You can install AvahiPlatform by running:
pip install avahiplatform -U
Hey there, AI enthusiast! 👋 Are you ready to supercharge your Gen-AI projects? Look no further than AvahiPlatform - your new best friend in the world of Large Language Models (LLMs)!
With AvahiPlatform, you can create and deploy GenAI applications on Bedrock in just 60 seconds. It's that fast and easy!
AvahiPlatform is not just a library; it's your ticket to effortless AI-powered applications. We've taken the complexity out of working with LLMs on AWS Bedrock, so you can focus on what really matters - bringing your brilliant ideas to life!
With the provided Google Colab notebook, you can easily test and explore the features of this project. Simply click the "Open In Colab" badge above to get started!
import avahiplatform
import os
import sys
# Add the parent directory of logicsdk to sys.path
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
# Configure AvahiPlatform
avahiplatform.configure(default_model_name='amazon.nova-pro-v1:0')
# You can pass all this configuration: aws_access_key_id, aws_secret_access_key, aws_session_token, region_name, input_tokens_price, output_tokens_price, input_bucket_name_for_medical_scribing, iam_arn_for_medical_scribing, default_model_name
# Initialize observability
avahiplatform.initialize_observability(metrics_file='./metrics.jsonl', start_prometheus=True, prometheus_port=8007)
# Launch Chat UI
chatbot = avahiplatform.chatbot
chatbot.launch_chat_ui()
# Summarize any document Example
summary = avahiplatform.summarize_document(document_path='./Test/df1.csv')
print("Summary:", summary['response_text'])
# Summarize text Example
summary = avahiplatform.summarize_text(text="sameple text")
print("Summary:", summary['response_text'])
# Summarize s3 document Example
summary = avahiplatform.summarize_s3_document(s3_path='s3://avahi-python-package-data/Patient’s clinical history.txt')
print("Summary:", summary['response_text'])
# Summarize image Example
summary = avahiplatform.summarize_image(image_path='./Test/041_400NB_Ramp_to_Glenridge_Conn_2024-09-13T17-30-20.340+00-00.jpg')
print("Summary:", summary['response_text'])
# Summarize video Example
summary = avahiplatform.summarize_video(video_path='./Test/041_400NB_Ramp_to_Glenridge_Conn_2024-09-13T17-30-20.340+00-00.mp4')
print("Summary:", summary['response_text'])
# Data Masking Example
masked_data = avahiplatform.mask_data(input_content="s3://avahi-python-package-data/Patient’s clinical history.txt")
print("Masked Data:", masked_data['response_text'])
# Structured Extraction Example
extraction = avahiplatform.structuredExtraction(input_content="s3://avahi-python-package-data/Patient’s clinical history.txt")
print("Extraction:", extraction['response_text'])
# Grammar Correction Example
corrected_text = avahiplatform.grammar_assistant(input_content="I in am english speaker and I like write a sentence english in.")
print("Corrected Text:", corrected_text['response_text'])
# Product Description Generation Example
description = avahiplatform.product_description_assistant("SKU123", "Summer Sale", "Young Adults")
print("Product Description:", description['response_text'])
# Image Generation Example
avahiplatform.configure(default_model_name='stability.stable-diffusion-xl-v1')
result = avahiplatform.generate_image(image_prompt="A beautiful sunset over mountains")
output[0].save('./Test/output.png')
print(f"output: {output[1]}")
# ICD-10 Code Generation Example
icd_codes = avahiplatform.generate_icdcode(input_content="s3://avahi-python-package-data/Patient’s clinical history.txt")
print("ICD-10 Codes:", icd_codes)
# Medical Scribing Example
medical_summary = avahiplatform.medicalscribing(audio_filepath="./Test/Doctor-patient-conversation.mp3")
print("Medical Summary:", medical_summary)
# CSV Querying Example
csv_files = {
"df1": "./Test/df1.csv",
"df2": "./Test/df2.csv"
}
csv_query_answer = avahiplatform.query_csv("How many entries are there in df1?", csv_file_paths=csv_files)
print(f"CSV Query Answer: {csv_query_answer}")
# Image similarity Example
image1 = "./Test/image_1.jpg"
image2 = "./Test/image_2.jpg"
similarity = avahiplatform.get_similar_images(image1, image2)
print(f"Similarity: {similarity}")
| Category | Feature | Description |
|---|---|---|
| Text Processing | Text Summarization | Summarize plain text, local files, or files stored in S3 |
| Grammar Correction | Automatically correct grammatical errors in your text | |
| Multimedia | Image and Video Summarization | Generate summaries for images and videos |
| Image Generation | Create custom images using state-of-the-art AI models | |
| Image Similarity | Find and compare similar images in your dataset | |
| Data Processing | Structured Information Extraction | Extract structured data from unstructured text sources |
