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Eden AI aims to simplify the use and deployment of AI technologies by providing a unique API that connects to all the best AI engines.
With the rise of AI as a Service , a lot of companies provide off-the-shelf trained models that you can access directly through an API. These companies are either the tech giants (Google, Microsoft , Amazon) or other smaller, more specialized companies, and there are hundreds of them. Some of the most known are : DeepL (translation), OpenAI (text and image analysis), AssemblyAI (speech analysis).
There are hundreds of companies doing that. We're regrouping the best ones in one place !
why aren't you regrouping Open Source models (instead of proprietary APIs) into one repo? : Because it doesn't make sens to deploy and maintain large pytorch (or other framework) AI models in every solution that wants AI capabilities (especially for document parsing, image and video moderation or speech recognition) . So using APIs makes way more sens. Deployed OpenSource models are being included using different APIs like HuggingFace and other equivalents.
You can install the package with pip :
pip install git+https://github.com/edenai/edenai-apis To make call to different AI providers, first add the api-keys/secrets for the provider you will use in edenai_apis.api_keys.<provider_name>_settings_templates.json, then rename the file to <provider_name>_settings.json
When it's done you can directly start using edenai_apis. Here is a quick example using Microsoft and IBM Keyword Extraction apis:
from edenai_apis import Text
keyword_extraction = Text.keyword_extraction("microsoft")
microsoft_res = keyword_extraction(language="en", text="as simple as that")
# Provider's response
print(microsoft_res.original_response)
# Standardized version of Provider's response
print(microsoft_res.standardized_response)
for item in microsoft_res.standardized_response.items:
print(f"keyword: {item.keyword}, importance: {item.importance}")
# What if we want to try an other provider?
ibm_kw = Text.keyword_extraction("ibm")
ibm_res = ibm_kw(language="en", text="same api & unified inputs for all providers")
# `original_response` will obviously be different and you will have to check
# the doc of each individual providers to know how to parse them
print(ibm_res.original_response)
# We can however easily parse `standardized_response`
# the same way as we did for microsoft:
for item in ibm_res.standardized_response.items:
print(f"keyword: {item.keyword}, importance: {item.importance}")If you need to use features like speech to text, object extraction from videos, etc. Then you will have to use asynchrounous operations. This means that you will first make a call to launch an asynchrounous job, it will then return a job ID allowing you to make other calls to get the job status or response if the job is finished
from edenai_apis import Audio
provider = "google" # it could also be assamblyai, deepgram, microsoft ...etc
stt_launch = Audio.speech_to_text_async__launch_job(provider)
stt_get_result = Audio.speech_to_text_async__get_job_result(provider)
res = stt_launch(
file=your_file.wav,
language="en",
speakers=2,
profanity_filter=False,
)
job_id = stt_launch.provider_job_id
res = stt_get_result(provider_job_id=job_id)
print(res.status) # "pending" | "succeeded" | "failed"We would love to have your contribution. Please follow our gidelines for adding a new AI provider's API or a new AI feature. You can check the package structure for more details on how it is organized. We use GitHub issues for tracking requests and bugs. For broader discussions you can join our discord.
You can create an account on Eden AI and have access to all the AI technologies and providers directly through our API.
Join our friendly community to improve your skills, focus on the integration of AI engines, get help to use Eden AI API and much more !
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