notebooks/clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sweepai.config.client import SweepConfig\n",
    "from sweepai.core.lexical_search import prepare_lexical_search_index\n",
    "from sweepai.utils.github_utils import ClonedRepo, MockClonedRepo\n",
    "\n",
    "\n",
    "cloned_repo = MockClonedRepo(\n",
    "    \"/tmp/sweep\",\n",
    "    \"sweepai/sweep\",\n",
    ")\n",
    "\n",
    "_, snippets, lexical_index = prepare_lexical_search_index(\n",
    "    cloned_repo.cached_dir,\n",
    "    SweepConfig(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "openai embedding:   0%|          | 0/3 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "openai embedding: 100%|██████████| 3/3 [00:00<00:00, 15.35it/s]\n"
     ]
    }
   ],
   "source": [
    "from sweepai.core.lexical_search import SNIPPET_FORMAT\n",
    "from sweepai.core.vector_db import embed_text_array\n",
    "\n",
    "snippet_formats = [\n",
    "    SNIPPET_FORMAT.format(\n",
    "        file_path=snippet.file_path,\n",
    "        contents=snippet.get_snippet(add_ellipsis=False, add_lines=False),\n",
    "    ) for snippet in snippets\n",
    "]\n",
    "\n",
    "embeddings = embed_text_array(snippet_formats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "embeddings = np.concatenate(embeddings, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[-0.02411441, -0.02171271,  0.05348661, ...,  0.03586329,\n",
       "         -0.02171271, -0.00821123],\n",
       "        [ 0.0182037 ,  0.02335203,  0.05350372, ...,  0.04868567,\n",
       "         -0.04487785, -0.0305014 ],\n",
       "        [-0.05113083, -0.00523904,  0.08606402, ...,  0.03495184,\n",
       "         -0.06658205, -0.03491452],\n",
       "        ...,\n",
       "        [-0.09475309, -0.01271244,  0.0428106 , ...,  0.00565865,\n",
       "         -0.00643427, -0.04768874],\n",
       "        [-0.07259093,  0.00631516,  0.03578078, ...,  0.05537992,\n",
       "          0.00829669, -0.02684588],\n",
       "        [ 0.03295828, -0.04015078,  0.0591305 , ...,  0.01009071,\n",
       "         -0.02944155, -0.04382655]]),\n",
       " (546, 512))"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings, embeddings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Clustering\n",
    "kmeans = KMeans(n_clusters=10, random_state=42)\n",
    "clusters = kmeans.fit_predict(embeddings)\n",
    "\n",
    "# Dimensionality Reduction\n",
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "reduced_embeddings = tsne.fit_transform(embeddings)\n",
    "\n",
    "# Visualization\n",
    "plt.figure(figsize=(10, 8))\n",
    "scatter = plt.scatter(reduced_embeddings[:, 0], reduced_embeddings[:, 1], c=clusters, cmap='viridis', alpha=0.6)\n",
    "plt.colorbar(scatter)\n",
    "plt.title('t-SNE visualization of Embeddings Clustered by K-Means')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0\n",
      "docs/pages/blogs/openai-proxy.mdx\n",
      "docs/public/redis_vector_db.py\n",
      "docs/sdk/src/agent.py\n",
      "sweepai/core/vector_db.py\n",
      "sweepai/core/chat.py\n",
      "sweepai/utils/chat_logger.py\n",
      "sweepai/utils/openai_proxy_test.py\n",
      "sweepai/utils/progress.py\n",
      "sweepai/utils/event_logger.py\n",
      "sweepai/utils/openai_proxy.py\n",
      "sweepai/utils/anthropic_client.py\n",
      "sweepai/chat/api.py\n",
      "sweepai/config/server.py\n",
      "sweepai/agents/agent_utils.py\n",
      "sweepai/agents/assistant_wrapper.py\n",
      "sweepai/agents/image_description_bot.py\n",
      "tests/notebooks/src/mod3.py\n",
