Integration Guide
Signet + LlamaIndex
Integrate Signet trust scoring with LlamaIndex data agents to verify trust in RAG pipelines and data source interactions.
Why integrate Signet with LlamaIndex?
LlamaIndex powers data-aware AI agents that query, retrieve, and synthesize information from diverse data sources. Signet integration adds trust verification for agents in multi-agent retrieval architectures, ensuring data sources and retrieval agents meet trust requirements.
Integration steps
Step 1: Register data agents
Register your LlamaIndex query engines and data agents with Signet to establish trust identities.
Step 2: Verify retrieval sources
Before accepting retrieved data from external agents, check their Signet Scores to ensure data quality.
Step 3: Add trust context to queries
Include trust metadata in your query engine context so responses can be weighted by source agent trust.
Step 4: Build trust through usage
Report successful retrieval and synthesis outcomes to Signet to improve agent scores over time.
Code example
from signet import SignetClient
signet = SignetClient(api_key=os.environ["SIGNET_API_KEY"])
# Verify data source agent trust before retrieval
async def trusted_retrieve(agent_sid: str, query: str):
score = signet.lookup_score(agent_sid)
if score["recommendation"] == "Caution":
raise ValueError(f"Data agent {agent_sid} has low trust: {score['composite_score']}")
# Proceed with retrieval
return await query_engine.aquery(query)Ready to integrate?
Apply for API access to start building trust-verified agent interactions with LlamaIndex.