sarvam-conv-ai-sdk 1.0.5

A Python SDK for building multilingual, context-aware conversational agents on Sarvam's Samvaad platform.

Sarvam Conv AI SDK

The Sarvam Conversational AI SDK is a Python package that helps developers build and extend conversational agents. It provides core components to manage conversation flow, language preferences, and messaging, making it easier to develop interactive and context-aware AI experiences.

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Overview

The Sarvam Conv AI SDK enables developers to create tools that can:

• Facilitate agentic capabilities like API calling in the middle of a conversation.
• Manage agent-specific variables
• Control and modify the language used during conversations
• Send dynamic messages to both the user and the underlying language model (LLM)

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Installation

Install the SDK via pip:

pip install sarvam-conv-ai-sdk

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Example Usage

import httpx
from pydantic import Field

from sarvam_conv_ai_sdk import (
    SarvamInteractionTurnRole,
    SarvamOnEndTool,
    SarvamOnEndToolContext,
    SarvamOnStartTool,
    SarvamOnStartToolContext,
    SarvamTool,
    SarvamToolContext,
    SarvamToolLanguageName,
    SarvamToolOutput,
)

class OnStart(SarvamOnStartTool): #Name of the class has to be OnStart
    async def run(self, context: SarvamOnStartToolContext):
        user_id = context.get_user_identifier()
        async with httpx.AsyncClient() as client:
            response = await client.get(f"https://sarvam-flights.com/users/{user_id}")
            response.raise_for_status()
            user_data = response.json()

        source_destination = user_data.get("home_city")
        context.set_agent_variable("source_destination", source_destination)
        context.set_agent_variable("passenger_name", user_data.get("name"))
        context.set_initial_language_name(SarvamToolLanguageName.ENGLISH)
        context.set_initial_bot_message(
            f"Hello! Would you like to book a flight from {source_destination}? Where would you like to go?",
        )
        return context


class BookFlight(SarvamTool):
    """Book a flight based on the user's travel preferences."""

    destination: str = Field(description="City of destination")
    travel_date: str = Field(description="Date of travel (YYYY-MM-DD)")

    async def run(self, context: SarvamToolContext) -> SarvamToolOutput:
        source_destination = context.get_agent_variable("source_destination")
        booking_data = {
            "source": source_destination,
            "destination": self.destination,
            "travel_date": self.travel_date,
            "passenger_name": context.get_agent_variable("passenger_name"),
        }

        async with httpx.AsyncClient() as client:
            response = await client.post(
                "https://sarvam-flights.com/book", json=booking_data
            )
            response.raise_for_status()
            booking_result = response.json()

        if booking_result.get("status") == "confirmed":
            context.set_agent_variable("booking_id", booking_result.get("booking_id"))
            context.set_end_conversation()
            return SarvamToolOutput(
                message_to_user=f"Flight booked successfully to {self.destination}!",
                context=context,
            )
        else:
            context.change_state("recommend_destinations")
            return SarvamToolOutput(
                message_to_llm="Booking failed. Please suggest similar destinations.",
                context=context,
            )


class OnEnd(SarvamOnEndTool):  #Name of the class has to be OnEnd
    async def run(self, context: SarvamOnEndToolContext):
        feedback = context.get_agent_variable("feedback")
        negative_words = ["bad", "poor", "disappointed", "unhappy", "problem"]
        interaction_transcript = context.get_interaction_transcript()
        if interaction_transcript:
            for turn in interaction_transcript.interaction_transcript:
                if turn.role == SarvamInteractionTurnRole.USER:
                    is_negative = any(word in feedback.lower() for word in negative_words)
            context.set_agent_variable("feedback_sentiment", is_negative)

        return context

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Base Classes

The SDK exposes three base classes for tool development:

1. SarvamTool

Primary base class for all operational tools invoked during conversation flow.

Example:

class MyCustomTool(SarvamTool):
    """Brief description of the tool's purpose."""

    tool_variable: type = Field(description="Description of this input parameter")

    async def run(self, context: SarvamToolContext) -> SarvamToolOutput:
        # Custom tool logic
        return SarvamToolOutput(
            message_to_user="Response to user",
            message_to_llm="Context for LLM",
            context=context
        )

2. SarvamOnStartTool

Executed at the beginning of a conversation, typically for initialization. The class must be named OnStart.

