extractor = GLiNERLinkExtractor(
    labels=["Person", "Award", "Date", "Competitions", "Teams"]
)
results = extractor.extract_one("some long text...")

.. _GLiNER: https://github.com/urchade/GLiNER

.. seealso::

    - :mod:`How to use a graph vector store <langchain_community.graph_vectorstores>`
    - :class:`How to create links between documents <langchain_community.graph_vectorstores.links.Link>`

How to link Documents on common named entities

Preliminaries

Install the gliner package:

.. code-block:: bash

pip install -q langchain_community gliner

Usage

We load the state_of_the_union.txt file, chunk it, then for each chunk we extract named entity links and add them to the chunk.

Using extract_one() ^^^^^^^^^^^^^^^^^^^

We can use :meth:extract_one on a document to get the links and add the links to the document metadata with :meth:~langchain_community.graph_vectorstores.links.add_links::

from langchain_community.document_loaders import TextLoader
from langchain_community.graph_vectorstores import CassandraGraphVectorStore
from langchain_community.graph_vectorstores.extractors import GLiNERLinkExtractor
from langchain_community.graph_vectorstores.links import add_links
from langchain_text_splitters import CharacterTextSplitter

loader = TextLoader("state_of_the_union.txt")
raw_documents = loader.load()

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)

ner_extractor = GLiNERLinkExtractor(["Person", "Topic"])
for document in documents:
    links = ner_extractor.extract_one(document)
    add_links(document, links)

print(documents[0].metadata)

.. code-block:: output

{'source': 'state_of_the_union.txt', 'links': [Link(kind='entity:Person', direction='bidir', tag='President Zelenskyy'), Link(kind='entity:Person', direction='bidir', tag='Vladimir Putin')]}

Using LinkExtractorTransformer ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

Using the :class:~langchain_community.graph_vectorstores.extractors.link_extractor_transformer.LinkExtractorTransformer, we can simplify the link extraction::

from langchain_community.document_loaders import TextLoader
from langchain_community.graph_vectorstores.extractors import (
    GLiNERLinkExtractor,
    LinkExtractorTransformer,
)
from langchain_text_splitters import CharacterTextSplitter

loader = TextLoader("state_of_the_union.txt")
raw_documents = loader.load()

text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
documents = text_splitter.split_documents(raw_documents)

ner_extractor = GLiNERLinkExtractor(["Person", "Topic"])
transformer = LinkExtractorTransformer([ner_extractor])
documents = transformer.transform_documents(documents)

print(documents[0].metadata)

.. code-block:: output

{'source': 'state_of_the_union.txt', 'links': [Link(kind='entity:Person', direction='bidir', tag='President Zelenskyy'), Link(kind='entity:Person', direction='bidir', tag='Vladimir Putin')]}

The documents with named entity links can then be added to a :class:~langchain_community.graph_vectorstores.base.GraphVectorStore::

from langchain_community.graph_vectorstores import CassandraGraphVectorStore

store = CassandraGraphVectorStore.from_documents(documents=documents, embedding=...)