| Data Masking | Protect sensitive information by masking data in your datasets | |
| CSV Querying | Perform natural language queries on your CSV files | |
| Healthcare | ICD-10 Code Generation | Generate ICD-10 codes from clinical documents |
| Medical Scribing | Transcribe and summarize medical conversations from audio files | |
| E-commerce | Product Description Generation | Generate compelling product descriptions based on SKU, campaign, and target audience |
| Chatbot | Deploy interactive chatbots with customizable prompts and behaviors | |
| Platform Features | Global Gradio URL | Generate shareable URLs for any of your deployed functionalities |
| Custom Prompts and Models | Support for custom prompts and different Anthropic Claude model versions | |
| Error Handling | Receive clear and actionable error messages for common issues |
import avahiplatform
# Summarize plain text
summary = avahiplatform.summarize_text(text="sample text")
print("Summary:", summary['response_text'])
# Summarize a local file
summary = avahiplatform.summarize_document(document_path='path/to/local/file.txt')
print("Summary:", summary['response_text'])
# Summarize a file from S3
summary = avahiplatform.summarize_s3_document(s3_path='s3://bucket-name/file.txt')
print("Summary:", summary['response_text'])
# Summarize an image
image_summary = avahiplatform.summarize_image(image_path='./Test/image.jpg')
print("Image Summary:", image_summary['response_text'])
# Summarize a video
video_summary = avahiplatform.summarize_video(video_path='./Test/video.mp4', system_prompt="Summarize the video content.")
print("Video Summary:", video_summary['response_text'])
extraction = avahiplatform.structuredExtraction(input_content="s3://avahi-python-package-data/Patient’s clinical history.txt") # anything local path, plain text or s3 path
print("Extraction:", extraction['response_text'])
masked_data = avahiplatform.mask_data(input_content="s3://avahi-python-package-data/Patient’s clinical history.txt") # anything local path, plain text or s3 path
print("Masked Data:", masked_data['response_text'])
corrected_text = avahiplatform.grammar_assistant(input_content="I in am english speaker and I like write a sentence english in.") # plain text or s3 path
print("Corrected Text:", corrected_text['response_text'])
description = avahiplatform.product_description_assistant("SKU123", "Summer Sale", "Young Adults") # product name, season, target audience
print("Product Description:", description['response_text'])
# Remember to give us-west-2 in region name in configuration as many stability models are in us-west-2.
avahiplatform.configure(default_model_name='stability.stable-diffusion-xl-v1')
result = avahiplatform.generate_image(image_prompt="A beautiful sunset over mountains")
result[0].save('./Test/output.png')
print(f"output: {result[1]}")
image1 = "./Test/image_1.jpg"
image2 = "./Test/image_2.jpg"
similarity = avahiplatform.get_similar_images(image1, image2)
print(f"Similarity: {similarity}")
# You can also compare multiple images contained in the folder
image1 = "./Test/image_1.jpg"
folder = "./Test/"
similarity = avahiplatform.get_similar_images(image1, folder)
print(f"Similarity: {similarity}")
# You can also compare multiple s3 images contained in the folder
image1 = "s3://bucket-name/image_1.jpg"
folder = "s3://bucket-name/"
similarity = avahiplatform.get_similar_images(image1, folder)
print(f"Similarity: {similarity}")
icd_codes = avahiplatform.generate_icdcode(input_content="s3://avahi-python-package-data/Patient’s clinical history.txt") # anything local path, plain text or s3 path
print("ICD-10 Codes:", icd_codes)
medical_summary = avahiplatform.medicalscribing(audio_filepath="./Test/Doctor-patient-conversation.mp3")
print("Medical Summary:", medical_summary)
csv_files = {
"df1": "./Test/df1.csv",
"df2": "./Test/df2.csv"
}
csv_query_answer = avahiplatform.query_csv("How many entries are there in df1?", csv_file_paths=csv_files) # local filepath or s3 path
print(f"CSV Query Answer: {csv_query_answer}")
result = avahiplatform.nl2sql(
"Show me all users who signed up in the last month.",
db_type="postgresql",
username="user",
password="pass",
host="localhost",
port=5432,
dbname="mydb"
)
print("SQL Result:", result)
answer, sources = avahiplatform.perform_rag_with_sources(
"What is Kafka?",
s3_path="s3://your-bucket-path-where-doc-is-present/"
)
print(f"Generated Answer: {answer}")
print(f"Retrieved Sources: {sources}")
similar_docs = avahiplatform.perform_semantic_search(
"What is Kafka?",
s3_path="s3://your-bucket-path-where-doc-is-present/"
)
print(f"Similar Documents: {similar_docs}")
chatbot = avahiplatform.chatbot
chatbot.initialize_system(system_prompt="You are a Python developer. You only answer queries related to Python. If you receive any other queries, please respond with 'I don't know.'")
