Open In Colab

Entity Metadata Extraction#

In this demo, we use the new EntityExtractor to extract entities from each node, stored in metadata. The default model is tomaarsen/span-marker-mbert-base-multinerd, which is downloaded an run locally from HuggingFace.

For more information on metadata extraction in LlamaIndex, see our documentation.

If you’re opening this Notebook on colab, you will probably need to install LlamaIndex 🦙.

!pip install llama-index

# Needed to run the entity extractor
# !pip install span_marker

import os
import openai

os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
openai.api_key = os.getenv("OPENAI_API_KEY")

Setup the Extractor and Parser#

from llama_index.extractors.metadata_extractors import EntityExtractor
from llama_index.node_parser import SentenceSplitter

entity_extractor = EntityExtractor(
    prediction_threshold=0.5,
    label_entities=False,  # include the entity label in the metadata (can be erroneous)
    device="cpu",  # set to "cuda" if you have a GPU
)

node_parser = SentenceSplitter()

transformations = [node_parser, entity_extractor]

/Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm
/Users/loganmarkewich/llama_index/llama-index/lib/python3.9/site-packages/bitsandbytes/cextension.py:34: UserWarning: The installed version of bitsandbytes was compiled without GPU support. 8-bit optimizers, 8-bit multiplication, and GPU quantization are unavailable.
  warn("The installed version of bitsandbytes was compiled without GPU support. "

'NoneType' object has no attribute 'cadam32bit_grad_fp32'

Load the data#

Here, we will download the 2023 IPPC Climate Report - Chapter 3 on Oceans and Coastal Ecosystems (172 Pages)

!curl https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_Chapter03.pdf --output IPCC_AR6_WGII_Chapter03.pdf

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100 20.7M  100 20.7M    0     0  22.1M      0 --:--:-- --:--:-- --:--:-- 22.1M

Next, load the documents.

from llama_index import SimpleDirectoryReader

documents = SimpleDirectoryReader(
    input_files=["./IPCC_AR6_WGII_Chapter03.pdf"]
).load_data()

Extracting Metadata#

Now, this is a pretty long document. Since we are not running on CPU, for now, we will only run on a subset of documents. Feel free to run it on all documents on your own though!

from llama_index.ingestion import IngestionPipeline

import random

random.seed(42)
# comment out to run on all documents
# 100 documents takes about 5 minutes on CPU
documents = random.sample(documents, 100)

pipeline = IngestionPipeline(transformations=transformations)

nodes = pipeline.run(documents=documents)

huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...
To disable this warning, you can either:
	- Avoid using `tokenizers` before the fork if possible
	- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)

Examine the outputs#

samples = random.sample(nodes, 5)
for node in samples:
    print(node.metadata)

{'page_label': '387', 'file_name': 'IPCC_AR6_WGII_Chapter03.pdf'}
{'page_label': '410', 'file_name': 'IPCC_AR6_WGII_Chapter03.pdf', 'entities': {'Parmesan', 'Boyd', 'Riebesell', 'Gattuso'}}
{'page_label': '391', 'file_name': 'IPCC_AR6_WGII_Chapter03.pdf', 'entities': {'Gulev', 'Fox-Kemper'}}
{'page_label': '430', 'file_name': 'IPCC_AR6_WGII_Chapter03.pdf', 'entities': {'Kessouri', 'van der Sleen', 'Brodeur', 'Siedlecki', 'Fiechter', 'Ramajo', 'Carozza'}}
{'page_label': '388', 'file_name': 'IPCC_AR6_WGII_Chapter03.pdf'}

Try a Query!#

from llama_index import ServiceContext, VectorStoreIndex
from llama_index.llms import OpenAI

service_context = ServiceContext.from_defaults(
    llm=OpenAI(model="gpt-3.5-turbo", temperature=0.2)
)

index = VectorStoreIndex(nodes, service_context=service_context)

query_engine = index.as_query_engine()
response = query_engine.query("What is said by Fox-Kemper?")
print(response)

According to the provided context information, Fox-Kemper is mentioned in relation to the observed and projected trends of ocean warming and marine heatwaves. It is stated that Fox-Kemper et al. (2021) reported that ocean warming has increased on average by 0.88°C from 1850-1900 to 2011-2020. Additionally, it is mentioned that Fox-Kemper et al. (2021) projected that ocean warming will continue throughout the 21st century, with the rate of global ocean warming becoming scenario-dependent from the mid-21st century. Fox-Kemper is also cited as a source for the information on the increasing frequency, intensity, and duration of marine heatwaves over the 20th and early 21st centuries, as well as the projected increase in frequency of marine heatwaves in the future.

Contrast without metadata#

Here, we re-construct the index, but without metadata

for node in nodes:
    node.metadata.pop("entities", None)

print(nodes[0].metadata)

{'page_label': '542', 'file_name': 'IPCC_AR6_WGII_Chapter03.pdf'}

from llama_index import ServiceContext, VectorStoreIndex
from llama_index.llms import OpenAI

service_context = ServiceContext.from_defaults(
    llm=OpenAI(model="gpt-3.5-turbo", temperature=0.2)
)

index = VectorStoreIndex(nodes, service_context=service_context)

query_engine = index.as_query_engine()
response = query_engine.query("What is said by Fox-Kemper?")
print(response)

According to the provided context information, Fox-Kemper is mentioned in relation to the decline of the AMOC (Atlantic Meridional Overturning Circulation) over the 21st century. The statement mentions that there is high confidence in the decline of the AMOC, but low confidence for quantitative projections.

As we can see, our metadata-enriched index is able to fetch more relevant information.