langchain-taguru (Python)

Official LangChain integration for the Taguru long-term semantic memory server. The TypeScript twin (langchain-taguru on npm) exposes the identical surface.

pip install langchain-taguru

from langchain_openai import ChatOpenAI
from taguru_langchain import TaguruIngester, TaguruRetriever

# Write: an LLM decomposes documents into the association graph
# (the LangChain twin of `taguru extract`; per-source replace, idempotent).
ingester = TaguruIngester(
    context="sake",
    llm=ChatOpenAI(model="gpt-4.1", temperature=0),
    create_context=True,
    context_description="青嶺酒造という架空の酒蔵の知識",
)
ingester.ingest_documents(docs)          # docs[*].metadata["source"] required

# Read: graph lane (resolve → activate → citations) + text lane
# (search_passages), merged by Reciprocal Rank Fusion.
retriever = TaguruRetriever(context="sake", k=8)
documents = retriever.invoke("青嶺酒造")

Runnable use-case examples (RAG QA with citations, governed ingestion, conversational long-term memory — each mirrored in TypeScript) live in examples/langchain; they work offline, no API key needed.

Not provided, deliberately: a VectorStore facade (Taguru's retrieval is structural-first — similarity_search would misrepresent it), a Memory class (deprecated upstream in favor of LangGraph state), and agent Tools (the MCP bridge taguru-mcp already serves the identical tools; pair it with langchain-mcp-adapters).

The behavioral contract is the server's protocol document (GET /protocol); the ingestion prompt/validation mirror taguru extract (PROMPT_VERSION is kept in sync with src/extract.rs).