# Querent
The Asynchronous Data Dynamo and Graph Neural Network Catalyst
## Unlock Insights, Asynchronous Scaling, and Forge a Knowledge-Driven Future
🚀 **Async at its Core**: Querent thrives in an asynchronous world. With asynchronous processing, we handle multiple data sources seamlessly, eliminating bottlenecks for utmost efficiency.
💡 **Knowledge Graphs Made Easy**: Constructing intricate knowledge graphs is a breeze. Querent's robust architecture simplifies building comprehensive knowledge graphs, enabling you to uncover hidden data relationships.
🌐 **Scalability Redefined**: Scaling your data operations is effortless with Querent. We scale horizontally, empowering you to process multiple data streams without breaking a sweat.
🔬 **GNN Integration**: Querent seamlessly integrates with Graph Neural Networks (GNNs), enabling advanced data analysis, recommendation systems, and predictive modeling.
🔍 **Data-Driven Insights**: Dive deep into data-driven insights with Querent's tools. Extract actionable information and make data-informed decisions with ease.
🧠 **Leverage Language Models**: Utilize state-of-the-art language models (LLMs) for text data. Querent empowers natural language processing, tackling complex text-based tasks.
📈 **Efficient Memory Usage**: Querent is mindful of memory constraints. Our framework uses memory-efficient techniques, ensuring you can handle large datasets economically.
## Table of Contents
- [Querent](#querent)
- [Unlock Insights, Asynchronous Scaling, and Forge a Knowledge-Driven Future](#unlock-insights-asynchronous-scaling-and-forge-a-knowledge-driven-future)
- [Table of Contents](#table-of-contents)
- [Introduction](#introduction)
- [Features](#features)
- [Getting Started](#getting-started)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Usage](#usage)
- [Configuration](#configuration)
- [Querent: an asynchronous engine for LLMs](#querent-an-asynchronous-engine-for-llms)
- [Ease of Use](#ease-of-use)
- [Contributing](#contributing)
- [License](#license)
## Introduction
Querent is designed to simplify and optimize data collection and processing workflows. Whether you need to scrape web data, ingest files, preprocess text, or create complex knowledge graphs, Querent offers a flexible framework for building and scaling these processes.
## Features
- **Collectors:** Gather data from various sources asynchronously, including web scraping and file collection.
- **Ingestors:** Process collected data efficiently with custom transformations and filtering.
- **Processors:** Apply asynchronous data processing, including text preprocessing, cleaning, and feature extraction.
- **Engines:** Execute a suite of LLM engines to extract insights from data, leveraging parallel processing for enhanced efficiency.
- **Storage:** Store processed data in various storage systems, such as databases or cloud storage.
- **Workflow Management:** Efficiently manage and scale data workflows with task orchestration.
- **Scalability:** Querent is designed to scale horizontally, handling large volumes of data with ease.
## Getting Started
Let's get Querent up and running on your local machine.
### Prerequisites
- Python 3.9+
- Virtual environment (optional but recommended)
### Installation
1. Create a virtual environment (recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
```
2. Install latest Querent Workflow Orchestrator package:
```bash
pip install querent
```
3. Install the project dependencies:
```bash
python3 -m spacy download en_core_web_lg
```
4. Apt install the project dependencies:
```bash
sudo apt install tesseract-ocr
sudo apt install libtesseract-dev
sudo apt-get install ffmpeg
sudo apt install antiword
```
## Usage
Querent provides a flexible framework that adapts to your specific data collection and processing needs. Here's how to get started:
1. **Configuration:** Set up collector, ingestor, and processor configurations as needed.
2. **Collecting Data:** Implement collector classes to gather data from chosen sources. Handle errors and edge cases gracefully.
3. **Processing Data:** Create ingestors and processors to clean, transform, and filter collected data. Apply custom logic to meet your requirements.
4. **Storage:** Choose your storage system (e.g., databases) and configure connections. Store processed data efficiently.
5. **Task Orchestration:** For large tasks, implement a task orchestrator to manage and distribute the workload.
6. **Scaling:** To handle scalability, consider running multiple instances of collectors and ingestors in parallel.
7. **Monitoring:** Implement monitoring and logging to track task progress, detect errors, and ensure smooth operation.
8. **Documentation:** Maintain thorough project documentation to make it easy for others (and yourself) to understand and contribute.
## Configuration
Querent relies on configuration files to define how collectors, ingestors, and processors operate. These files are typically located in the `config` directory. Ensure that you configure the components according to your project's requirements.
