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The UpstashKnowledgeBase class provides integration with Upstash Vector, a vector database service that enables semantic search and similarity matching. The class facilitates document indexing, searching, and topic-based organization using Upstash Vector’s vector search capabilities.

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Prerequisites

Before using the Upstash knowledge base, you need:
  1. An Upstash account
  2. A Vector Index created in the Upstash Console
  3. Your Vector Index URL and token

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Dependencies

The following external dependencies are required: 
uv add "trusttest[rag-upstash]"

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Usage Example

import os

from dotenv import load_dotenv

from trusttest.knowledge_base.upstash import UpstashKnowledgeBase
from trusttest.probes.rag import RAGProbe, MaliciousQuestion
from trusttest.evaluation_scenarios import EvaluationScenario
from trusttest.evaluator_suite import EvaluatorSuite
from trusttest.evaluators import RAGPoisoningEvaluator
from trusttest.targets.testing import DummyTarget

load_dotenv(override=True)

knowledge_base = UpstashKnowledgeBase(
    url=os.getenv("UPSTASH_VECTOR_REST_URL"),
    token=os.getenv("UPSTASH_VECTOR_REST_TOKEN"),
)

probe = RAGProbe(
    target=DummyTarget(),
    knowledge_base=knowledge_base,
    num_questions=10,
    question_types=[MaliciousQuestion.SPECIAL_TOKEN, MaliciousQuestion.HYPOTHETICAL],
)

scenario = EvaluationScenario(
    name="RAG Poisoning",
    evaluator_suite=EvaluatorSuite(evaluators=[RAGPoisoningEvaluator()], criteria="any_fail"),
)

test_set = probe.get_test_set()
results = scenario.evaluate(test_set)
results.display_summary()