SheetCopilot

We release a general framework for prompting LLMs to manipulate software in a closed-loop manner.

Stars: 82

Visit

SheetCopilot is an assistant agent that manipulates spreadsheets by following user commands. It leverages Large Language Models (LLMs) to interact with spreadsheets like a human expert, enabling non-expert users to complete tasks on complex software such as Google Sheets and Excel via a language interface. The tool observes spreadsheet states, polishes generated solutions based on external action documents and error feedback, and aims to improve success rate and efficiency. SheetCopilot offers a dataset with diverse task categories and operations, supporting operations like entry & manipulation, management, formatting, charts, and pivot tables. Users can interact with SheetCopilot in Excel or Google Sheets, executing tasks like calculating revenue, creating pivot tables, and plotting charts. The tool's evaluation includes performance comparisons with leading LLMs and VBA-based methods on specific datasets, showcasing its capabilities in controlling various aspects of a spreadsheet.

README:

SheetCopilot: Bringing Software Productivity to the Next Level through Large Language Models

SheetCopilot Icon

Overview • Setup • Dataset • Sheetcopilot Usage • Evaluation • Poster • Paper • Citation

Explore the project website »

We release the SheetCopilot agent as well as the evaluation environment in this repository.

SheetCopilot is an assistant agent that manipulates spreadsheets by following user commands. It breaks new ground in human-computer interaction, opening up possibilities for enabling non-expert users to complete their mundane work on complex software (e.g. Google Sheets and Excel) via a language interface.

What's New

[2024/02/24] 🛠 Full SheetCopilot was released.
[2023/12/26] 🛠 SheetCopilot equipped with Chain-of-Thoughts and external document retrieval was released.
[2023/11/15] ✨ SheetCopilot for Google Sheets was released! You can now use SheetCopilot directly on Google Sheets. Check out our Google Sheets plugin store page and watch this tutorial for installation and usage guide.
[2023/10/27] 🛠 More ground truths! We added more reference solutions to our benchmark (dataset/task_sheet_answers_v2) to obtain more accurate evaluation results.
[2023/10/25] SheetCopilot benchmark was open-sourced.
[2023/9/22] 🎉 Our paper was accepted to NeurIPS 2023.
[2023/5/19] 👷🏻‍♂️ SheetCopilot was completed.

TODO

Update the function call parsing code to fix the quote parsing errors
Update API implementations
Update the evaluation script to improve the checking accuracy

Overview

SheetCopilot employs a novel way of directing Large Language Models (LLMs) to manipulate spreadsheets like a human expert. To achieve elegant closed-loop control, SheetCopilot observes the spreadsheet state and polishes generated solutions according to external action documents and error feedback, thereby improving its success rate and efficiency.

Setup

1. Prepare the Conda environment

SheetCopilot is only available on Windows. Python 3.10 is required to support the asynchronous implementation of SheetCopilot.

conda create -n sheetcopilot python=3.10

2. In this conda env, run:

pip install -r requirements.txt

Dataset

We released a spreadsheet task dataset containing 28 workbooks and 221 tasks applied to these workbooks. Each task is given one or more hand-made solutions.

Here is the overview of the dataset:

Our dataset contains diverse task categories and involves a wide range of operations:

Our dataset provides tasks with diverse complexity:

44 operations are supported and more will be added:

Entry & manipulation: Write, CopyPaste, CutPaste, SetHyperlink, RemoveHyperlink, AutoFill, InsertRow, InsertColumn, Delete, Clear
Management: Sort, Filter, DeleteFilter, MoveRow, MoveColumn, RemoveDuplicate
Formatting: SetFormat, DeleteFormat, SetDataType, SetCellMerge, AutoFit, ResizeRowColumn, SetConditionalFormat, SetDataValidation, SetCellLock, FreezePanes, UnfreezePanes
Chart: CreateChart, SetChartTrendline, SetChartTitle, SetChartHasAxis, SetChartAxis, SetChartHasLegend, SetChartLegend, SetChartType, AddChartErrorBars, RemoveChartErrorBars, AddDataLabels, RemoveDataLabels, SetChartMarker
Pivot Table: CreatePivotTable, CreateChartFromPivotTable, CreateSheet, RemoveSheet

This dataset can be used to evaluate any spreadsheet agent including RL, LLM-based, or rule-based methods.

In the dataset folder, dataset.xlsx lists the 221 tasks, containing the target workbook name, task number, instruction, task categories, and involved atomic actions.