## Querent: an asynchronous engine for LLMs
**Sequence Diagram:** *Asynchronous Data Processing in Querent*
```mermaid
sequenceDiagram
participant User
participant Collector
participant Ingestor
participant Processor
participant LLM
participant Querent
participant Storage
participant Callback
User->>Collector: Initiate Data Collection
Collector->>Ingestor: Collect Data
Ingestor->>Processor: Ingest Data
Processor->>LLM: Process Data (IngestedTokens)
LLM->>Processor: Processed Data (EventState)
Processor->>Storage: Store Processed Data (CollectedBytes)
Ingestor->>Querent: Send Ingested Data (IngestedTokens)
Querent->>Processor: Process Ingested Data (IngestedTokens)
Processor->>LLM: Process Data (IngestedTokens)
LLM->>Processor: Processed Data (EventState)
Callback->>Storage: Store Processed Data (EventState)
Querent->>Processor: Processed Data Available (EventState)
Processor->>Callback: Return Processed Data (EventState)
Callback->>User: Deliver Processed Data (CollectedBytes)
Note right of User: Asynchronous Flow
Note right of Collector: Data Collection
Note right of Ingestor: Data Ingestion
Note right of Processor: Data Processing
Note right of LLM: Language Model Processing
Note right of Querent: Query Execution
Note right of Storage: Data Storage
Note right of Callback: Callback Invocation
```
## Ease of Use
With Querent, creating scalable workflows with any LLM is just a few lines of code.
```python
import pytest
import uuid
from pathlib import Path
import asyncio
from querent.callback.event_callback_interface import EventCallbackInterface
from querent.common.types.ingested_tokens import IngestedTokens
from querent.common.types.ingested_code import IngestedCode
from querent.common.types.ingested_images import IngestedImages
from querent.common.types.ingested_messages import IngestedMessages
from querent.common.types.querent_event import EventState, EventType
from querent.common.types.querent_queue import QuerentQueue
from querent.core.base_engine import BaseEngine
from querent.querent.querent import Querent
from querent.querent.resource_manager import ResourceManager
from querent.collectors.collector_resolver import CollectorResolver
from querent.common.uri import Uri
from querent.config.collector.collector_config import FSCollectorConfig
from querent.ingestors.ingestor_manager import IngestorFactoryManager
# Create input and output queues
input_queue = QuerentQueue()
resource_manager = ResourceManager()
# Define a simple mock LLM engine for testing
class MockLLMEngine(BaseEngine):
def __init__(self, input_queue: QuerentQueue):
super().__init__(input_queue)
async def process_tokens(self, data: IngestedTokens):
if data is None or data.is_error():
# the LLM developer can raise an error here or do something else
# the developers of Querent can customize the behavior of Querent
# to handle the error in a way that is appropriate for the use case
self.set_termination_event()
return
# Set the state of the LLM
# At any given point during the execution of the LLM, the LLM developer
# can set the state of the LLM using the set_state method
# The state of the LLM is stored in the state attribute of the LLM
# The state of the LLM is published to subscribers of the LLM
current_state = EventState(EventType.Graph, 1.0, "anything", "dummy.txt")
await self.set_state(new_state=current_state)
async def process_code(self, data: IngestedCode):
pass
async def process_messages(self, data: IngestedMessages):
return super().process_messages(data)
async def process_images(self, data: IngestedImages):
return super().process_images(data)
def validate(self):
return True
@pytest.mark.asyncio
async def test_example_workflow_with_querent():
# Initialize some collectors to collect the data
directory_path = "path/to/your/data/directory"
collectors = [
CollectorResolver().resolve(
Uri("file://" + str(Path(directory_path).resolve())),
FSCollectorConfig(root_path=directory_path, id=str(uuid.uuid4())),
)
]
# Connect to the collector
for collector in collectors:
await collector.connect()
# Set up the result queue
result_queue = asyncio.Queue()
# Create the IngestorFactoryManager
ingestor_factory_manager = IngestorFactoryManager(
collectors=collectors, result_queue=result_queue
)
# Start the ingest_all_async in a separate task
ingest_task = asyncio.create_task(ingestor_factory_manager.ingest_all_async())
### A Typical Use Case ###
# Create an engine to harness the LLM
llm_mocker = MockLLMEngine(input_queue)
# Define a callback function to subscribe to state changes
class StateChangeCallback(EventCallbackInterface):
async def handle_event(self, event_type: EventType, event_state: EventState):
print(f"New state: {event_state}")
print(f"New state type: {event_type}")
assert event_state.event_type == EventType.Graph
# Subscribe to state change events
# This pattern is ideal as we can expose multiple events for each use case of the LLM
llm_mocker.subscribe(EventType.Graph, StateChangeCallback())
