text-extract-api

text-extract-api

Document (PDF, Word, PPTX ...) extraction and parse API using state of the art modern OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured JSON or Markdown

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The text-extract-api is a powerful tool that allows users to convert images, PDFs, or Office documents to Markdown text or JSON structured documents with high accuracy. It is built using FastAPI and utilizes Celery for asynchronous task processing, with Redis for caching OCR results. The tool provides features such as PDF/Office to Markdown and JSON conversion, improving OCR results with LLama, removing Personally Identifiable Information from documents, distributed queue processing, caching using Redis, switchable storage strategies, and a CLI tool for task management. Users can run the tool locally or on cloud services, with support for GPU processing. The tool also offers an online demo for testing purposes.

README:

text-extract-api

Convert any image, PDF or Office document to Markdown text or JSON structured document with super-high accuracy, including tabular data, numbers or math formulas.

The API is built with FastAPI and uses Celery for asynchronous task processing. Redis is used for caching OCR results.

hero doc extract

Features:

  • No Cloud/external dependencies all you need: PyTorch based OCR (EasyOCR) + Ollama are shipped and configured via docker-compose no data is sent outside your dev/server environment,
  • PDF/Office to Markdown conversion with very high accuracy using different OCR strategies including llama3.2-vision, easyOCR
  • PDF/Office to JSON conversion using Ollama supported models (eg. LLama 3.1)
  • LLM Improving OCR results LLama is pretty good with fixing spelling and text issues in the OCR text
  • Removing PII This tool can be used for removing Personally Identifiable Information out of document - see examples
  • Distributed queue processing using Celery)
  • Caching using Redis - the OCR results can be easily cached prior to LLM processing,
  • Storage Strategies switchable storage strategies (Google Drive, Local File System ...)
  • CLI tool for sending tasks and processing results

Screenshots

Converting MRI report to Markdown + JSON.

python client/cli.py ocr_upload --file examples/example-mri.pdf --prompt_file examples/example-mri-2-json-prompt.txt

Before running the example see getting started

Converting MRI report to Markdown

Converting Invoice to JSON and remove PII

python client/cli.py ocr_upload --file examples/example-invoice.pdf --prompt_file examples/example-invoice-remove-pii.txt

Before running the example see getting started

Converting Invoice to JSON

Getting started

You might want to run the app directly on your machine for development purposes OR to use for example Apple GPUs (which are not supported by Docker at the moment).

Prerequisites

To have it up and running please execute the following steps:

Download and install Ollama Download and install Docker

Setting Up Ollama on a Remote Host

To connect to an external Ollama instance, set the environment variable: OLLAMA_HOST=http://address:port, e.g.:

OLLAMA_HOST=http(s)://127.0.0.1:5000

If you want to disable the local Ollama model, use env DISABLE_LOCAL_OLLAMA=1, e.g.

DISABLE_LOCAL_OLLAMA=1 make install

Note: When local Ollama is disabled, ensure the required model is downloaded on the external instance.

Currently, the DISABLE_LOCAL_OLLAMA variable cannot be used to disable Ollama in Docker. As a workaround, remove the ollama service from docker-compose.yml or docker-compose.gpu.yml.

Support for using the variable in Docker environments will be added in a future release.

Clone the Repository

First, clone the repository and change current directory to it:

git clone https://github.com/CatchTheTornado/text-extract-api.git
cd text-extract-api

Setup with Makefile

Be default application create virtual python env: .venv. You can disable this functionality on local setup by adding DISABLE_VENV=1 before running script:

DISABLE_VENV=1 make install 
DISABLE_VENV=1 make run

Manual setup

Configure environment variables:

cp .env.localhost.example .env.localhost

You might want to just use the defaults - should be fine. After ENV variables are set, just execute:

python3 -m venv .venv
source .venv/bin/activate
pip install -e .
chmod +x run.sh
run.sh

This command will install all the dependencies - including Redis (via Docker, so it is not entirely docker free method of running text-extract-api anyways :)

(MAC) - Dependencies

brew update && brew install libmagic poppler pkg-config ghostscript ffmpeg automake autoconf

