Package 'llmflow' reference manual

Title:	Reasoning and Acting Workflow for Automated Data Analysis
Description:	Provides a framework for integrating Large Language Models ('LLMs') with R programming through workflow automation. Built on the 'ReAct' (Reasoning and Acting) architecture, enables bi-directional communication between 'LLMs' and R environments. Features include automated code generation and execution, intelligent error handling with retry mechanisms, persistent session management, structured JSON output validation, and context-aware conversation management.
Authors:	Zaoqu Liu [aut, cre] (ORCID: <https://orcid.org/0000-0002-0452-742X>)
Maintainer:	Zaoqu Liu <[email protected]>
License:	GPL (>= 3)
Version:	3.0.2
Built:	2026-05-22 07:44:09 UTC
Source:	https://github.com/Zaoqu-Liu/llmflow

AutoFlow - Automated R Analysis Workflow with LLM

Description

AutoFlow - Automated R Analysis Workflow with LLM

Usage

AutoFlow(
  react_llm,
  task_prompt,
  rag_llm = NULL,
  max_turns = 15,
  pkgs_to_use = c(),
  objects_to_use = list(),
  existing_session = NULL,
  verbose = TRUE,
  r_session_options = list(),
  context_window_size = 3000,
  max_observation_length = 800,
  error_escalation_threshold = 3
)
AutoFlow(
  react_llm,
  task_prompt,
  rag_llm = NULL,
  max_turns = 15,
  pkgs_to_use = c(),
  objects_to_use = list(),
  existing_session = NULL,
  verbose = TRUE,
  r_session_options = list(),
  context_window_size = 3000,
  max_observation_length = 800,
  error_escalation_threshold = 3
)

Arguments

react_llm

Chat object for ReAct task execution (required)

task_prompt

Task description (required)

rag_llm

Chat object for RAG documentation retrieval (default: NULL, uses react_llm)

max_turns

Maximum ReAct turns (default: 15)

pkgs_to_use

Packages to load in R session

objects_to_use

Named list of objects to load

existing_session

Existing callr R session

verbose

Verbose output (default: TRUE)

r_session_options

Options for callr R session

context_window_size

Context window size for history

max_observation_length

Maximum observation length

error_escalation_threshold

Error count threshold

Details

**Dual-LLM Architecture:**

AutoFlow supports using different models for different purposes: - 'rag_llm': Retrieval-Augmented Generation - retrieves relevant function documentation - 'react_llm': ReAct execution - performs reasoning and action loops

**Why separate models?** - RAG tasks are simple (extract function names) - use fast/cheap models - ReAct tasks are complex (coding, reasoning) - use powerful models - Cost savings: ~70

If 'rag_llm' is NULL, both operations use 'react_llm'.

Value

ReAct result object

Examples

## Not run: 
# Simple: same model for both
llm <- llm_openai(model = "gpt-4o")
result <- AutoFlow(llm, "Load mtcars and plot mpg vs hp")

# Optimized: lightweight RAG, powerful ReAct
rag <- llm_openai(model = "gpt-3.5-turbo") # Fast & cheap
react <- llm_openai(model = "gpt-4o") # Powerful
result <- AutoFlow(
  react_llm = react,
  task_prompt = "Perform PCA on iris dataset",
  rag_llm = rag
)

# Cross-provider: DeepSeek RAG + Claude ReAct
rag <- chat_deepseek(model = "deepseek-chat")
react <- chat_anthropic(model = "claude-sonnet-4-20250514")
result <- AutoFlow(react, "Complex analysis", rag_llm = rag)

# Batch evaluation with shared RAG
rag <- chat_deepseek(model = "deepseek-chat")
react <- chat_openai(model = "gpt-4o")

for (task in tasks) {
  result <- AutoFlow(react, task, rag_llm = rag, verbose = FALSE)
}

## End(Not run)
## Not run: 
# Simple: same model for both
llm <- llm_openai(model = "gpt-4o")
result <- AutoFlow(llm, "Load mtcars and plot mpg vs hp")

# Optimized: lightweight RAG, powerful ReAct
rag <- llm_openai(model = "gpt-3.5-turbo") # Fast & cheap
react <- llm_openai(model = "gpt-4o") # Powerful
result <- AutoFlow(
  react_llm = react,
  task_prompt = "Perform PCA on iris dataset",
  rag_llm = rag
)

# Cross-provider: DeepSeek RAG + Claude ReAct
rag <- chat_deepseek(model = "deepseek-chat")
react <- chat_anthropic(model = "claude-sonnet-4-20250514")
result <- AutoFlow(react, "Complex analysis", rag_llm = rag)

# Batch evaluation with shared RAG
rag <- chat_deepseek(model = "deepseek-chat")
react <- chat_openai(model = "gpt-4o")

for (task in tasks) {
  result <- AutoFlow(react, task, rag_llm = rag, verbose = FALSE)
}

## End(Not run)

Extract Bash/Shell code from a string

Description

This function extracts Bash/Shell code from a string by matching all content between '“‘bash’, '“‘sh’, '“‘shell’ and '“''.

