Merge pull request openai#27 from openai/ted/restore_qa_notebooks

Ted/restore qa notebooks

examples/fine-tuned_qa/answers_with_ft.py

@@ -0,0 +1,156 @@
+"""
+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).
+"""
+
+import argparse
+
+import openai
+
+
+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 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 ""
+
+
+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}")