PyckLM is trained on code structure — not general text. Better retrieval, better similarity, better results for any codebase application.
Most embedding APIs treat code like prose. PyckLM is fine-tuned on real codebases — it understands function signatures, call patterns, docstrings, and the semantic relationships between code units.
The result: more relevant retrieval, better similarity scoring, and context that actually makes sense to the AI reading it.
Top-5 retrieval accuracy on held-out code benchmarks.
One endpoint. Accepts text or code. Returns a PyckLM embedding vector.
import requests response = requests.post( "https://api.pyckle.co/v1/embed", headers={ "Authorization": "Bearer YOUR_API_KEY", "Content-Type": "application/json", }, json={ "text": "authentication middleware for JWT tokens", "model": "pycklelm-v2", } ) data = response.json() embedding = data["embedding"] # list[float], 512 dimensions tokens = data["tokens_used"] # billed per token
512
dimensions
<80ms
typical latency
REST
any stack, any language
PyckLM embeddings are the foundation for any application that needs to understand code at semantic depth.
Build search that finds code by meaning — "find where we handle auth errors" returns the right files even when the words don't match exactly.
Score how similar two code snippets are — find duplicates, detect refactoring candidates, or surface related implementations across repos.
Power retrieval-augmented generation with code context that AI models actually understand — precise chunk retrieval, fewer hallucinations.
Start free — no credit card required.
~$8.33/month
Endpoint: https://api.pyckle.co/mcp/embeddings · Hosted on Fly.io
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Questions? Email [email protected]