Usage

extract_bash_code(input_string)
extract_bash_code(input_string)

Arguments

input_string

A string containing Bash/Shell code blocks, typically a response from an LLM

Value

A character vector containing the extracted Bash/Shell code

Examples

# Simple bash example
text <- "Run this:\n```bash\necho 'Hello'\n```"
extract_bash_code(text)

# Using 'sh' tag
text <- "```sh\nls -la\npwd\n```"
extract_bash_code(text)

# Using 'shell' tag
text <- "```shell\nfor i in {1..5}; do echo $i; done\n```"
extract_bash_code(text)

# Multiple blocks with different tags
response <- "
Setup script:
```bash
#!/bin/bash
mkdir -p /tmp/test
cd /tmp/test
```

Installation:
```sh
apt-get update
apt-get install -y git
```

Configuration:
```shell
export PATH=$PATH:/usr/local/bin
source ~/.bashrc
```
"
codes <- extract_bash_code(response)
length(codes) # Returns 3

# Complex script example
script_response <- "
Here's a backup script:
```bash
#!/bin/bash

# Set variables
BACKUP_DIR='/backup'
DATE=$(date +%Y%m%d)

# Create backup
tar -czf ${BACKUP_DIR}/backup_${DATE}.tar.gz /home/user/

# Check if successful
if [ $? -eq 0 ]; then
    echo 'Backup completed successfully'
else
    echo 'Backup failed'
    exit 1
fi
```
"
extract_bash_code(script_response)

# Simple bash example
text <- "Run this:\n```bash\necho 'Hello'\n```"
extract_bash_code(text)

# Using 'sh' tag
text <- "```sh\nls -la\npwd\n```"
extract_bash_code(text)

# Using 'shell' tag
text <- "```shell\nfor i in {1..5}; do echo $i; done\n```"
extract_bash_code(text)

# Multiple blocks with different tags
response <- "
Setup script:
```bash
#!/bin/bash
mkdir -p /tmp/test
cd /tmp/test
```

Installation:
```sh
apt-get update
apt-get install -y git
```

Configuration:
```shell
export PATH=$PATH:/usr/local/bin
source ~/.bashrc
```
"
codes <- extract_bash_code(response)
length(codes) # Returns 3

# Complex script example
script_response <- "
Here's a backup script:
```bash
#!/bin/bash

# Set variables
BACKUP_DIR='/backup'
DATE=$(date +%Y%m%d)

# Create backup
tar -czf ${BACKUP_DIR}/backup_${DATE}.tar.gz /home/user/

# Check if successful
if [ $? -eq 0 ]; then
    echo 'Backup completed successfully'
else
    echo 'Backup failed'
    exit 1
fi
```
"
extract_bash_code(script_response)

Extract chat history from ellmer chat object

Description

Extract chat history from ellmer chat object

Usage

extract_chat_history(
  chat_obj,
  include_tokens = TRUE,
  include_time = TRUE,
  tz = "Asia/Shanghai"
)
extract_chat_history(
  chat_obj,
  include_tokens = TRUE,
  include_time = TRUE,
  tz = "Asia/Shanghai"
)

Arguments

chat_obj

An ellmer chat object

include_tokens

Whether to include token information

include_time

Whether to include timestamp information

tz

Time zone for timestamps (default "Asia/Shanghai" for CST)

Value

A data frame with chat history

Generic function to extract code of any specified language

Description

This function provides a flexible way to extract code blocks of any language from a string by specifying the language identifier(s).

Usage

extract_code(input_string, language, case_sensitive = FALSE)
extract_code(input_string, language, case_sensitive = FALSE)

Arguments

input_string

A string containing code blocks

language

Language identifier(s) to extract (e.g., "r", "python", c("bash", "sh"))

case_sensitive

Whether the language matching should be case-sensitive (default: FALSE)

Value

A character vector containing the extracted code

Examples

# Extract R code
text <- "```r\nx <- 1:10\n```"
extract_code(text, "r")

# Extract multiple language variants
text <- "```bash\necho 'test'\n```\n```sh\nls -la\n```"
extract_code(text, c("bash", "sh"))

# Case-sensitive extraction
text <- "```R\nplot(1:10)\n```\n```r\nprint('hello')\n```"
extract_code(text, "r", case_sensitive = TRUE) # Only matches lowercase 'r'
extract_code(text, "r", case_sensitive = FALSE) # Matches both 'R' and 'r'

