ragleap-graph

Knowledge-graph-augmented retrieval for RAG systems — entity extraction, co-occurrence graphs, and graph-based document retrieval via Neo4j.

pip install ragleap-graph

Quickstart

from ragleap_graph import GraphConfig, GraphIndex

graph = GraphIndex(config=GraphConfig(
    uri="bolt://localhost:7687",
    user="neo4j",
    password="...",
))

graph.upsert_document(
    document_id="doc-1",
    title="Q3 Report",
    chunks=[{"text": "Acme Corp reported strong Q3 revenue growth."}],
)

docs = graph.find_documents_by_entities(["Acme Corp"])
related = graph.search_related_entities(["Acme Corp"], max_depth=2)

Current: v0.6.5 · 87 tests (86 passed, 1 skipped)

Architecture

LLM-based extraction and dedup (v0.2.0+)

The default entity extraction is regex/heuristic-based (fast, free, zero dependencies). For messier input — e.g. inconsistent capitalization like "Acme Corp" vs "ACME Corp." — LLM-based extraction and dedup produce cleaner graphs. Requires the llm extra: pip install ragleap-graph[llm]

from ragleap.generation import ProviderConfig
from ragleap_graph import GraphConfig, GraphIndex, ExtractionConfig

graph = GraphIndex(
    config=GraphConfig(uri="bolt://localhost:7687", user="neo4j", password="..."),
    extraction=ExtractionConfig(
        method="llm",
        provider=ProviderConfig(provider="gemini", api_key="...", model="gemini-3.6-flash"),
        dedup_enabled=True,
    ),
)

Note: EntityDeduplicator merges spelling variants of an already-extracted name; it does not fix fragmentation caused by the regex extractor splitting one real-world entity into multiple candidates in the first place — see CHANGELOG.md for a real, measured example of this and how method="llm" avoids it at the source.

Status

v0.6.5. Ported from a real production GraphService, adapted for standalone open-source use — see HANDOFF.md for the full design history. ragleap-rag >=0.12.0 is an optional dependency, required only for method="llm".

License

MIT