(Mac) - You need to startup the celery worker

source .venv/bin/activate && celery -A text_extract_api.celery_app worker --loglevel=info --pool=solo

Then you're good to go with running some CLI commands like:

python client/cli.py ocr_upload --file examples/example-mri.pdf --ocr_cache --prompt_file=examples/example-mri-remove-pii.txt

Scaling the parallell processing

To have multiple tasks running at once - for concurrent processing please run the following command to start single worker process:

celery -A text_extract_api.tasks worker --loglevel=info --pool=solo & # to scale by concurrent processing please run this line as many times as many concurrent processess you want to have running

Online demo

To try out the application with our hosted version you can skip the Getting started and try out the CLI tool against our cloud:

Open in the browser: demo.doctractor.com

... or run n the terminal:

python3 -m venv .venv
source .venv/bin/activate
pip install -e .
export OCR_UPLOAD_URL=https://doctractor:[email protected]/ocr/upload
export RESULT_URL=https://doctractor:[email protected]/ocr/result/

python client/cli.py ocr_upload --file examples/example-mri.pdf --ocr_cache --prompt_file=examples/example-mri-remove-pii.txt

Demo Source code

Note: In the free demo we don't guarantee any processing times. The API is Open so please do not send any secret documents neither any documents containing personal information, If you do - you're doing it on your own risk and responsiblity.

Demo screenshot

Join us on Discord

In case of any questions, help requests or just feedback - please join us on Discord!

Getting started with Docker

Prerequisites

  • Docker
  • Docker Compose

Clone the Repository

git clone https://github.com/CatchTheTornado/text-extract-api.git
cd text-extract-api

Using Makefile

You can use the make install and make run command to setup the Docker environment for text-extract-api. You can find the manual steps required to do so described below.

Manual setup

Create .env file in the root directory and set the necessary environment variables. You can use the .env.example file as a template:

# defaults for docker instances
cp .env.example .env

or

# defaults for local run
cp .env.example.localhost .env

Then modify the variables inside the file:

#APP_ENV=production # sets the app into prod mode, otherwise dev mode with auto-reload on code changes
REDIS_CACHE_URL=redis://localhost:6379/1
STORAGE_PROFILE_PATH=./storage_profiles
LLAMA_VISION_PROMPT="You are OCR. Convert image to markdown."

# CLI settings
OCR_URL=http://localhost:8000/ocr/upload
OCR_UPLOAD_URL=http://localhost:8000/ocr/upload
OCR_REQUEST_URL=http://localhost:8000/ocr/request
RESULT_URL=http://localhost:8000/ocr/result/
CLEAR_CACHE_URL=http://localhost:8000/ocr/clear_cach
LLM_PULL_API_URL=http://localhost:8000/llm_pull
LLM_GENEREATE_API_URL=http://localhost:8000/llm_generate

CELERY_BROKER_URL=redis://localhost:6379/0
CELERY_RESULT_BACKEND=redis://localhost:6379/0
OLLAMA_HOST=http://localhost:11434
APP_ENV=development  # Default to development mode

Note: In order to properly save the output files you might need to modify storage_profiles/default.yaml to change the default storage path according to the volumes path defined in the docker-compose.yml

Build and Run the Docker Containers

Build and run the Docker containers using Docker Compose:

docker-compose up --build

... for GPU support run:

docker-compose -f docker-compose.gpu.yml -p text-extract-api-gpu up --build

Note: While on Mac - Docker does not support Apple GPUs. In this case you might want to run the application natively without the Docker Compose please check how to run it natively with GPU support

This will start the following services:

  • FastAPI App: Runs the FastAPI application.
  • Celery Worker: Processes asynchronous OCR tasks.
  • Redis: Caches OCR results.
  • Ollama: Runs the Ollama model.

Cloud - paid edition

If the on-prem is too much hassle ask us about the hosted/cloud edition of text-extract-api, we can setup it you, billed just for the usage.

CLI tool

Note: While on Mac, you may need to create a virtual Python environment first:

python3 -m venv .venv
source .venv/bin/activate
# now you've got access to `python` and `pip` within your virutal env.
pip install -e . # install main project requirements

The project includes a CLI for interacting with the API. To make it work first run:

cd client
pip install -e .