# Extract custom language
text <- "```julia\nprintln(\"Julia code\")\n```"
extract_code(text, "julia")

# Extract YAML configuration
config_text <- "
Here's the configuration:
```yaml
database:
  host: localhost
  port: 5432
  name: mydb
```
"
extract_code(config_text, "yaml")

# Extract multiple TypeScript and JavaScript blocks
mixed_text <- "
TypeScript:
```typescript
interface User {
    name: string;
    age: number;
}
```

JavaScript:
```js
const user = {name: 'John', age: 30};
```
"
# Extract TypeScript
extract_code(mixed_text, "typescript")
# Extract both TypeScript and JavaScript
extract_code(mixed_text, c("typescript", "js"))

# Extract R code
text <- "```r\nx <- 1:10\n```"
extract_code(text, "r")

# Extract multiple language variants
text <- "```bash\necho 'test'\n```\n```sh\nls -la\n```"
extract_code(text, c("bash", "sh"))

# Case-sensitive extraction
text <- "```R\nplot(1:10)\n```\n```r\nprint('hello')\n```"
extract_code(text, "r", case_sensitive = TRUE) # Only matches lowercase 'r'
extract_code(text, "r", case_sensitive = FALSE) # Matches both 'R' and 'r'

# Extract custom language
text <- "```julia\nprintln(\"Julia code\")\n```"
extract_code(text, "julia")

# Extract YAML configuration
config_text <- "
Here's the configuration:
```yaml
database:
  host: localhost
  port: 5432
  name: mydb
```
"
extract_code(config_text, "yaml")

# Extract multiple TypeScript and JavaScript blocks
mixed_text <- "
TypeScript:
```typescript
interface User {
    name: string;
    age: number;
}
```

JavaScript:
```js
const user = {name: 'John', age: 30};
```
"
# Extract TypeScript
extract_code(mixed_text, "typescript")
# Extract both TypeScript and JavaScript
extract_code(mixed_text, c("typescript", "js"))

Extract Examples from a Package Function

Description

This function extracts and cleans the examples section from a specific function's documentation in an R package. It uses the 'tools' package to access the Rd database and extracts examples using 'tools::Rd2ex()'. The output is cleaned to remove metadata headers and formatting artifacts.

Usage

extract_function_examples(package_name, function_name)
extract_function_examples(package_name, function_name)

Arguments

package_name

A character string specifying the name of the package

function_name

A character string specifying the name of the function

Value

A character string containing the cleaned examples code, or 'NA' if no examples are found or an error occurs

Examples

## Not run: 
# Extract examples from ggplot2's geom_point function
examples <- extract_function_examples("ggplot2", "geom_point")
cat(examples)

## End(Not run)

## Not run: 
# Extract examples from ggplot2's geom_point function
examples <- extract_function_examples("ggplot2", "geom_point")
cat(examples)

## End(Not run)

Extract JavaScript code from a string

Description

This function extracts JavaScript code from a string by matching all content between '“‘javascript’, '“‘js’, '“‘jsx’ and '“''.

Usage

extract_javascript_code(input_string)
extract_javascript_code(input_string)

Arguments

input_string

A string containing JavaScript code blocks, typically a response from an LLM

Value

A character vector containing the extracted JavaScript code

Examples

# Simple JavaScript example
text <- "Code:\n```javascript\nconsole.log('Hello');\n```"
extract_javascript_code(text)

# Using 'js' tag
text <- "```js\nconst x = 42;\n```"
extract_javascript_code(text)

# Using 'jsx' tag for React
text <- "```jsx\n<div>Hello World</div>\n```"
extract_javascript_code(text)

# Multiple blocks with different tags
response <- "
Frontend code:
```javascript
function fetchData() {
    return fetch('/api/data')
        .then(response => response.json());
}
```

React component:
```jsx
const MyComponent = () => {
    const [data, setData] = useState([]);

    useEffect(() => {
        fetchData().then(setData);
    }, []);

    return (
        <div>
            {data.map(item => <p key={item.id}>{item.name}</p>)}
        </div>
    );
};
```

Node.js backend:
```js
const express = require('express');
const app = express();

app.get('/api/data', (req, res) => {
    res.json([{id: 1, name: 'Item 1'}]);
});

app.listen(3000);
```
"
codes <- extract_javascript_code(response)
length(codes) # Returns 3

