Integrate LLMs into spaCy NLP Pipeline

To leverage the power of LLM models in NLP workflows, need to integrate LLMs into the spaCy NLP pipeline. spaCy is a popular NLP library that offers robust tools for various language processing requirements.

Combining the strengths of spaCy with the flexibility of the LLM prompt using the ‘spacy-llm’ library helps to enhance NLP capabilities.

Here let’s explore steps to know how to integrate LLMs into spaCy NLP pipeline.

Steps to Integration of LLMs into spaCy NLP Pipeline

For the integration of LLMs into spaCy we use the spaCy-llm library. Our AI and ML software development experts provide a complete example of LLM provider OpenAI. (We can also use other LLMs compatible APIs to execute this example).

Step 1: Install Required Libraries
Ensure the following libraries are installed in your Python environment.

$ pip install spacy
$ pip install spacy-llm

Step 2: Import Necessary Modules
For this example, we need to import spacy and os modules to execute the complete process.

import spacy
import os

Step 3: Set Up Your LLM API Key
If you intend to use a cloud-based LLM, obtain an API key from the provider (e.g., OpenAI) and set the OPENAI_API_KEY environment variable:

os.environ["OPENAI_API_KEY"] = "your_api_key"  # Replace with your actual key

Step 4: Load a Blank spaCy Model (or Create a Custom One)
Create a blank spaCy nlp object to serve as the foundation for your custom pipeline:

try:
    nlp = spacy.blank("en")

Step 5: Create the NER Component and Add it to the Pipeline
Extend the nlp pipeline by adding the llm_ner component using nlp.add_pipe().

llm_ner = nlp.add_pipe("llm_ner")

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Simple Way to Integrate LLMs into spaCy NLP Pipeline