
ryoma
Common AI agent framework solving your data problems
Stars: 130

Ryoma is an AI Powered Data Agent framework that offers a comprehensive solution for data analysis, engineering, and visualization. It leverages cutting-edge technologies like Langchain, Reflex, Apache Arrow, Jupyter Ai Magics, Amundsen, Ibis, and Feast to provide seamless integration of language models, build interactive web applications, handle in-memory data efficiently, work with AI models, and manage machine learning features in production. Ryoma also supports various data sources like Snowflake, Sqlite, BigQuery, Postgres, MySQL, and different engines like Apache Spark and Apache Flink. The tool enables users to connect to databases, run SQL queries, and interact with data and AI models through a user-friendly UI called Ryoma Lab.
README:
AI Powered Data Agent framework, a comprehensive solution for data analysis, engineering, and visualization.
Our platform leverages a combination of cutting-edge technologies and frameworks:
- Langchain: Facilitates the seamless integration of language models into application workflows, significantly enhancing AI interaction capabilities.
- Reflex: An open-source framework for quickly building beautiful, interactive web applications in pure Python
- Apache Arrow: A cross-language development platform for in-memory data that specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.
- Jupyter Ai Magics: A JupyterLab extension that provides a set of magics for working with AI models.
- Amundsen: A data discovery and metadata platform that helps users discover, understand, and trust the data they use.
- Ibis: A Python data analysis framework that provides a pandas-like API for analytics on large datasets.
- Feast: An operational feature store for managing and serving machine learning features to models in production.
Simply install the package using pip:
pip install ryoma_ai
Or with extra dependencies:
pip install ryoma_ai[snowflake]
Below is an example of using SqlAgent to connect to a PostgreSQL database and ask a question. You can read more details in the documentation.
from ryoma_ai.agent.sql import SqlAgent
from ryoma_ai.datasource.postgresql import PostgreSqlDataSource
# Connect to a PostgreSQL catalog
datasource = PostgreSqlDataSource("postgresql://user:password@localhost:5432/dbname")
# Create a SQL agent
sql_agent = SqlAgent("gpt-3.5-turbo").add_datasource(datasource)
# ask question to the agent
sql_agent.stream("I want to get the top 5 customers which making the most purchases", display=True)
The Sql agent will try to run the tool as shown below:
================================ Human Message =================================
I want to get the top 5 customers which making the most purchases
================================== Ai Message ==================================
Tool Calls:
sql_database_query (call_mWCPB3GQGOTLYsvp21DGlpOb)
Call ID: call_mWCPB3GQGOTLYsvp21DGlpOb
Args:
query: SELECT C.C_NAME, SUM(L.L_EXTENDEDPRICE) AS TOTAL_PURCHASES FROM CUSTOMER C JOIN ORDERS O ON C.C_CUSTKEY = O.O_CUSTKEY JOIN LINEITEM L ON O.O_ORDERKEY = L.L_ORDERKEY GROUP BY C.C_NAME ORDER BY TOTAL_PURCHASES DESC LIMIT 5
result_format: pandas
Continue to run the tool with the following code:
sql_agent.stream(tool_mode=ToolMode.ONCE)
Output will look like after running the tool:
================================== Ai Message ==================================
The top 5 customers who have made the most purchases are as follows:
1. Customer#000143500 - Total Purchases: $7,154,828.98
2. Customer#000095257 - Total Purchases: $6,645,071.02
3. Customer#000087115 - Total Purchases: $6,528,332.52
4. Customer#000134380 - Total Purchases: $6,405,556.97
5. Customer#000103834 - Total Purchases: $6,397,480.12
Ryoma lab is an application that allows you to interact with your data and AI models in UI. The ryoma lab is built with Reflex.
- Create Ryoma lab configuration file
rxconfig.py
in your project:
import logging
import reflex as rx
from reflex.constants import LogLevel
config = rx.Config(
app_name="ryoma_lab",
loglevel=LogLevel.INFO,
)
# Setup basic configuration for logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
- You can start the ryoma lab by running the following command:
ryoma_lab run
the ryoma lab will be available at http://localhost:3000
.
Model provider are supported by jupyter ai magics. Ensure the corresponding environment variables are set before using the Ryoma agent.
Provider | Provider ID | Environment variable(s) | Python package(s) |
---|---|---|---|
AI21 | ai21 |
AI21_API_KEY |
ai21 |
Anthropic | anthropic |
ANTHROPIC_API_KEY |
langchain-anthropic |
Anthropic (playground) | anthropic-playground |
ANTHROPIC_API_KEY |
langchain-anthropic |
Bedrock | bedrock |
N/A | boto3 |
Bedrock (playground) | bedrock-playground |
N/A | boto3 |
Cohere | cohere |
COHERE_API_KEY |
cohere |
ERNIE-Bot | qianfan |
QIANFAN_AK , QIANFAN_SK
|
qianfan |
Gemini | gemini |
GOOGLE_API_KEY |
langchain-google-genai |
GPT4All | gpt4all |
N/A | gpt4all |
Hugging Face Hub | huggingface_hub |
HUGGINGFACEHUB_API_TOKEN |
huggingface_hub , ipywidgets , pillow
|
NVIDIA | nvidia-playground |
NVIDIA_API_KEY |
langchain_nvidia_ai_endpoints |
OpenAI | openai |
OPENAI_API_KEY |
langchain-openai |
OpenAI (playground) | openai-playground |
OPENAI_API_KEY |
langchain-openai |
SageMaker | sagemaker-endpoint |
N/A | boto3 |
- [x] Snowflake
- [x] Sqlite
- [x] BigQuery
- [x] Postgres
- [x] MySQL
- [x] File (CSV, Excel, Parquet, etc.)
- [ ] Redshift
- [ ] DynamoDB
- [x] Apache Spark
- [x] Apache Flink
- [ ] Presto
This project is licensed under the terms of the Apache Software License 2.0
license. See LICENSE for more details.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for ryoma
Similar Open Source Tools

