Usage of llama-3.1-sonar-small-128k-online

perplexity-api-test-files.py

import os
import sys
from dotenv import load_dotenv
from langchain_community.chat_models import ChatPerplexity
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

# Load environment variables
load_dotenv()

# Set up the Perplexity API client
chat = ChatPerplexity(
    model="llama-3.1-sonar-small-128k-online",
    temperature=0,
    pplx_api_key=os.getenv("PPLX_API_KEY"),
)


def evaluate_text(file_path, prompt):
    # Read the file content
    with open(file_path, "r", encoding="utf-8") as file:
        file_content = file.read()

    # Create a prompt template
    prompt_template = ChatPromptTemplate.from_messages(
        [
            ("system", "You are a helpful assistant."),
            ("human", "{prompt}\n\n{file_content}"),
        ]
    )

    # Create a runnable sequence
    chain = prompt_template | chat | StrOutputParser()

    # Invoke the chain
    return chain.invoke({"prompt": prompt, "file_content": file_content})


if __name__ == "__main__":
    if len(sys.argv) != 3:
        print("Usage: python script_name.py <prompt> <path_to_text_file>")
        sys.exit(1)

    prompt = sys.argv[1]
    file_path = sys.argv[2]

    # Make the API call and get the evaluation
    evaluation = evaluate_text(file_path, prompt)

    # Print the evaluation
    print(evaluation)