noclocksai

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Overview

The noclocksai package provides a comprehensive toolkit for working with AI services at No Clocks, LLC. It simplifies interactions with various AI models, provides utilities for embedding, database operations, and includes a flexible agent framework for creating AI assistants.

Key Features

• Agent Framework: Create and manage AI agents with memory, tools, and vector store capabilities
• Chat Interfaces: Simplified interactions with popular AI providers (OpenAI, Claude, Gemini)
• Embeddings: Generate and manage vector embeddings for RAG applications
• Database Integration: Store and retrieve embeddings and documents
• API Connectors: Wrappers for OpenAI, Google Maps, Geocoding, FireCrawl, and more
• Utility Functions: Tools for caching, logging, data extraction, and markdown processing
• Mermaid Diagrams: Generate mermaid.js diagrams with natural language
• Configuration Management: Secure API key and environment management

AI & Agentic Capabilities

LLM Provider Integrations

• OpenAI: GPT-4o, GPT-4, and other models
• Anthropic: Claude models
• Google: Gemini models
• Ollama: Local model hosting
• Groq: High-performance inference

AI Agents

• Base Agent: R6 class with memory, tools, and vector store capabilities
• Specialized Agents:
  • EDA Agent: For exploratory data analysis and anomaly detection
  • Mermaid Agent: For generating diagrams from natural language
  • Git Agent: For repository management and commit messages

Included Prompt Templates

• Code Analysis: For analyzing and explaining code
• Document Processing: For document summarization and analysis
• Exploratory Data Analysis: For statistical analysis
• Markdown Enhancement: For improving markdown documents
• Git Commits: For generating commit messages
• Synthetic Data: For generating sample data
• SQL to Text: For explaining SQL queries
• Execute R Code: For safe code execution
• Anomaly Detection: For identifying outliers in data

AI Tools

• Code Tools: Extract, format, and execute code
• Data Analysis: Anomaly detection, correlation analysis
• Geospatial: Google Maps integration, geocoding, weather data
• Database: Query execution with LLM assistance
• Time & Date: Timezone-aware functions

External API Integrations

• OpenWeather: Weather data retrieval by coordinates or location
• Google Maps: Geocoding, place search, and location data
• FireCrawl: Web scraping and crawling capabilities
• Langfuse: Comprehensive LLM observability and analytics
  • Trace chat sessions
  • Record user feedback
  • Manage prompt templates
  • Create evaluation datasets
  • Run experiments

Type System

The package includes a robust type system for parameter validation:

• Primitive Types: String, number, integer, boolean, array, object
• Complex Types: For specialized data formats
  • type_gmaps_geocode_response: Google Maps geocoding responses
  • type_gmaps_places_search_response: Places API responses
  • type_dataset_docs: Document dataset format

Installation

You can install the development version of noclocksai from GitHub:

R pak::pak("noclocks/noclocksai")

System Requirements

This package requires: - R 4.1 or later - Node.js with npm/npx (for certain features) - Database connections (for vector stores)

Basic Usage

Creating a Chat Agent

```r library(noclocksai)

Initialize a simple chat

chat <- initializechat( model = "gpt-4o", systemprompt = "You are a helpful AI assistant." )

Chat with the agent

chat$chat("What is the R programming language?") ```

Using Geocoding and Weather APIs

Below is an example of registering some tools for the chat agent to use:

```r registertools( chat, tools = list( toolcurrenttime(), toolgeocodelocation(), toolget_weather() ) )

✔ Successfully registered tool: getcurrenttime

✔ Successfully registered tool: geocode_location

✔ Successfully registered tool: get_weather

✔ Successfully registered 3 tools.

```

Now you can ask the agent about the current time or weather:

```r

chat with tools

chat$chat("What is the current time?")

[1] "The current time is 21:06:05 EDT on March 21, 2025."

chat$chat("What is the weather in Atlanta?")

[1] "The current weather in Atlanta is clear with a temperature of 11.75°C. The wind is blowing at 2.75 m/s from the west, and the humidity is 34%."

```

Creating Mermaid Diagrams

```r

initialize agent

mermaidagent <- initializechat(systemprompt = promptmermaid_sys())

example code for a diagram

example <- "starwars |> group_by(species) |> summarise( n = n(), mass = mean(mass, na.rm = TRUE) ) |> filter( n > 1, mass > 50 )"

prompt for mermaid diagram

resp <- createmermaiddiagram( mermaidagent, promptmermaid_user(code = example) ) resp

```mermaid

graph TD

A[starwars dataset] --> B[Group by species]

B --> C[Summarise]

C -->|Calculate n = count of species| D

C -->|Calculate mass = mean mass of species| D

D --> E[Filter]

E -->|n > 1| F[Filtered Data]

E -->|mass > 50| F[Filtered Data]

```

extract code & render diagram

extract_code(resp, "mermaid") |> Diagrammer::Diagrammer() ```


Extracting Code from Text

r text <- "Here's an example: r library(dplyr) mtcars |> groupby(cyl) |> summarise(avgmpg = mean(mpg)) ```"

Extract the R code

code <- extract_code(text, lang = "r") ```

Working with PostgreSQL Databases

```r

Connect to a PostgreSQL database

conn <- dbconnect(list( driver = "PostgreSQL", host = "localhost", dbname = "mydatabase", user = "postgres", password = "password", port = 5432 ))

Query the database and store documents for vector search

dbstoredocument( conn, title = "Sample Document", content = "This is a sample document for vector search.", metadata = list(author = "Jane Doe", date = Sys.Date()) )

Retrieve documents using semantic search

results <- dbgetdocuments(conn, "sample vector search") ```

Complex Workflow Example

The following example shows how to create an agent that uses multiple tools:

```r

Create an agent with vector store capabilities

agent <- Agent$new( provider = "openai", model = "gpt-4o", system_prompt = "You are an assistant that helps analyze real estate data.", name = "real-estate-assistant" )

Register external API tools with the agent

agent$registertool(toolgeocodelocation()) agent$registertool(toolgetweather()) agent$registertool(toolgmapsplacessearch())

Connect the agent to a database for document storage

agent$dbconnect(dbconfig = list( driver = "PostgreSQL", host = "34.75.86.90", dbname = "gmhleasingdev", user = "postgres", password = "password", port = 5432 ))

Add a resource to the agent

agent$addresource( name = "marketdata", content = read.csv("market_data.csv"), type = "dataframe" )

Ask the agent a question that requires using multiple tools

response <- agent$chat( "What are the current market conditions for apartment rentals in New York City, and how might the current weather impact showings this weekend?" )

Save the agent state for later use

agent$savestate("agentstate.rds") ```

Documentation

For more detailed information, see the package vignettes:

```r

List available vignettes

vignette(package = "noclocksai")

View a specific vignette

vignette("noclocksai", package = "noclocksai") ```

License

This package is licensed under a proprietary license - see the LICENSE file for details.

Contributing

This package is primarily maintained by No Clocks, LLC. Please file issues for bugs or feature requests.