Answers with ft

In [ ]:

"""
Note: To answer questions based on text documents, we recommend the procedure in 
[Question Answering using Embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb).
Some of the code below may rely on [deprecated API endpoints](https://github.com/openai/openai-cookbook/tree/main/transition_guides_for_deprecated_API_endpoints).
"""

""" Note: To answer questions based on text documents, we recommend the procedure in [Question Answering using Embeddings](https://github.com/openai/openai-cookbook/blob/main/examples/Question_answering_using_embeddings.ipynb). Some of the code below may rely on [deprecated API endpoints](https://github.com/openai/openai-cookbook/tree/main/transition_guides_for_deprecated_API_endpoints). """

In [ ]:

import argparse

import argparse

In [ ]:

import openai

import openai

In [ ]:

def create_context(
    question, search_file_id, max_len=1800, search_model="ada", max_rerank=10
):
    """
    Create a context for a question by finding the most similar context from the search file.
    :param question: The question
    :param search_file_id: The file id of the search file
    :param max_len: The maximum length of the returned context (in tokens)
    :param search_model: The search model to use
    :param max_rerank: The maximum number of reranking
    :return: The context
    """
    results = openai.Engine(search_model).search(
        search_model=search_model,
        query=question,
        max_rerank=max_rerank,
        file=search_file_id,
        return_metadata=True,
    )
    returns = []
    cur_len = 0
    for result in results["data"]:
        cur_len += int(result["metadata"]) + 4
        if cur_len > max_len:
            break
        returns.append(result["text"])
    return "\n\n###\n\n".join(returns)

def create_context( question, search_file_id, max_len=1800, search_model="ada", max_rerank=10 ): """ Create a context for a question by finding the most similar context from the search file. :param question: The question :param search_file_id: The file id of the search file :param max_len: The maximum length of the returned context (in tokens) :param search_model: The search model to use :param max_rerank: The maximum number of reranking :return: The context """ results = openai.Engine(search_model).search( search_model=search_model, query=question, max_rerank=max_rerank, file=search_file_id, return_metadata=True, ) returns = [] cur_len = 0 for result in results["data"]: cur_len += int(result["metadata"]) + 4 if cur_len > max_len: break returns.append(result["text"]) return "\n\n###\n\n".join(returns)

In [ ]:

def answer_question(
    search_file_id="<SEARCH_FILE_ID>",
    fine_tuned_qa_model="<FT_QA_MODEL_ID>",
    question="Which country won the European Football championship in 2021?",
    max_len=1800,
    search_model="ada",
    max_rerank=10,
    debug=False,
    stop_sequence=["\n", "."],
    max_tokens=100,
):
    """
    Answer a question based on the most similar context from the search file, using your fine-tuned model.
    :param question: The question
    :param fine_tuned_qa_model: The fine tuned QA model
    :param search_file_id: The file id of the search file
    :param max_len: The maximum length of the returned context (in tokens)
    :param search_model: The search model to use
    :param max_rerank: The maximum number of reranking
    :param debug: Whether to output debug information
    :param stop_sequence: The stop sequence for Q&A model
    :param max_tokens: The maximum number of tokens to return
    :return: The answer
    """
    context = create_context(
        question,
        search_file_id,
        max_len=max_len,
        search_model=search_model,
        max_rerank=max_rerank,
    )
    if debug:
        print("Context:\n" + context)
        print("\n\n")
    try:
        # fine-tuned models requires model parameter, whereas other models require engine parameter
        model_param = (
            {"model": fine_tuned_qa_model}
            if ":" in fine_tuned_qa_model
            and fine_tuned_qa_model.split(":")[1].startswith("ft")
            else {"engine": fine_tuned_qa_model}
        )
        response = openai.Completion.create(
            prompt=f"Answer the question based on the context below\n\nText: {context}\n\n---\n\nQuestion: {question}\nAnswer:",
            temperature=0,
            max_tokens=max_tokens,
            top_p=1,
            frequency_penalty=0,
            presence_penalty=0,
            stop=stop_sequence,
            **model_param,
        )
        return response["choices"][0]["text"]
    except Exception as e:
        print(e)
        return ""

