LangGraph-Expense-Tracker

LangGraph - FastAPI - Postgresql - AI project

Stars: 82

Visit

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.

README:

LangGraph Expense tracker

In short

Small project exploring the possibilitys of LangGraph. It lets you sent pictures of invoices, it structures and categorize the expenses and puts them in a database.

Visual overview of the project

To zoom visit the whiteboard here.

Some invoice extraction in action

Project structure

.
├── LICENCE.txt
├── README.MD
├── config.yaml
├── data
│   └── walmart-bon.jpeg
├── documents
│   ├── api_documentation.png
│   ├── langgraph.png
│   ├── openapi.json
│   └── pg_admin_screenshot.png
├── env.example
├── requirements.txt
└── src
    ├── api
    │   ├── __init__.py
    │   ├── category_routes.py
    │   ├── expenses_routes.py
    │   ├── payment_methods_routes.py
    │   └── run_api.py
    ├── chain
    │   ├── __init__.py
    │   ├── graphstate.py
    │   ├── helpers
    │   │   ├── __init__.py
    │   │   └── get_payment_methods_and_categories.py
    │   └── nodes
    │       ├── __init__.py
    │       ├── categorizer.py
    │       ├── correct.py
    │       ├── db_entry.py
    │       ├── humancheck.py
    │       ├── imageencoder.py
    │       └── jsonparser.py
    └── database
        ├── __init__.py
        ├── create_categories_and_payment_methods.py
        ├── create_tables.py
        └── db_connection.py

9 directories, 30 files

Step by step

1. Create Project

1.1 Create virtual environment
Using Conda, venv or any other tool of your liking.

1.2 activate virtual environment

1.3 clone repo
!TO DO!

1.4 install requirements
!TO DO!

1.5 create .env file
See example here.

2. Set up the database

2.1 Prerequisites

2.1.1 Install postgresql:

brew install postgresql

(other ways to install Postgresql)

2.1.2 Install Docker:

brew install docker

(other ways to install Docker)

2.2 Make a Docker container

2.2.1 Create:

docker run -d \
  --name postgres-expenses \
  -e POSTGRES_USER=expenses \
  -e POSTGRES_PASSWORD=money$ \
  -e POSTGRES_DB=expenses \
  -p 6025:5432 \
  postgres:latest

2.2.2 Control:
Use the following command to see if the container is running correctly:

docker ps

it should show a list of running containers.

2.3 Configure database

2.3.1 Create tables
Add tables for our expense tracking by running the /src/database/create_tables.py script (link)

2.3.2 Inspect tables
Using a tool link PGAdmin, you can inspect if the tables in the database are all there.

3. set up API

-Go to the root folder of your project and activate virtual environment

CD path/to/your/projectfolder
workon expense-tracker

i have some shell aliases set up, the [workon] command should probably be something like [conda activate] or [source [env]]

-activate virtual environment

(expense_tracker)
~/Developer/expense_tracker
▶ uvicorn src.api.run_api:app --reload
INFO:     Will watch for changes in these directories: ['/Users/jw/developer/expense_tracker']
INFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)
INFO:     Started reloader process [12588] using StatReload
INFO:     Started server process [12590]
INFO:     Waiting for application startup.
INFO:     Application startup complete.

You can visit http://localhost:8000/docs#/ for a page with documentation about the API:

Database

The database consists of three main tables: categories, payment_methods, and expenses.

Table: categories

This table contains a list of categories for expenses. Each category has a unique ID and a name.

Columns:
- category_id (SERIAL, Primary Key): The unique ID for the category.
- category_name (VARCHAR(100), Unique): The name of the category.

Table: payment_methods

This table contains various payment methods that can be used for expenses.

Columns:
- payment_method_id (SERIAL, Primary Key): The unique ID for the payment method.
- payment_method_name (VARCHAR(50), Unique): The name of the payment method.

Table: expenses

This is the main table for tracking expenses. It contains information such as the date, the category (with a reference to the categories table), the payment method (with a reference to the payment_methods table), the amount, VAT, and other details.

Columns:
- transaction_id (SERIAL, Primary Key): The unique ID for the transaction.
- date (DATE): The date of the expense.
- category_id (INTEGER, Foreign Key): Reference to the categories table.
- description (TEXT): A short description of the expense.
- amount (DECIMAL(10, 2)): The amount of the expense.
- vat (DECIMAL(10, 2)): The VAT for the expense.
- payment_method_id (INTEGER, Foreign Key): Reference to the payment_methods table.
- business_personal (VARCHAR(50)): Indicates whether the expense is business or personal.
- declared_on (DATE): The date when the expense was declared.

