Sparrow

Data processing with ML and LLM

The Principle

Sparrow is an innovative open-source solution for efficient data extraction and processing from various documents and images. It seamlessly handles forms, invoices, receipts, and other unstructured data sources. Sparrow stands out with its modular architecture, offering independent services and pipelines all optimized for robust performance. One of the critical functionalities of Sparrow - pluggable architecture. You can easily integrate and run data extraction pipelines using tools and frameworks like LlamaIndex, Haystack, or Unstructured. Sparrow enables local LLM data extraction pipelines through Ollama or Apple MLX. With Sparrow solution you get API, which helps to process and transform your data into structured output, ready to be integrated with custom workflows.

Sparrow Agents - with Sparrow you can build independent LLM agents, and use API to invoke them from your system.

List of available agents:

llamaindex - RAG pipeline with LlamaIndex for PDF processing
vllamaindex - RAG pipeline with LLamaIndex multimodal for image processing
vprocessor - RAG pipeline with OCR and LlamaIndex for image processing
haystack - RAG pipeline with Haystack for PDF processing
fcall - Function call pipeline
unstructured-light - RAG pipeline with Unstructured and LangChain

Services

sparrow-data-ocr - OCR service, providing optical character recognition as part of the Sparrow.
sparrow-ml-llm - LLM RAG pipeline, Sparrow service for data extraction and document processing.
sparrow-ui - Dashboard UI for LLM RAG pipeline.

Sparrow implementation with Donut ML model - sparrow-donut

Installation

LLM

Install Weaviate local DB with Docker:

docker compose up -d

Sparrow setup

Setup Python Environment (Sparrow is tested with Python 3.10.4) with pyenv:

Install pyenv:

If you haven't already installed pyenv, you can do so using Homebrew with the following command:

brew update
brew install pyenv

Install the desired Python version:

With pyenv installed, you can now install a specific version of Python. For example, to install Python 3.10.4, you would use:

pyenv install 3.10.4

You can check available Python versions by running pyenv install --list.

Set the global Python version:

Once the installation is complete, you can set the desired Python version as the default (global) version on your system:

pyenv global 3.10.4

This command sets Python 3.10.4 as the default version for all shells.

Verify the change:

To ensure the change was successful, you can verify the current Python version by running:

python --version

If the output doesn’t reflect the change, you may need to restart your terminal or add pyenv to your shell's initialization script as follows:

Configure your shell's initialization script:

Add pyenv to your shell by adding the following lines to your ~/.bash_profile, ~/.zprofile, ~/.bashrc, or ~/.zshrc file:

export PATH="$HOME/.pyenv/bin:$PATH"
eval "$(pyenv init --path)"
eval "$(pyenv init -)"

After adding these lines, restart your terminal or source your profile script with source ~/.bash_profile (or the appropriate file for your shell).

Create Virtual Environments to Run Sparrow Agents

Create virtual environments in sparrow-ml/llm folder:

python -m venv .env_llamaindex
python -m venv .env_haystack
python -m venv .env_instructor
python -m venv .env_unstructured

.env_llamaindex is used for LLM RAG with llamaindex, vllamaindex and vprocessor agents, .env_haystack is used for LLM RAG with haystack agent, and .env_instructor is used for LLM function calling with fcall agent. .env_unstructured is used for unstructured-light agent.

Create virtual environment in sparrow-data/ocr folder:

python -m venv .env_ocr

Activate Virtual Environments and Install Dependencies

Activate each environment and install its dependencies using the corresponding requirements.txt file.

For llamaindex environment:

Activate the environment:

source .env_llamaindex/bin/activate

Install dependencies:

pip install -r requirements_llamaindex.txt

Repeat the same for haystack, instructor and unstructured environments.

Run Sparrow

You can run Sparrow on CLI or through API. To run on CLI, use sparrow.sh script. Run it from corresponding virtual environment, depending which agent you want to execute.