ryoma
Ryoma is an AI Powered Data Agent framework that offers a comprehensive solution for data analysis, engineering, and visualization. It leverages cutting-edge technologies like Langchain, Reflex, Apache Arrow, Jupyter Ai Magics, Amundsen, Ibis, and Feast to provide seamless integration of language models, build interactive web applications, handle in-memory data efficiently, work with AI models, and manage machine learning features in production. Ryoma also supports various data sources like Snowflake, Sqlite, BigQuery, Postgres, MySQL, and different engines like Apache Spark and Apache Flink. The tool enables users to connect to databases, run SQL queries, and interact with data and AI models through a user-friendly UI called Ryoma Lab.

dvc
DVC, or Data Version Control, is a command-line tool and VS Code extension that helps you develop reproducible machine learning projects. With DVC, you can version your data and models, iterate fast with lightweight pipelines, track experiments in your local Git repo, compare any data, code, parameters, model, or performance plots, and share experiments and automatically reproduce anyone's experiment.

llms-from-scratch-rs
This project provides Rust code that follows the text 'Build An LLM From Scratch' by Sebastian Raschka. It translates PyTorch code into Rust using the Candle crate, aiming to build a GPT-style LLM. Users can clone the repo, run examples/exercises, and access the same datasets as in the book. The project includes chapters on understanding large language models, working with text data, coding attention mechanisms, implementing a GPT model, pretraining unlabeled data, fine-tuning for classification, and fine-tuning to follow instructions.

repopack
Repopack is a powerful tool that packs your entire repository into a single, AI-friendly file. It optimizes your codebase for AI comprehension, is simple to use with customizable options, and respects Gitignore files for security. The tool generates a packed file with clear separators and AI-oriented explanations, making it ideal for use with Generative AI tools like Claude or ChatGPT. Repopack offers command line options, configuration settings, and multiple methods for setting ignore patterns to exclude specific files or directories during the packing process. It includes features like comment removal for supported file types and a security check using Secretlint to detect sensitive information in files.