3. SarvamOnEndTool

Executed at the end of a conversation, typically for cleanup or post-processing. The class must be named OnEnd.

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Context Classes and Methods

SarvamToolContext

The context object passed to SarvamTool.run() methods.

Variable Management

• get_agent_variable(variable_name: str) -> Any Retrieve the value of a variable.

• set_agent_variable(variable_name: str, value: Any) -> None Update a variable's value.

Language Control

• get_current_language() -> SarvamToolLanguageName Returns the current language of the agent.

• change_language(language: SarvamToolLanguageName) -> None Update the language preference.

Conversation Flow

• set_end_conversation() -> None Explicitly end the conversation.

State Management

• get_current_state() -> str Returns the current state of the conversation.

• change_state(state: str) -> None Transition to a new state. Note: The new state must be one of the next valid states defined in the agent configuration.

Engagement Metadata

• get_engagement_metadata() -> EngagementMetadata Retrieve the engagement metadata containing information about the current interaction.

---

SarvamOnStartToolContext

The context object passed to SarvamOnStartTool.run() methods.

Variable Management

• get_agent_variable(variable_name: str) -> Any Retrieve the value of a variable.

• set_agent_variable(variable_name: str, value: Any) -> None Update a variable's value.

User Information

• get_user_identifier() -> str Get the user identifier.

Initialization Methods

• set_initial_bot_message(message: str) -> None Set the first message sent by the agent when the conversation starts.

• set_initial_state_name(state_name: str) -> None Set the initial state from which the agent should start.

• set_initial_language_name(language: SarvamToolLanguageName) -> None Define the initial language preference for the user.

Engagement Metadata

• get_engagement_metadata() -> EngagementMetadata Retrieve the engagement metadata containing information about the current interaction.

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SarvamOnEndToolContext

The context object passed to SarvamOnEndTool.run() methods.

Variable Management

• get_agent_variable(variable_name: str) -> Any Retrieve the value of a variable.

• set_agent_variable(variable_name: str, value: Any) -> None Update a variable's value.

User Information

• get_user_identifier() -> str Get the user identifier.

Engagement Metadata

• get_engagement_metadata() -> EngagementMetadata Retrieve the engagement metadata containing information about the current interaction.

Interaction Reattempt

• set_retry_interaction The user will be reattempted with the same agent. Useful when any business goal has not been met.

Interaction Transcript

• get_interaction_transcript() -> SarvamInteractionTranscript Retrieve the conversation history containing user and agent messages in English and the timestamp when the conversation began and ended. Format: yyyy-mm-dd hh:mm:ss

Example transcript:

[
    SarvamInteractionTurn(role=<SarvamInteractionTurnRole.AGENT: 'agent'>, en_text='Hello! How can I help you today?'),
    SarvamInteractionTurn(role=<SarvamInteractionTurnRole.USER: 'user'>, en_text='I need to book a flight'),
    SarvamInteractionTurn(role=<SarvamInteractionTurnRole.AGENT: 'agent'>, en_text='I can help you with that. Where would you like to go?'),
    SarvamInteractionTurn(role=<SarvamInteractionTurnRole.USER: 'user'>, en_text='I want to go to Mumbai'),
    SarvamInteractionTurn(role=<SarvamInteractionTurnRole.AGENT: 'agent'>, en_text='Great! When would you like to travel?')
]

---

Return Types

SarvamToolOutput

The return type for SarvamTool.run() methods. Contains:

• message_to_user: Optional[str] - Message that is sent directly to the user
• message_to_llm: Optional[str] - Message that is sent to the LLM, which then responds
• context: SarvamToolContext - The updated context object

Note: At least one of message_to_llm or message_to_user must be set.

Important: When both message_to_user and message_to_llm are set, only the message_to_user is actually sent to the user, but the message_to_llm overrides the message_to_user when adding to the chat thread for the LLM's context.

