In the ever-evolving landscape of artificial intelligence, the ability to efficiently process and analyze vast amounts of textual data has become more crucial than ever. As we step into 2025, AI engineers face increasingly complex challenges in natural language processing (NLP) and machine learning tasks. One of the most powerful tools in our arsenal is the OpenAI API's batch embedding feature, which has undergone significant improvements since its inception. This comprehensive guide will walk you through the intricacies of batch embedding, providing you with the knowledge and skills to leverage this technology effectively in your AI projects.
The Evolution of Embedding Technology
Before we dive into the technical aspects, let's take a moment to appreciate how far embedding technology has come. In the early 2020s, we were working with models like GPT-3 and BERT. Now, in 2025, we have access to more advanced models that offer higher dimensionality, better semantic understanding, and improved efficiency.
The latest OpenAI models, as of 2025, can generate embeddings with up to 4096 dimensions, a significant leap from the 1536 dimensions available in previous years. This increase in dimensionality allows for more nuanced representations of text, capturing subtle semantic relationships that were previously overlooked.
Why Batch Embedding Matters More Than Ever
As AI systems become more sophisticated, the demand for high-quality embeddings has skyrocketed. Here's why batch embedding remains a critical skill for AI engineers in 2025:
- Exponential Data Growth: With the proliferation of IoT devices and digital services, the volume of text data generated daily has increased tenfold since 2020. Batch embedding allows us to process this data efficiently.
- Cost Optimization: Despite advancements in AI, computing resources remain a significant expense. Batch embedding offers substantial cost savings compared to individual API calls.
- Real-time Applications: Many AI applications now require near-real-time processing. Batch embedding enables us to prepare large datasets quickly, supporting responsive AI systems.
- Multi-modal AI: As AI increasingly integrates text, image, and audio data, embeddings serve as a universal language for these diverse data types.
Setting Up Your Environment for 2025
To get started with batch embedding using the latest OpenAI API, you'll need an up-to-date development environment. Here's what you'll need:
- Python 3.11 or later (3.13 recommended as of 2025)
- OpenAI Python library (version 2.x or later)
- Pandas for data manipulation
- Requests for API interactions
Install the required libraries using pip:
pip install openai==2.x pandas requests
Next, set up your OpenAI API key. In 2025, OpenAI has introduced a more secure method of API authentication:
import os
from openai import OpenAI
# Use the new secure key management system
client = OpenAI(api_key=os.environ.get("OPENAI_API_KEY_2025"))
Preparing Your Data: A 2025 Perspective
For this tutorial, we'll use an updated dataset of ICD-11 codes, which have become the standard for medical classification in 2025. The process of downloading and processing this data has been streamlined:
import requests
import pandas as pd
def download_and_process_icd_codes():
link = 'https://icd.who.int/api/v2025/icd11/mms/releases/11'
response = requests.get(link, headers={'Authorization': 'Bearer YOUR_WHO_API_KEY'})
icd_codes = pd.DataFrame(response.json()['linearization'])
icd_codes = icd_codes[['code', 'title', 'definition']]
icd_codes.columns = ['code', 'short_description', 'long_description']
icd_codes = icd_codes.dropna()
return icd_codes
icd_codes = download_and_process_icd_codes()
print(icd_codes.head())
Creating Batch Files: Optimized for 2025
The OpenAI batch embedding service now supports larger batch sizes and more efficient file formats. We'll create these files in batches of 50,000 items each, using the new optimized JSONL format:
import json
import os
def create_batch_files(data, batch_size=50000, output_folder='./batch_files'):
if not os.path.exists(output_folder):
os.makedirs(output_folder)
num_files = len(data) // batch_size + 1
for num_file in range(num_files):
output_file = f'{output_folder}/batch_part{num_file}.jsonl'
with open(output_file, 'w') as file:
for index, row in data.iloc[batch_size*num_file : min(batch_size*(num_file+1), len(data))].iterrows():
payload = {
"custom_id": f"custom_id_{index}",
"method": "POST",
"url": "/v1/embeddings",
"body": {
"input": row["long_description"],
"model": "text-embedding-4-large",
"encoding_format": "float16",
'dimensions': 4096
}
}
file.write(json.dumps(payload) + '\n')
create_batch_files(icd_codes)
Note the use of the new text-embedding-4-large model and float16 encoding format, which offer improved performance and reduced file size.