      "tests/archive/test_api.py\n",
      "sweep_chat/backend/api.py\n",
      "\n",
      "\n",
      "Cluster 1\n",
      "package.json\n",
      "docs/package.json\n",
      "docs/theme.config.tsx\n",
      "docs/pages/_app.tsx\n",
      "docs/pages/blogs/index.mdx\n",
      "docs/components/counters.module.css\n",
      "docs/components/counters.tsx\n",
      "docs/components/PRPreview.jsx\n",
      "docs/components/RepoPreview.jsx\n",
      "docs/components/Cards.tsx\n",
      "docs/components/ShowMore.jsx\n",
      "docs/tailwind.config.js\n",
      "docs/tsconfig.json\n",
      "docs/next-env.d.ts\n",
      "docs/styles/global.css\n",
      "docs/next.config.js\n",
      "tests/archive/test_modify_bot.py\n",
      "platform/cypress.config.ts\n",
      "platform/cypress/support/e2e.ts\n",
      "platform/cypress/support/commands.ts\n",
      "\n",
      "\n",
      "Cluster 2\n",
      "tests/jsons/move_file_comment.json\n",
      "tests/jsons/pr_activate_hey_button.json\n",
      "tests/jsons/pull_request_closed.json\n",
      "tests/jsons/e2e_pr_review_comment.json\n",
      "tests/jsons/e2e_pr_comment.json\n",
      "tests/jsons/e2e_branch_change.json\n",
      "tests/jsons/sweep_yaml.json\n",
      "tests/jsons/opened_pull.json\n",
      "tests/jsons/merge_webhook.json\n",
      "tests/jsons/e2e_button_to_green.json\n",
      "tests/jsons/sweep_rules_webhook.json\n",
      "\n",
      "\n",
      "Cluster 3\n",
      "docs/pages/assistant.mdx\n",
      "docs/pages/_meta.json\n",
      "docs/pages/about/_meta.json\n",
      "docs/pages/about/roadmap.mdx\n",
      "docs/pages/deployment.mdx.old\n",
      "docs/pages/usage/extra-self-host.mdx\n",
      "docs/pages/usage/_meta.json\n",
      "docs/pages/usage/config.mdx\n",
      "docs/pages/usage/sandbox.mdx\n",
      "docs/pages/usage/tutorial.mdx\n",
      "docs/pages/blogs/soc2.mdx\n",
      "docs/pages/blogs/super-linter.mdx\n",
      "docs/pages/blogs/openai-proxy.mdx\n",
      "docs/pages/blogs/zero-downtime-deployment.mdx\n",
      "docs/pages/deployment.mdx\n",
      "docs/pages/videos/_meta.json\n",
      "docs/pages/videos/ai_junior_developer_adds_an_easter_egg_to_our_logo.mdx\n",
      "docs/pages/videos/adding_walrus.mdx\n",
      "docs/pages/videos/introducing_sweep.mdx\n",
      "docs/pages/videos/adding_banner.mdx\n",
      "\n",
      "\n",
      "Cluster 4\n",
      "sweepai/core/lexical_search.py\n",
      "sweepai/core/context_pruning.py\n",
      "sweepai/core/prompts.py\n",
      "sweepai/core/update_prompts.py\n",
      "sweepai/core/sweep_bot.py\n",
      "sweepai/core/planning_prompts.py\n",
      "sweepai/core/external_searcher.py\n",
      "sweepai/core/reflection_utils.py\n",
      "sweepai/core/annotate_code_openai.py\n",
      "sweepai/core/entities.py\n",
      "sweepai/logn/logn.py\n",
      "sweepai/utils/multi_query.py\n",
      "sweepai/utils/ticket_utils.py\n",
      "sweepai/utils/search_and_replace.py\n",
      "sweepai/utils/str_utils.py\n",
      "sweepai/utils/openai_listwise_reranker.py\n",
      "sweepai/utils/convert_openai_anthropic.py\n",
      "sweepai/utils/fuzzy_diff.py\n",
      "sweepai/utils/utils.py\n",
      "sweepai/utils/code_tree.py\n",
      "\n",
      "\n",
      "Cluster 5\n",
      "docs/public/file_cache.py\n",
      "sweepai/core/repo_parsing_utils.py\n",
      "sweepai/logn/cache.py\n",
      "sweepai/logn/trace_util.py\n",
      "sweepai/logn/__init__.py\n",
      "sweepai/handlers/on_ticket.py\n",
      "sweepai/handlers/on_button_click_test.py\n",
      "sweepai/web/health.py\n",