# Simple JavaScript example
text <- "Code:\n```javascript\nconsole.log('Hello');\n```"
extract_javascript_code(text)

# Using 'js' tag
text <- "```js\nconst x = 42;\n```"
extract_javascript_code(text)

# Using 'jsx' tag for React
text <- "```jsx\n<div>Hello World</div>\n```"
extract_javascript_code(text)

# Multiple blocks with different tags
response <- "
Frontend code:
```javascript
function fetchData() {
    return fetch('/api/data')
        .then(response => response.json());
}
```

React component:
```jsx
const MyComponent = () => {
    const [data, setData] = useState([]);

    useEffect(() => {
        fetchData().then(setData);
    }, []);

    return (
        <div>
            {data.map(item => <p key={item.id}>{item.name}</p>)}
        </div>
    );
};
```

Node.js backend:
```js
const express = require('express');
const app = express();

app.get('/api/data', (req, res) => {
    res.json([{id: 1, name: 'Item 1'}]);
});

app.listen(3000);
```
"
codes <- extract_javascript_code(response)
length(codes) # Returns 3

Extract and parse JSONs from a string (LLM response)

Description

This function extracts JSON blocks from a string and parses them using 'jsonlite::fromJSON()'. This can be used to extract all JSONs from LLM responses, immediately converting them to R objects.

Usage

extract_json(llm_response)
extract_json(llm_response)

Arguments

llm_response

A character string

Details

CRITICAL FIX: Now uses simplifyVector = FALSE to preserve array structure. This ensures that JSON arrays remain as R lists, preventing single-element arrays from being simplified to character vectors. This is essential for proper schema validation when used with auto_unbox = TRUE in toJSON().

Value

A list of parsed JSON objects

Extract Python code from a string

Description

This function extracts Python code from a string by matching all content between '“‘python’, '“‘py’ and '“''.

Usage

extract_python_code(input_string)
extract_python_code(input_string)

Arguments

input_string

A string containing Python code blocks, typically a response from an LLM

Value

A character vector containing the extracted Python code

Examples

# Simple example
text <- "Python code:\n```python\nprint('Hello World')\n```"
extract_python_code(text)

# Using 'py' tag
text <- "```py\nimport numpy as np\n```"
extract_python_code(text)

# Multiple blocks with different tags
response <- "
Data processing:
```python
import pandas as pd
df = pd.read_csv('data.csv')
df.head()
```

Visualization:
```py
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()
```
"
codes <- extract_python_code(response)
length(codes) # Returns 2

# Complex example with classes and functions
llm_response <- "
Here's a complete Python solution:
```python
class DataProcessor:
    def __init__(self, data):
        self.data = data

    def process(self):
        return [x * 2 for x in self.data]

processor = DataProcessor([1, 2, 3])
result = processor.process()
print(result)
```
"
extract_python_code(llm_response)

# Simple example
text <- "Python code:\n```python\nprint('Hello World')\n```"
extract_python_code(text)

# Using 'py' tag
text <- "```py\nimport numpy as np\n```"
extract_python_code(text)

# Multiple blocks with different tags
response <- "
Data processing:
```python
import pandas as pd
df = pd.read_csv('data.csv')
df.head()
```

Visualization:
```py
import matplotlib.pyplot as plt
plt.plot([1, 2, 3], [4, 5, 6])
plt.show()
```
"
codes <- extract_python_code(response)
length(codes) # Returns 2

# Complex example with classes and functions
llm_response <- "
Here's a complete Python solution:
```python
class DataProcessor:
    def __init__(self, data):
        self.data = data

    def process(self):
        return [x * 2 for x in self.data]

processor = DataProcessor([1, 2, 3])
result = processor.process()
print(result)
```
"
extract_python_code(llm_response)

Extract R code from a string

Description

This function extracts R code from a string by matching all content between '“‘r’ or '“‘R’ and '“''.

Usage

extract_r_code(input_string)
extract_r_code(input_string)

Arguments

input_string

A string containing R code blocks, typically a response from an LLM

Value

A character vector containing the extracted R code

Examples

# Simple example
text <- "Here is some R code:\n```r\nprint('Hello')\n```"
extract_r_code(text)

# Multiple code blocks
response <- "
First block:
```r
x <- 1:10
mean(x)
```

Second block:
```R
library(ggplot2)
ggplot(mtcars, aes(mpg, hp)) + geom_point()
```
"
codes <- extract_r_code(response)
length(codes) # Returns 2

# With surrounding text
llm_response <- "
To calculate the mean, use this code:
```r
data <- c(1, 2, 3, 4, 5)
result <- mean(data)
print(result)
```
The result will be 3.
"
extract_r_code(llm_response)

# Simple example
text <- "Here is some R code:\n```r\nprint('Hello')\n```"
extract_r_code(text)

# Multiple code blocks
response <- "
First block:
```r
x <- 1:10
mean(x)
```

Second block:
```R
library(ggplot2)
ggplot(mtcars, aes(mpg, hp)) + geom_point()
```
"
codes <- extract_r_code(response)
length(codes) # Returns 2

# With surrounding text
llm_response <- "
To calculate the mean, use this code:
```r
data <- c(1, 2, 3, 4, 5)
result <- mean(data)
print(result)
```
The result will be 3.
"
extract_r_code(llm_response)

Extract SQL code from a string

Description

This function extracts SQL code from a string by matching all content between '“‘sql’ and '“'' (case-insensitive).