## one can also subscribe to other events, e.g. EventType.CHAT_COMPLETION ...
# Create a Querent instance with a single MockLLM
# here we see the simplicity of the Querent
# massive complexity is hidden in the Querent,
# while being highly configurable, extensible, and scalable
# async architecture helps to scale to multiple querenters
# How async architecture works:
# 1. Querent starts a worker task for each querenter
# 2. Querenter starts a worker task for each worker
# 3. Each worker task runs in a loop, waiting for input data
# 4. When input data is received, the worker task processes the data
# 5. The worker task notifies subscribers of state changes
# 6. The worker task repeats steps 3-5 until termination
querent = Querent(
[llm_mocker],
resource_manager=resource_manager,
)
# Start the querent
querent_task = asyncio.create_task(querent.start())
await asyncio.gather(ingest_task, querent_task)
if __name__ == "__main__":
asyncio.run(test_example_workflow_with_querent())
```
## Contributing
Contributions to Querent are welcome! Please follow our [contribution guidelines](CONTRIBUTING.md) to get started.
## License
This project is licensed under the BSL-1.1 License - see the [LICENSE](LICENCE) file for details.
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"description": "# Querent\n\nThe Asynchronous Data Dynamo and Graph Neural Network Catalyst\n\n\n\n\n\n## Unlock Insights, Asynchronous Scaling, and Forge a Knowledge-Driven Future\n\n\ud83d\ude80 **Async at its Core**: Querent thrives in an asynchronous world. With asynchronous processing, we handle multiple data sources seamlessly, eliminating bottlenecks for utmost efficiency.\n\n\ud83d\udca1 **Knowledge Graphs Made Easy**: Constructing intricate knowledge graphs is a breeze. Querent's robust architecture simplifies building comprehensive knowledge graphs, enabling you to uncover hidden data relationships.\n\n\ud83c\udf10 **Scalability Redefined**: Scaling your data operations is effortless with Querent. We scale horizontally, empowering you to process multiple data streams without breaking a sweat.\n\n\ud83d\udd2c **GNN Integration**: Querent seamlessly integrates with Graph Neural Networks (GNNs), enabling advanced data analysis, recommendation systems, and predictive modeling.\n\n\ud83d\udd0d **Data-Driven Insights**: Dive deep into data-driven insights with Querent's tools. Extract actionable information and make data-informed decisions with ease.\n\n\ud83e\udde0 **Leverage Language Models**: Utilize state-of-the-art language models (LLMs) for text data. Querent empowers natural language processing, tackling complex text-based tasks.\n\n\ud83d\udcc8 **Efficient Memory Usage**: Querent is mindful of memory constraints. Our framework uses memory-efficient techniques, ensuring you can handle large datasets economically.\n\n## Table of Contents\n\n- [Querent](#querent)\n - [Unlock Insights, Asynchronous Scaling, and Forge a Knowledge-Driven Future](#unlock-insights-asynchronous-scaling-and-forge-a-knowledge-driven-future)\n - [Table of Contents](#table-of-contents)\n - [Introduction](#introduction)\n - [Features](#features)\n - [Getting Started](#getting-started)\n - [Prerequisites](#prerequisites)\n - [Installation](#installation)\n - [Usage](#usage)\n - [Configuration](#configuration)\n - [Querent: an asynchronous engine for LLMs](#querent-an-asynchronous-engine-for-llms)\n - [Ease of Use](#ease-of-use)\n - [Contributing](#contributing)\n - [License](#license)\n\n## Introduction\n\nQuerent is designed to simplify and optimize data collection and processing workflows. Whether you need to scrape web data, ingest files, preprocess text, or create complex knowledge graphs, Querent offers a flexible framework for building and scaling these processes.\n\n## Features\n\n- **Collectors:** Gather data from various sources asynchronously, including web scraping and file collection.\n\n- **Ingestors:** Process collected data efficiently with custom transformations and filtering.\n\n- **Processors:** Apply asynchronous data processing, including text preprocessing, cleaning, and feature extraction.\n\n- **Engines:** Execute a suite of LLM engines to extract insights from data, leveraging parallel processing for enhanced efficiency.\n\n- **Storage:** Store processed data in various storage systems, such as databases or cloud storage.\n\n- **Workflow Management:** Efficiently manage and scale data workflows with task orchestration.