      "sweepai/web/health_test.py\n",
      "sweepai/utils/scorer_test.py\n",
      "sweepai/utils/docker_utils_test.py\n",
      "sweepai/utils/comment_utils_test.py\n",
      "sweepai/utils/html_extractor.py\n",
      "sweepai/utils/buttons_test.py\n",
      "sweepai/utils/cohere_utils.py\n",
      "sweepai/utils/autoimport.py\n",
      "sweepai/utils/scorer.py\n",
      "sweepai/utils/code_tree_test.py\n",
      "sweepai/utils/regex_utils.py\n",
      "sweepai/utils/tree_utils.py\n",
      "\n",
      "\n",
      "Cluster 6\n",
      "sweepai/core/post_merge.py\n",
      "sweepai/core/sweep_bot.py\n",
      "sweepai/core/pr_reader.py\n",
      "sweepai/handlers/on_merge.py\n",
      "sweepai/handlers/on_ticket.py\n",
      "sweepai/handlers/on_button_click.py\n",
      "sweepai/handlers/on_check_suite.py\n",
      "sweepai/handlers/create_pr.py\n",
      "sweepai/handlers/on_comment.py\n",
      "sweepai/handlers/on_review.py\n",
      "sweepai/handlers/on_jira_ticket.py\n",
      "sweepai/web/events.py\n",
      "sweepai/web/event_utils.py\n",
      "sweepai/utils/ticket_rendering_utils.py\n",
      "sweepai/utils/issue_validator.py\n",
      "sweepai/utils/user_settings.py\n",
      "sweepai/utils/buttons.py\n",
      "sweepai/utils/github_utils.py\n",
      "sweepai/utils/prompt_constructor.py\n",
      "sweepai/utils/image_utils.py\n",
      "\n",
      "\n",
      "Cluster 7\n",
      "sweepai/utils/patch_utils.py\n",
      "sweepai/utils/diff_test.py\n",
      "sweepai/utils/comment_utils.py\n",
      "tests/archive/planning.xml\n",
      "tests/archive/test_diff_parsing2.py\n",
      "tests/archive/example_diff.diff\n",
      "tests/archive/test_diff_parsing.py\n",
      "tests/archive/modify_prompt.txt\n",
      "tests/archive/test_diff_parsing4.py\n",
      "tests/archive/test_modify.py\n",
      "tests/archive/test_diff_code.py\n",
      "tests/archive/test_diff.py\n",
      "platform/components/dashboard/DashboardActions.tsx\n",
      "platform/components/dashboard/DashboardPlanning.tsx\n",
      "platform/lib/patchUtils.test.ts\n",
      "platform/lib/patchUtils.ts\n",
      "platform/lib/types.ts\n",
      "platform/lib/tree.test.ts.unused\n",
      "platform/lib/diff.test.ts\n",
      "platform/lib/search.test.ts\n",
      "\n",
      "\n",
      "Cluster 8\n",
      "docs/Recipes.md\n",
      "docs/pages/cli.mdx\n",
      "docs/pages/about/limitations.mdx\n",
      "docs/pages/about/examples.mdx\n",
      "docs/pages/getting-started.md\n",
      "docs/pages/usage/advanced.mdx\n",
      "docs/pages/index.mdx\n",
      "docs/pages/blogs/generating-50k-embeddings-with-gte.mdx\n",
      "docs/pages/blogs/_meta.json\n",
      "docs/pages/blogs/gpt-4-modification.mdx\n",
      "docs/pages/blogs/sweeps-core-algo.mdx\n",
      "docs/pages/blogs/chunking-2m-files.mdx\n",
      "docs/pages/blogs/llm-sdk.mdx\n",
      "docs/pages/blogs/gpt-32k-open-source.mdx\n",
      "docs/pages/blogs/building-code-search.mdx\n",
      "docs/pages/blogs/ai-code-planning.mdx\n",
      "docs/pages/blogs/search-infra.mdx\n",
      "docs/pages/blogs/chunking-improvements.mdx\n",
      "docs/pages/blogs/super-linter.mdx\n",
      "docs/pages/blogs/vector-db-implementation.mdx\n",
      "\n",
      "\n",
      "Cluster 9\n",
      "sweepai/utils/openai_listwise_reranker.py\n",
      "tests/archive/test_langchain_chunker.py\n",
      "tests/archive/test_cst_splitter.py\n",
      "platform/package.json\n",
      "requirements.txt\n",
      "package-lock.json\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "cluster_indices = {}\n",