Initiating the Batch Embedding Process: 2025 Edition
The process of initiating batch jobs has been simplified in the 2025 OpenAI API:
def create_batch_jobs():
batch_folder = './batch_files'
job_creations = []
for file in os.listdir(batch_folder):
job_creations.append(client.batches.create(
input_file=open(f'{batch_folder}/{file}', "rb"),
endpoint="/v1/embeddings",
completion_window="6h", # Reduced from 24h due to improved processing speed
metadata={
"description": f"icd11_embeddings_{file}"
}
))
return job_creations
job_creations = create_batch_jobs()
for job in job_creations:
print(job)
Monitoring Batch Jobs: Real-time Insights
In 2025, OpenAI has introduced a real-time monitoring system for batch jobs:
import time
def monitor_batch_jobs(job_creations):
job_ids = [job.id for job in job_creations]
while True:
all_completed = True
for job_id in job_ids:
job = client.batches.retrieve(job_id)
if job.status != "completed":
all_completed = False
print(f'Job {job_id}: {job.status}, {job.request_counts.completed}/{job.request_counts.total} requests completed')
else:
print(f"Job {job_id} has finished")
if all_completed:
break
time.sleep(60) # Check every minute
# Final status check
for job_id in job_ids:
job = client.batches.retrieve(job_id)
print(f'{job.request_counts.failed}/{job.request_counts.total} requests failed in job {job_id}')
monitor_batch_jobs(job_creations)
Downloading and Processing Results: Enhanced Efficiency
The 2025 OpenAI API offers a more efficient way to download and process batch results:
def download_and_process_results(job_creations):
embedding_results = []
for job in job_creations:
output_file = client.files.retrieve_content(job.output_file_id)
for line in output_file.split('\n')[:-1]:
data = json.loads(line)
custom_id = data.get('custom_id')
embedding = data['response']['body']['data'][0]['embedding']
embedding_results.append([custom_id, embedding])
return pd.DataFrame(embedding_results, columns=['custom_id', 'embedding'])
embedding_results = download_and_process_results(job_creations)
Merging Results with Original Data: Streamlined Process
The process of merging embedding results with the original ICD-11 data has been optimized:
def merge_results_with_original_data(icd_codes, embedding_results):
icd_codes = icd_codes.reset_index().rename(columns={'index': 'id'})
embedding_results['id'] = embedding_results['custom_id'].apply(lambda x: int(x.split('custom_id_')[1]))
icd_codes_with_embedding = icd_codes.merge(embedding_results[['id', 'embedding']], on='id', how='left')
icd_codes_with_embedding.to_parquet('./data/icd11_codes_with_embedding_4096.parquet')
return icd_codes_with_embedding
final_dataset = merge_results_with_original_data(icd_codes, embedding_results)
print(final_dataset.head())
Note the use of the Parquet format for efficient storage of large datasets with high-dimensional embeddings.
