process_grok

This function uses the new BaseAPIProcessor architecture for improved
maintainability and consistent logging across all API providers.

internal

Automated cell type annotation for single-cell RNA sequencing data
using consensus predictions from multiple large language models (LLMs). LLMs are
artificial intelligence models trained on vast text corpora to understand and
generate human-like text. This package integrates with 'Seurat' objects and
provides uncertainty quantification for annotations. Supports various LLM
providers including 'OpenAI', 'Anthropic', and 'Google'. The package leverages
these models through their respective APIs (Application Programming Interfaces)
<https://platform.openai.com/docs>, <https://docs.anthropic.com/>, and
<https://ai.google.dev/gemini-api/docs>.
For details see Yang et al. (2025) <doi:10.1101/2025.04.10.647852>.

Chen Yang

mLLMCelltype

Cell Type Annotation Using Large Language Models

process_grok function

<dl><dt>prompt</dt>
<dd>Input prompt text</dd>
<dt>model</dt>
<dd>Model identifier</dd>
<dt>api_key</dt>
<dd>Grok API key</dd>
<dt>base_url</dt>
<dd>Optional custom base URL for Grok API</dd></dl>

Arguments

Process request using Grok models — process_grok

<dl>

<dt>prompt</dt>
<dd>Input prompt text</dd>


<dt>model</dt>
<dd>Model identifier</dd>


<dt>api_key</dt>
<dd>Grok API key</dd>


<dt>base_url</dt>
<dd>Optional custom base URL for Grok API</dd>

</dl>

process_grok: Process request using Grok models

Description

Usage

Value

Arguments