API

See API documentation here: openapi.json

LangChain

the way this chain works is best described by showing the LangSmith Trace: click here to have a look

For Tasks:

Click tags to check more tools for each tasks

track expenses categorize invoices manage financial data generate expense reports automate expense tracking

For Jobs:

accountant financial analyst data analyst business administrator bookkeeper

Alternative AI tools for LangGraph-Expense-Tracker

Similar Open Source Tools

LangGraph-Expense-Tracker

github

: 82

RainbowGPT

RainbowGPT is a versatile tool that offers a range of functionalities, including Stock Analysis for financial decision-making, MySQL Management for database navigation, and integration of AI technologies like GPT-4 and ChatGlm3. It provides a user-friendly interface suitable for all skill levels, ensuring seamless information flow and continuous expansion of emerging technologies. The tool enhances adaptability, creativity, and insight, making it a valuable asset for various projects and tasks.

github

: 86

py-llm-core

PyLLMCore is a light-weighted interface with Large Language Models with native support for llama.cpp, OpenAI API, and Azure deployments. It offers a Pythonic API that is simple to use, with structures provided by the standard library dataclasses module. The high-level API includes the assistants module for easy swapping between models. PyLLMCore supports various models including those compatible with llama.cpp, OpenAI, and Azure APIs. It covers use cases such as parsing, summarizing, question answering, hallucinations reduction, context size management, and tokenizing. The tool allows users to interact with language models for tasks like parsing text, summarizing content, answering questions, reducing hallucinations, managing context size, and tokenizing text.

github

: 118

gpt-computer-assistant

GPT Computer Assistant (GCA) is an open-source framework designed to build vertical AI agents that can automate tasks on Windows, macOS, and Ubuntu systems. It leverages the Model Context Protocol (MCP) and its own modules to mimic human-like actions and achieve advanced capabilities. With GCA, users can empower themselves to accomplish more in less time by automating tasks like updating dependencies, analyzing databases, and configuring cloud security settings.

github

: 5.8k

openai-chat-api-workflow

**OpenAI Chat API Workflow for Alfred** An Alfred 5 Workflow for using OpenAI Chat API to interact with GPT-3.5/GPT-4 🤖💬 It also allows image generation 🖼️, image understanding 👀, speech-to-text conversion 🎤, and text-to-speech synthesis 🔈 **Features:** * Execute all features using Alfred UI, selected text, or a dedicated web UI * Web UI is constructed by the workflow and runs locally on your Mac 💻 * API call is made directly between the workflow and OpenAI, ensuring your chat messages are not shared online with anyone other than OpenAI 🔒 * OpenAI does not use the data from the API Platform for training 🚫 * Export chat data to a simple JSON format external file 📄 * Continue the chat by importing the exported data later 🔄

github

: 308

julep

Julep is an advanced platform for creating stateful and functional AI apps powered by large language models. It offers features like statefulness by design, automatic function calling, production-ready deployment, cron-like asynchronous functions, 90+ built-in tools, and the ability to switch between different LLMs easily. Users can build AI applications without the need to write code for embedding, saving, and retrieving conversation history, and can connect to third-party applications using Composio. Julep simplifies the process of getting started with AI apps, whether they are conversational, functional, or agentic.

github

: 4.4k

code2prompt

Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks. It bridges the gap between codebases and LLMs by converting projects into AI-friendly prompts, enabling users to leverage AI for various software development tasks. The tool offers features like holistic codebase representation, intelligent source tree generation, customizable prompt templates, smart token management, Gitignore integration, flexible file handling, clipboard-ready output, multiple output options, and enhanced code readability.

github

: 492

OpenAI-Api-Unreal

The OpenAIApi Plugin provides access to the OpenAI API in Unreal Engine, allowing users to generate images, transcribe speech, and power NPCs using advanced AI models. It offers blueprint nodes for making API calls, setting parameters, and accessing completion values. Users can authenticate using an API key directly or as an environment variable. The plugin supports various tasks such as generating images, transcribing speech, and interacting with NPCs through chat endpoints.

github

: 631

sec-parser

The `sec-parser` project simplifies extracting meaningful information from SEC EDGAR HTML documents by organizing them into semantic elements and a tree structure. It helps in parsing SEC filings for financial and regulatory analysis, analytics and data science, AI and machine learning, causal AI, and large language models. The tool is especially beneficial for AI, ML, and LLM applications by streamlining data pre-processing and feature extraction.

