feat(integrations): add LangChain integration — retriever, vectorstor… (#1155)

* feat(integrations): add LangChain integration — retriever, vectorstore, tools

Co-authored-by: Cursor <cursoragent@cursor.com>

* fix(langchain): address Qodo review on HybridSearch hits and tools

Read nested HybridSearch metadata so retriever/vectorstore Documents
are not empty, make the agent tools real BaseTool subclasses, and
stop slicing tool JSON into invalid payloads.

Co-authored-by: Cursor <cursoragent@cursor.com>

---------

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Derek Tapley
2026-08-26 18:29:22 +05:00
committed by GitHub
co-authored by Cursor
parent 8a990c8bf5
commit f0aa581318
14 changed files with 1469 additions and 14 deletions
+1
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@@ -103,6 +103,7 @@
"pages": [
"integrations/agno",
"integrations/crewai",
"integrations/langchain",
"integrations/docling",
"integrations/snowflake",
"integrations/databricks"
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---
title: "LangChain Integration"
description: "Drop Semantica into LangChain / LangGraph pipelines via a GraphRAG retriever, VectorStore adapter, and agent tools."
icon: "link"
---
> Three drop-in adapters that bring Semantica's context graph and hybrid search into LangChain chains and LangGraph agents.
## Installation
```bash
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
<Tabs>
<Tab title="SemanticaRetriever">
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)
```
</Tab>
<Tab title="SemanticaVectorStore">
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`).
</Tab>
<Tab title="Agent tools">
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 |
</Tab>
</Tabs>