Pull the LLama3.1 and LLama3.2-vision models

You might want to test out different models supported by LLama

python client/cli.py llm_pull --model llama3.1
python client/cli.py llm_pull --model llama3.2-vision

These models are required for most features supported by text-extract-api.

Upload a File for OCR (converting to Markdown)

python client/cli.py ocr_upload --file examples/example-mri.pdf --ocr_cache

or alternatively

python client/cli.py ocr_request --file examples/example-mri.pdf --ocr_cache

The difference is just that the first call uses ocr/upload - multipart form data upload, and the second one is a request to ocr/request sending the file via base64 encoded JSON property - probable a better suit for smaller files.

Upload a File for OCR (processing by LLM)

Important note: To use LLM you must first run the llm_pull to get the specific model required by your requests.

For example you must run:

python client/cli.py llm_pull --model llama3.1
python client/cli.py llm_pull --model llama3.2-vision

and only after to run this specific prompt query:

python client/cli.py ocr_upload --file examples/example-mri.pdf --ocr_cache --prompt_file=examples/example-mri-remove-pii.txt --language en

Note: The language argument is used for the OCR strategy to load the model weights for the selected language. You can specify multiple languages as a list: en,de,pl etc.

The ocr command can store the results using the storage_profiles:

  • storage_profile: Used to save the result - the default profile (./storage_profiles/default.yaml) is used by default; if empty file is not saved
  • storage_filename: Outputting filename - relative path of the root_path set in the storage profile - by default a relative path to /storage folder; can use placeholders for dynamic formatting: {file_name}, {file_extension}, {Y}, {mm}, {dd} - for date formatting, {HH}, {MM}, {SS} - for time formatting

Upload a File for OCR (processing by LLM), store result on disk

python client/cli.py ocr_upload --file examples/example-mri.pdf --ocr_cache --prompt_file=examples/example-mri-remove-pii.txt  --storage_filename "invoices/{Y}/{file_name}-{Y}-{mm}-{dd}.md"

Get OCR Result by Task ID

python client/cli.py result --task_id {your_task_id_from_upload_step}

List file results archived by storage_profile 

python client/cli.py list_files

to use specific (in this case google drive) storage profile run:

python client/cli.py list_files  --storage_profile gdrive

Load file result archived by storage_profile 

python client/cli.py load_file --file_name "invoices/2024/example-invoice-2024-10-31-16-33.md"

Delete file result archived by storage_profile 

python client/cli.py delete_file --file_name "invoices/2024/example-invoice-2024-10-31-16-33.md" --storage_profile gdrive

or for default profile (local file system):

python client/cli.py delete_file --file_name "invoices/2024/example-invoice-2024-10-31-16-33.md"

Clear OCR Cache

python client/cli.py clear_cache

Test LLama

python llm_generate --prompt "Your prompt here"

API Clients

You might want to use the decdicated API clients to use text-extract-api

Typescript

There's a dedicated API client for Typescript - text-extract-api-client and the npm package by the same name:

npm install text-extract-api-client

Usage:

import { ApiClient, OcrRequest } from 'text-extract-api-client';
const apiClient = new ApiClient('https://api.doctractor.com/', 'doctractor', 'Aekie2ao');
const formData = new FormData();
formData.append('file', fileInput.files[0]);
formData.append('prompt', 'Convert file to JSON and return only JSON'); // if not provided, no LLM transformation will gonna happen - just the OCR
formData.append('strategy', 'llama_vision');
formData.append('model', 'llama3.1')
formData.append('ocr_cache', 'true');

apiClient.uploadFile(formData).then(response => {
    console.log(response);
});

Endpoints

OCR Endpoint via File Upload / multiform data

  • URL: /ocr/upload
  • Method: POST
  • Parameters:
    • file: PDF, image or Office file to be processed.
    • strategy: OCR strategy to use (llama_vision or easyocr).
    • ocr_cache: Whether to cache the OCR result (true or false).
    • prompt: When provided, will be used for Ollama processing the OCR result
    • model: When provided along with the prompt - this model will be used for LLM processing
    • storage_profile: Used to save the result - the default profile (./storage_profiles/default.yaml) is used by default; if empty file is not saved
    • storage_filename: Outputting filename - relative path of the root_path set in the storage profile - by default a relative path to /storage folder; can use placeholders for dynamic formatting: {file_name}, {file_extension}, {Y}, {mm}, {dd} - for date formatting, {HH}, {MM}, {SS} - for time formatting
    • language: One or many (en or en,pl,de) language codes for the OCR to load the language weights