Usage

extract_sql_code(input_string)
extract_sql_code(input_string)

Arguments

input_string

A string containing SQL code blocks, typically a response from an LLM

Value

A character vector containing the extracted SQL code

Examples

# Simple SQL query
text <- "Query:\n```sql\nSELECT * FROM users;\n```"
extract_sql_code(text)

# Case-insensitive matching
text <- "```SQL\nSELECT COUNT(*) FROM orders;\n```"
extract_sql_code(text)

# Multiple SQL blocks
response <- "
Create table:
```sql
CREATE TABLE employees (
    id INT PRIMARY KEY,
    name VARCHAR(100),
    department VARCHAR(50),
    salary DECIMAL(10, 2)
);
```

Insert data:
```sql
INSERT INTO employees (id, name, department, salary)
VALUES
    (1, 'John Doe', 'IT', 75000),
    (2, 'Jane Smith', 'HR', 65000);
```

Query data:
```sql
SELECT name, salary
FROM employees
WHERE department = 'IT'
ORDER BY salary DESC;
```
"
codes <- extract_sql_code(response)
length(codes) # Returns 3

# Complex query with joins
complex_query <- "
Here's the analysis query:
```sql
WITH monthly_sales AS (
    SELECT
        DATE_TRUNC('month', order_date) as month,
        SUM(total_amount) as total_sales,
        COUNT(DISTINCT customer_id) as unique_customers
    FROM orders
    WHERE order_date >= '2024-01-01'
    GROUP BY DATE_TRUNC('month', order_date)
)
SELECT
    month,
    total_sales,
    unique_customers,
    total_sales / unique_customers as avg_per_customer
FROM monthly_sales
ORDER BY month;
```
"
extract_sql_code(complex_query)

# Simple SQL query
text <- "Query:\n```sql\nSELECT * FROM users;\n```"
extract_sql_code(text)

# Case-insensitive matching
text <- "```SQL\nSELECT COUNT(*) FROM orders;\n```"
extract_sql_code(text)

# Multiple SQL blocks
response <- "
Create table:
```sql
CREATE TABLE employees (
    id INT PRIMARY KEY,
    name VARCHAR(100),
    department VARCHAR(50),
    salary DECIMAL(10, 2)
);
```

Insert data:
```sql
INSERT INTO employees (id, name, department, salary)
VALUES
    (1, 'John Doe', 'IT', 75000),
    (2, 'Jane Smith', 'HR', 65000);
```

Query data:
```sql
SELECT name, salary
FROM employees
WHERE department = 'IT'
ORDER BY salary DESC;
```
"
codes <- extract_sql_code(response)
length(codes) # Returns 3

# Complex query with joins
complex_query <- "
Here's the analysis query:
```sql
WITH monthly_sales AS (
    SELECT
        DATE_TRUNC('month', order_date) as month,
        SUM(total_amount) as total_sales,
        COUNT(DISTINCT customer_id) as unique_customers
    FROM orders
    WHERE order_date >= '2024-01-01'
    GROUP BY DATE_TRUNC('month', order_date)
)
SELECT
    month,
    total_sales,
    unique_customers,
    total_sales / unique_customers as avg_per_customer
FROM monthly_sales
ORDER BY month;
```
"
extract_sql_code(complex_query)

Generate Function Extraction Prompt for LLM Analysis

Description

Creates a highly refined prompt that guides LLMs to identify ONLY the most documentation-critical, domain-specific R functions from a task description. The prompt uses sophisticated filtering criteria to exclude common, well-known functions (like read.csv, mean, order) that any LLM can use correctly without explicit documentation, focusing instead on specialized functions where examples truly add value.

