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
1 maintainer
Release files for ragleap-graph 0.6.9
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Total release size: 111.2 kB
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