Example:

curl -X POST -H "Content-Type: multipart/form-data" -F "file=@examples/example-mri.pdf" -F "strategy=easyocr" -F "ocr_cache=true" -F "prompt=" -F "model=" "http://localhost:8000/ocr/upload"

OCR Endpoint via JSON request

  • URL: /ocr/request
  • Method: POST
  • Parameters (JSON body):
    • file: Base64 encoded PDF file content.
    • strategy: OCR strategy to use (llama_vision or easyocr).
    • ocr_cache: Whether to cache the OCR result (true or false).
    • prompt: When provided, will be used for Ollama processing the OCR result.
    • model: When provided along with the prompt - this model will be used for LLM processing.
    • storage_profile: Used to save the result - the default profile (/storage_profiles/default.yaml) is used by default; if empty file is not saved.
    • storage_filename: Outputting filename - relative path of the root_path set in the storage profile - by default a relative path to /storage folder; can use placeholders for dynamic formatting: {file_name}, {file_extension}, {Y}, {mm}, {dd} - for date formatting, {HH}, {MM}, {SS} - for time formatting.
    • language: One or many (en or en,pl,de) language codes for the OCR to load the language weights

Example:

curl -X POST "http://localhost:8000/ocr/request" -H "Content-Type: application/json" -d '{
  "file": "<base64-encoded-file-content>",
  "strategy": "easyocr",
  "ocr_cache": true,
  "prompt": "",
  "model": "llama3.1",
  "storage_profile": "default",
  "storage_filename": "example.pdf"
}'

OCR Result Endpoint

  • URL: /ocr/result/{task_id}
  • Method: GET
  • Parameters:
    • task_id: Task ID returned by the OCR endpoint.

Example:

curl -X GET "http://localhost:8000/ocr/result/{task_id}"

Clear OCR Cache Endpoint

  • URL: /ocr/clear_cache
  • Method: POST

Example:

curl -X POST "http://localhost:8000/ocr/clear_cache"

Ollama Pull Endpoint

  • URL: /llm/pull
  • Method: POST
  • Parameters:
    • model: Pull the model you are to use first

Example:

curl -X POST "http://localhost:8000/llm/pull" -H "Content-Type: application/json" -d '{"model": "llama3.1"}'

Ollama Endpoint

  • URL: /llm/generate
  • Method: POST
  • Parameters:
    • prompt: Prompt for the Ollama model.
    • model: Model you like to query

Example:

curl -X POST "http://localhost:8000/llm/generate" -H "Content-Type: application/json" -d '{"prompt": "Your prompt here", "model":"llama3.1"}'

List storage files:

  • URL: /storage/list
  • Method: GET
  • Parameters:
    • storage_profile: Name of the storage profile to use for listing files (default: default).

Download storage file:

  • URL: /storage/load
  • Method: GET
  • Parameters:
    • file_name: File name to load from the storage
    • storage_profile: Name of the storage profile to use for listing files (default: default).

Delete storage file:

  • URL: /storage/delete
  • Method: DELETE
  • Parameters:
    • file_name: File name to load from the storage
    • storage_profile: Name of the storage profile to use for listing files (default: default).

Storage profiles

The tool can automatically save the results using different storage strategies and storage profiles. Storage profiles are set in the /storage_profiles by a yaml configuration files.

Local File System

strategy: local_filesystem
settings:
  root_path: /storage # The root path where the files will be stored - mount a proper folder in the docker file to match it
  subfolder_names_format: "" # eg: by_months/{Y}-{mm}/
  create_subfolders: true

Google Drive

strategy: google_drive
settings:
## how to enable GDrive API: https://developers.google.com/drive/api/quickstart/python?hl=pl

  service_account_file: /storage/client_secret_269403342997-290pbjjlb06nbof78sjaj7qrqeakp3t0.apps.googleusercontent.com.json
  folder_id:

Where the service_account_file is a json file with authorization credentials. Please read on how to enable Google Drive API and prepare this authorization file here.

