{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Code search\n",
"\n",
"We index our own openai-python code repository, and show how it can be searched. We implement a simple version of file parsing and extracting of functions from python files."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of py files: 40\n",
"Total number of functions extracted: 64\n"
]
}
],
"source": [
"import os\n",
"from glob import glob\n",
"import pandas as pd\n",
"\n",
"def get_function_name(code):\n",
" \"\"\"\n",
" Extract function name from a line beginning with \"def \"\n",
" \"\"\"\n",
" assert code.startswith(\"def \")\n",
" return code[len(\"def \"): code.index(\"(\")]\n",
"\n",
"def get_until_no_space(all_lines, i) -> str:\n",
" \"\"\"\n",
" Get all lines until a line outside the function definition is found.\n",
" \"\"\"\n",
" ret = [all_lines[i]]\n",
" for j in range(i + 1, i + 10000):\n",
" if j < len(all_lines):\n",
" if len(all_lines[j]) == 0 or all_lines[j][0] in [\" \", \"\\t\", \")\"]:\n",
" ret.append(all_lines[j])\n",
" else:\n",
" break\n",
" return \"\\n\".join(ret)\n",
"\n",
"def get_functions(filepath):\n",
" \"\"\"\n",
" Get all functions in a Python file.\n",
" \"\"\"\n",
" whole_code = open(filepath).read().replace(\"\\r\", \"\\n\")\n",
" all_lines = whole_code.split(\"\\n\")\n",
" for i, l in enumerate(all_lines):\n",
" if l.startswith(\"def \"):\n",
" code = get_until_no_space(all_lines, i)\n",
" function_name = get_function_name(code)\n",
" yield {\"code\": code, \"function_name\": function_name, \"filepath\": filepath}\n",
"\n",
"\n",
"# get user root directory\n",
"root_dir = os.path.expanduser(\"~\")\n",
"\n",
"# path to code repository directory\n",
"code_root = root_dir + \"/openai-python\"\n",
"code_files = [y for x in os.walk(code_root) for y in glob(os.path.join(x[0], '*.py'))]\n",
"print(\"Total number of py files:\", len(code_files))\n",
"all_funcs = []\n",
"for code_file in code_files:\n",
" funcs = list(get_functions(code_file))\n",
" for func in funcs:\n",
" all_funcs.append(func)\n",
"\n",
"print(\"Total number of functions extracted:\", len(all_funcs))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For code search models we use code-search-{model}-code to obtain embeddings for code snippets, and code-search-{model}-text to embed natural language queries."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>function_name</th>\n",
" <th>filepath</th>\n",
" <th>code_embedding</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>def semantic_search(engine, query, documents):...</td>\n",
" <td>semantic_search</td>\n",
" <td>/examples/semanticsearch/semanticsearch.py</td>\n",
" <td>[-0.038976121693849564, -0.0031428150832653046...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>def main():\\n parser = argparse.ArgumentPar...</td>\n",
" <td>main</td>\n",
" <td>/examples/semanticsearch/semanticsearch.py</td>\n",
" <td>[-0.024289356544613838, -0.017748363316059113,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>def get_candidates(\\n prompt: str,\\n sto...</td>\n",
" <td>get_candidates</td>\n",
" <td>/examples/codex/backtranslation.py</td>\n",
" <td>[-0.04161201789975166, -0.0169310811907053, 0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>def rindex(lst: List, value: str) -> int:\\n ...</td>\n",
" <td>rindex</td>\n",
" <td>/examples/codex/backtranslation.py</td>\n",
" <td>[-0.027255680412054062, -0.007931121625006199,...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>def eval_candidate(\\n candidate_answer: str...</td>\n",
" <td>eval_candidate</td>\n",
" <td>/examples/codex/backtranslation.py</td>\n",
" <td>[-0.00999179296195507, -0.01640152558684349, 0...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" code function_name \\\n",
"0 def semantic_search(engine, query, documents):... semantic_search \n",
"1 def main():\\n parser = argparse.ArgumentPar... main \n",
"2 def get_candidates(\\n prompt: str,\\n sto... get_candidates \n",
"3 def rindex(lst: List, value: str) -> int:\\n ... rindex \n",
"4 def eval_candidate(\\n candidate_answer: str... eval_candidate \n",
"\n",
" filepath \\\n",
"0 /examples/semanticsearch/semanticsearch.py \n",
"1 /examples/semanticsearch/semanticsearch.py \n",
"2 /examples/codex/backtranslation.py \n",
"3 /examples/codex/backtranslation.py \n",
"4 /examples/codex/backtranslation.py \n",
"\n",
" code_embedding \n",
"0 [-0.038976121693849564, -0.0031428150832653046... \n",
"1 [-0.024289356544613838, -0.017748363316059113,... \n",
"2 [-0.04161201789975166, -0.0169310811907053, 0.... \n",
"3 [-0.027255680412054062, -0.007931121625006199,... \n",
"4 [-0.00999179296195507, -0.01640152558684349, 0... "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from openai.embeddings_utils import get_embedding\n",
"\n",
"df = pd.DataFrame(all_funcs)\n",
"df['code_embedding'] = df['code'].apply(lambda x: get_embedding(x, engine='code-search-babbage-code-001'))\n",
"df['filepath'] = df['filepath'].apply(lambda x: x.replace(code_root, \"\"))\n",
"df.to_csv(\"output/code_search_openai-python.csv\", index=False)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/openai/tests/test_endpoints.py:test_completions_multiple_prompts score=0.681\n",
"def test_completions_multiple_prompts():\n",
" result = openai.Completion.create(\n",