\n\n- **Scalability:** Querent is designed to scale horizontally, handling large volumes of data with ease.\n\n## Getting Started\n\nLet's get Querent up and running on your local machine.\n\n### Prerequisites\n\n- Python 3.9+\n- Virtual environment (optional but recommended)\n\n### Installation\n\n1. Create a virtual environment (recommended):\n\n ```bash\n python -m venv venv\n source venv/bin/activate # On Windows, use `venv\\Scripts\\activate`\n ```\n2. Install latest Querent Workflow Orchestrator package:\n\n ```bash\n pip install querent\n ```\n \n3. Install the project dependencies:\n\n ```bash\n python3 -m spacy download en_core_web_lg\n ```\n\n4. Apt install the project dependencies:\n ```bash\n sudo apt install tesseract-ocr\n sudo apt install libtesseract-dev\n sudo apt-get install ffmpeg\n sudo apt install antiword\n ```\n\n## Usage\n\nQuerent provides a flexible framework that adapts to your specific data collection and processing needs. Here's how to get started:\n\n1. **Configuration:** Set up collector, ingestor, and processor configurations as needed.\n\n2. **Collecting Data:** Implement collector classes to gather data from chosen sources. Handle errors and edge cases gracefully.\n\n3. **Processing Data:** Create ingestors and processors to clean, transform, and filter collected data. Apply custom logic to meet your requirements.\n\n4. **Storage:** Choose your storage system (e.g., databases) and configure connections. Store processed data efficiently.\n\n5. **Task Orchestration:** For large tasks, implement a task orchestrator to manage and distribute the workload.\n\n6. **Scaling:** To handle scalability, consider running multiple instances of collectors and ingestors in parallel.\n\n7. **Monitoring:** Implement monitoring and logging to track task progress, detect errors, and ensure smooth operation.\n\n8. **Documentation:** Maintain thorough project documentation to make it easy for others (and yourself) to understand and contribute.\n\n## Configuration\n\nQuerent relies on configuration files to define how collectors, ingestors, and processors operate. These files are typically located in the `config` directory. Ensure that you configure the components according to your project's requirements.\n\n## Querent: an asynchronous engine for LLMs\n\n**Sequence Diagram:** *Asynchronous Data Processing in Querent*\n\n```mermaid\nsequenceDiagram\n participant User\n participant Collector\n participant Ingestor\n participant Processor\n participant LLM\n participant Querent\n participant Storage\n participant Callback\n\n User->>Collector: Initiate Data Collection\n Collector->>Ingestor: Collect Data\n Ingestor->>Processor: Ingest Data\n Processor->>LLM: Process Data (IngestedTokens)\n LLM->>Processor: Processed Data (EventState)\n Processor->>Storage: Store Processed Data (CollectedBytes)\n Ingestor->>Querent: Send Ingested Data (IngestedTokens)\n Querent->>Processor: Process Ingested Data (IngestedTokens)\n Processor->>LLM: Process Data (IngestedTokens)\n LLM->>Processor: Processed Data (EventState)\n Callback->>Storage: Store Processed Data (EventState)\n Querent->>Processor: Processed Data Available (EventState)\n Processor->>Callback: Return Processed Data (EventState)\n Callback->>User: Deliver Processed Data (CollectedBytes)\n\n Note right of User: Asynchronous Flow\n Note right of Collector: Data Collection\n Note right of Ingestor: Data Ingestion\n Note right of Processor: Data Processing\n Note right of LLM: Language Model Processing\n Note right of Querent: Query Execution\n Note right of Storage: Data Storage\n Note right of Callback: Callback Invocation\n\n```\n\n## Ease of Use\n\nWith Querent, creating scalable workflows with any LLM is just a few lines of code.\n\n```python\nimport pytest\nimport uuid\nfrom pathlib import Path\nimport asyncio\n\nfrom querent.callback.event_callback_interface import EventCallbackInterface\nfrom querent.common.types.ingested_tokens import IngestedTokens\nfrom querent.common.types.ingested_code import IngestedCode\nfrom querent.common.types.ingested_images import IngestedImages\nfrom querent.common.types.ingested_messages import IngestedMessages\nfrom querent.common.types.querent_event import EventState, EventType\nfrom querent.common.types.querent_queue import QuerentQueue\nfrom querent.core.base_engine import BaseEngine\nfrom querent.querent.querent import Querent\nfrom querent.querent.resource_manager import ResourceManager\nfrom querent.collectors.collector_resolver import CollectorResolver\nfrom querent.common.uri import Uri\nfrom querent.config.collector.collector_config import FSCollectorConfig\nfrom querent.ingestors.ingestor_manager import IngestorFactoryManager\n\n# Create input and output queues\ninput_queue = QuerentQueue()\nresource_manager = ResourceManager()\n\n\n# Define a simple mock LLM engine for testing\nclass MockLLMEngine(BaseEngine):\n def __init__(self, input_queue: QuerentQueue):\n super().