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Get Started Free →majiayu000/AgentDB Vector Search
Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying.
Passed 24 of 24 test cases with the skill, and 9 of 24 without it.
and 17 more in the full report
15 fixed9 passed either way0 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
I have a vector store backup file at `./backup.json`. Which AgentDB CLI command imports this backup into the database?
| Checked | Without skill | With skill |
|---|---|---|
| Executes npx agentdb@latest import ./backup.json | Missed | Passed |
The instructions your model reads when the skill loads.
Implements vector-based semantic search using AgentDB's high-performance vector database with 150x-12,500x faster operations than traditional solutions. Features HNSW indexing, quantization, and sub-millisecond search (<100µs).
# Initialize with default dimensions (1536 for OpenAI ada-002)
npx agentdb@latest init ./vectors.db
# Custom dimensions for different embedding models
npx agentdb@latest init ./vectors.db --dimension 768 # sentence-transformers
npx agentdb@latest init ./vectors.db --dimension 384 # all-MiniLM-L6-v2
# Use preset configurations
npx agentdb@latest init ./vectors.db --preset small # <10K vectors
npx agentdb@latest init ./vectors.db --preset medium # 10K-100K vectors
npx agentdb@latest init ./vectors.db --preset large # >100K vectors
# In-memory database for testing
npx agentdb@latest init ./vectors.db --in-memory
# Basic similarity search
npx agentdb@latest query ./vectors.db "[0.1,0.2,0.3,...]"
# Top-k results
test cases passed, vs 9 / 24 without
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