LangChain Integration

Three drop-in adapters that bring Semantica’s context graph and hybrid search into LangChain chains and LangGraph agents.

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Installation

pip install "semantica[langchain]"

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Components at a Glance

• SemanticaRetriever (BaseRetriever): hybrid-search seeds retrieval, then graph edges are walked hops steps (default 2) for GraphRAG-style results.
• SemanticaVectorStore (VectorStore): add_texts / similarity_search / similarity_search_with_score / from_texts over HybridSearch.
• SemanticaKGTool / SemanticaDecisionTool (BaseTool subclasses): semantica_query_graph and semantica_query_decisions for LangGraph / tool-calling agents.

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Component Details

from integrations.langchain import SemanticaRetriever
from semantica.context import ContextGraph
from semantica.vector_store import HybridSearch

graph = ContextGraph()
hybrid = HybridSearch()

retriever = SemanticaRetriever(graph=graph, hybrid=hybrid, hops=2, top_k=10)

from langchain.chains import RetrievalQA

qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)

from integrations.langchain import SemanticaVectorStore

store = SemanticaVectorStore(hybrid=hybrid)
store.add_texts(
    ["document one", "document two"],
    metadatas=[{"source": "a"}, {"source": "b"}],
)
docs = store.similarity_search("document", k=2)
docs, scores = store.similarity_search_with_score("document", k=2)

from integrations.langchain import SemanticaKGTool, SemanticaDecisionTool
from langgraph.prebuilt import create_react_agent

tools = [
    SemanticaKGTool(graph),
    SemanticaDecisionTool(graph),
]
agent = create_react_agent(model, tools)