    "for i in range(kmeans.n_clusters):\n",
    "    cluster_indices[i] = np.where(clusters == i)[0]\n",
    "\n",
    "for cluster, indices in cluster_indices.items():\n",
    "    print(f\"Cluster {cluster}\")\n",
    "    count = 0\n",
    "    vis_file_path = set()\n",
    "    for index in indices:\n",
    "        if count == 20:\n",
    "            break\n",
    "        if snippets[index].file_path in vis_file_path:\n",
    "            continue\n",
    "        vis_file_path.add(snippets[index].file_path)\n",
    "        print(snippets[index].file_path.removeprefix(cloned_repo._repo_dir + \"/\"))\n",
    "        count += 1\n",
    "    print(\"\\n\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Subdirectories with more than 20 files: ['/tmp/sweep/docs', '/tmp/sweep/docs/pages', '/tmp/sweep/docs/pages/blogs', '/tmp/sweep/docs/public', '/tmp/sweep/docs/public/deployment', '/tmp/sweep/docs/public/covers', '/tmp/sweep/sweepai', '/tmp/sweep/sweepai/core', '/tmp/sweep/sweepai/utils', '/tmp/sweep/sweepai/agents', '/tmp/sweep/tests', '/tmp/sweep/tests/notebooks', '/tmp/sweep/tests/jsons', '/tmp/sweep/tests/events', '/tmp/sweep/tests/archive', '/tmp/sweep/platform', '/tmp/sweep/platform/components', '/tmp/sweep/platform/components/ui', '/tmp/sweep/platform/components/dashboard', '/tmp/sweep/platform/lib', '/tmp/sweep/platform/app', '/tmp/sweep/sweep_chat', '/tmp/sweep/sweep_chat/components', '/tmp/sweep/sweep_chat/components/ui', '/tmp/sweep/.github']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "def count_files(directory):\n",
    "    total_count = 0\n",
    "    for root, dirs, files in os.walk(directory):\n",
    "        total_count += len(files)\n",
    "    return total_count\n",
    "\n",
    "def find_large_subdirs(directory, file_threshold):\n",
    "    large_subdirs = []\n",
    "    for root, dirs, files in os.walk(directory):\n",
    "        if \".git\" in root.split(os.sep):\n",
    "            continue\n",
    "        subdir_file_count = count_files(root)\n",
    "        if subdir_file_count > file_threshold:\n",
    "            large_subdirs.append(root)\n",
    "    large_subdirs.remove(directory)\n",
    "    return large_subdirs\n",
    "\n",
    "\n",
    "# Usage: Specify the directory path and the file threshold\n",
    "directory_path = cloned_repo._repo_dir\n",
    "file_threshold = 10\n",
    "large_subdirectories = find_large_subdirs(directory_path, file_threshold)\n",
    "print(\"Subdirectories with more than 20 files:\", large_subdirectories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Snippet(start=0, end=19, file_path='/tmp/sweep/docs/Recipes.md', score=0.0), Snippet(start=0, end=66, file_path='/tmp/sweep/docs/package.json', score=0.0), Snippet(start=0, end=52, file_path='/tmp/sweep/docs/theme.config.tsx', score=0.0), Snippet(start=0, end=137, file_path='/tmp/sweep/docs/pages/assistant.mdx', score=0.0), Snippet(start=0, end=37, file_path='/tmp/sweep/docs/pages/_meta.json', score=0.0), Snippet(start=0, end=18, file_path='/tmp/sweep/docs/pages/_app.tsx', score=0.0), Snippet(start=0, end=133, file_path='/tmp/sweep/docs/pages/cli.mdx', score=0.0), Snippet(start=0, end=6, file_path='/tmp/sweep/docs/pages/about/_meta.json', score=0.0), Snippet(start=0, end=11, file_path='/tmp/sweep/docs/pages/about/limitations.mdx', score=0.0), Snippet(start=0, end=62, file_path='/tmp/sweep/docs/pages/about/examples.mdx', score=0.0), Snippet(start=0, end=7, file_path='/tmp/sweep/docs/pages/about/roadmap.mdx', score=0.0), Snippet(start=0, end=200, 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      "       -0.04764944, -0.00485071,  0.05325302,  0.02542574, -0.01636282,\n",