FalkorDB
FalkorDB is the first queryable Property Graph database to use sparse matrices to represent the adjacency matrix in graphs and linear algebra to query the graph. Primary features: * Adopting the Property Graph Model * Nodes (vertices) and Relationships (edges) that may have attributes * Nodes can have multiple labels * Relationships have a relationship type * Graphs represented as sparse adjacency matrices * OpenCypher with proprietary extensions as a query language * Queries are translated into linear algebra expressions

XLICON-V2-MD
XLICON-V2-MD is a versatile Multi-Device WhatsApp bot developed by Salman Ahamed. It offers a wide range of features, making it an advanced and user-friendly bot for various purposes. The bot supports multi-device operation, AI photo enhancement, downloader commands, hidden NSFW commands, logo generation, anime exploration, economic activities, games, and audio/video editing. Users can deploy the bot on platforms like Heroku, Replit, Codespace, Okteto, Railway, Mongenius, Coolify, and Render. The bot is maintained by Salman Ahamed and Abraham Dwamena, with contributions from various developers and testers. Misusing the bot may result in a ban from WhatsApp, so users are advised to use it at their own risk.

chatglm.cpp
ChatGLM.cpp is a C++ implementation of ChatGLM-6B, ChatGLM2-6B, ChatGLM3-6B and more LLMs for real-time chatting on your MacBook. It is based on ggml, working in the same way as llama.cpp. ChatGLM.cpp features accelerated memory-efficient CPU inference with int4/int8 quantization, optimized KV cache and parallel computing. It also supports P-Tuning v2 and LoRA finetuned models, streaming generation with typewriter effect, Python binding, web demo, api servers and more possibilities.

evalscope
Eval-Scope is a framework designed to support the evaluation of large language models (LLMs) by providing pre-configured benchmark datasets, common evaluation metrics, model integration, automatic evaluation for objective questions, complex task evaluation using expert models, reports generation, visualization tools, and model inference performance evaluation. It is lightweight, easy to customize, supports new dataset integration, model hosting on ModelScope, deployment of locally hosted models, and rich evaluation metrics. Eval-Scope also supports various evaluation modes like single mode, pairwise-baseline mode, and pairwise (all) mode, making it suitable for assessing and improving LLMs.

unsloth
Unsloth is a tool that allows users to fine-tune large language models (LLMs) 2-5x faster with 80% less memory. It is a free and open-source tool that can be used to fine-tune LLMs such as Gemma, Mistral, Llama 2-5, TinyLlama, and CodeLlama 34b. Unsloth supports 4-bit and 16-bit QLoRA / LoRA fine-tuning via bitsandbytes. It also supports DPO (Direct Preference Optimization), PPO, and Reward Modelling. Unsloth is compatible with Hugging Face's TRL, Trainer, Seq2SeqTrainer, and Pytorch code. It is also compatible with NVIDIA GPUs since 2018+ (minimum CUDA Capability 7.0).

agentops
AgentOps is a toolkit for evaluating and developing robust and reliable AI agents. It provides benchmarks, observability, and replay analytics to help developers build better agents. AgentOps is open beta and can be signed up for here. Key features of AgentOps include: - Session replays in 3 lines of code: Initialize the AgentOps client and automatically get analytics on every LLM call. - Time travel debugging: (coming soon!) - Agent Arena: (coming soon!) - Callback handlers: AgentOps works seamlessly with applications built using Langchain and LlamaIndex.