github

: 99

LLM-as-HH

LLM-as-HH is a codebase that accompanies the paper ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution. It introduces Language Hyper-Heuristics (LHHs) that leverage LLMs for heuristic generation with minimal manual intervention and open-ended heuristic spaces. Reflective Evolution (ReEvo) is presented as a searching framework that emulates the reflective design approach of human experts while surpassing human capabilities with scalable LLM inference, Internet-scale domain knowledge, and powerful evolutionary search. The tool can improve various algorithms on problems like Traveling Salesman Problem, Capacitated Vehicle Routing Problem, Orienteering Problem, Multiple Knapsack Problems, Bin Packing Problem, and Decap Placement Problem in both black-box and white-box settings.

github

: 78

web-ui

WebUI is a user-friendly tool built on Gradio that enhances website accessibility for AI agents. It supports various Large Language Models (LLMs) and allows custom browser integration for seamless interaction. The tool eliminates the need for re-login and authentication challenges, offering high-definition screen recording capabilities.

github

: 2.9k

trustgraph

TrustGraph is a tool that deploys private GraphRAG pipelines to build a RDF style knowledge graph from data, enabling accurate and secure `RAG` requests compatible with cloud LLMs and open-source SLMs. It showcases the reliability and efficiencies of GraphRAG algorithms, capturing contextual language flags missed in conventional RAG approaches. The tool offers features like PDF decoding, text chunking, inference of various LMs, RDF-aligned Knowledge Graph extraction, and more. TrustGraph is designed to be modular, supporting multiple Language Models and environments, with a plug'n'play architecture for easy customization.

github

: 219

preswald

Preswald is a full-stack platform for building, deploying, and managing interactive data applications in Python. It simplifies the process by combining ingestion, storage, transformation, and visualization into one lightweight SDK. With Preswald, users can connect to various data sources, customize app themes, and easily deploy apps locally. The platform focuses on code-first simplicity, end-to-end coverage, and efficiency by design, making it suitable for prototyping internal tools or deploying production-grade apps with reduced complexity and cost.

github

: 518

videodb-python

VideoDB Python SDK allows you to interact with the VideoDB serverless database. Manage videos as intelligent data, not files. It's scalable, cost-efficient & optimized for AI applications and LLM integration. The SDK provides functionalities for uploading videos, viewing videos, streaming specific sections of videos, searching inside a video, searching inside multiple videos in a collection, adding subtitles to a video, generating thumbnails, and more. It also offers features like indexing videos by spoken words, semantic indexing, and future indexing options for scenes, faces, and specific domains like sports. The SDK aims to simplify video management and enhance AI applications with video data.

github

: 63

xFasterTransformer

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

github

: 247

extension-gen-ai

The Looker GenAI Extension provides code examples and resources for building a Looker Extension that integrates with Vertex AI Large Language Models (LLMs). Users can leverage the power of LLMs to enhance data exploration and analysis within Looker. The extension offers generative explore functionality to ask natural language questions about data and generative insights on dashboards to analyze data by asking questions. It leverages components like BQML Remote Models, BQML Remote UDF with Vertex AI, and Custom Fine Tune Model for different integration options. Deployment involves setting up infrastructure with Terraform and deploying the Looker Extension by creating a Looker project, copying extension files, configuring BigQuery connection, connecting to Git, and testing the extension. Users can save example prompts and configure user settings for the extension. Development of the Looker Extension environment includes installing dependencies, starting the development server, and building for production.

github

: 59

For similar tasks

LangGraph-Expense-Tracker

github

: 82

travel-planner-ai

Travel Planner AI is a Software as a Service (SaaS) product that simplifies travel planning by generating comprehensive itineraries based on user preferences. It leverages cutting-edge technologies to provide tailored schedules, optimal timing suggestions, food recommendations, prime experiences, expense tracking, and collaboration features. The tool aims to be the ultimate travel companion for users looking to plan seamless and smart travel adventures.

github

: 73

For similar jobs

SheetCopilot

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.

github

: 82

LangGraph-Expense-Tracker

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

: 91

lollms-webui

LoLLMs WebUI (Lord of Large Language Multimodal Systems: One tool to rule them all) is a user-friendly interface to access and utilize various LLM (Large Language Models) and other AI models for a wide range of tasks. With over 500 AI expert conditionings across diverse domains and more than 2500 fine tuned models over multiple domains, LoLLMs WebUI provides an immediate resource for any problem, from car repair to coding assistance, legal matters, medical diagnosis, entertainment, and more. The easy-to-use UI with light and dark mode options, integration with GitHub repository, support for different personalities, and features like thumb up/down rating, copy, edit, and remove messages, local database storage, search, export, and delete multiple discussions, make LoLLMs WebUI a powerful and versatile tool.

github

: 4.4k

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.

github

: 136

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

mage-ai

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

github

: 7.8k