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      "       -0.03556555,  0.03834827, -0.02012712,  0.02994291,  0.05264311,\n",
      "       -0.08386302, -0.06693794]), array([ 3.56908385e-03,  1.14885914e-02,  1.66685854e-02, -3.52085309e-02,\n",
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      "       -0.09374824, -0.0157594 , -0.07559539,  0.08342847, -0.1237543 ,\n",
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      "       -8.20394977e-02,  5.40481618e-02,  5.96148405e-02,  3.59021489e-04,\n",
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      "       -2.48292748e-02,  7.32128077e-02, -1.28340521e-02, -3.05165230e-02,\n",
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      "       -4.39125854e-03,  9.57604801e-02,  4.61019244e-02,  5.84242916e-03,\n",
      "        3.74787835e-02,  3.10365924e-02, -7.13338365e-02, -2.78322743e-02]), array([-0.03765706,  0.03683219,  0.06828479, -0.043467  , -0.03130915,\n",
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      "       -0.0439784 ,  0.01415155,  0.0478846 ,  0.00490379, -0.0728708 ,\n",
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      "       -0.03293329, -0.03187256,  0.00862058, -0.07314019,  0.0230331 ,\n",
      "       -0.0363007 , -0.06957074, -0.01389899,  0.0209453 , -0.04519067,\n",
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      "       -8.89533500e-02,  2.08288651e-02, -2.52486936e-02,  6.73066451e-02,\n",
      "       -2.87114704e-02, -7.79994951e-03, -3.87343824e-02, -8.41507090e-02,\n",
      "       -3.15826152e-02, -4.67387908e-02,  2.16641078e-02, -4.57991421e-02,\n",
      "        1.07537501e-02,  2.11942834e-02, -2.72671957e-02, -2.34389992e-02,\n",
      "       -3.72161554e-03,  8.20626023e-02,  2.10202758e-02,  2.97729237e-02,\n",
      "       -3.88039854e-02,  3.20176395e-02,  5.78405591e-02, -2.74412049e-02,\n",
      "       -2.71039267e-05,  1.50604710e-02, -1.88973661e-02, -1.03709303e-01,\n",
      "       -1.76184020e-02,  6.22951891e-02, -2.41002327e-02,  8.65694295e-03,\n",
      "        8.72654690e-03,  3.07821741e-02,  2.19077215e-02, -2.40306282e-02,\n",
      "       -2.93901039e-02,  7.04301037e-03, -1.82883356e-02,  9.60529141e-03,\n",
      "        5.36382457e-03, -2.88332750e-02,  2.68495729e-02,  2.62231424e-02,\n",
      "        4.59383467e-02, -1.23198308e-02, -2.90246871e-02, -4.71912137e-02,\n",
      "        3.37229264e-02, -7.32142483e-03,  1.71485777e-02,  4.61471632e-02,\n",
      "        6.43136898e-02,  4.83744731e-02,  9.77059979e-03,  4.06136769e-02,\n",
      "       -1.62089304e-02, -5.24810849e-02, -9.45216422e-02, -1.32333780e-02,\n",
      "        1.49647664e-02,  7.45454154e-02, -4.32586125e-02, -3.33749096e-02,\n",
      "        1.81491294e-02,  1.58174091e-02, -3.58458346e-02,  5.78057591e-02,\n",
      "        7.00211838e-02, -2.97903252e-02,  1.11539706e-02,  5.08106053e-02,\n",
      "        3.16348197e-02, -7.38493818e-02,  3.17740273e-02,  2.02937884e-03,\n",
      "       -4.39198490e-02,  4.35457292e-03,  1.70963754e-02,  1.23894339e-02,\n",
      "        1.02056223e-02, -6.52098370e-03,  7.27357181e-03,  2.29343747e-02,\n",
      "        1.38772101e-02,  6.09379147e-02, -2.81546414e-02,  4.30150003e-02,\n",
      "        4.78176471e-02, -2.35956068e-02, -2.14552986e-02,  6.78286682e-02,\n",
      "       -9.11806657e-03, -2.75804125e-02, -7.92784562e-02,  2.56663135e-02,\n",