The fields are explained one by one as follows:

Sheet Name: The name of the sheet this task is applied to.
No.: The number of this task.
Context: The brief description of the sheet this task is applied to. This context will be added to the prompt to inform the LLM of the spreadsheet usage.
Instructions: The task content.
Categories: Each task is classified into multiple categories according to the atomic actions involved in the task.
Atomic actions: The atomic actions used to solve the task
Seed task: The number of the seed task (stored in dataset/seed_tasks.xlsx) this task originates from. Our 221 tasks were produced by adapting the 67 seed tasks to apply them to the task sheets (the task_sheets folder).

The task_sheets folder contains the 28 evaluation workbooks these tasks are applied to.

The task_sheet_answers folder contains the reference solutions of the tasks. Each solution consists of a reference workbook showing the expected outcome of the corresponding task and a *.yaml file listing the necessary sheet states to compare. If the necessary states of the result match those of one of the references, the result is seen as correct. (The v1 version is used in our paper while the v2 version contains more reference solutions collected after our paper was submitted)

Each solution folder (e.g. 1_BoomerangSales) contains at least 1 reference, which comprises a final spreadsheet (1_BoomerangSales_gt1.xlsx) and a checking list (1_BoomerangSales_gt1_check.yaml). Different tasks need different atomic actions so the checking lists are tailored to corresponding tasks.

The dataset_20Samples.xlsx file lists the 20 selected tasks used to compare the representative LLMs in our experiments (Table 1).

To dive deeper into the dataset collection details, refer to this tutorial.

SheetCopilot Usage

For Excel

This repo releases a simplified version of the SheetCopilot agent, whose state machine can do CoT reasoning and retrieve external documents.

SheetCopilot calls customized atomic actions to execute its generated solutions. We implement each atomic action using the pywin32 library. Please refer to API definitions to see the details. To compare with our SheetCopilot, your own agents should also adopt this action space.

Before running an experiment, please set max tokens, temperature, model_name, and API keys in config/config.yaml. (As launching multiple Excels still encounters certain unknown issues, we recommend worker=1. This can finish the evaluation in 1-2 hours.)

You can see two ChatGPT configs in this file - ChatGPT_1 is used to do planning while ChatGPT_2 is used to revise the format of the planning results. You can set use_same_LLM: true to use ChatGPT_1 to carry out both two jobs.

The underlying implementation of SheetCopilot is a state machine that implements planning by transitioning among 4 states (See the below figure). max_cycle_times is used to limit the number of times the agent visits the states.

SheetCopilot State Machine

Interactive mode

Open an Excel workbook before running this command:

python interaction.py -c config/config.yaml

Now you can enter instructions and wait for SheetCoilot to finish them without human intervention.

Example

To try SheetCopilot quickly, please open dataset/task_sheets/BoomerangSales.xlsx and then enter these instructions in order:

Calculate the revenue for each transaction considering the corresponding retail price and discount.
Highlight the Revenue cells greater than 500 in blue text.
Create a pivot table in a new sheet to show the counts of the websites on which boomerangs were sold.
Plot a bar chart for the pivot table in the same sheet.
Set the y-axis title as "Count" and turn off legends.
Create another pivot table in a new sheet to show the revenue sums of each product.
Plot a pie chart for the pivot table with the chart title "Revenue by Product" in this sheet.

You can also try more vague instructions like: Analyze the data and plot charts for the results.

Afterward, you may see SheetCopilot create pivot tables and plot proper charts for you (see the figure below).

Result of the example task

[Caution] Any operation executed by SheetCopilot cannot be undone by clicking the "Undo" button! We strongly recommend that our users use SheetCopilot on GoogleSheets to automate their spreadsheet tasks.

For Google Sheets

Open a GoogleSheets spreadsheet and install SheetCopilot on the Google Workspace Market like this:

Install SheetCopilot for GoogleSheets

Then you can hack SheetCopilot happily via chatting ...

Let SheetCopilot solve complex tasks for you

You can undo any operations executed by SheetCopilot by just using Ctrl + Z.

Evaluation

The results generated by any method should be organized like this:

results
  └── ([Order]_[Sheet Name])
  └── 1_BoomerangSales
  |   └── ([Order]_[Sheet Name]_[Repeat_No.].xlsx)
  |   └── 1_BoomerangSales_log.yaml
  ...
  └── 9_BoomerangSales
  └── 10_DemographicProfile
  ...
  └── 17_Dragging
  ...
  └── 24_Dragging
  ...
  └── 221_XYScatterPlot

[Order] is the row index of the task minus 1 and [Sheet Name] is the items of column A in dataset.xlsx. [Repeat_NO.] is used to differentiate multiple repeats of the same task. If you run each task only once (controlled by repeat in the config file), [Repeat_NO.] is 1.

