LlamaIndex with SUTRA

!pip install -qU llama-index llama-index-llms-openai-like llama-index-vector-stores-faiss faiss-cpu ipywidgets

import os
from llama_index.llms.openai_like import OpenAILike

# Initialize Sutra as an OpenAI-compatible LLM
llm = OpenAILike(
    model="sutra-v2",                             # Model name
    api_base="https://api.two.ai/v2",            # Sutra API base
    api_key=os.getenv("SUTRA_API_KEY"),          # API Key from environment
    is_chat_model=True                           # Set True for chat-based models
)

# Send a message in English
response = llm.complete("Give me 3 tips to stay productive while working from home.")
print(response.text)

from llama_index.llms.openai_like import OpenAILike

# Initialize Sutra LLM with necessary parameters
llm = OpenAILike(
    model="sutra-v2",                    # Sutra model name
    api_base="https://api.two.ai/v2",    # Sutra API base URL
    api_key=os.getenv("SUTRA_API_KEY"),  # Sutra API key
    is_chat_model=True,                  # Mandatory: Set to True for chat-based models
)

# Multilingual prompts
prompts = [
    "తెలుగులో ఒక కథ చెప్పు?",                         # Telugu
    "Une histoire en français, s'il vous plaît.",     # French
    "Por favor, cuéntame una historia en español.",   # Spanish
    "कृपया हिंदी में एक कहानी सुनाइए।",                    # Hindi
    "Bitte erzähle mir eine Geschichte auf Deutsch."  # German
]

# Loop through each prompt and print the response
for prompt in prompts:
    response = llm.complete(prompt)
    print(f"\nPrompt: {prompt}")
    print(f"Response: {str(response)}\n")

import os
from llama_index.llms.openai_like import OpenAILike
from llama_index.core.llms import ChatMessage


# Initialize the Sutra model via LlamaIndex
llm = OpenAILike(
    model="sutra-v2",                    # Sutra model name
    api_base="https://api.two.ai/v2",    # Sutra API base URL
    api_key=os.getenv("SUTRA_API_KEY"),  # Sutra API key
    is_chat_model=True,                  # Mandatory for chat-based models
)

# Start the chatbot conversation loop
print("Chatbot: Hello! Type 'exit' to end the conversation.\n")

chat_history = []

while True:
    user_input = input("You: ")  # Get user input

    if user_input.lower() == "exit":
        print("Chatbot: Goodbye! 👋")
        break

    # Add user message to chat history
    chat_history.append(ChatMessage(role="user", content=user_input))

    # Get response from Sutra via LlamaIndex
    response = llm.chat(chat_history)

    # Print response content
    print("Chatbot:", response.message.content)

    # Add AI response to chat history
    chat_history.append(response.message)