actions/actions_llm.py

# # run_search.py
# import os
# import sys
# import openai

# # Add "/actions" to the sys.path
# actions_path = os.path.abspath("/actions")
# sys.path.insert(0, actions_path)

# # Import search_content.py from /actions folder
# from search_content import main_search


# # Import api key from secrets
# secret_value_0 = os.environ.get("openai")

# openai.api_key = secret_value_0
# # Provide your OpenAI API key

# def generate_openai_response(query, model_engine="text-davinci-002", max_tokens=124, temperature=0.8):
#     """Generate a response using the OpenAI API."""
#     # Run the main function from search_content.py and store the results in a variable
#     results = main_search(query)

#     # Create context from the results
#     context = "".join([f"#{str(i)}" for i in results])[:2014] # Trim the context to 2014 characters - Modify as necessory
#     prompt_template = f"Relevant context: {context}\n\n Answer the question in detail: {query}"

#     # Generate a response using the OpenAI API
#     response = openai.Completion.create(
#         engine=model_engine,
#         prompt=prompt_template,
#         max_tokens=max_tokens,
#         temperature=temperature,
#         n=1,
#         stop=None,
#     )

#     return response.choices[0].text.strip()

# def main():
#     query = "What is omdena local chapters, how a developer can benifit from it"
#     response = generate_openai_response(query)
#     print(response)

# if __name__ == "__main__":
#     main()