Note: Service Account is different account that the one you're using for Google workspace (files will not be visible in the UI)

Amazon S3 - Cloud Object Storage

strategy: aws_s3
settings:
  bucket_name: ${AWS_S3_BUCKET_NAME}
  region: ${AWS_REGION}
  access_key: ${AWS_ACCESS_KEY_ID}
  secret_access_key: ${AWS_SECRET_ACCESS_KEY}

Requirements for AWS S3 Access Key

  1. Access Key Ownership
    The access key must belong to an IAM user or role with permissions for S3 operations.

  2. IAM Policy Example
    The IAM policy attached to the user or role must allow the necessary actions. Below is an example of a policy granting access to an S3 bucket:

    {
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:PutObject",
                "s3:GetObject",
                "s3:ListBucket",
                "s3:DeleteObject"
            ],
            "Resource": [
                "arn:aws:s3:::your-bucket-name",
                "arn:aws:s3:::your-bucket-name/*"
            ]
        }
    ]
}

Next, populate the appropriate .env file (e.g., .env, .env.localhost) with the required AWS credentials:

AWS_ACCESS_KEY_ID=your-access-key-id
AWS_SECRET_ACCESS_KEY=your-secret-access-key
AWS_REGION=your-region
AWS_S3_BUCKET_NAME=your-bucket-name

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

In case of any questions please contact us at: [email protected]

For Tasks:

Click tags to check more tools for each tasks
convert documents to markdown extract json from documents remove pii from documents process ocr results manage document storage

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exif-photo-blog

EXIF Photo Blog is a full-stack photo blog application built with Next.js, Vercel, and Postgres. It features built-in authentication, photo upload with EXIF extraction, photo organization by tag, infinite scroll, light/dark mode, automatic OG image generation, a CMD-K menu with photo search, experimental support for AI-generated descriptions, and support for Fujifilm simulations. The application is easy to deploy to Vercel with just a few clicks and can be customized with a variety of environment variables.

github
: 992
SillyTavern Screenshot
SillyTavern

SillyTavern is a user interface you can install on your computer (and Android phones) that allows you to interact with text generation AIs and chat/roleplay with characters you or the community create. SillyTavern is a fork of TavernAI 1.2.8 which is under more active development and has added many major features. At this point, they can be thought of as completely independent programs.

github
: 13.2k
Twitter-Insight-LLM Screenshot
Twitter-Insight-LLM

This project enables you to fetch liked tweets from Twitter (using Selenium), save it to JSON and Excel files, and perform initial data analysis and image captions. This is part of the initial steps for a larger personal project involving Large Language Models (LLMs).

github
: 401
AISuperDomain Screenshot
AISuperDomain

Aila Desktop Application is a powerful tool that integrates multiple leading AI models into a single desktop application. It allows users to interact with various AI models simultaneously, providing diverse responses and insights to their inquiries. With its user-friendly interface and customizable features, Aila empowers users to engage with AI seamlessly and efficiently. Whether you're a researcher, student, or professional, Aila can enhance your AI interactions and streamline your workflow.

github
: 1.2k
ChatGPT-On-CS Screenshot
ChatGPT-On-CS

This project is an intelligent dialogue customer service tool based on a large model, which supports access to platforms such as WeChat, Qianniu, Bilibili, Douyin Enterprise, Douyin, Doudian, Weibo chat, Xiaohongshu professional account operation, Xiaohongshu, Zhihu, etc. You can choose GPT3.5/GPT4.0/ Lazy Treasure Box (more platforms will be supported in the future), which can process text, voice and pictures, and access external resources such as operating systems and the Internet through plug-ins, and support enterprise AI applications customized based on their own knowledge base.

github
: 768
obs-localvocal Screenshot
obs-localvocal

LocalVocal is a live-streaming AI assistant plugin for OBS that allows you to transcribe audio speech into text and perform various language processing functions on the text using AI / LLMs (Large Language Models). It's privacy-first, with all data staying on your machine, and requires no GPU, cloud costs, network, or downtime.

github
: 248