EAGLE
Eagle is a family of Vision-Centric High-Resolution Multimodal LLMs that enhance multimodal LLM perception using a mix of vision encoders and various input resolutions. The model features a channel-concatenation-based fusion for vision experts with different architectures and knowledge, supporting up to over 1K input resolution. It excels in resolution-sensitive tasks like optical character recognition and document understanding.

fittencode.nvim
Fitten Code AI Programming Assistant for Neovim provides fast completion using AI, asynchronous I/O, and support for various actions like document code, edit code, explain code, find bugs, generate unit test, implement features, optimize code, refactor code, start chat, and more. It offers features like accepting suggestions with Tab, accepting line with Ctrl + Down, accepting word with Ctrl + Right, undoing accepted text, automatic scrolling, and multiple HTTP/REST backends. It can run as a coc.nvim source or nvim-cmp source.

ollama4j
Ollama4j is a Java library that serves as a wrapper or binding for the Ollama server. It allows users to communicate with the Ollama server and manage models for various deployment scenarios. The library provides APIs for interacting with Ollama, generating fake data, testing UI interactions, translating messages, and building web UIs. Users can easily integrate Ollama4j into their Java projects to leverage the functionalities offered by the Ollama server.

aiohttp
aiohttp is an async http client/server framework that supports both client and server side of HTTP protocol. It also supports both client and server Web-Sockets out-of-the-box and avoids Callback Hell. aiohttp provides a Web-server with middleware and pluggable routing.

Liger-Kernel
Liger Kernel is a collection of Triton kernels designed for LLM training, increasing training throughput by 20% and reducing memory usage by 60%. It includes Hugging Face Compatible modules like RMSNorm, RoPE, SwiGLU, CrossEntropy, and FusedLinearCrossEntropy. The tool works with Flash Attention, PyTorch FSDP, and Microsoft DeepSpeed, aiming to enhance model efficiency and performance for researchers, ML practitioners, and curious novices.

aiodocker
Aiodocker is a simple Docker HTTP API wrapper written with asyncio and aiohttp. It provides asynchronous bindings for interacting with Docker containers and images. Users can easily manage Docker resources using async functions and methods. The library offers features such as listing images and containers, creating and running containers, and accessing container logs. Aiodocker is designed to work seamlessly with Python's asyncio framework, making it suitable for building asynchronous Docker management applications.
For similar tasks

Azure-Analytics-and-AI-Engagement
The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.

sorrentum
Sorrentum is an open-source project that aims to combine open-source development, startups, and brilliant students to build machine learning, AI, and Web3 / DeFi protocols geared towards finance and economics. The project provides opportunities for internships, research assistantships, and development grants, as well as the chance to work on cutting-edge problems, learn about startups, write academic papers, and get internships and full-time positions at companies working on Sorrentum applications.

tidb
TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

zep-python
Zep is an open-source platform for building and deploying large language model (LLM) applications. It provides a suite of tools and services that make it easy to integrate LLMs into your applications, including chat history memory, embedding, vector search, and data enrichment. Zep is designed to be scalable, reliable, and easy to use, making it a great choice for developers who want to build LLM-powered applications quickly and easily.

telemetry-airflow
This repository codifies the Airflow cluster that is deployed at workflow.telemetry.mozilla.org (behind SSO) and commonly referred to as "WTMO" or simply "Airflow". Some links relevant to users and developers of WTMO: * The `dags` directory in this repository contains some custom DAG definitions * Many of the DAGs registered with WTMO don't live in this repository, but are instead generated from ETL task definitions in bigquery-etl * The Data SRE team maintains a WTMO Developer Guide (behind SSO)

mojo
Mojo is a new programming language that bridges the gap between research and production by combining Python syntax and ecosystem with systems programming and metaprogramming features. Mojo is still young, but it is designed to become a superset of Python over time.

pandas-ai
PandasAI is a Python library that makes it easy to ask questions to your data in natural language. It helps you to explore, clean, and analyze your data using generative AI.

databend
Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.
For similar jobs

sweep
Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

teams-ai
The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

ai-guide
This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

classifai
Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

chatbot-ui
Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents
uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

griptape
Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.