goldengraph

Build an own-your-KG knowledge graph from text:

text → LLM extraction → goldenmatch entity resolution → a durable, bi-temporal store.

Entity resolution is the differentiator — duplicate surface forms across documents collapse into one durable entity, the thing most GraphRAG frameworks do badly.

from goldengraph import ingest, OpenAIClient
from goldengraph_native import _native as gg

store = gg.PyStore()                 # durable bi-temporal store (SP2 engine)
llm = OpenAIClient()                 # or any object with .complete(prompt) -> str
ingest("Acme Inc, founded by ...", store, at=1, llm=llm)

snapshot = store.snapshot()          # canonical JSON; persist + reopen later
view = store.as_of(valid_t=1, tx_t=1)   # bi-temporal slice -> queryable graph

• extract(text, llm) — text → typed entities + relationships (LLM).
• resolve(mentions) — goldenmatch's zero-config dedupe_df → merged entities + :h1: record keys.
• ingest(text, store, *, at, llm) — the end-to-end path into the store.

Part of the goldengraph program. The engine (goldengraph-native: store / query / communities) is pyo3-free Rust; this package is the Python host pipeline. LLM access is provider-agnostic (LLMClient protocol); an OpenAIClient ships behind the [openai] extra.

Scope: SP4b (this package) is the build path. Retrieval + synthesis + NL query (SP4c) and WASM/C surfaces (SP5) are separate slices.