      "        4.07528845e-02, -9.09740348e-04, -2.12812895e-02, -1.94193935e-02,\n",
      "        8.84661227e-02, -9.54264806e-02, -2.13508925e-02, -3.47669797e-02,\n",
      "        2.08288651e-02,  6.92555442e-02,  6.99863837e-02,  3.16478690e-04,\n",
      "       -2.52660936e-02, -2.42742433e-02, -8.59168960e-03, -3.42971553e-02,\n",
      "        1.74335161e-03, -4.06832814e-02,  3.32357020e-02, -2.67625684e-02,\n",
      "       -1.19805140e-02, -1.72181822e-02,  6.68890209e-02, -7.23877071e-02,\n",
      "       -1.13649568e-03, -5.59960676e-02,  3.04863603e-02,  2.73193973e-02,\n",
      "        7.07781248e-03,  4.44766750e-02,  3.64026620e-02, -2.77718202e-02,\n",
      "        4.56599388e-02, -1.24851385e-02,  5.39427581e-02, -9.03454230e-02,\n",
      "       -2.37348159e-02,  2.69191775e-02,  1.94193935e-02, -1.88277645e-02,\n",
      "       -1.67222549e-02,  3.33401066e-02,  3.61068483e-03,  9.38952102e-02,\n",
      "       -6.99515778e-02, -8.20452008e-03, -1.04318336e-02, -2.08462666e-02,\n",
      "       -5.90586231e-02, -2.41176357e-02,  3.24091579e-03,  2.09158711e-02,\n",
      "        3.13042029e-02,  5.82581805e-02,  7.01603914e-02,  1.09016581e-02,\n",
      "        3.14608106e-02, -3.91737545e-03, -2.82764475e-02, -1.92975874e-02,\n",
      "       -2.69365804e-02, -6.31652304e-02,  9.94460893e-03, -1.31267966e-03,\n",
      "       -9.84890376e-03, -2.35608067e-02, -1.97152087e-02,  1.86015523e-02,\n",
      "       -4.90270081e-03,  4.72260152e-02,  1.93149889e-02, -6.27128075e-02,\n",
      "       -4.16577303e-02,  4.07528845e-02,  5.74229407e-02, -4.22493622e-02,\n",
      "        1.09712613e-02, -3.51497995e-02, -7.21788921e-02,  7.15524601e-02,\n",
      "        1.32333780e-02,  1.95237995e-02,  2.90420871e-02, -8.03225110e-02,\n",
      "        3.74119153e-02, -2.34477007e-03, -3.31834975e-02,  2.34389992e-02,\n",
      "        1.14845867e-02, -2.83286506e-02,  2.83460506e-02,  2.61535393e-02,\n",
      "        1.16933974e-02,  3.70638956e-02,  3.35663187e-02, -4.76436394e-02,\n",
      "       -5.95110474e-03,  9.38778000e-03,  5.20286620e-02, -5.38383549e-02,\n",
      "       -4.70172060e-02,  5.39427581e-02,  1.37641040e-02,  1.28853598e-02,\n",
      "        1.88973661e-02,  6.75589603e-03, -2.38740235e-02,  2.91116916e-02,\n",
      "       -2.16989108e-02,  8.12621597e-03,  5.83277835e-02,  4.39894491e-02,\n",
      "        7.69119387e-02, -4.55207312e-02,  2.96859206e-02,  7.56590733e-02,\n",
      "        3.74119153e-02, -5.02189705e-02,  9.67380680e-04, -5.33163275e-02,\n",
      "        3.46103721e-02, -5.92326322e-02,  4.09790967e-03,  5.48476096e-02,\n",
      "       -4.06832814e-02, -4.13793180e-02, -1.73834898e-02, -4.84788806e-02,\n",
      "        5.01493660e-02,  1.85145477e-02,  3.55500208e-02, -7.14828585e-02,\n",
      "        2.09158711e-02, -1.06145429e-02,  1.59131137e-02, -3.46973752e-02,\n",
      "        1.40947222e-01, -4.99753554e-02,  3.51149965e-02, -3.37229264e-02,\n",
      "       -3.61590498e-02,  7.44323108e-03,  5.94066399e-02,  5.52652266e-02,\n",
      "       -4.44070734e-02, -4.05788768e-02, -4.47550916e-02,  4.61123573e-02,\n",
      "       -1.69049655e-02, -3.56718254e-02, -1.13540810e-02, -4.25973804e-02,\n",
      "        4.94881310e-02, -1.92627859e-02,  6.20167725e-02, -4.04396692e-02,\n",
      "       -6.83855044e-03,  1.55215953e-02,  7.64943145e-02, -7.52414490e-02,\n",
      "        9.87500586e-03, -4.45114794e-02,  2.43786479e-02,  1.04405348e-02,\n",
      "        1.05588603e-01,  4.91053127e-02, -6.68194193e-02,  9.24422312e-04,\n",
