Where to Find the Documentation for Semantica AGI: Complete Repository Guide

All documentation for Semantica AGI lives inside the semantica-agi/semantica repository, organized into markdown files covering quick-start guides, architecture deep-dives, API references, and platform integration tutorials.

Semantica AGI is an open-source, graph-native infrastructure platform for building context-aware AI systems. Whether you are deploying a decision-intelligence pipeline or integrating with enterprise data warehouses, the comprehensive documentation for Semantica AGI provides the technical reference and practical examples you need. This guide maps every documentation resource in the repository, from high-level architecture diagrams to runnable code snippets.

Documentation Structure and Organization

The documentation follows a logical hierarchy inside the repository root. Each section serves a specific purpose in the development lifecycle, from initial setup to advanced integrations.

Entry Point and Overview Documentation

Start with the repository's README.md at the root level. This file provides badges for website links, installation instructions, and quick navigation to critical resources. For system architecture, consult ARCHITECTURE.md, which contains Mermaid diagrams illustrating the full data pipeline and decision-intelligence lifecycle.

Guided Learning and Tutorials

The docs/guides/ directory contains end-to-end recipes for common workflows. These include GraphRAG pipeline construction, AML (Anti-Money Laundering) rules engine implementation, and audit-trail generation. Each guide provides step-by-step instructions combined with production-ready code patterns.

API and Module Reference

Detailed API specifications live in docs/modules.md and docs/reference/. The modules file provides an auto-generated index of all top-level packages including semantica.ingest, semantica.semantic_extract, and semantica.kg. The reference subdirectory drills deeper into individual components such as the vector store, provenance tracking, ontology management, and reasoning engines.

Essential Documentation Files

Understanding the role of each markdown file helps you navigate the codebase efficiently.

• docs/quickstart.md: Installation instructions and a "Hello World" example using ContextGraph.
• ARCHITECTURE.md: High-level data flow and pipeline stages with visual diagrams.
• docs/cli-setup.md: Complete REST API endpoint documentation, MCP server tools, and the semantica command-line interface reference.
• docs/integrations/*.md: Connection guides for Databricks, Snowflake, Agno, and other enterprise platforms.
• LICENSE and CONTRIBUTING.md: Legal terms and contribution guidelines for open-source development.

Practical Code Examples from the Documentation

The documentation includes runnable Python snippets demonstrating core workflows. These examples illustrate the platform's semantic extraction and knowledge graph capabilities.

First, create a ContextGraph and record a decision for audit trails:

from semantica.context import ContextGraph

graph = ContextGraph(advanced_analytics=True)

decision_id = graph.record_decision(
    category="vendor_selection",
    scenario="Choose cloud provider for HIPAA workload",
    reasoning="AWS offers BAA, mature HIPAA tooling, and existing team expertise",
    outcome="selected_aws",
    confidence=0.93,
)
chain = graph.trace_decision_chain(decision_id)

To ingest unstructured documents and build a knowledge graph, use the FileIngestor and GraphBuilder:

from semantica.ingest import FileIngestor
from semantica.kg import GraphBuilder

docs = FileIngestor().ingest_directory("./contracts/", recursive=True)
kg = GraphBuilder(merge_entities=True, enable_temporal=True).build(docs)

For semantic search capabilities, initialize the VectorStore with HybridSearch:

from semantica.vector_store import VectorStore, HybridSearch

vs = VectorStore(backend="faiss", dimension=1536)
vs.store_decision(
    scenario="Personal loan A-7291, $85k income, 31% DTI, 3yr employment",
    outcome="approved",
    confidence=0.94,
    category="loan_underwriting",
)
results = HybridSearch(vector_store=vs).search("low‑DTI loan approval")

Finally, export the enriched knowledge graph to standard formats using RDFExporter:

from semantica.export import RDFExporter

kg_dict = graph.to_dict()
RDFExporter().export(kg_dict, "kg_audit.ttl", format="turtle")

Accessing Documentation Offline and Online

All documentation files are plain Markdown, enabling flexible consumption workflows. You can read them directly on GitHub through the web interface, which renders Mermaid diagrams and syntax-highlighted code blocks automatically. For offline development, clone the repository locally—the docs/ directory mirrors the online structure exactly, allowing you to browse documentation in your IDE alongside the source code in the semantica/ package folder.

Summary

• The complete documentation for Semantica AGI resides in the semantica-agi/semantica repository as markdown files.
• README.md and ARCHITECTURE.md provide entry points and system design diagrams.
• The docs/quickstart.md file contains installation steps and your first ContextGraph example with record_decision().
• API references are organized under docs/modules.md and docs/reference/ for vector stores, provenance, and reasoning components.
• Integration guides for enterprise platforms live in docs/integrations/.
• All documentation is plain Markdown, readable both on GitHub and in local clones alongside the semantica/ source package.

Frequently Asked Questions

Where can I find the API reference for Semantica AGI modules?

The auto-generated module reference is located in docs/modules.md, which catalogs all top-level packages including semantica.ingest, semantica.semantic_extract, and semantica.kg. For deeper component-level details, consult the files in docs/reference/ covering specific implementations like the vector store and ontology manager.

How do I access the Semantica AGI architecture documentation?

The high-level architecture is documented in ARCHITECTURE.md at the repository root. This file contains Mermaid diagrams illustrating the data pipeline stages, decision-intelligence lifecycle, and system data flow, providing visual context for how components interact.

Can I read the documentation for Semantica AGI offline?

Yes. Because all documentation uses plain Markdown format, you can clone the repository locally using git clone and browse the docs/ directory in any text editor or Markdown viewer. The local files maintain the same structure as the GitHub web interface, including all guides, references, and integration documents.

Where are the integration guides for enterprise platforms?

Integration documentation for Databricks, Snowflake, Agno, and other platforms is stored in docs/integrations/. Each connector has its own markdown file explaining authentication, configuration parameters, and usage examples specific to that platform's interaction with the Semantica AGI graph infrastructure.

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