__init__(input_queue)\n\n async def process_tokens(self, data: IngestedTokens):\n if data is None or data.is_error():\n # the LLM developer can raise an error here or do something else\n # the developers of Querent can customize the behavior of Querent\n # to handle the error in a way that is appropriate for the use case\n self.set_termination_event()\n return\n # Set the state of the LLM\n # At any given point during the execution of the LLM, the LLM developer\n # can set the state of the LLM using the set_state method\n # The state of the LLM is stored in the state attribute of the LLM\n # The state of the LLM is published to subscribers of the LLM\n current_state = EventState(EventType.Graph, 1.0, \"anything\", \"dummy.txt\")\n await self.set_state(new_state=current_state)\n\n async def process_code(self, data: IngestedCode):\n pass\n\n async def process_messages(self, data: IngestedMessages):\n return super().process_messages(data)\n\n async def process_images(self, data: IngestedImages):\n return super().process_images(data)\n\n def validate(self):\n return True\n\n\n@pytest.mark.asyncio\nasync def test_example_workflow_with_querent():\n # Initialize some collectors to collect the data\n directory_path = \"path/to/your/data/directory\"\n collectors = [\n CollectorResolver().resolve(\n Uri(\"file://\" + str(Path(directory_path).resolve())),\n FSCollectorConfig(root_path=directory_path, id=str(uuid.uuid4())),\n )\n ]\n\n # Connect to the collector\n for collector in collectors:\n await collector.connect()\n\n # Set up the result queue\n result_queue = asyncio.Queue()\n\n # Create the IngestorFactoryManager\n ingestor_factory_manager = IngestorFactoryManager(\n collectors=collectors, result_queue=result_queue\n )\n\n # Start the ingest_all_async in a separate task\n ingest_task = asyncio.create_task(ingestor_factory_manager.ingest_all_async())\n\n ### A Typical Use Case ###\n # Create an engine to harness the LLM\n llm_mocker = MockLLMEngine(input_queue)\n\n # Define a callback function to subscribe to state changes\n class StateChangeCallback(EventCallbackInterface):\n async def handle_event(self, event_type: EventType, event_state: EventState):\n print(f\"New state: {event_state}\")\n print(f\"New state type: {event_type}\")\n assert event_state.event_type == EventType.Graph\n\n # Subscribe to state change events\n # This pattern is ideal as we can expose multiple events for each use case of the LLM\n llm_mocker.subscribe(EventType.Graph, StateChangeCallback())\n\n ## one can also subscribe to other events, e.g. EventType.CHAT_COMPLETION ...\n\n # Create a Querent instance with a single MockLLM\n # here we see the simplicity of the Querent\n # massive complexity is hidden in the Querent,\n # while being highly configurable, extensible, and scalable\n # async architecture helps to scale to multiple querenters\n # How async architecture works:\n # 1. Querent starts a worker task for each querenter\n # 2. Querenter starts a worker task for each worker\n # 3. Each worker task runs in a loop, waiting for input data\n # 4. When input data is received, the worker task processes the data\n # 5. The worker task notifies subscribers of state changes\n # 6. The worker task repeats steps 3-5 until termination\n querent = Querent(\n [llm_mocker],\n resource_manager=resource_manager,\n )\n # Start the querent\n querent_task = asyncio.create_task(querent.start())\n await asyncio.gather(ingest_task, querent_task)\n\n\nif __name__ == \"__main__\":\n asyncio.run(test_example_workflow_with_querent())\n\n\n```\n\n## Contributing\n\nContributions to Querent are welcome! Please follow our [contribution guidelines](CONTRIBUTING.md) to get started.\n\n## License\n\nThis project is licensed under the BSL-1.1 License - see the [LICENSE](LICENCE) file for details.\n",
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