langchain-schift 0.1.0

An integration package connecting Schift and LangChain

langchain-schift

LangChain integration for Schift -- vector store with server-side embedding and graph edges.

Installation

pip install langchain-schift

Quick Start

Schift handles embedding server-side, so no Embeddings object is needed:

from langchain_schift import SchiftVectorStore

store = SchiftVectorStore(
    api_key="sk-...",
    bucket="my-bucket",
)

# Add documents (embedded server-side)
store.add_texts(["Contract A supersedes Contract B", "Contract B dated 2024-01"])

# Search
results = store.similarity_search("which contract is newer?", k=3)

Graph-Enhanced Retrieval

Schift supports edges between documents. This is useful for legal citations, document versioning, knowledge graphs, and more:

# Add edges between documents
store.add_edges([
    {"source": "contract-a", "target": "contract-b", "relation": "supersedes"},
    {"source": "clause-1", "target": "contract-a", "relation": "has_child"},
])

# Search with graph expansion -- follows edges from top results
results = store.similarity_search(
    "contract terms",
    k=5,
    graph_expand=True,
    graph_depth=1,
    graph_relations=["supersedes", "has_child"],
)

Supported relation types: contradicts, supersedes, caused_by, is_a, related_to, has_child, follows.

Use with LangChain chains

from langchain_schift import SchiftVectorStore
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI

store = SchiftVectorStore(api_key="sk-...", bucket="legal-docs")
retriever = store.as_retriever(search_kwargs={"k": 5})

# Use in a chain
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

prompt = ChatPromptTemplate.from_template(
    "Answer based on context:\n{context}\n\nQuestion: {question}"
)
llm = ChatOpenAI()

chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

answer = chain.invoke("What are the key contract terms?")

Modes

Mode	Use case	Embedding
Bucket (recommended)	Upload files/texts, Schift handles everything	Server-side
Collection	Raw vector operations with your own embeddings	Client-side or server-side

# Bucket mode (server-side embedding)
store = SchiftVectorStore(api_key="sk-...", bucket="my-bucket")

# Collection mode (bring your own embeddings)
from langchain_openai import OpenAIEmbeddings
store = SchiftVectorStore(
    api_key="sk-...",
    collection="my-collection",
    embedding=OpenAIEmbeddings(),
)

Environment Variables

Set SCHIFT_API_KEY to avoid passing api_key explicitly:

export SCHIFT_API_KEY=sk-...