Usage

package_extraction_prompt(
  task_description,
  include_criteria = NULL,
  exclude_criteria = NULL,
  prioritization_factors = NULL,
  emphasis = NULL
)
package_extraction_prompt(
  task_description,
  include_criteria = NULL,
  exclude_criteria = NULL,
  prioritization_factors = NULL,
  emphasis = NULL
)

Arguments

task_description

Character string. Detailed description of the R task or analysis workflow that needs to be performed. Should include: - Data types and sources involved - Analytical objectives and methods - Expected outputs or deliverables - Domain-specific context (e.g., bioinformatics, spatial analysis) The more domain-specific the description, the better the function selection.

include_criteria

Character vector. Additional inclusion criteria beyond the defaults. Specify domain-specific requirements or function characteristics that should be documented. Default is NULL (use standard criteria).

exclude_criteria

Character vector. Additional exclusion criteria beyond the defaults. Specify function types or patterns that should be skipped (e.g., "Basic ggplot2 themes", "Standard dplyr verbs"). Default is NULL.

prioritization_factors

Character vector. Additional factors for prioritizing functions beyond the defaults. Specify what makes certain functions more important to document. Default is NULL (use standard priorities).

emphasis

Character string. Additional emphasis or context to guide the extraction process. Use this to highlight specific aspects of the task or to emphasize certain types of functions. Default is NULL.

Details

This function applies a "documentation necessity test": only include functions where a proficient LLM would struggle without explicit documentation and examples. This dramatically improves output quality and reduces token waste.

The enhanced prompt applies a rigorous "documentation necessity test" with four key questions:

1. Would a proficient LLM struggle without documentation? 2. Is this function domain-specific or universally known? 3. Does it use specialized terminology or workflows? 4. Would examples significantly improve usage accuracy?

**Automatic exclusions** (common functions that waste tokens): - Data I/O: read.csv, write.csv, readLines - Basic operations: order, sort, subset, head, tail - Simple statistics: mean, median, sd, sum - Core structures: c, list, data.frame - Well-known tidyverse: simple dplyr::filter, dplyr::mutate - Basic control flow: if, for, while - Common utilities: paste, grep, unique

**What gets included** (documentation-critical functions): - Domain-specific methods (clusterProfiler::enrichGO for GO analysis) - Complex statistical procedures (DESeq2::DESeq) - Specialized transformations (sf::st_transform for spatial data) - Functions with many non-obvious parameters - Methods where wrong usage produces plausible but incorrect results

This approach ensures that "GO enrichment analysis" returns clusterProfiler functions, NOT read.csv or order.

Value

Character string containing the complete extraction prompt with:

Clear documentation necessity principle
Strict inclusion criteria for domain-specific functions
Comprehensive exclusion rules with concrete examples
Four-question decision heuristic for each function
Concrete good/bad examples from multiple domains
Prioritization by domain specialization and complexity
Quality-over-quantity guidance

Examples

# Basic usage
prompt <- package_extraction_prompt(
  "Perform GO enrichment analysis on differentially expressed genes"
)

# With domain-specific guidance
prompt <- package_extraction_prompt(
  task_description = "Single-cell RNA-seq analysis with Seurat",
  include_criteria = c(
    "Seurat-specific normalization and scaling methods"
  ),
  exclude_criteria = c(
    "Standard dplyr data manipulation"
  )
)

## Not run: 
# Use with retrieve_docs (requires LLM client)
docs <- retrieve_docs(
  chat_obj = llm,
  prompt = package_extraction_prompt(
    task_description = "Perform differential expression analysis"
  )
)

## End(Not run)

# Basic usage
prompt <- package_extraction_prompt(
  "Perform GO enrichment analysis on differentially expressed genes"
)

# With domain-specific guidance
prompt <- package_extraction_prompt(
  task_description = "Single-cell RNA-seq analysis with Seurat",
  include_criteria = c(
    "Seurat-specific normalization and scaling methods"
  ),
  exclude_criteria = c(
    "Standard dplyr data manipulation"
  )
)

## Not run: 
# Use with retrieve_docs (requires LLM client)
docs <- retrieve_docs(
  chat_obj = llm,
  prompt = package_extraction_prompt(
    task_description = "Perform differential expression analysis"
  )
)

## End(Not run)

Create JSON Schema for Package Function Validation

Description

Create JSON Schema for Package Function Validation

Usage

package_function_schema(
  min_functions = 0,
  max_functions = 10,
  description = NULL
)
package_function_schema(
  min_functions = 0,
  max_functions = 10,
  description = NULL
)

Arguments

min_functions

Integer. Minimum number of functions (default: 0 to allow empty)

max_functions

Integer. Maximum number of functions (default: 10)

description

Character string. Custom description

Value

List containing JSON schema

Build prompt from chat history

Description

Build prompt from chat history

Usage

prompt_from_history(
  chat_obj,
  add_text = NULL,
  add_role = "user",
  start_turn_index = 1
)
prompt_from_history(
  chat_obj,
  add_text = NULL,
  add_role = "user",
  start_turn_index = 1
)

Arguments

chat_obj

Chat object

add_text

Additional text to append

add_role

Role for add_text ("user" or "assistant")

start_turn_index

Starting turn index (default 1)

Value

Formatted prompt string

Simplified interface - Enhanced react_r

Description

Simplified interface - Enhanced react_r

Usage

react_r(chat_obj, task, ...)
react_r(chat_obj, task, ...)