Install Ollama and pull LLM model specified in config.yml

OCR

Follow the install steps outlined here:

Sparrow OCR services install steps

Usage

Copy text PDF files to the data folder or use sample data provided in the data folder.

Ingest

This step is required for llamaindex or haystack agents only.

Run the script, to convert text to vector embeddings and save in Weaviate. By default it will use llamaindex agent. Example with llamaindex agent:

./sparrow.sh ingest --file-path /data/invoice_1.pdf --agent llamaindex --index-name Sparrow_llamaindex_doc1

Example with haystack agent:

./sparrow.sh ingest --file-path /data/invoice_1.pdf --agent haystack --index-name Sparrow_haystack_doc1

Inference

Run the script, to process data with LLM RAG and return the answer. By default, it will use llamaindex agent. You can specify other agents (see ingest example), such as haystack:

./sparrow.sh "invoice_number, invoice_date, client_name, client_address, client_tax_id, seller_name, seller_address,
seller_tax_id, iban, names_of_invoice_items, gross_worth_of_invoice_items, total_gross_worth" "int, str, str, str, str,
str, str, str, str, List[str], List[float], str" --agent llamaindex --index-name Sparrow_llamaindex_doc1

Answer:

{
    "invoice_number": 61356291,
    "invoice_date": "09/06/2012",
    "client_name": "Rodriguez-Stevens",
    "client_address": "2280 Angela Plain, Hortonshire, MS 93248",
    "client_tax_id": "939-98-8477",
    "seller_name": "Chapman, Kim and Green",
    "seller_address": "64731 James Branch, Smithmouth, NC 26872",
    "seller_tax_id": "949-84-9105",
    "iban": "GB50ACIE59715038217063",
    "names_of_invoice_items": [
        "Wine Glasses Goblets Pair Clear Glass",
        "With Hooks Stemware Storage Multiple Uses Iron Wine Rack Hanging Glass",
        "Replacement Corkscrew Parts Spiral Worm Wine Opener Bottle Houdini",
        "HOME ESSENTIALS GRADIENT STEMLESS WINE GLASSES SET OF 4 20 FL OZ (591 ml) NEW"
    ],
    "gross_worth_of_invoice_items": [
        66.0,
        123.55,
        8.25,
        14.29
    ],
    "total_gross_worth": "$212,09"
}

Example with haystack agent:

./sparrow.sh "invoice_number, invoice_date, client_name, client_address, client_tax_id, seller_name, seller_address,
seller_tax_id, iban, names_of_invoice_items, gross_worth_of_invoice_items, total_gross_worth" "int, str, str, str, str,
str, str, str, str, List[str], List[float], str" --agent haystack --index-name Sparrow_haystack_doc1

To run multimodal agent, use vllamaindex flag:

./sparrow.sh "guest_no, cashier_name" "int, str" --agent vllamaindex --file-path /data/inout-20211211_001.jpg

Use vprocessor agent to run OCR + LLM, this works best to process scanned docs

./sparrow.sh "guest_no, cashier_name, transaction_number, names_of_receipt_items, authorized_amount, receipt_date" "int, str, int, List[str], str, str" --agent vprocessor --file-path /data/inout-20211211_001.jpg

LLM function call example:

./sparrow.sh assistant --agent "fcall" --query "Exxon"

Answer:

{
  "company": "Exxon",
  "ticker": "XOM"
}

The stock price of the Exxon is 111.2699966430664. USD

Use unstructured-light agent to run RAG pipeline with Unstructured library. It helps to improve data pre-processing for LLM

./sparrow.sh "invoice_number, invoice_date, total_gross_worth" "int, str, str" --agent unstructured-light --file-path
/Users/andrejb/infra/shared/katana-git/sparrow/sparrow-ml/llm/data/invoice_1.pdf

With unstructured-light it is possible to specify option for table data processing only

./sparrow.sh "names_of_invoice_items, gross_worth_of_invoice_items, total_gross_worth" "List[str], List[str], str"
--agent unstructured-light --file-path /Users/andrejb/infra/shared/katana-git/sparrow/sparrow-ml/llm/data/invoice_1.pdf
--options tables

FastAPI Endpoint for Local LLM RAG

Sparrow enables you to run a local LLM RAG as an API using FastAPI, providing a convenient and efficient way to interact with our services. You can pass the name of the plugin to be used for the inference. By default, llamaindex agent is used.