# Chat with the chatbot
response = chatbot.chat(user_input="Create me a function to add 2 numbers")
print(f"Chatbot Response: {response["response_text"]}")
response = chatbot.chat(user_input="What is Avahi?")
print(f"Chatbot Response: {response["response_text"]}")
# Get chat history
history = chatbot.get_history()
print(f"Chat History: {history}")
# Clear chat history
chatbot.clear_history()
# For Summarization
avahiplatform.summarize_document.create_url()
# For Medical Scribing
avahiplatform.medicalscribing.create_url()
# For CSV Querying
avahiplatform.query_csv.create_url()
# For RaG with Sources
avahiplatform.perform_rag_with_sources.create_url()
# For Chatbot
chatbot = avahiplatform.chatbot
chatbot.create_url()
This will generate a global URL which you can share with anyone, allowing them to explore and utilize any of the features running in your environment using the AvahiPlatform SDK.
AvahiPlatform requires AWS credentials to access AWS Bedrock and S3 services. You have two options for providing your AWS credentials:
~/.aws/credentials file.💡 Tip: For detailed instructions on setting up AWS credentials, please refer to the AWS CLI Configuration Guide.
Ensuring your AWS credentials are correctly set up will allow you to seamlessly use all of AvahiPlatform's powerful features. If you encounter any issues with authentication, double-check your credential configuration or reach out to our support team for assistance.
To use medical scribing features, configure the IAM role and input bucket name:
avahiplatform.configure(
iam_arn_for_medical_scribing="arn:aws:iam::<account-id>:role/<role-name>",
input_bucket_name_for_medical_scribing="your-input-bucket-name",
default_model_name='amazon.nova-pro-v1:0'
)
IAM Policy for Medical Scribing:
Ensure the IAM role has the following inline policy:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"iam:GetRole",
"iam:PassRole"
],
"Resource": [
"arn:aws:iam::<account-id>:role/<role-name>"
]
}
]
}
Additionally, the role/user should have full access to both Transcribe and Comprehend services.
AvahiPlatform provides user-friendly error messages for common issues, ensuring you can quickly identify and resolve any problems. Here are some examples:
Our detailed error messages will guide you towards quick resolutions, keeping your development process smooth and efficient.
To use AvahiPlatform, make sure you have the following:
boto3>=1.34.160
python-docx>=1.1.2
PyMuPDF>=1.24.9
loguru>=0.7.2
setuptools>=72.1.0
sqlalchemy>=2.0.35
gradio>=4.44.0
tabulate>=0.9.0
python-magic-bin>=0.4.14
pillow>=10.4.0
pandas>=2.2.3
gradio>=4.44.0
typing_extensions>=4.0.0
prometheus-client>=0.21.0
python-magic>=0.4.27
anthropic>=0.42.0
You can install these dependencies using pip. We recommend using a virtual environment for your project.
pip install -r requirements.txt
We would like to thank the following individuals for their valuable contributions:
This project is licensed under the MIT License. See the Open-source MIT license file for details.
We welcome contributions from the community! Whether you've found a bug or have a feature in mind, we'd love to hear from you. Here's how you can contribute:
Let's make AvahiPlatform even better together!
We're here to help! If you have any questions, suggestions, or just want to say hi, feel free to reach out:
Lead by Dhruv Motwani
Contributors:
Thank you for choosing AvahiPlatform! We are excited to see the amazing applications you'll build.
FAQs
An avahiai library which makes your Gen-AI tasks effortless
We found that avahiplatform demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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