shadai 0.1.25

SHADAI Client

SHADAI Intelligence Client

A Python client for interacting with the SHADAI API. This client provides a simple interface for document processing, querying, and session management.

Installation

pip install shadai

Requirements

• Python >= 3.9
• Environment Variables:
  • SHADAI_API_KEY: Your SHADAI API key

Features

• Asynchronous API interactions
• Automatic session management
• File ingestion with progress tracking
• Interactive query interface
• Robust error handling and retries
• Rich console output

Getting Started with SHADAI

SHADAI Intelligence Client is a powerful tool for document processing, querying, and AI-assisted analysis. This guide will walk you through various use cases and show you how to leverage the full capabilities of the platform.

Session Configuration

When working with SHADAI, you'll always start by creating a Session. Here are the configuration options available:

Parameter	Type	Description	Default
type	str	Processing type ("light", "standard", "deep")	"standard"
llm_model	str	Language model to use	AIModels.CLAUDE_3_5_HAIKU
llm_temperature	float	Model temperature	0.7
llm_max_tokens	int	Maximum tokens for response	4096
query_mode	str	Query processing mode	hybrid
language	str	Response language	es
delete	bool	Auto-delete session on exit	True

Working with Documents

Ingesting Documents

The first step in most SHADAI workflows is ingesting documents. SHADAI can process various document formats and extract relevant information.

# examples/ingest_data.py
import asyncio
import os
from shadai.core.session import Session

# Define the directory containing your documents
input_dir = os.path.join(os.path.dirname(__file__), "data")

async def ingest_with_alias(alias: str) -> None:
    """
    Creating a session with an alias allows you to easily reference it later.
    This is useful for long-running projects where you might need to
    come back to the same document set.
    """
    async with Session(alias=alias, type="standard", delete=True) as session:
        # The ingest method processes all documents in the input directory
        # It automatically handles different file formats and extracts text content
        await session.ingest(input_dir=input_dir)
        print(f"Successfully ingested documents into session with alias: {alias}")

async def ingest_without_alias() -> None:
    """
    If you don't need to reference the session later, you can create one
    without an alias. This is useful for one-off queries.
    """
    async with Session(type="standard", delete=True) as session:
        await session.ingest(input_dir=input_dir)
        print(f"Successfully ingested documents into session with ID: {session.id}")

async def main():
    # Example using both approaches
    await ingest_with_alias(alias="constitution_docs")
    await ingest_without_alias()

if __name__ == "__main__":
    asyncio.run(main())

Managing Sessions

SHADAI provides tools for managing your sessions, including creating, listing, retrieving, and deleting them.

# examples/handle_sessions.py
import asyncio
from shadai.core.manager import Manager
from shadai.core.session import Session
from shadai.core.constants import AIModels, QueryMode

async def create_session() -> Session:
    """
    This function creates a new session with specific parameters.
    You can customize model, temperature, tokens, and query mode.
    If you set delete to False, you need to manually delete the session.

    Returns:
        Session: The session object.
    """
    async with Session(
        type="standard",
        llm_model=AIModels.CLAUDE_3_7_SONNET,   # Specify which LLM to use
        llm_temperature=0.7,                    # Control response creativity (0.0-1.0)
        llm_max_tokens=4096,                    # Set maximum response length
        query_mode=QueryMode.HYBRID,            # Choose between HYBRID, SEMANTIC_HYBRID, TEXT_SEARCH, etc
        delete=False,                           # No auto-delete session when done
    ) as session:
        # Session is now ready for document ingestion and queries
        return session

async def get_existing_session_with_session_id(session_id: str) -> Session:
    """
    Retrieve a previously created session using its ID.
    Setting delete=False ensures the session isn't deleted when the context manager exits.
    """
    async with Session(session_id=session_id, type="standard", delete=False) as session:
        print(f"Retrieved session with ID: {session.id}")
        return session

async def get_existing_session_with_alias(alias: str) -> Session:
    """
    Retrieve a session using its alias - this is often more convenient than using IDs.
    """
    async with Session(alias=alias, type="standard", delete=False) as session:
        print(f"Retrieved session with alias: {alias} (ID: {session.id})")
        return session

async def list_sessions():
    """
    The Manager class provides administrative functions for working with sessions.
    This method lists all sessions in your current namespace.
    """
    async with Manager() as manager:
        # show_in_console=True displays a nicely formatted table in the terminal
        sessions = await manager.list_sessions(show_in_console=True)
        return sessions

async def cleanup_namespace() -> None:
    """
    Remove all sessions in your current namespace.
    Useful for cleaning up after testing or when starting a new project.
    """
    async with Manager() as manager:
        await manager.cleanup_namespace()
        print("All sessions in the current namespace have been deleted")

async def delete_session(session_id: str) -> None:
    """
    Delete a specific session by its ID.
    """
    async with Manager() as manager:
        await manager.delete_session(session_id=session_id)
        print(f"Session {session_id} has been deleted")

async def main():
    # Example workflow for session management
    session = await create_session()  # Create a session with custom parameters
    await list_sessions()  # See what sessions exist
    # await cleanup_namespace()  # Uncomment to delete all sessions
    # await delete_session(session_id="YOUR SESSION ID")  # Delete a specific session

if __name__ == "__main__":
    asyncio.run(main())

