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SpiceDB DocumentationOperationsSecure your RAG Pipelines using LangChain & LangGraph

Fine-Grained Authorization for RAG Applications using LangChain (or LangGraph)

This guide explains how to enforce fine-grained, per-document authorization in Retrieval-Augmented Generation (RAG) pipelines using SpiceDB, LangChain, and LangGraph.

It demonstrates how to plug authorization directly into an LLM workflow using a post-retrieval filter powered by SpiceDB — ensuring that every document used by the LLM has been explicitly authorized for the requesting user.

Overview

Modern AI-assisted applications use RAG to retrieve documents and generate responses.
However, standard RAG pipelines do not consider permissions - meaning LLMs may hallucinate or leak information from unauthorized sources.

This guide shows how to solve that problem using:

  • SpiceDB as the source of truth for authorization
  • spicedb-rag-authorization for fast post-retrieval filtering
  • LangChain for LLM pipelines (or)
  • LangGraph for stateful, multi-step workflows and agents

The library implements post-filter authorization, meaning:

  1. Retrieve the best semantic matches.
  2. Filter them using SpiceDB permission checks.
  3. Feed only authorized documents to the LLM.

1. Prerequisites

Run SpiceDB

To run locally, use:

docker run --rm -p 50051:50051 authzed/spicedb serve --grpc-preshared-key "sometoken" --grpc-no-tls

2. Installation

The package is not yet published on PyPI. Install directly from GitHub:

pip install "git+https://github.com/sohanmaheshwar/spicedb-rag-authorization.git#egg=spicedb-rag-auth[all]"

Or clone locally with git clone https://github.com/sohanmaheshwar/spicedb-rag-authorization.git and then run:

import sys
sys.path.append("/path/to/spicedb-rag-authorization")

Create a SpiceDB schema

We will use the zed  CLI to write schema and relationships. In your production application, this would be replaced with an API call.

zed context set local localhost:50051 sometoken --insecure
zed schema write --insecure <(cat << EOF
definition user {}
definition article {
    relation viewer: user
    permission view = viewer
}
EOF
)

Add relationships

zed relationship create article:doc1 viewer user:alice --insecure
zed relationship create article:doc2 viewer user:bob --insecure
zed relationship create article:doc4 viewer user:alice --insecure

3. Document Metadata Requirements

Every document used in RAG must include a resource ID in metadata. This is what enables SpiceDB to check which user has what permissions for each doc.

Document(
    page_content="Example text",
    metadata={"article_id": "doc4"}
)

The metadata key must match the configured resource_id_key which in this case is article_id.

4. LangChain Integration

This is the simplest way to add authorization to a LangChain RAG pipeline.

LangChain  is a framework for building LLM-powered applications by composing modular components such as retrievers, prompts, memory, tools, and models. It provides a high-level abstraction called the LangChain Expression Language (LCEL) which lets you construct RAG pipelines as reusable, declarative graphs — without needing to manually orchestrate each step.

You would typically use LangChain when:

  • You want a composable pipeline that chains together retrieval, prompting, model calls, and post-processing.
  • You are building a RAG system where each step (retriever → filter → LLM → parser) should be easily testable and swappable.
  • You need integrations with many LLM providers, vector stores, retrievers, and tools.
  • You want built-in support for streaming, parallelism, or structured output.

LangChain is an excellent fit for straightforward RAG pipelines where the control flow is mostly linear. For more complex, branching, stateful, or agent-style workflows, you would likely choose LangGraph instead.

Core component: SpiceDBAuthFilter or SpiceDBAuthLambda.

Example Pipeline

auth = SpiceDBAuthFilter(
    spicedb_endpoint="localhost:50051",
    spicedb_token="sometoken",
    resource_type="article",
    resource_id_key="article_id",
)

Build your chain once:

chain = (
    RunnableParallel({
        "context": retriever | auth,  # Authorization happens here
        "question": RunnablePassthrough(),
    })
    | prompt
    | llm
    | StrOutputParser()
)

Invoke:

# Pass user at runtime - reuse same chain for different users
answer = await chain.ainvoke(
    "Your question?",
    config={"configurable": {"subject_id": "alice"}}
)
 
# Different user, same chain
answer = await chain.ainvoke(
    "Another question?",
    config={"configurable": {"subject_id": "bob"}}
)

5. LangGraph Integration

LangGraph  is a framework for building stateful, multi-step, and branching LLM applications using a graph-based architecture. Unlike LangChain’s linear pipelines, LangGraph allows you to define explicit nodes, edges, loops, and conditional branches — enabling deterministic, reproducible, agent-like workflows.

You would choose LangGraph when:

  • You are building multi-step RAG pipelines (retrieve → authorize → rerank → generate → reflect).
  • Your application needs state management across steps (conversation history, retrieved docs, user preferences).
  • You require a strong separation of responsibilities (e.g., retriever node, authorization node, generator node).

LangGraph is ideal for more advanced AI systems, such as conversational RAG assistants, agents with tool-use, or pipelines with complex authorization or business logic.