1_BoomerangSales_log.yaml is the running log of the task saving the content of the planning process. Likewise, your method should also record a log for each task.

You can also use the "[No.]_[Sheet Name]" naming convention as follows ([No.] are the items of column B in dataset.xlsx):

results
  └── ([No.]_[Sheet Name])
  └── 1_BoomerangSales
  |   └── ([No.]_[Sheet Name]_[Repeat_No.].xlsx)
  |   └── 1_BoomerangSales_log.yaml
  ...
  └── 9_BoomerangSales
  ...
  └── 1_Dragging
  ...
  └── 8_Dragging
  ...

You should set the global variable USE_NO_AND_SHEETNAME in evaluation.py as True to use such a naming convention.

As different agents may present plans in various formats, we recommend that each method outputs each step using this Chain-of-Thoughts (CoT) format:

Step X. [Thought]
Action API: @[Action call]@

For example,

Step 3. Fill the formula to other cells.
Action API: @AutoFill(source="Sheet1!A2", destination="Sheet1!A2:A36")@

agent/SheetCopilot_example_logs shows examples of the required log format (use the "[Order]_[Sheet Name]" naming convention).

Specify the correct paths in agent/config/config.yaml and then run this code within the agent folder to evaluate your results:

python evaluation.py

The evaluation results will be recorded in a file named eval_result.yaml under the result folder.

The evaluation can restart from a checkpoint if it has been aborted. If you want to re-evaluate, just delete the eval_result.yaml in the result folder.

Important: NOTE that

Every new sheet must be created to the left of the very first sheet for correct matching with the references since sheet names are not to be checked.
The sheet content must start from cell A1 and each sheet is required to contain contiguous tables.

Evaluation results

The performances of SheetCopilot with 3 leading LLMs as its back-end on dataset/dataset_20Samples.xlsx.

Models	Exec@1	Pass@1	A50	A90
GPT-3.5-Turbo	85.0%	45.0%	2.00	4.50
GPT-4	65.0%	55.0%	1.33	2.00
Claude	80.0%	40.0%	1.50	4.40

The performances of SheetCopilot and a VBA-based method were evaluated on dataset/dataset.xlsx using dataset/task_sheet_answers as the ground truths. (Note: as we also included the functionally correct results generated by GPT-3.5-Turbo to dataset/task_sheet_answers_v2, the evaluation results for this model remain the same whether you use v1 or v2 ground truths.)

Methods	Exec@1	Pass@1
GPT-3.5-Turbo	87.3%	44.3%
VBA-based	77.8%	16.3%

The aspects of a spreadsheet SheetCopilot can control

(1) Manipulation: Writing values and formulas, deleting cells, inserting a row/column, auto-filling, copy-pasting values, find-and-replacing, setting hyperlinks, removing duplicates, creating sheets, clearing formats.

(2) Management: Sorting, filtering, and freezing panes.

(3) Formatting: Setting format and conditional format (font, bold, italic, underline, text color, and fill color), setting data type (date, text, number, currency, time, general, percentage), and merging.

(4) Charts: Creating charts, creating charts from pivot tables, setting chart title/axis title/legends/chart type/marker/trendline/data labels.

(5) Pivot table: Creating pivot tables.

(More operations will be added once the developers finish testing them. Besides, you can raise issues to ask for more supported operations or pull requests to upload your implementations.)

Demo

This video shows that SheetCopilot conducts GDP data analysis successfully.

The video below shows SheetCopilot deployed on Google Sheets.

You can upload task_sheets/BoomerangSales.xlsx and type in these instructions to reproduce the results in the demo:

Calculate the revenue for each transaction in the sales table considering the corresponding retail price and discount.
Highlight the Revenue cells greater than 500 in blue text.
Create a pivot table in a new sheet to show the counts of the websites on which boomerangs were sold.
Plot a bar chart for the pivot table in the same sheet.
Set the y-axis title as "Count" and turn off legends.
Create another pivot table in a new sheet to show the revenue sums of each product.
Plot a pie chart for the pivot table with chart title "Revenue by Product" in this sheet.

Citation

SheetCopilot and the dataset can only be used for non-commercial purposes.

