langchain-wiproai 0.1.0

Wipro AI integration for LangChain

langchain-wiproai

Wipro AI integration for LangChain, providing seamless access to Wipro AI models with full tool calling support.

Installation

pip install langchain-wiproai

Quick Start

from langchain_wiproai import ChatWiproAI

# Initialize the model
llm = ChatWiproAI(
    api_token="your-api-token",
    model_name="gpt-4o",
    temperature=0.0
)

# Simple usage
response = llm.invoke("Hello, how are you?")
print(response.content)

# With tool calling
from langchain_core.tools import tool

@tool
def get_weather(location: str) -> str:
    \"\"\"Get the weather for a location.\"\"\"
    return f"The weather in {location} is sunny!"

llm_with_tools = llm.bind_tools([get_weather])
response = llm_with_tools.invoke("What's the weather in Paris?")
print(response.tool_calls)

Configuration

Environment Variables

You can set your API token as an environment variable:

export WIPROAI_API_TOKEN="your-api-token"

Then use without passing the token:

from langchain_wiproai import ChatWiproAI

llm = ChatWiproAI()  # Will use WIPROAI_API_TOKEN from environment

Parameters

• api_token (str): Your Wipro AI API token
• api_url (str): API endpoint URL (default: Wipro AI endpoint)
• model_name (str): Model to use (default: "gpt-4o")
• temperature (float): Temperature for generation (default: 0.0, range: 0.0-2.0)
• max_output_tokens (int): Maximum tokens to generate (default: 2000)
• top_p (float): Top-p sampling parameter (default: 1.0, range: 0.0-1.0)
• top_k (int): Top-k sampling parameter (default: 1)

Advanced Usage

Streaming

for chunk in llm.stream("Tell me a story"):
    print(chunk.content, end="", flush=True)

Async

import asyncio

async def main():
    response = await llm.ainvoke("Hello!")
    print(response.content)

asyncio.run(main())

With LangChain Agents

from langchain.agents import create_react_agent
from langchain_wiproai import ChatWiproAI
from langchain_core.tools import tool

@tool
def calculator(expression: str) -> str:
    \"\"\"Calculate a mathematical expression.\"\"\"
    return str(eval(expression))

llm = ChatWiproAI(temperature=0)
agent = create_react_agent(llm, [calculator])
result = agent.invoke({"input": "What is 25 * 47?"})

Features

• ✅ Full LangChain integration
• ✅ Tool/function calling support
• ✅ Streaming support
• ✅ Async support
• ✅ Automatic JSON tool call parsing
• ✅ Multiple response format handling
• ✅ Pydantic v2 compatibility