" prompt=[\"This was a test\", \"This was another test\"], n=5, engine=\"ada\"\n",
" )\n",
" assert len(result.choices) == 10\n",
"\n",
"----------------------------------------------------------------------\n",
"/openai/tests/test_endpoints.py:test_completions score=0.675\n",
"def test_completions():\n",
" result = openai.Completion.create(prompt=\"This was a test\", n=5, engine=\"ada\")\n",
" assert len(result.choices) == 5\n",
"\n",
"\n",
"----------------------------------------------------------------------\n",
"/openai/tests/test_api_requestor.py:test_requestor_sets_request_id score=0.635\n",
"def test_requestor_sets_request_id(mocker: MockerFixture) -> None:\n",
" # Fake out 'requests' and confirm that the X-Request-Id header is set.\n",
"\n",
" got_headers = {}\n",
"\n",
" def fake_request(self, *args, **kwargs):\n",
" nonlocal got_headers\n",
"----------------------------------------------------------------------\n"
]
}
],
"source": [
"from openai.embeddings_utils import cosine_similarity\n",
"\n",
"def search_functions(df, code_query, n=3, pprint=True, n_lines=7):\n",
" embedding = get_embedding(code_query, engine='code-search-babbage-text-001')\n",
" df['similarities'] = df.code_embedding.apply(lambda x: cosine_similarity(x, embedding))\n",
"\n",
" res = df.sort_values('similarities', ascending=False).head(n)\n",
" if pprint:\n",
" for r in res.iterrows():\n",
" print(r[1].filepath+\":\"+r[1].function_name + \" score=\" + str(round(r[1].similarities, 3)))\n",
" print(\"\\n\".join(r[1].code.split(\"\\n\")[:n_lines]))\n",
" print('-'*70)\n",
" return res\n",
"res = search_functions(df, 'Completions API tests', n=3)\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/openai/validators.py:format_inferrer_validator score=0.655\n",
"def format_inferrer_validator(df):\n",
" \"\"\"\n",
" This validator will infer the likely fine-tuning format of the data, and display it to the user if it is classification.\n",
" It will also suggest to use ada and explain train/validation split benefits.\n",
" \"\"\"\n",
" ft_type = infer_task_type(df)\n",
" immediate_msg = None\n",
"----------------------------------------------------------------------\n",
"/openai/validators.py:long_examples_validator score=0.649\n",
"def long_examples_validator(df):\n",
" \"\"\"\n",
" This validator will suggest to the user to remove examples that are too long.\n",
" \"\"\"\n",
" immediate_msg = None\n",
" optional_msg = None\n",
" optional_fn = None\n",
"----------------------------------------------------------------------\n",
"/openai/validators.py:non_empty_completion_validator score=0.646\n",
"def non_empty_completion_validator(df):\n",
" \"\"\"\n",
" This validator will ensure that no completion is empty.\n",
" \"\"\"\n",
" necessary_msg = None\n",
" necessary_fn = None\n",
" immediate_msg = None\n",
"----------------------------------------------------------------------\n"
]
}
],
"source": [
"res = search_functions(df, 'fine-tuning input data validation logic', n=3)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/openai/validators.py:common_completion_suffix_validator score=0.665\n",
"def common_completion_suffix_validator(df):\n",
" \"\"\"\n",
" This validator will suggest to add a common suffix to the completion if one doesn't already exist in case of classification or conditional generation.\n",
" \"\"\"\n",
" error_msg = None\n",
" immediate_msg = None\n",
" optional_msg = None\n",
" optional_fn = None\n",
"\n",
" ft_type = infer_task_type(df)\n",
"----------------------------------------------------------------------\n",
"/openai/validators.py:get_outfnames score=0.66\n",
"def get_outfnames(fname, split):\n",
" suffixes = [\"_train\", \"_valid\"] if split else [\"\"]\n",
" i = 0\n",
" while True:\n",
" index_suffix = f\" ({i})\" if i > 0 else \"\"\n",
" candidate_fnames = [\n",
" fname.split(\".\")[0] + \"_prepared\" + suffix + index_suffix + \".jsonl\"\n",
" for suffix in suffixes\n",
" ]\n",
" if not any(os.path.isfile(f) for f in candidate_fnames):\n",
"----------------------------------------------------------------------\n"
]
}
],
"source": [
"res = search_functions(df, 'find common suffix', n=2, n_lines=10)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/openai/cli.py:tools_register score=0.651\n",
"def tools_register(parser):\n",
" subparsers = parser.add_subparsers(\n",
" title=\"Tools\", help=\"Convenience client side tools\"\n",
" )\n",
"\n",
" def help(args):\n",
" parser.print_help()\n",
"\n",
" parser.set_defaults(func=help)\n",
"\n",
" sub = subparsers.add_parser(\"fine_tunes.prepare_data\")\n",
" sub.add_argument(\n",
" \"-f\",\n",
" \"--file\",\n",
" required=True,\n",
" help=\"JSONL, JSON, CSV, TSV, TXT or XLSX file containing prompt-completion examples to be analyzed.\"\n",
" \"This should be the local file path.\",\n",
" )\n",
" sub.add_argument(\n",
" \"-q\",\n",
"----------------------------------------------------------------------\n"
]
}
],
"source": [
"res = search_functions(df, 'Command line interface for fine-tuning', n=1, n_lines=20)"
]
}
],
"metadata": {
"interpreter": {
"hash": "be4b5d5b73a21c599de40d6deb1129796d12dc1cc33a738f7bac13269cfcafe8"
},
"kernelspec": {
"display_name": "Python 3.7.3 64-bit ('base': conda)",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}