:

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 supports pluggable backends. For GPU-accelerated semantic search:

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:

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) 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 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, 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. 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 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. Ensure CUDA dependencies are installed; the abstraction handles device allocation automatically.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}