Our library  provides:

  • RAGAuthState — a TypedDict defining the required state fields
  • create_auth_node() — auto-configured authorization node
  • AuthorizationNode — reusable class-based node

5.1 LangGraph Example

from langgraph.graph import StateGraph, END
from spicedb_rag_auth import create_auth_node, RAGAuthState
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
 
# Use the provided RAGAuthState TypedDict
graph = StateGraph(RAGAuthState)
 
# Define your nodes
def retrieve_node(state):
    """Retrieve documents from vector store"""
    docs = retriever.invoke(state["question"])
    return {"retrieved_documents": docs}
 
def generate_node(state):
    """Generate answer from authorized documents"""
    # Create prompt
    prompt = ChatPromptTemplate.from_messages([
        ("system", "Answer based only on the provided context."),
        ("human", "Question: {question}\n\nContext:\n{context}")
    ])
 
    # Format context from authorized documents
    context = "\n\n".join([doc.page_content for doc in state["authorized_documents"]])
 
    # Generate answer
    llm = ChatOpenAI(model="gpt-4o-mini")
    messages = prompt.format_messages(question=state["question"], context=context)
    answer = llm.invoke(messages)
 
    return {"answer": answer.content}
 
# Add nodes
graph.add_node("retrieve", retrieve_node)
graph.add_node("authorize", create_auth_node(
    spicedb_endpoint="localhost:50051",
    spicedb_token="sometoken",
    resource_type="article",
    resource_id_key="article_id",
))
graph.add_node("generate", generate_node)
 
# Wire it up
graph.set_entry_point("retrieve")
graph.add_edge("retrieve", "authorize")
graph.add_edge("authorize", "generate")
graph.add_edge("generate", END)
 
# Compile and run
app = graph.compile()
result = await app.ainvoke({
    "question": "What is SpiceDB?",
    "subject_id": "alice",
})
 
print(result["answer"])  # The actual answer to the question

5.2 Extending State with LangGraph

Add custom fields to track additional state like conversation history, user preferences, or metadata.

class MyCustomState(RAGAuthState):
    user_preferences: dict
    conversation_history: list
 
graph = StateGraph(MyCustomState)
# ... add nodes and edges

When to use:

  • Multi-turn conversations that need history
  • Personalized responses based on user preferences
  • Complex workflows requiring additional context

Example use case: A chatbot that remembers previous questions and tailors responses based on user role (engineer vs manager).

5.3 Reusable Class-Based Authorization Node

Create reusable authorization node instances that can be shared across multiple graphs or configured with custom state key mappings.

from spicedb_rag_auth import AuthorizationNode
 
auth_node = AuthorizationNode(
    spicedb_endpoint="localhost:50051",
    spicedb_token="sometoken",
    resource_type="article",
    resource_id_key="article_id",
)
 
graph = StateGraph(RAGAuthState)
graph.add_node("authorize", auth_node)

You can define it once and reuse everywhere.

article_auth = AuthorizationNode(resource_type="article", ...)
video_auth = AuthorizationNode(resource_type="video", ...)
 
# Use in multiple graphs
blog_graph.add_node("auth", article_auth)
media_graph.add_node("auth", video_auth)
learning_graph.add_node("auth_articles", article_auth)

When to use:

  • Multiple graphs need the same authorization logic
  • Your state uses different key names than the defaults
  • Building testable code (easy to swap prod/test instances)
  • Team collaboration (security team provides authZ nodes)

Example use case: A multi-resource platform (articles, videos, code snippets) where each resource type has its own authorization node that’s reused across different workflows.

For production applications, you’ll often use a mix of Option 2 and 3: A custom state for your workflow + reusable authZ nodes for flexibility. Here’s an example:

class CustomerSupportState(RAGAuthState):
    conversation_history: list
    customer_tier: str
    sentiment_score: float
 
docs_auth = AuthorizationNode(resource_type="support_doc", ...)
kb_auth = AuthorizationNode(resource_type="knowledge_base", ...)
 
graph = StateGraph(CustomerSupportState)
graph.add_node("auth_docs", docs_auth)
graph.add_node("auth_kb", kb_auth)

6. Metrics & Observability

The library exposes:

  • number of retrieved documents
  • number authorized
  • denied resource IDs
  • latency per SpiceDB check

In LangChain

auth = SpiceDBAuthFilter(..., subject_id="alice", return_metrics=True)
result = await auth.ainvoke(docs)
 
print(result.authorized_documents)
print(result.total_authorized)
print(result.check_latency_ms)
# ... all other metrics

In LangGraph

Metrics appear in auth_results in the graph state.

graph = StateGraph(RAGAuthState)
# ... add nodes including create_auth_node()
 
result = await app.ainvoke({"question": "...", "subject_id": "alice"})
 
# Access metrics from state
print(result["auth_results"]["total_retrieved"])
print(result["auth_results"]["total_authorized"])
print(result["auth_results"]["authorization_rate"])
print(result["auth_results"]["denied_resource_ids"])
print(result["auth_results"]["check_latency_ms"])

7. Complete Example

See the full example in the repo here 

  • langchain_example.py
  • README_langchain.md

8. Next Steps

  • Read this guide  on creating a production-grade RAG with SpiceDB & Motia.dev
  • Check out this self-guided workshop  for a closer look at how fine-grained authorization with SpiceDB works in RAG. This guide also includes the pre-filtration technique.
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