EngagementMetadata

The engagement metadata object that can be retrieved from context objects using get_engagement_metadata(). Contains:

• interaction_id: str - Unique identifier for each conversation between user & agent.
• attempt_id: Optional[str] - Unique identifier for each attempt created on the platform
• campaign_id: Optional[str] - Campaign ID for the interaction
• app_id: str - Application identifier of the agent for the interaction
• app_version: int - Version number of the agent

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Supported Languages

The SDK supports multilingual conversations using the SarvamToolLanguageName enum. Available languages include:

• Bengali
• Gujarati
• Kannada
• Malayalam
• Tamil
• Telugu
• Punjabi
• Odia
• Marathi
• Hindi
• English

Note: The allowed languages are actually a subset that is preselected while defining the agent configurations.

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Best Practices

1. Always implement run(): The run() method is the entry point for tool execution logic.
2. Use Field() for parameters: Ensures type safety and adds descriptive metadata necessary for LLM to use in the prompt.
3. Gracefully handle errors: Avoid accessing unset variables or using invalid types.
4. Return the appropriate type: SarvamTool.run() must return SarvamToolOutput, while SarvamOnStartTool.run() and SarvamOnEndTool.run() return their respective context objects.
5. Write meaningful docstrings: Clearly describe what each tool is intended to do as this directly impacts the performance of tool calling capabilities of the agent.
6. Use async operations for I/O: For the best performance, use async/await for external API calls to avoid blocking.
7. Use context methods: Use the provided context methods for variable management, language control, and messaging instead of directly accessing context attributes.

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Error Handling

The SDK includes built-in error handling for common scenarios:

• Variable not found: Raises ValueError when accessing undefined variables
• Variable not defined: Raises ValueError when setting variables that haven't been initialized
• Non-serializable values: Raises ValueError when variable values cannot be JSON serialized
• Invalid output: Raises ValueError when SarvamToolOutput is created without at least one message

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Testing Your Tools

After creating a tool, you can test it locally to ensure it works as expected. Here's how to test your tools:

Testing Steps

1. Create the ToolContext: Initialize the appropriate context object with test data
2. Instantiate the tool class: Use tool.model_validate(tool_args) to create a tool instance
3. Run the tool: Call the tool's run() method with the context
4. Observe the returned object: Check if the necessary changes have been made to the context

Example Test

import asyncio
from sarvam_conv_ai_sdk import SarvamToolLanguageName, SarvamToolContext

# Test the BookFlight tool
async def test_book_flight():
    # 1. Create the ToolContext
    context = SarvamToolContext(
        language=SarvamToolLanguageName.ENGLISH,
        allowed_languages=[SarvamToolLanguageName.ENGLISH],
        state="booking",
        next_valid_states=["recommend_destinations", "end"],
        agent_variables={
            "source_destination": "Mumbai",
            "passenger_name": "John Doe"
        }
    )
    
    # 2. Instantiate the tool class
    tool_args = {
        "destination": "Delhi",
        "travel_date": "2024-03-15"
    }
    tool_instance = BookFlight.model_validate(tool_args)
    
    # 3. Run the tool
    result = await tool_instance.run(context)
    
    # 4. Observe the returned object
    print(f"Message to user: {result.message_to_user}")
    print(f"Message to LLM: {result.message_to_llm}")
    print(f"End conversation: {result.context.end_conversation}")
    print(f"Current state: {result.context.get_current_state()}")
    print(f"Agent variables: {result.context.agent_variables}")

# Run the test
asyncio.run(test_book_flight())

Testing OnStart and OnEnd Tools

For SarvamOnStartTool and SarvamOnEndTool, the testing approach is similar but they return the context object directly:

# Testing OnStart tool
async def test_on_start():
    context = SarvamOnStartToolContext(
        user_identifier="user123",
        initial_state_name="start",
        initial_language_name=SarvamToolLanguageName.ENGLISH,
        agent_variables={}
    )
    
    tool_instance = OnStart()
    result = await tool_instance.run(context)
    
    print(f"Initial bot message: {result.initial_bot_message}")
    print(f"Initial state: {result.initial_state_name}")
    print(f"Agent variables: {result.agent_variables}")