Querying and Analysis

Making a Single Query

The most basic way to interact with your documents is through a single query:

# examples/make_single_query.py
import asyncio
import os
from shadai.core.session import Session

input_dir = os.path.join(os.path.dirname(__file__), "data")

async def main():
    async with Session(type="standard", delete=True) as session:
        # First, ingest the documents
        await session.ingest(input_dir=input_dir)

        # Then, query the documents
        # The query method searches through your documents and returns relevant information
        # You can customize the response by providing a role for the AI
        await session.query(
            query="¿De qué habla la quinta enmienda de la constitución?",
            # The role parameter influences how the AI responds - here as a Harvard law expert
            role="Eres un abogado experto de harvard",
            display_in_console=True,  # Print the response in the console
        )
        # The response will include relevant passages from the documents and an answer to your question

if __name__ == "__main__":
    asyncio.run(main())

Making Multiple Queries

For more complex analysis, you might want to ask multiple related questions:

# examples/make_multiple_queries.py
import asyncio
import os
from typing import List
from shadai.core.schemas import Query
from shadai.core.session import Session

input_dir = os.path.join(os.path.dirname(__file__), "data")

# Define a list of queries to process in sequence
queries: List[Query] = [
    Query(
        query="¿De qué habla la quinta enmienda de la constitución?",
        role="Eres un abogado experto de harvard",
        display_in_console=True,
    ),
    Query(
        query="""
        ¿Cómo se detalla en el Artículo I, Sección 2 el mecanismo de asignación de representantes a
        los estados, y qué implicaciones podría tener la fórmula de prorrateo
        (incluyendo consideraciones como "las tres quintas partes" y la exclusión de indígenas)
        en términos de representación política y distribución de poder?
        """,
        display_in_console=True,
    ),
    # You can add as many queries as needed to analyze different aspects of your documents
]

async def main():
    async with Session(type="standard", delete=True) as session:
        # Ingest documents once
        await session.ingest(input_dir=input_dir)

        # Run all queries in sequence
        # This is more efficient than running separate query calls
        # as the session maintains context between queries
        await session.multiple_queries(queries=queries)
        # Each query and its response will be displayed in the console

if __name__ == "__main__":
    asyncio.run(main())

Getting a Session Summary

SHADAI can generate concise summaries of the documents in your session:

# examples/get_session_summary.py
import asyncio
import os
from shadai.core.session import Session

input_dir = os.path.join(os.path.dirname(__file__), "data")

async def main():
    async with Session(type="standard", delete=True) as session:
        # Ingest the documents
        await session.ingest(input_dir=input_dir)

        # Generate a summary of all documents in the session
        # This provides a high-level overview of the key topics and information
        await session.summarize(display_in_console=True)
        # The summary will be displayed in the console and can be used for further analysis

if __name__ == "__main__":
    asyncio.run(main())

Creating an Article

SHADAI can generate comprehensive articles on topics related to the documents in your session:

# examples/create_article.py
import asyncio
import os
from shadai.core.session import Session

input_dir = os.path.join(os.path.dirname(__file__), "data")

async def main():
    async with Session(type="standard", delete=True) as session:
        # Ingest the documents
        await session.ingest(input_dir=input_dir)

        # Generate an article on a specific topic
        # This creates a well-structured, detailed piece of content
        # based on information from your documents
        await session.article(
            topic="Enmiendas de la constitución y su impacto social",
            display_in_console=True,
        )
        # The article will be displayed in the console and can be saved or further processed

if __name__ == "__main__":
    asyncio.run(main())

Advanced Interactions

Simple LLM Completion

Sometimes you just need a direct answer from the language model:

# examples/call_llm.py
import asyncio
import os
from shadai.core.session import Session

input_dir = os.path.join(os.path.dirname(__file__), "data")

async def main():
    async with Session(type="standard", delete=True) as session:
        # Ingest documents to provide context
        await session.ingest(input_dir=input_dir)

        # Use complete for direct language model responses
        # This bypasses the document retrieval step but still considers the document context
        await session.llm_call(
            prompt="¿Cual es el estado con la mayor economía en los Estados Unidos?",
            display_prompt=True,  # Show the prompt in the output
            display_in_console=True,  # Print the response in the console
        )
        # The LLM will generate a response based on its knowledge without document context

if __name__ == "__main__":
    asyncio.run(main())

