hashai 0.3.5

A powerful SDK for building AI assistants with RAG capabilities.

HashAI: The Mother of Your AI Agents

HashAI is an advanced SDK designed to simplify the creation of AI agents. Whether you’re building a research assistant, a customer support bot, or a personal AI, HashAI provides all the tools and integrations to make it easy.

We currently support Groq, OpenAI, and Anthropic LLMs, along with a Retrieval-Augmented Generation (RAG) system for enhanced context-awareness.

Installation

Install HashAI via pip:

pip install hashai

Features

• Seamless LLM Integration: Plug-and-play support for Groq, OpenAI, and Anthropic.
• RAG Support: Retrieval-Augmented Generation for contextually aware responses.
• Customizable Agents: Define the personality, behavior, and instructions for your AI agents.
• Extensibility: Add new tools or modify behavior with ease.

Example: Blockchain Research Assistant

This example demonstrates how to create a blockchain research assistant using HashAI and the Groq LLM.

Prerequisites

1. Set up your Groq API key as an environment variable or directly in the code.

import os
os.environ["GROQ_API_KEY"] = "your-api-key"  # Replace with your Groq API key

2. Import the HashAI Assistant class and configure your agent.

Code Example

# Set the Groq API key (either via environment variable or explicitly)
import os
os.environ["GROQ_API_KEY"] = "your-api-key"  # Set the API key here

# Initialize the Assistant
from hashai.assistant import Assistant

healthcare_research_assistant = Assistant(
    name="Healthcare Assistant",
    description="Extract and structure medical information from the provided text into a JSON format used in healthcare",
    instructions=[
        "Always use medical terminology while creating json",
        "Extract and structure medical information from the provided text into a JSON format used in healthcare",
    ],
    model="Groq",
    show_tool_calls=True,
    user_name="Researcher",
    emoji=":chains:",
    markdown=True,
)
patient_text = """
Patient Complaints of High grade fever, chest pain, radiating towards right shoulder. Sweating,
patient seams to have high grade fever ,  patient is allergic to pollution , diagnosis high grade fever , plan of care comeback after 2 days , instructions take rest and drink lot of water  Palpitation since 5 days.
Advice investigation: CBC, LFT, Chest X ray, Abdomen Ultrasound
Medication: Diclofenac 325mg twice a day for 5 days, Amoxiclave 625mg once a day for 5 days, Azithromycin 500mg Once a day
Ibuprofen SOS, Paracetamol sos, Pentoprazol before breakfast  , follow up after 2 days
"""
# Test the Assistant
healthcare_research_assistant.print_response(patient_text)

File Structure

The HashAI SDK is organized as follows:

opData/
├── hashai/                      # Core package
│   ├── __init__.py              # Package initialization
│   ├── assistant.py             # Core Assistant class
│   ├── agent.py                 # Core Agent class
│   ├── rag.py                   # RAG functionality
│   ├── memory.py                # Conversation memory management
│   ├── llm/                     # LLM integrations
│   │   ├── __init__.py
│   │   ├── openai.py            # OpenAI integration
│   │   ├── anthropic.py         # Anthropic (Claude) integration
│   │   ├── llama.py             # Llama 2 integration
│   │   └── base_llm.py          # Base class for LLMs
│   ├── knowledge_base/          # Knowledge base integration
│   │   ├── __init__.py
│   │   ├── vector_store.py      # Vector store for embeddings
│   │   ├── document_loader.py   # Load documents into the knowledge base
│   │   └── retriever.py         # Retrieve relevant documents
│   ├── tools/                   # Tools for assistants
│   │   ├── __init__.py
│   │   ├── calculator.py        # Example tool: Calculator
│   │   ├── web_search.py        # Example tool: Web search
│   │   └── base_tool.py         # Base class for tools
│   ├── storage/                 # Storage for memory and data
│   │   ├── __init__.py
│   │   ├── local_storage.py     # Local file storage
│   │   └── cloud_storage.py     # Cloud storage (e.g., S3, GCP)
│   ├── utils/                   # Utility functions
│   │   ├── __init__.py
│   │   ├── logger.py            # Logging utility
│   │   └── config.py            # Configuration loader
│   └── cli/                     # Command-line interface
│       ├── __init__.py
│       └── main.py              # CLI entry point
├── tests/                       # Unit tests
│   ├── __init__.py
│   ├── test_assistant.py
│   ├── test_rag.py
│   └── test_memory.py
├── examples/                    # Example usage
│   ├── basic_assistant.py
│   ├── customer_support.py
│   └── research_assistant.py
├── requirements.txt             # Dependencies
├── setup.py                     # Installation script
├── README.md                    # Documentation
└── LICENSE                      # License file

Contributing

Contributions are welcome! Please fork the repository, create a feature branch, and submit a pull request with a detailed description of your changes.

License

This project is licensed under the MIT License.

Support

For issues, feature requests, or questions, please open an issue in the repository or reach out to the team.