rallyio 0.1.3

A Python library for interacting with Rally.io services

Rally.io Python Package

A Python package for interacting with Rally.io services, including search and RAG (Retrieval Augmented Generation) capabilities.

Installation

Quick Install (Recommended)

Simply use pip to install the package:

pip install rallyio

Development Installation

If you want to install the package in development mode or contribute to the project:

1. Clone the repository:

git clone https://github.com/jatinarora2409/rallyio.git
cd rallyio

2. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

3. Install the package in development mode:

pip install -e .

Usage

Search Tool

import rallyio

# Initialize the search tool
search_tool = rallyio.get_search_tool(
    project_id="your-google-cloud-project-id",
    location="us-central1",
    index_endpoint_id="your-index-endpoint-id"
)

# Perform a search
results = search_tool.search(query_vector=[0.1, 0.2, 0.3])  # Your vector here
print(results)

# Get information about the index
index_info = search_tool.get_index_info()
print(index_info)

RAG Tool

import rallyio
import inspect

# Initialize the RAG function with custom names
my_rag = rallyio.get_rag_function(
    project_id="your-google-cloud-project-id",
    rag_corpus_id="your-rag-corpus-id",
    location="us-central1",  # optional, defaults to "us-central1"
    function_name="ask_ai",  # optional, customizes the function name
    parameter_name="question"  # optional, customizes the parameter name
)

# The function's name and parameter will be customized
print(my_rag.__name__)  # Output: ask_ai
print(inspect.signature(my_rag))  # Output: (question: str) -> str

# Use the RAG function with the custom parameter name
response = my_rag(question="What is the capital of France?")
print(response)

# You can also use the default function without custom names
default_rag = rallyio.get_rag_function(
    project_id="your-google-cloud-project-id",
    rag_corpus_id="your-rag-corpus-id"
)
print(default_rag.__name__)  # Output: function
print(inspect.signature(default_rag))  # Output: (query: str) -> str

# Use the default function
response = default_rag(query="Tell me about quantum computing")
print(response)

Requirements

• Python 3.8 or higher
• Google Cloud credentials configured (for RAG functionality)
• Required Python packages (automatically installed with the package):
  • requests
  • google-cloud-aiplatform
  • google-generativeai

Google Cloud Setup

To use the RAG functionality, you need to:

1. Have a Google Cloud project set up
2. Enable the Vertex AI API
3. Create a RAG corpus in your project
4. Set up authentication:

   # Install Google Cloud CLI
   # Then authenticate:
   gcloud auth application-default login
   

Development

To run tests:

pytest

To format code:

black .

License

[Your chosen license]