sulcus-llamaindex

LlamaIndex storage integration for Sulcus — a thermodynamic memory system for AI agents.

What is Sulcus?

Sulcus stores AI memories as nodes with a heat value (0–1) that represents accessibility. Memories naturally cool over time; pinned memories retain their heat indefinitely. This creates a biologically-inspired attention gradient across your agent's knowledge graph.

Installation

pip install sulcus-llamaindex

Note: Only llama-index-core is required — not the full llama-index package.

Components

Usage

SulcusVectorStore — RAG Pipeline

from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.schema import Document
from sulcus_llamaindex import SulcusVectorStore

# Set up the store
vector_store = SulcusVectorStore(
    api_key="sk-...",
    namespace="my-project",       # logical namespace in Sulcus
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

# Index documents — they're persisted to Sulcus automatically
docs = [
    Document(
        text="Pinned memories resist heat decay.",
        metadata={
            "memory_type": "semantic",   # episodic | semantic | preference | procedural
            "heat": 0.9,                 # 0.0–1.0, higher = more accessible
            "namespace": "my-project",
        },
    ),
]
index = VectorStoreIndex.from_documents(docs, storage_context=storage_context)

# Query
response = index.as_query_engine(similarity_top_k=5).query("What are pinned memories?")
print(response)

Metadata Mapping

LlamaIndex node metadata fields are mapped to Sulcus fields on add():

Returned nodes (from query()) include all Sulcus fields in metadata: sulcus_id, memory_type, heat, base_utility, is_pinned, modality, namespace.

SulcusReader — Load Existing Memories

from sulcus_llamaindex import SulcusReader

reader = SulcusReader(api_key="sk-...", namespace="research")

# Load all memories of a specific type
docs = reader.load_by_type("semantic")

# Load only pinned (high-importance) memories
pinned = reader.load_pinned()

# Filter by namespace, type, and pinned status simultaneously
docs = reader.load_data(
    memory_type="episodic",
    namespace="project-x",
    pinned=False,
    search="deployment",   # optional server-side substring filter
)

# Quick search returning Documents
results = reader.search("thermodynamic heat", limit=10)

SulcusDocumentStore — Document Persistence

from llama_index.core import StorageContext
from sulcus_llamaindex import SulcusDocumentStore

doc_store = SulcusDocumentStore(api_key="sk-...", namespace="my-project")
storage_context = StorageContext.from_defaults(docstore=doc_store)

Document nodes are stored under a docstore:<namespace> Sulcus namespace as procedural memories, keeping them separate from your semantic memory graph.

Full Example

See examples/rag_pipeline.py for a complete workflow:

1. Load existing Sulcus memories as LlamaIndex Documents
2. Store new documents into the vector store
3. Build a VectorStoreIndex
4. Run natural-language queries

SULCUS_API_KEY=sk-... python examples/rag_pipeline.py

Thermodynamic Tips

• Use heat=0.9 for core knowledge you want to remain accessible long-term.
• Pin critical memories via the Sulcus API (client.pin(memory_id)) to prevent any heat decay.
• Use namespaces to partition memory by project, agent, or session — they're free and unlimited.
• memory_type matters: Sulcus's UI and search can filter by type, so labelling memories correctly improves discoverability.

Requirements

• Python ≥ 3.9
• sulcus >= 0.1.0
• llama-index-core >= 0.12.0

License

MIT