Semantic Search Over Smart Contract Events: Embedding Guide

Frequently Asked Questions

01.What is semantic search over smart contract events?

Semantic search over smart contract events converts event log data fetched via eth_getLogs into high-dimensional vector embeddings using models like text-embedding-ada-002, then stores those vectors in a database such as Pinecone or Weaviate. Queries are embedded using the same model and matched against stored vectors by cosine similarity, returning contextually relevant events even when the exact parameter values are unknown to the caller. This approach complements exact-match topic filtering with natural-language intent retrieval.

02.How does eth_getLogs differ from semantic vector retrieval for on-chain events?

eth_getLogs performs exact-match filtering on indexed topics and block ranges, returning only events whose keccak256 signature and indexed parameters match the specified filter criteria. Vector retrieval encodes a natural-language or structured query into an embedding, then ranks all ingested events by cosine similarity score, surfacing semantically related events across contract addresses, event types, and parameter value ranges. The two approaches are complementary: use eth_getLogs for narrow high-precision pulls and vector retrieval for exploratory or cross-protocol semantic queries.

03.Which vector database is best for smart contract event embeddings in production?

Pinecone suits teams that need managed horizontal scaling with no operational overhead, offering cosine similarity search across billions of vectors with sub-100ms latency at paid tiers. Weaviate suits teams requiring hybrid BM25 plus vector search in a self-hosted configuration with GraphQL querying. Qdrant suits Rust-native stacks needing on-premise deployment with payload filtering before vector scoring. For most DeFi event pipelines ingesting under one billion vectors, any of the three is viable. The correct choice depends on operational model, query pattern, and whether metadata filtering or pure semantic recall is the dominant workload.