Getting Started

from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(advanced_analytics=True),
    decision_tracking=True,
)

# Store a fact with provenance
context.store("GPT-4 outperforms GPT-3.5 on reasoning by 40%")

# Record a decision with full causal chain
decision_id = context.record_decision(
    category="model_selection",
    scenario="Choose LLM for production pipeline",
    reasoning="GPT-4 benchmark advantage justifies cost",
    outcome="selected_gpt4",
    confidence=0.91,
)

# Search past decisions before making a new one
precedents = context.find_precedents("model selection", limit=5)

from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore

context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(advanced_analytics=True),
)

# Load your knowledge graph
context.load_graph("company_kg.json")

# Multi-hop GraphRAG query
result = context.query(
    "What companies were founded by people who worked at Apple?",
    mode="graphrag",
    reasoning=True,
)

# Every claim links back to a source node
for claim in result.claims:
    print(f"{claim.text}  →  source: {claim.source_node}")

pip install semantica

{
  "mcpServers": {
    "semantica": {
      "command": "semantica-mcp"
    }
  }
}