Advanced Applications in 2025
With our ICD-11 codes now embedded using state-of-the-art technology, we can explore cutting-edge applications:
1. Multi-lingual Medical Coding
Leverage the improved semantic understanding of the 4096-dimensional embeddings to create a system that can automatically assign ICD-11 codes across multiple languages:
from sklearn.neighbors import NearestNeighbors
def create_multilingual_coding_system(embeddings, codes):
nn = NearestNeighbors(n_neighbors=5, metric='cosine')
nn.fit(embeddings)
def code_text(text, source_lang='en', target_lang='es'):
# Translate text to English if necessary
if source_lang != 'en':
text = translate_to_english(text)
# Generate embedding for the text
text_embedding = client.embeddings.create(
model="text-embedding-4-large",
input=text,
dimensions=4096
).data[0].embedding
# Find nearest neighbors
distances, indices = nn.kneighbors([text_embedding])
# Get corresponding ICD-11 codes
suggested_codes = codes.iloc[indices[0]]
# Translate descriptions if necessary
if target_lang != 'en':
suggested_codes['long_description'] = suggested_codes['long_description'].apply(
lambda x: translate_to_language(x, target_lang)
)
return suggested_codes
return code_text
multilingual_coder = create_multilingual_coding_system(final_dataset['embedding'].tolist(), final_dataset[['code', 'short_description', 'long_description']])
# Example usage
spanish_text = "Paciente con dolor abdominal agudo y fiebre"
suggested_codes = multilingual_coder(spanish_text, source_lang='es', target_lang='es')
print(suggested_codes)
2. Medical Knowledge Graph Enhancement
Use the high-dimensional embeddings to improve medical knowledge graphs, enabling more sophisticated reasoning:
import networkx as nx
from scipy.spatial.distance import cosine
def enhance_medical_knowledge_graph(embeddings, codes, threshold=0.8):
G = nx.Graph()
for i, code in codes.iterrows():
G.add_node(code['code'], description=code['short_description'])
for i in range(len(codes)):
for j in range(i+1, len(codes)):
similarity = 1 - cosine(embeddings[i], embeddings[j])
if similarity > threshold:
G.add_edge(codes.iloc[i]['code'], codes.iloc[j]['code'], weight=similarity)
return G
medical_kg = enhance_medical_knowledge_graph(final_dataset['embedding'].tolist(), final_dataset[['code', 'short_description']])
# Example: Find related conditions
related_conditions = list(medical_kg.neighbors('MB40'))
print(f"Conditions related to MB40: {related_conditions}")
3. AI-Assisted Differential Diagnosis
Create a system that uses embeddings to suggest potential diagnoses based on patient symptoms:
from sklearn.metrics.pairwise import cosine_similarity
def create_differential_diagnosis_system(embeddings, codes):
def suggest_diagnoses(symptoms, top_n=5):
# Generate embedding for symptoms
symptoms_embedding = client.embeddings.create(
model="text-embedding-4-large",
input=symptoms,
dimensions=4096
).data[0].embedding
# Calculate similarity with all ICD-11 codes
similarities = cosine_similarity([symptoms_embedding], embeddings)[0]
# Get top N similar codes
top_indices = similarities.argsort()[-top_n:][::-1]
return codes.iloc[top_indices][['code', 'short_description', 'long_description']]
return suggest_diagnoses
diff_diagnosis = create_differential_diagnosis_system(final_dataset['embedding'].tolist(), final_dataset[['code', 'short_description', 'long_description']])
# Example usage
patient_symptoms = "Persistent cough, fever, and shortness of breath for the past week"
suggested_diagnoses = diff_diagnosis(patient_symptoms)
print(suggested_diagnoses)
Ethical Considerations and Future Directions
As AI engineers working with powerful embedding technologies in 2025, we must be mindful of the ethical implications of our work:
Privacy and Data Protection: Ensure that all patient data used in embedding generation is properly anonymized and protected.
Bias Mitigation: Regularly audit your embeddings and downstream applications for potential biases, especially in healthcare applications where fairness is crucial.
Transparency: Develop methods to explain how embedding-based systems arrive at their conclusions, particularly in critical applications like medical diagnosis.
Continuous Learning: Stay updated with the latest advancements in embedding technology and ethical AI practices.
Conclusion
Mastering batch embedding with the OpenAI API is more important than ever for AI engineers in 2025. The advancements we've seen in embedding technology have opened up new possibilities in healthcare, natural language processing, and beyond. By leveraging these powerful tools responsibly and ethically, we can create AI systems that truly enhance human capabilities and improve lives.
As we look to the future, the potential applications of high-dimensional embeddings are boundless. From personalized medicine to global health monitoring systems, the insights we can derive from these advanced representations of text will continue to drive innovation in AI and healthcare.
Remember, as AI engineers, our role is not just to implement these technologies, but to do so in a way that benefits society as a whole. Let's embrace the power of batch embedding while always keeping in mind the ethical implications and potential impacts of our work.
