Source: https://docs.nygen.io/CyteOnto/reference/configuration/
Revision: cb82698da3fc9e2bc385eb9a70e07f9304835eba
Structured page: https://docs.nygen.io/CyteOnto/reference.configuration.json
Environment and limits
Environment variables
Variable Read Purpose Default
NEBIUS_API_KEY, DEEPINFRA_API_KEY, FIREWORKS_API_KEY, GOOGLE_API_KEY, OPENAI_API_KEY, OPENROUTER_API_KEY, TOGETHER_API_KEY When CyteOnto is created The API key of the embedding provider, when EmbdConfig.apiKey is None Unset
EMBEDDING_MODEL_API_KEY When cyteonto is imported The embedding API key when the provider’s own variable is unset Empty
NCBI_API_KEY When cyteonto is imported Sent with PubMed searches, to raise NCBI’s rate limit Empty
CYTEONTO_LOG_LEVEL When cyteonto is imported The log level, such as DEBUG, INFO or WARNING DEBUG
CYTEONTO_LOG_FILE When cyteonto is imported A file to write the log to as well, rotated at 10 MB Unset
Set the variables that are read on import before the first import cyteonto. When it is imported, cyteonto loads a .env file with python-dotenv: in a notebook or another interactive session, the nearest one from the working directory up, and in a script, the nearest one from the installed package’s directory up. Variables that are already set keep their values. In a script, call load_dotenv() from the dotenv package yourself to load the .env file next to the script.
CyteOnto does not read the API key of the LLM: you give it to the pydantic-ai provider of your agent, which may read its own variable, such as OPENAI_API_KEY, when you do not.
Logging
CyteOnto logs with loguru to standard output, as LEVEL: message, and to CYTEONTO_LOG_FILE with timestamps. When imported, it removes the handlers of loguru’s global logger and adds its own, so configure your own loguru handlers after importing cyteonto.
At the INFO level, the log shows the progress of descriptions and embeddings, the generated run IDs and the files that are read and written. DEBUG, the default, adds details such as missing embeddings. WARNING shows only failed attempts, labels that stayed undescribed and other problems.
Retries and limits
These rules are set in the code, mostly in cyteonto.config.Config. Only the concurrency limits are options of CyteOnto.
Rule Value
Attempts per LLM request, to describe or to split a label 5, with waits of 1, 2, 4 and 8 seconds
Time limit per LLM attempt 120 seconds
Second pass over the labels whose description failed Once, after 5 seconds, with 5 attempts per label
Usage limits per description or split 50 requests and 60,000 input tokens
PubMed searches per description At most 3, as the prompt asks
Attempts per embedding request 3, with waits of 1 and 2 seconds
Time limit per embedding request 5 minutes, the default of aiohttp
Concurrent LLM requests max_description_concurrency, 100 by default
Concurrent embedding requests EmbdConfig.maxConcurrent, 10 by default
LLM requests are retried after errors of the model’s API, such as HTTP and connection errors, unexpected model behavior, output that fails validation and attempts that run out of time. A request that exceeds the usage limits is not retried. When all attempts fail:
A description is tried again in the second pass, and then by the fallback agent, in two passes as well, if there is one. A label that is still undescribed is embedded from its text and is not cached, so the next call tries again. With use_cache=False, a label with a cached description keeps it instead.
A split leaves the label whole for this call, and the next call with the run tries again. With use_cache=False, a label with a cached split keeps it instead. A split that exceeds the usage limits leaves the label whole, and this is cached.
An embedding fails the batch. CyteOnto then switches to the fallback embedding configuration, if there is one that differs from embedding, as Fall back to other models describes, and otherwise raises a RuntimeError.
Troubleshooting lists the errors and what to do about them.