From Dataset Labeling to Deployment: The Power of NLP and LLMs Combined.
Project description
PromptedGraphs
From Dataset Labeling to Deployment: The Power of NLP and LLMs Combined.
Description
PromptedGraphs is a Python library that aims to seamlessly integrate traditional NLP methods with the capabilities of modern Large Language Models (LLMs) in the realm of knowledge graphs. Our library offers tools tailored for dataset labeling, model training, and smooth deployment to production environments. We leverage the strengths of spacy for core NLP tasks, snorkel for effective data labeling, and async
to ensure enhanced performance. Our mission is to provide a harmonized solution to knowledge graph development when you have to merge traditional and LLM-driven approaches, squarely addressing the challenges associated with accuracy, efficiency, and affordability.
✨ Features
- Named Entity Recognition (NER): Customize ER labels based on your domain.
- Structured Data Extraction: Extract structured data from unstructured text.
- Entity Resolution: Deduplication and normalization
- Relationship Extraction: Either open ended labels or constrain to your domain
- Entity Linking: Link references in text to entities in a graph
- Graph Construction: Create or update knowledge graphs
Core Functions
- Dataset Labeling: Efficient tools for labeling datasets, powered by
haystack
. - Model Training: Combine the reliability of NLP and the prowess of LLMs.
- Deployment: Streamlined processes to ensure smooth transition to production.
Requirements
- Python 3.10 or newer.
📦 Installation
To install PromptedGraphs
via pip:
pip install promptedgraphs
# or
poetry add promptedgraphs
Usage
Entity Recognition
from examples/er_reviews.ipynb
from spacy import displacy
from promptedgraphs.config import Config
from promptedgraphs.entity_recognition import extract_entities
labels = {
"POSITIVE": "A postive review of a product or service.",
"NEGATIVE": "A negative review of a product or service.",
"NEUTRAL": "A neutral review of a product or service.",
}
text_of_reviews = """
1. "I absolutely love this product. It's been a game changer!"
2. "The service was quite poor and the staff was rude."
3. "The item is okay. Nothing special, but it gets the job done."
""".strip()
# Label Sentiment
ents = []
async for msg in extract_entities(
name="sentiment",
description="Sentiment Analysis of Customer Reviews",
text=text_of_reviews,
labels=labels,
config=Config(), # Reads `OPENAI_API_KEY` from .env file or environment
):
ents.append(msg)
# Show Results using spacy.displacy
displacy.render(
{
"text": text_of_reviews,
"ents": [e.to_dict() for e in ents],
},
style="ent",
jupyter=True,
manual=True,
options={
"colors": {"POSITIVE": "#7aecec", "NEGATIVE": "#f44336", "NEUTRAL": "#f4f442"}
},
)
Structured Data Extraction
from examples/de_chatintents.ipynb
from pydantic import BaseModel, Field
from promptedgraphs.config import Config
from promptedgraphs.data_extraction import extract_data
class UserIntent(BaseModel):
"""The UserIntent entity, representing the canonical description of what a user desires to achieve in a given conversation."""
intent_name: str = Field(
title="Intent Name",
description="Canonical name of the user's intent",
examples=[
"question",
"command",
"clarification",
"chit_chat",
"greeting",
"feedback",
"nonsensical",
"closing",
"harrassment",
"unknown",
],
)
description: str | None = Field(
title="Intent Description",
description="A detailed explanation of the user's intent",
)
msg = """It's a busy day, I need to send an email and to buy groceries"""
async for intent in extract_data(
text=msg, output_type=list[UserIntent], config=Config()
):
print(intent)
intent_name='task' description='User wants to complete a task'
intent_name='communication' description='User wants to send an email'
intent_name='shopping' description='User wants to buy groceries'
📚 Resources
Related Libraries
Contributing
We welcome contributions! Please DM me @seankruzel or create issues or pull requests.
📝 License
This project is licensed under the terms of the MIT license.
Built using quantready using template https://github.com/closedloop-technologies/quantready-api
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