Arguments

chat_obj

Chat object

task

Task description

...

Additional arguments passed to react_using_r

Value

Formatted result display

ReAct (Reasoning and Acting) using R code execution - Optimized Version

Description

ReAct (Reasoning and Acting) using R code execution - Optimized Version

Usage

react_using_r(
  chat_obj,
  task,
  max_turns = 15,
  pkgs_to_use = c(),
  objects_to_use = list(),
  existing_session = NULL,
  verbose = TRUE,
  r_session_options = list(),
  context_window_size = 3000,
  max_observation_length = 800,
  error_escalation_threshold = 3
)
react_using_r(
  chat_obj,
  task,
  max_turns = 15,
  pkgs_to_use = c(),
  objects_to_use = list(),
  existing_session = NULL,
  verbose = TRUE,
  r_session_options = list(),
  context_window_size = 3000,
  max_observation_length = 800,
  error_escalation_threshold = 3
)

Arguments

chat_obj

Chat object from ellmer

task

Character string. The task description to be solved

max_turns

Integer. Maximum number of ReAct turns (default: 15)

pkgs_to_use

Character vector. R packages to load in session

objects_to_use

Named list. Objects to load in R session

existing_session

Existing callr session to continue from (optional)

verbose

Logical. Whether to print progress information

r_session_options

List. Options for callr R session

context_window_size

Integer. Maximum characters before history summary (default: 3000)

max_observation_length

Integer. Maximum observation length (default: 800)

error_escalation_threshold

Integer. Error count threshold for escalation (default: 3)

Value

List with complete ReAct results

Get JSON response from LLM with validation and retry

Description

Get JSON response from LLM with validation and retry

Usage

response_as_json(
  chat_obj,
  prompt,
  schema = NULL,
  schema_strict = FALSE,
  max_iterations = 3
)
response_as_json(
  chat_obj,
  prompt,
  schema = NULL,
  schema_strict = FALSE,
  max_iterations = 3
)

Arguments

chat_obj

Chat object (LLM client). Must be a properly initialized LLM client instance.

prompt

Character string. The user prompt to send to the LLM requesting JSON output.

schema

List or NULL. Optional JSON schema for response validation (as R list structure). When provided, validates the LLM response against this schema. Default is NULL.

schema_strict

Logical. Whether to use strict schema validation (no additional properties allowed). Only applies when schema is provided. Default is FALSE.

max_iterations

Integer. Maximum number of retry attempts for invalid JSON or schema validation failures. Must be positive. Default is 3.

Value

List. Parsed JSON response from the LLM.

Examples

## Not run: 
# Basic usage without schema
result <- response_as_json(
  chat_obj = llm_client,
  prompt = "List three colors"
)

# With schema validation
schema <- list(
  type = "object",
  properties = list(
    equation = list(type = "string"),
    solution = list(type = "number")
  ),
  required = c("equation", "solution")
)
result <- response_as_json(
  chat_obj = llm_client,
  prompt = "How can I solve 8x + 7 = -23?",
  schema = schema,
  schema_strict = TRUE,
  max_iterations = 3
)

## End(Not run)

## Not run: 
# Basic usage without schema
result <- response_as_json(
  chat_obj = llm_client,
  prompt = "List three colors"
)

# With schema validation
schema <- list(
  type = "object",
  properties = list(
    equation = list(type = "string"),
    solution = list(type = "number")
  ),
  required = c("equation", "solution")
)
result <- response_as_json(
  chat_obj = llm_client,
  prompt = "How can I solve 8x + 7 = -23?",
  schema = schema,
  schema_strict = TRUE,
  max_iterations = 3
)

## End(Not run)

Response to R code generation and execution with session continuity

Description

Response to R code generation and execution with session continuity

Usage

response_to_r(
  chat_obj,
  prompt,
  add_text = NULL,
  pkgs_to_use = c(),
  objects_to_use = list(),
  existing_session = NULL,
  list_packages = TRUE,
  list_objects = TRUE,
  return_session_info = TRUE,
  evaluate_code = TRUE,
  r_session_options = list(),
  return_mode = c("full", "code", "console", "object", "formatted_output", "llm_answer",
    "session"),
  max_iterations = 3
)
response_to_r(
  chat_obj,
  prompt,
  add_text = NULL,
  pkgs_to_use = c(),
  objects_to_use = list(),
  existing_session = NULL,
  list_packages = TRUE,
  list_objects = TRUE,
  return_session_info = TRUE,
  evaluate_code = TRUE,
  r_session_options = list(),
  return_mode = c("full", "code", "console", "object", "formatted_output", "llm_answer",
    "session"),
  max_iterations = 3
)

