semantica/docs/integrations/langchain.md

title, description, icon

title	description	icon
LangChain Integration	Drop Semantica into LangChain / LangGraph pipelines via a GraphRAG retriever, VectorStore adapter, and agent tools.	link

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

Installation

pip install "semantica[langchain]"

Requires langchain-core >= 0.3. If langchain-core is not installed, the integration still imports. Every class carries the full Semantica API and degrades gracefully (build() returns None; branch on LANGCHAIN_AVAILABLE).

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.

Component Details

Hybrid search seeds retrieval; then graph edges are walked `hops` steps so results go beyond flat vector similarity. If hybrid search is omitted or fails, the retriever falls back to a `ContextGraph.query` keyword scan.

```python
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)
```

Drop-in `VectorStore` for RetrievalQA / LCEL chains. `from_texts` requires a pre-configured `hybrid` instance.

```python
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)
```

`add_texts` delegates to a Semantica vector store with `add_documents` (pass `vector_store=` to `HybridSearch` or to `SemanticaVectorStore`).

Instances are LangChain `BaseTool`s and can be passed to an agent directly. `.build()` returns the tool, or `None` when langchain-core is absent.

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

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

| Tool | Description |
| :------ | :------------- |
| `semantica_query_graph` | Keyword / NL query over the shared context graph |
| `semantica_query_decisions` | Search the recorded decision log |