If you use the SheetCopilot agent and benchmark, feel free to cite us.

@inproceedings{li_sheetcopilot_2023,
	title = {{SheetCopilot}: {Bringing} {Software} {Productivity} to the {Next} {Level} through {Large} {Language} {Models}},
	volume = {36},
	url = {https://proceedings.neurips.cc/paper_files/paper/2023/file/0ff30c4bf31db0119a6219e0d250e037-Paper-Conference.pdf},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
	publisher = {Curran Associates, Inc.},
	author = {Li, Hongxin and Su, Jingran and Chen, Yuntao and Li, Qing and ZHANG, ZHAO-XIANG},
	editor = {Oh, A. and Neumann, T. and Globerson, A. and Saenko, K. and Hardt, M. and Levine, S.},
	year = {2023},
	pages = {4952--4984},
}

For Tasks:

Click tags to check more tools for each tasks

calculate revenue highlight cells create pivot table plot charts manage data

For Jobs:

data analyst financial analyst business intelligence developer operations manager accountant

Alternative AI tools for SheetCopilot

Similar Open Source Tools

SheetCopilot

github

: 82

RepoAgent

RepoAgent is an LLM-powered framework designed for repository-level code documentation generation. It automates the process of detecting changes in Git repositories, analyzing code structure through AST, identifying inter-object relationships, replacing Markdown content, and executing multi-threaded operations. The tool aims to assist developers in understanding and maintaining codebases by providing comprehensive documentation, ultimately improving efficiency and saving time.

github

: 425

carla

CARLA is an open-source simulator for autonomous driving research. It provides open-source code, protocols, and digital assets (urban layouts, buildings, vehicles) for developing, training, and validating autonomous driving systems. CARLA supports flexible specification of sensor suites and environmental conditions.

github

: 12.2k

fuse-med-ml

FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.

github

: 138

zipnn

ZipNN is a lossless and near-lossless compression library optimized for numbers/tensors in the Foundation Models environment. It automatically prepares data for compression based on its type, allowing users to focus on core tasks without worrying about compression complexities. The library delivers effective compression techniques for different data types and structures, achieving high compression ratios and rates. ZipNN supports various compression methods like ZSTD, lz4, and snappy, and provides ready-made scripts for file compression/decompression. Users can also manually import the package to compress and decompress data. The library offers advanced configuration options for customization and validation tests for different input and compression types.

github

: 217

premsql

PremSQL is an open-source library designed to help developers create secure, fully local Text-to-SQL solutions using small language models. It provides essential tools for building and deploying end-to-end Text-to-SQL pipelines with customizable components, ideal for secure, autonomous AI-powered data analysis. The library offers features like Local-First approach, Customizable Datasets, Robust Executors and Evaluators, Advanced Generators, Error Handling and Self-Correction, Fine-Tuning Support, and End-to-End Pipelines. Users can fine-tune models, generate SQL queries from natural language inputs, handle errors, and evaluate model performance against predefined metrics. PremSQL is extendible for customization and private data usage.

github

: 90

ChatDev

ChatDev is a virtual software company powered by intelligent agents like CEO, CPO, CTO, programmer, reviewer, tester, and art designer. These agents collaborate to revolutionize the digital world through programming. The platform offers an easy-to-use, highly customizable, and extendable framework based on large language models, ideal for studying collective intelligence. ChatDev introduces innovative methods like Iterative Experience Refinement and Experiential Co-Learning to enhance software development efficiency. It supports features like incremental development, Docker integration, Git mode, and Human-Agent-Interaction mode. Users can customize ChatChain, Phase, and Role settings, and share their software creations easily. The project is open-source under the Apache 2.0 License and utilizes data licensed under CC BY-NC 4.0.

github

: 25.1k

deepeval

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

github

: 5.6k

Controllable-RAG-Agent

This repository contains a sophisticated deterministic graph-based solution for answering complex questions using a controllable autonomous agent. The solution is designed to ensure that answers are solely based on the provided data, avoiding hallucinations. It involves various steps such as PDF loading, text preprocessing, summarization, database creation, encoding, and utilizing large language models. The algorithm follows a detailed workflow involving planning, retrieval, answering, replanning, content distillation, and performance evaluation. Heuristics and techniques implemented focus on content encoding, anonymizing questions, task breakdown, content distillation, chain of thought answering, verification, and model performance evaluation.

