skeg-llamaindex 0.1.1

LlamaIndex VectorStore adapter for skeg.

skeg-llamaindex

LlamaIndex VectorStore adapter for skeg. Compatible with llama-index-core >= 0.10.

Install

pip install skeg-llamaindex

Pulls in skeg (Python client) and llama-index-core automatically. Server install (skeg engine) is separate:

brew tap skegdb/tap
brew install skeg

Usage

from llama_index.core import VectorStoreIndex, StorageContext, Document
from llama_index.core.node_parser import SentenceSplitter
from skeg_llamaindex import SkegVectorStore

# 1. Start a skeg server:
#    skeg --data-dir ./data --addr 127.0.0.1:7379

# 2. Point the adapter at it. `dim` must match the embedding model.
store = SkegVectorStore.from_uri(
    "skeg://127.0.0.1:7379/notes",
    dim=1024,             # mxbai-embed-large-v1 dimension
    kind="int8",          # tier-1 quantisation
    backend="flat",       # in-RAM flat index for <50K vectors
)

# 3. Wire into LlamaIndex.
ctx = StorageContext.from_defaults(vector_store=store)
docs = [Document(text="hello world"), Document(text="goodbye world")]
index = VectorStoreIndex.from_documents(docs, storage_context=ctx)

# 4. Query as usual.
engine = index.as_query_engine()
print(engine.query("what does the first doc say?"))

Index backend choice

Use case	backend	Notes
Personal AI, < 50K nodes	flat	Exhaustive scan; fast on M-series CPUs
RAG over a fixed corpus, > 50K nodes	disk_vamana (pre-build)	Use skeg-cli build, then serve read-only
Streaming insert with eventual large size	disk_vamana (RW)	Delta WAL handles streaming

For the pre-build path, build offline once and start the server in serve mode; this adapter then queries it read-only:

# Build the index offline (one shell):
skeg-cli build --input embeddings.npy --output ./data --name notes

# Serve it read-only (another shell):
skeg --mode serve --data-dir ./data --tier pq:128:256

What this adapter handles

• add(nodes): VSET each embedding + KV-store the text + metadata
• query(VectorStoreQuery): VSEARCH top-k, returns node_ids + similarity
• delete(ref_doc_id): VDEL + drop KV keys
• Stable mapping node_id (str) → vec_id (u64) via xxh3

What this adapter does not do

• Metadata filter pushdown: LlamaIndex post-filters returned hits.
• Batched VSET: one VSET per node on the synchronous path. For large corpora build the index offline with skeg-cli build and serve it read-only.
• Hybrid sparse+dense search: skeg's surface is dense-only.
• Async API: this adapter is synchronous.

Test-suite safety

The pytest suite spawns its own skeg-server via the conftest fixture and tears it down at the end. The tests create VINDEX entries with names like notes-<test_name> and drop them after each test. If you ever override the fixture to point at an external server, those VINDEX names may collide with yours. The fixture is the safe default.

License

Apache-2.0.