jvelezmagic

---
title: "Cydar batch correction"
author: ""
date: "`r Sys.Date()`"
output: 
  html_document:
    toc: true
    toc_float: true
    toc_depth: 3
    code_download: true

jvelezmagic

# Include docstring package to render documentation
if (!requireNamespace("docstring", quietly = TRUE)) {
  install.packages("docstring")
}

library(docstring)

average_expression_heatmaps <- function(object,
                                        assay = NULL,
                                        columns_to_keep = NULL,

jvelezmagic

# Include docstring package to render documentation
if (!requireNamespace("docstring", quietly = TRUE)) {
  install.packages("docstring")
}

library(docstring)

catalyst_expression_heatmaps <- function(sce,
                                         by = "sample_id",
                                         by_keep = NULL,

jvelezmagic

render_md_as_rmd <- function(file, overwrite = TRUE, ...) {
  file_name <- fs::path_ext_remove(file)

  file_to_render <- fs::file_copy(
    path = file,
    new_path = fs::path(file_name, ext = "Rmd"),
    overwrite = overwrite
  )

  tryCatch(

jvelezmagic

---
title: "Tidytuesday - Week 29"
format: 
    html
---
    
## Libraries

```{r libraries}
library(tidyverse)

jvelezmagic

# The goal of this file is to provide a FastAPI application for handling
# chat requests amd generation AI-powered responses using conversation chains.
# The application uses the LangChaing library, which includes a chatOpenAI model
# for natural language processing.

# The `StreamingConversationChain` class is responsible for creating and storing
# conversation memories and generating responses. It utilizes the `ChatOpenAI` model
# and a callback handler to stream responses as they're generated.

# The application defines a `ChatRequest` model for handling chat requests,

jvelezmagic

"""QA Chatbot streaming using FastAPI, LangChain Expression Language , OpenAI, and Chroma.

Features
--------
- Persistent Chat Memory:
    Stores chat history in a local file.
- Persistent Vector Store:
    Stores document embeddings in a local vector store.
- Standalone Question Generation:
    Rephrases follow-up questions to standalone questions in their original language.