Chat with Data and History

For interactive conversations that maintain context:

# examples/chat_with_data_and_history.py
import asyncio
import os
import sys

# Add the parent directory to sys.path to access the shadai package
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from shadai.core.session import Session

input_dir = os.path.join(os.path.dirname(__file__), "data")


async def chat_with_data_and_history():
    """
    This function chats with the data and history.
    """
    async with Session(type="standard", delete=True) as session:
        await session.ingest(input_dir=input_dir)
        await session.chat(
            message="¿Qué dice la constitución sobre la libertad de expresión?",
            system_prompt="Eres un experto en derecho constitucional y tienes acceso a la constitución.",
            use_history=True,
            display_in_console=True,
        )
        # This is optional to run, it cleans up the chat history
        await session.cleanup_chat()


async def chat_only_with_history():
    """
    This function chats only with the history.
    """
    async with Session(type="standard", delete=True) as session:
        await session.chat(
            message="¿Qué dice la constitución sobre la libertad de expresión?",
            system_prompt="Eres un experto en derecho constitucional y tienes acceso a la constitución.",
            use_history=True,
            display_in_console=True,
        )
        # This is optional to run, it cleans up the chat history
        await session.cleanup_chat()


if __name__ == "__main__":
    asyncio.run(chat_with_data_and_history())
    asyncio.run(chat_only_with_history())

Chat with Images

You can also analyze images using the SHADAI client:

# examples/chat_with_images.py
import asyncio
import os
import sys

# Add the parent directory to sys.path to access the shadai package
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from shadai.core.session import Session

images_dir = os.path.join(os.path.dirname(__file__), "media", "images")


async def chat_with_images_without_history():
    """
    This function chats with the images.
    """
    async with Session(
        type="standard",
        delete=True,
    ) as session:
        await session.llm_call(
            prompt="Dame toda la información que puedas obtener de estas imágenes",
            images_path=images_dir,
            display_prompt=True,
            display_in_console=True,
        )


if __name__ == "__main__":
    asyncio.run(chat_with_images_without_history())

Creating a Tool Agent

For more complex AI-powered workflows, you can create a Tool Agent that combines document knowledge with custom functions:

# examples/create_shadai_agent.py
import asyncio
import os
from typing import Dict
from shadai.core.agents import ToolAgent
from shadai.core.session import Session

input_dir = os.path.join(os.path.dirname(__file__), "data")

# Define a function that the agent can use
def get_constitutional_article(article_id: str) -> str:
    """
    This function provides specific information about constitutional amendments.
    The agent can call this function to get detailed information.
    """
    articles: Dict[str, Dict[str, str]] = {
        "1": {
            "title": "Primera Enmienda",
            "description": "Libertad de expresión, religión, prensa y reunión",
            "content": "El Congreso no hará ley alguna con respecto al establecimiento de religión, ni prohibiendo la libre práctica de la misma; ni limitando la libertad de expresión, ni de prensa; ni el derecho del pueblo a reunirse pacíficamente.",
        },
        "2": {
            "title": "Segunda Enmienda",
            "description": "Derecho a portar armas",
            "content": "Siendo necesaria una milicia bien ordenada para la seguridad de un Estado libre, no se violará el derecho del pueblo a poseer y portar armas.",
        },
    }

    if article_id not in articles:
        return "Artículo no encontrado"

    article = articles[article_id]
    return f"Título: {article['title']}\nDescripción: {article['description']}\nContenido: {article['content']}"

async def main():
    async with Session(type="standard", delete=True) as session:
        # Ingest documents to provide context
        await session.ingest(input_dir=input_dir)

        # Create a Tool Agent that combines document knowledge with custom functions
        agent = ToolAgent(
            session=session,
            # Define a prompt template that uses both the function output and document summary
            prompt="""
                Analiza la relación entre la Primera Enmienda y las empresas digitales:

                Enmienda Constitucional:
                {function_output}

                Contexto de documentos:
                {summary}

                Considerando la Primera Enmienda y el contexto histórico, analiza:
                1. Cómo se aplican los principios de libertad de expresión en el entorno digital
                2. Desafíos y oportunidades para las empresas digitales en relación con estos derechos
                3. Recomendaciones para equilibrar la innovación tecnológica con los derechos constitucionales
            """,
            use_summary=True,  # Include document summary in the prompt
            function=get_constitutional_article,  # The function the agent can call
        )

        # Call the agent with specific parameters for the function
        await agent.call(article_id="1")
        # The agent will call the function, get the document summary,
        # and generate a comprehensive analysis based on both information sources

if __name__ == "__main__":
    asyncio.run(main())

Error Handling

The SHADAI client includes comprehensive error handling for:

• Configuration errors
• API communication issues
• File processing problems
• Session management failures

Author

SHADAI GROUP [e-mail]