Arguments

chat_obj

Chat object from ellmer

prompt

User prompt for R code generation

add_text

Additional instruction text

pkgs_to_use

Packages to load in R session

objects_to_use

Named list of objects to load in R session

existing_session

Existing callr session to continue from (optional)

list_packages

Whether to list available packages in prompt

list_objects

Whether to list available objects in prompt

return_session_info

Whether to return session state information

evaluate_code

Whether to evaluate the generated code

r_session_options

Options for callr R session

return_mode

Return mode specification

max_iterations

Maximum retry attempts

Value

Result based on return_mode

Retrieve and Format R Function Documentation for LLM Consumption

Description

Retrieve and Format R Function Documentation for LLM Consumption

Usage

retrieve_docs(
  chat_obj,
  prompt,
  schema = package_function_schema(),
  schema_strict = TRUE,
  skip_undocumented = TRUE,
  use_llm_fallback = FALSE,
  example_count = 2,
  warn_skipped = TRUE
)
retrieve_docs(
  chat_obj,
  prompt,
  schema = package_function_schema(),
  schema_strict = TRUE,
  skip_undocumented = TRUE,
  use_llm_fallback = FALSE,
  example_count = 2,
  warn_skipped = TRUE
)

Arguments

chat_obj

LLM chat client object

prompt

Task description

schema

JSON schema (default: package_function_schema())

schema_strict

Strict schema validation

skip_undocumented

Skip functions without docs

use_llm_fallback

Use LLM to generate examples

example_count

Number of examples per function

warn_skipped

Show warning for skipped functions

Value

Formatted documentation string

Save extracted code to file

Description

This function saves extracted code to a file with appropriate extension based on the programming language.

Usage

save_code_to_file(code_string, filename = NULL, language = "r")
save_code_to_file(code_string, filename = NULL, language = "r")

Arguments

code_string

String or character vector containing the code to save

filename

Output filename. If NULL, generates a timestamped filename

language

Programming language for determining file extension (default: "r")

Value

The path to the saved file

Examples

## Not run: 
# Extract and save R code
llm_response <- "```r\nplot(1:10)\n```"
code <- extract_r_code(llm_response)
save_code_to_file(code) # Saves as "code_20240101_120000.R"

# Save with custom filename
save_code_to_file(code, "my_plot.R")

# Save Python code with auto extension
py_code <- "import pandas as pd\ndf = pd.DataFrame()"
save_code_to_file(py_code, language = "python") # Creates .py file

# Save multiple code blocks
response <- "```r\nx <- 1\n```\n```r\ny <- 2\n```"
codes <- extract_r_code(response)
save_code_to_file(codes, "combined_code.R")

## End(Not run)

## Not run: 
# Extract and save R code
llm_response <- "```r\nplot(1:10)\n```"
code <- extract_r_code(llm_response)
save_code_to_file(code) # Saves as "code_20240101_120000.R"

# Save with custom filename
save_code_to_file(code, "my_plot.R")

# Save Python code with auto extension
py_code <- "import pandas as pd\ndf = pd.DataFrame()"
save_code_to_file(py_code, language = "python") # Creates .py file

# Save multiple code blocks
response <- "```r\nx <- 1\n```\n```r\ny <- 2\n```"
codes <- extract_r_code(response)
save_code_to_file(codes, "combined_code.R")

## End(Not run)

Package 'llmflow'

Help Index

AutoFlow - Automated R Analysis Workflow with LLM

Description

Usage

Arguments

Details

Value

Examples

Extract Bash/Shell code from a string

Description

Usage

Arguments

Value

Examples

Extract chat history from ellmer chat object

Description

Usage

Arguments

Value

Generic function to extract code of any specified language

Description

Usage

Arguments

Value

Examples

Extract Examples from a Package Function

Description

Usage

Arguments

Value

Examples

Extract JavaScript code from a string

Description

Usage

Arguments

Value

Examples

Extract and parse JSONs from a string (LLM response)

Description

Usage

Arguments

Details

Value

Extract Python code from a string

Description

Usage

Arguments

Value

Examples

Extract R code from a string

Description

Usage

Arguments

Value

Examples

Extract SQL code from a string

Description

Usage

Arguments

Value

Examples

Generate Function Extraction Prompt for LLM Analysis

Description

Usage

Arguments

Details

Value

See Also

Examples

Create JSON Schema for Package Function Validation

Description

Usage

Arguments

Value

Build prompt from chat history

Description

Usage

Arguments

Value