github

: 951

XLearning

XLearning is a scheduling platform for big data and artificial intelligence, supporting various machine learning and deep learning frameworks. It runs on Hadoop Yarn and integrates frameworks like TensorFlow, MXNet, Caffe, Theano, PyTorch, Keras, XGBoost. XLearning offers scalability, compatibility, multiple deep learning framework support, unified data management based on HDFS, visualization display, and compatibility with code at native frameworks. It provides functions for data input/output strategies, container management, TensorBoard service, and resource usage metrics display. XLearning requires JDK >= 1.7 and Maven >= 3.3 for compilation, and deployment on CentOS 7.2 with Java >= 1.7 and Hadoop 2.6, 2.7, 2.8.

github

: 1.7k

hold

This repository contains the code for HOLD, a method that jointly reconstructs hands and objects from monocular videos without assuming a pre-scanned object template. It can reconstruct 3D geometries of novel objects and hands, enabling template-free bimanual hand-object reconstruction, textureless object interaction with hands, and multiple objects interaction with hands. The repository provides instructions to download in-the-wild videos from HOLD, preprocess and train on custom videos, a volumetric rendering framework, a generalized codebase for single and two hand interaction with objects, a viewer to interact with predictions, and code to evaluate and compare with HOLD in HO3D. The repository also includes documentation for setup, training, evaluation, visualization, preprocessing custom sequences, and using HOLD on ARCTIC.

github

: 255

pgai

pgai simplifies the process of building search and Retrieval Augmented Generation (RAG) AI applications with PostgreSQL. It brings embedding and generation AI models closer to the database, allowing users to create embeddings, retrieve LLM chat completions, reason over data for classification, summarization, and data enrichment directly from within PostgreSQL in a SQL query. The tool requires an OpenAI API key and a PostgreSQL client to enable AI functionality in the database. Users can install pgai from source, run it in a pre-built Docker container, or enable it in a Timescale Cloud service. The tool provides functions to handle API keys using psql or Python, and offers various AI functionalities like tokenizing, detokenizing, embedding, chat completion, and content moderation.

github

: 4.5k

DemoGPT

DemoGPT is an all-in-one agent library that provides tools, prompts, frameworks, and LLM models for streamlined agent development. It leverages GPT-3.5-turbo to generate LangChain code, creating interactive Streamlit applications. The tool is designed for creating intelligent, interactive, and inclusive solutions in LLM-based application development. It offers model flexibility, iterative development, and a commitment to user engagement. Future enhancements include integrating Gorilla for autonomous API usage and adding a publicly available database for refining the generation process.

github

: 1.7k

bittensor

Bittensor is an internet-scale neural network that incentivizes computers to provide access to machine learning models in a decentralized and censorship-resistant manner. It operates through a token-based mechanism where miners host, train, and procure machine learning systems to fulfill verification problems defined by validators. The network rewards miners and validators for their contributions, ensuring continuous improvement in knowledge output. Bittensor allows anyone to participate, extract value, and govern the network without centralized control. It supports tasks such as generating text, audio, images, and extracting numerical representations.

github

: 987

TaskWeaver

TaskWeaver is a code-first agent framework designed for planning and executing data analytics tasks. It interprets user requests through code snippets, coordinates various plugins to execute tasks in a stateful manner, and preserves both chat history and code execution history. It supports rich data structures, customized algorithms, domain-specific knowledge incorporation, stateful execution, code verification, easy debugging, security considerations, and easy extension. TaskWeaver is easy to use with CLI and WebUI support, and it can be integrated as a library. It offers detailed documentation, demo examples, and citation guidelines.

github

: 5.4k

Endia

Endia is a dynamic Array library for Scientific Computing, offering automatic differentiation of arbitrary order, complex number support, dual API with PyTorch-like imperative or JAX-like functional interface, and JIT Compilation for speeding up training and inference. It can handle complex valued functions, perform both forward and reverse-mode automatic differentiation, and has a builtin JIT compiler. Endia aims to advance AI & Scientific Computing by pushing boundaries with clear algorithms, providing high-performance open-source code that remains readable and pythonic, and prioritizing clarity and educational value over exhaustive features.

github

: 190

For similar tasks

SheetCopilot

github

: 82

minio

MinIO is a High Performance Object Storage released under GNU Affero General Public License v3.0. It is API compatible with Amazon S3 cloud storage service. Use MinIO to build high performance infrastructure for machine learning, analytics and application data workloads.

github

: 46.0k

airbyte

Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.

