examples/Code_search.ipynb

{
 "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": {
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       "<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) -&gt; 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)"
   ]
  }
 ],
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