The Neo4jKnowledgeBase class provides access to documents stored in a Neo4j graph database. It enables efficient document storage, retrieval, and clustering based on topic similarity. The implementation supports topic discovery, document embedding, and language detection.

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Dependencies

The following external dependencies are required:

uv add "trusttest[rag-neo4j]"

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Usage Example

import os

from dotenv import load_dotenv

from trusttest.catalog import FunctionalRAGScenario
from trusttest.knowledge_base.neo4j import Neo4jKnowledgeBase
from trusttest.models.testing import DummyEndpoint

load_dotenv(override=True)

knowledge_base = Neo4jKnowledgeBase(
    uri=os.getenv("NEO4J_URI"),
    username=os.getenv("NEO4J_USERNAME"),
    password=os.getenv("NEO4J_PASSWORD"),
    database=os.getenv("NEO4J_DATABASE"),
    language="English",
    fields_mapping={"content": "chunk", "id": "chunk_id"},
    seed_topics=["AI", "Machine Learning"],
    max_doc_count=20
)

rag_test = FunctionalRAGScenario(
    model=DummyEndpoint(), knowledge_base=knowledge_base, num_questions=2
)

test_set = rag_test.probe.get_test_set()
results = rag_test.eval.evaluate(test_set)
results.display_summary()