github

: 17.6k

labelbox-python

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

github

: 134

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)

github

: 185

fasttrackml

FastTrackML is an experiment tracking server focused on speed and scalability, fully compatible with MLFlow. It provides a user-friendly interface to track and visualize your machine learning experiments, making it easy to compare different models and identify the best performing ones. FastTrackML is open source and can be easily installed and run with pip or Docker. It is also compatible with the MLFlow Python package, making it easy to integrate with your existing MLFlow workflows.

github

: 97

vertex-ai-samples

The Google Cloud Vertex AI sample repository contains notebooks and community content that demonstrate how to develop and manage ML workflows using Google Cloud Vertex AI.

github

: 264

argilla

Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency. It helps users improve AI output quality through data quality, take control of their data and models, and improve efficiency by quickly iterating on the right data and models. Argilla is an open-source community-driven project that provides tools for achieving and maintaining high-quality data standards, with a focus on NLP and LLMs. It is used by AI teams from companies like the Red Cross, Loris.ai, and Prolific to improve the quality and efficiency of AI projects.

github

: 4.2k

For similar jobs

SheetCopilot

github

: 82

LangGraph-Expense-Tracker

LangGraph Expense tracker is a small project that explores the possibilities of LangGraph. It allows users to send pictures of invoices, which are then structured and categorized into expenses and stored in a database. The project includes functionalities for invoice extraction, database setup, and API configuration. It consists of various modules for categorizing expenses, creating database tables, and running the API. The database schema includes tables for categories, payment methods, and expenses, each with specific columns to track transaction details. The API documentation is available for reference, and the project utilizes LangChain for processing expense data.

github

: 82

receipt-scanner

The receipt-scanner repository is an AI-Powered Receipt and Invoice Scanner for Laravel that allows users to easily extract structured receipt data from images, PDFs, and emails within their Laravel application using OpenAI. It provides a light wrapper around OpenAI Chat and Completion endpoints, supports various input formats, and integrates with Textract for OCR functionality. Users can install the package via composer, publish configuration files, and use it to extract data from plain text, PDFs, images, Word documents, and web content. The scanned receipt data is parsed into a DTO structure with main classes like Receipt, Merchant, and LineItem.

github

: 95

actual-ai

Actual AI is a project designed to categorize uncategorized transactions for Actual Budget using OpenAI or OpenAI specification compatible API. It sends requests to the OpenAI API to classify transactions based on their description, amount, and notes. Transactions that cannot be classified are marked as 'not guessed' in notes. The tool allows users to sync accounts before classification and classify transactions on a cron schedule. Guessed transactions are marked in notes for easy review.

github

: 139

gemini-android

Gemini-Android is a mobile application that allows users to track their expenses and manage their finances on the go. The app provides a user-friendly interface for adding and categorizing expenses, setting budgets, and generating reports to help users make informed financial decisions. With Gemini-Android, users can easily monitor their spending habits, identify areas for saving, and stay on top of their financial goals.

github

: 366

wealth-tracker

Wealth Tracker is a personal finance management tool designed to help users track their income, expenses, and investments in one place. With intuitive features and customizable categories, users can easily monitor their financial health and make informed decisions. The tool provides detailed reports and visualizations to analyze spending patterns and set financial goals. Whether you are budgeting, saving for a big purchase, or planning for retirement, Wealth Tracker offers a comprehensive solution to manage your money effectively.

github

: 376

vscode-dbt-power-user

The vscode-dbt-power-user is an open-source extension that enhances the functionality of Visual Studio Code to seamlessly work with dbt™. It provides features such as auto-complete for dbt™ code, previewing query results, column lineage visualization, generating dbt™ models, documentation generation, deferring model builds, running parent/child models and tests with a click, compiled query preview and explanation, project health check, SQL validation, BigQuery cost estimation, and other features like dbt™ logs viewer. The extension is fully compatible with dev containers, code spaces, and remote extensions, supporting dbt™ versions above 1.0.

github

: 489

wren-engine

Wren Engine is a semantic engine designed to serve as the backbone of the semantic layer for LLMs. It simplifies the user experience by translating complex data structures into a business-friendly format, enabling end-users to interact with data using familiar terminology. The engine powers the semantic layer with advanced capabilities to define and manage modeling definitions, metadata, schema, data relationships, and logic behind calculations and aggregations through an analytics-as-code design approach. By leveraging Wren Engine, organizations can ensure a developer-friendly semantic layer that reflects nuanced data relationships and dynamics, facilitating more informed decision-making and strategic insights.

github

: 189