To set this up:

Start the Endpoint

Launch the endpoint by executing the following command in your terminal:

python api.py

If you want to run agents from different Python virtual environments simultaneously, you can specify port, to avoid conflicts:

python api.py --port 8001

Access the Endpoint Documentation

You can view detailed documentation for the API by navigating to:

http://127.0.0.1:8000/api/v1/sparrow-llm/docs

For visual reference, a screenshot of the FastAPI endpoint

API calls:

Ingest

Ingest call with llamaindex agent:

curl -X 'POST' \
  'http://127.0.0.1:8000/api/v1/sparrow-llm/ingest' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'agent=llamaindex' \
  -F 'index_name=Sparrow_llamaindex_doc2' \
  -F 'file=@invoice_1.pdf;type=application/pdf'

Ingest call with haystack agent:

curl -X 'POST' \
  'http://127.0.0.1:8000/api/v1/sparrow-llm/ingest' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'agent=haystack' \
  -F 'index_name=Sparrow_haystack_doc2' \
  -F 'file=@invoice_1.pdf;type=application/pdf'

Inference

Inference call with llamaindex agent:

curl -X 'POST' \
  'http://127.0.0.1:8000/api/v1/sparrow-llm/inference' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'fields=invoice_number' \
  -F 'types=int' \
  -F 'agent=llamaindex' \
  -F 'index_name=Sparrow_llamaindex_doc2' \
  -F 'file='

Inference call with haystack agent:

curl -X 'POST' \
  'http://127.0.0.1:8000/api/v1/sparrow-llm/inference' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'fields=invoice_number' \
  -F 'types=int' \
  -F 'agent=haystack' \
  -F 'index_name=Sparrow_haystack_doc2' \
  -F 'file='

Inference call with multimodal agent vllamaindex:

curl -X 'POST' \
  'http://127.0.0.1:8000/api/v1/sparrow-llm/inference' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'fields=guest_no, cashier_name' \
  -F 'types=int, str' \
  -F 'agent=vllamaindex' \
  -F 'index_name=' \
  -F 'file=@inout-20211211_001.jpg;type=image/jpeg'

Inference call with OCR + LLM agent vprocessor:

curl -X 'POST' \
  'http://127.0.0.1:8000/api/v1/sparrow-llm/inference' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'fields=guest_no, cashier_name, transaction_number, names_of_receipt_items, authorized_amount, receipt_date' \
  -F 'types=int, str, int, List[str], str, str' \
  -F 'agent=vprocessor' \
  -F 'index_name=' \
  -F 'file=@inout-20211211_001.jpg;type=image/jpeg'

Inference call with unstructured-light agent:

curl -X 'POST' \
  'http://127.0.0.1:8000/api/v1/sparrow-llm/inference' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'fields=invoice_number, invoice_date, total_gross_worth' \
  -F 'types=int, str, str' \
  -F 'agent=unstructured-light' \
  -F 'index_name=' \
  -F 'options=' \
  -F 'file=@invoice_1.pdf;type=application/pdf'

Inference call with unstructured-light agent, using only tables option:

curl -X 'POST' \
  'http://127.0.0.1:8000/api/v1/sparrow-llm/inference' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'fields=names_of_invoice_items, gross_worth_of_invoice_items, total_gross_worth' \
  -F 'types=List[str], List[str], str' \
  -F 'agent=unstructured-light' \
  -F 'index_name=' \
  -F 'options=tables' \
  -F 'file=@invoice_1.pdf;type=application/pdf'

Examples

Inference with local LLM RAG

Document:

Request:

./sparrow.sh "invoice_number, invoice_date, client_name, client_address, client_tax_id, seller_name, seller_address,
seller_tax_id, iban, names_of_invoice_items, gross_worth_of_invoice_items, total_gross_worth" "int, str, str, str, str,
str, str, str, str, List[str], List[float], str" --agent llamaindex --index-name Sparrow_llamaindex_doc1

Response:

{
    "invoice_number": 61356291,
    "invoice_date": "09/06/2012",
    "client_name": "Rodriguez-Stevens",
    "client_address": "2280 Angela Plain, Hortonshire, MS 93248",
    "client_tax_id": "939-98-8477",
    "seller_name": "Chapman, Kim and Green",
    "seller_address": "64731 James Branch, Smithmouth, NC 26872",
    "seller_tax_id": "949-84-9105",
    "iban": "GB50ACIE59715038217063",
    "names_of_invoice_items": [
        "Wine Glasses Goblets Pair Clear Glass",
        "With Hooks Stemware Storage Multiple Uses Iron Wine Rack Hanging Glass",
        "Replacement Corkscrew Parts Spiral Worm Wine Opener Bottle Houdini",
        "HOME ESSENTIALS GRADIENT STEMLESS WINE GLASSES SET OF 4 20 FL OZ (591 ml) NEW"
    ],
    "gross_worth_of_invoice_items": [
        66.0,
        123.55,
        8.25,
        14.29
    ],
    "total_gross_worth": "$212,09"
}

Inference with OCR + local LLM RAG

Document:

Request:

./sparrow.sh "store_name, receipt_id, receipt_item_names, receipt_item_prices, receipt_date, receipt_store_id,
receipt_sold, receipt_returned, receipt_total" "str, str, List[str], List[str], str, int, int,
int, str" --agent vprocessor --file-path /data/ross-20211211_010.jpg

Response:

{
    "store_name": "Ross",
    "receipt_id": "Receipt # 0421-01-1602-1330-0",
    "receipt_item_names": [
        "400226513665 x hanes b1ue 4pk",
        "400239602790 fruit premium 4pk"
    ],
    "receipt_item_prices": [
        "$9.99R",
        "$12.99R"
    ],
    "receipt_date": "11/26/21 10:35:05 AM",
    "receipt_store_id": 421,
    "receipt_sold": 2,
    "receipt_returned": 0,
    "receipt_total": "$25.33"
}

Commercial usage

Sparrow is available under the GPL 3.0 license, promoting freedom to use, modify, and distribute the software while ensuring any modifications remain open source under the same license. This aligns with our commitment to supporting the open-source community and fostering collaboration.

Additionally, we recognize the diverse needs of organizations, including small to medium-sized enterprises (SMEs). Therefore, Sparrow is also offered for free commercial use to organizations with gross revenue below $5 million USD in the past 12 months, enabling them to leverage Sparrow without the financial burden often associated with high-quality software solutions.

For businesses that exceed this revenue threshold or require usage terms not accommodated by the GPL 3.0 license—such as integrating Sparrow into proprietary software without the obligation to disclose source code modifications—we offer dual licensing options. Dual licensing allows Sparrow to be used under a separate proprietary license, offering greater flexibility for commercial applications and proprietary integrations. This model supports both the project's sustainability and the business's needs for confidentiality and customization.

If your organization is seeking to utilize Sparrow under a proprietary license, or if you are interested in custom workflows, consulting services, or dedicated support and maintenance options, please contact us at abaranovskis@redsamuraiconsulting.com. We're here to provide tailored solutions that meet your unique requirements, ensuring you can maximize the benefits of Sparrow for your projects and workflows.

Author

Katana ML, Andrej Baranovskij

leiyuch/sparrow

Sparrow

The Principle

Services

Installation

LLM

OCR

Usage

Ingest

Inference

FastAPI Endpoint for Local LLM RAG

Ingest

Inference

Examples

Inference with local LLM RAG

Inference with OCR + local LLM RAG

Commercial usage

Author

License