def answer_question( search_file_id="", fine_tuned_qa_model="", question="Which country won the European Football championship in 2021?", max_len=1800, search_model="ada", max_rerank=10, debug=False, stop_sequence=["\n", "."], max_tokens=100, ): """ Answer a question based on the most similar context from the search file, using your fine-tuned model. :param question: The question :param fine_tuned_qa_model: The fine tuned QA model :param search_file_id: The file id of the search file :param max_len: The maximum length of the returned context (in tokens) :param search_model: The search model to use :param max_rerank: The maximum number of reranking :param debug: Whether to output debug information :param stop_sequence: The stop sequence for Q&A model :param max_tokens: The maximum number of tokens to return :return: The answer """ context = create_context( question, search_file_id, max_len=max_len, search_model=search_model, max_rerank=max_rerank, ) if debug: print("Context:\n" + context) print("\n\n") try: # fine-tuned models requires model parameter, whereas other models require engine parameter model_param = ( {"model": fine_tuned_qa_model} if ":" in fine_tuned_qa_model and fine_tuned_qa_model.split(":")[1].startswith("ft") else {"engine": fine_tuned_qa_model} ) response = openai.Completion.create( prompt=f"Answer the question based on the context below\n\nText: {context}\n\n---\n\nQuestion: {question}\nAnswer:", temperature=0, max_tokens=max_tokens, top_p=1, frequency_penalty=0, presence_penalty=0, stop=stop_sequence, **model_param, ) return response["choices"][0]["text"] except Exception as e: print(e) return ""

In [ ]:

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Rudimentary functionality of the answers endpoint with a fine-tuned Q&A model.",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    parser.add_argument(
        "--search_file_id", help="Search file id", required=True, type=str
    )
    parser.add_argument(
        "--fine_tuned_qa_model", help="Fine-tuned QA model id", required=True, type=str
    )
    parser.add_argument(
        "--question", help="Question to answer", required=True, type=str
    )
    parser.add_argument(
        "--max_len",
        help="Maximum length of the returned context (in tokens)",
        default=1800,
        type=int,
    )
    parser.add_argument(
        "--search_model", help="Search model to use", default="ada", type=str
    )
    parser.add_argument(
        "--max_rerank",
        help="Maximum number of reranking for the search",
        default=10,
        type=int,
    )
    parser.add_argument(
        "--debug", help="Print debug information (context used)", action="store_true"
    )
    parser.add_argument(
        "--stop_sequence",
        help="Stop sequences for the Q&A model",
        default=["\n", "."],
        nargs="+",
        type=str,
    )
    parser.add_argument(
        "--max_tokens",
        help="Maximum number of tokens to return",
        default=100,
        type=int,
    )
    args = parser.parse_args()
    response = answer_question(
        search_file_id=args.search_file_id,
        fine_tuned_qa_model=args.fine_tuned_qa_model,
        question=args.question,
        max_len=args.max_len,
        search_model=args.search_model,
        max_rerank=args.max_rerank,
        debug=args.debug,
        stop_sequence=args.stop_sequence,
        max_tokens=args.max_tokens,
    )
    print(f"Answer:{response}")

if __name__ == "__main__": parser = argparse.ArgumentParser( description="Rudimentary functionality of the answers endpoint with a fine-tuned Q&A model.", formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) parser.add_argument( "--search_file_id", help="Search file id", required=True, type=str ) parser.add_argument( "--fine_tuned_qa_model", help="Fine-tuned QA model id", required=True, type=str ) parser.add_argument( "--question", help="Question to answer", required=True, type=str ) parser.add_argument( "--max_len", help="Maximum length of the returned context (in tokens)", default=1800, type=int, ) parser.add_argument( "--search_model", help="Search model to use", default="ada", type=str ) parser.add_argument( "--max_rerank", help="Maximum number of reranking for the search", default=10, type=int, ) parser.add_argument( "--debug", help="Print debug information (context used)", action="store_true" ) parser.add_argument( "--stop_sequence", help="Stop sequences for the Q&A model", default=["\n", "."], nargs="+", type=str, ) parser.add_argument( "--max_tokens", help="Maximum number of tokens to return", default=100, type=int, ) args = parser.parse_args() response = answer_question( search_file_id=args.search_file_id, fine_tuned_qa_model=args.fine_tuned_qa_model, question=args.question, max_len=args.max_len, search_model=args.search_model, max_rerank=args.max_rerank, debug=args.debug, stop_sequence=args.stop_sequence, max_tokens=args.max_tokens, ) print(f"Answer:{response}")