      "        5.58568585e-03, -5.39427581e-02,  2.58229226e-02,  2.96337176e-02,\n",
      "        4.10313012e-02, -2.06722589e-02,  2.33693947e-02, -1.85645754e-03,\n",
      "       -7.93480622e-02, -4.06136769e-02, -9.29207599e-02,  4.61558633e-03,\n",
      "       -1.16498958e-02,  2.92508963e-02,  6.54969537e-02,  2.17163108e-02,\n",
      "       -3.87387331e-03, -2.62753440e-03, -2.33345946e-02,  2.46570617e-02,\n",
      "        1.96456057e-02,  1.59531361e-01, -2.85896628e-02, -6.89945349e-03,\n",
      "       -4.54511267e-02,  8.74220766e-02,  5.27595030e-02, -9.69229627e-03,\n",
      "        4.23189667e-02, -1.84449432e-02, -2.79110292e-02, -4.27365880e-02,\n",
      "        2.13334925e-02, -1.70311208e-03,  2.73411481e-03, -4.07528845e-02,\n",
      "       -3.06951695e-02,  4.56338344e-03, -2.13160910e-02,  3.81079504e-02,\n",
      "        2.10202758e-02, -1.32138012e-03, -4.87920959e-02, -6.38612698e-02,\n",
      "        4.05440768e-02,  6.36524548e-02, -2.93553038e-02, -8.77874949e-03])]\n"
     ]
    }
   ],
   "source": [
    "large_subdirectory = large_subdirectories[0]\n",
    "snippets_in_subdir = []\n",
    "embeddings_in_subdir = []\n",
    "\n",
    "for i, snippet in enumerate(snippets):\n",
    "    if snippet.file_path.startswith(large_subdirectory):\n",
    "        snippets_in_subdir.append(snippet)\n",
    "        embeddings_in_subdir.append(embeddings[i])\n",
    "\n",
    "print(snippets_in_subdir)\n",
    "print(embeddings_in_subdir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Snippet(start=0, end=107, file_path='/tmp/sweep/docs/public/file_cache.py', score=0.0), Snippet(start=0, end=118, file_path='/tmp/sweep/docs/components/Cards.tsx', score=0.0), Snippet(start=0, end=4, file_path='/tmp/sweep/docs/sdk/README.md', score=0.0), Snippet(start=0, end=200, file_path='/tmp/sweep/docs/pages/blogs/chunking-2m-files.mdx', score=0.0), Snippet(start=0, end=133, file_path='/tmp/sweep/docs/public/redis_vector_db.py', score=0.0)]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics.pairwise import cosine_distances\n",
    "\n",
    "def select_diverse_vectors(vectors, num_vectors=5):\n",
    "    # Start with a random vector\n",
    "    selected_indices = [np.random.randint(len(vectors))]\n",
    "    selected_vectors = [vectors[selected_indices[0]]]\n",
    "    \n",
    "    for _ in range(1, num_vectors):\n",
    "        max_min_distance = 0\n",
    "        next_index = -1\n",
    "        \n",
    "        for i in range(len(vectors)):\n",
    "            if i in selected_indices:\n",
    "                continue\n",
    "            \n",
    "            # Calculate minimum distance to all selected vectors\n",
    "            distances = cosine_distances([vectors[i]], selected_vectors)[0]\n",
    "            min_distance = np.min(distances)\n",
    "            \n",
    "            # Select the vector that maximizes the minimum distance\n",
    "            if min_distance > max_min_distance:\n",
    "                max_min_distance = min_distance\n",
    "                next_index = i\n",
    "        \n",
    "        selected_indices.append(next_index)\n",
    "        selected_vectors.append(vectors[next_index])\n",
    "    \n",
    "    return selected_indices\n",
    "\n",
    "# Example usage\n",
    "# Assuming 'embeddings' is your list of embeddings\n",
    "indices = select_diverse_vectors(embeddings_in_subdir)\n",
    "diverse_snippets = [snippets_in_subdir[i] for i in indices]\n",
    "diverse_vectors = [embeddings_in_subdir[i] for i in indices]\n",
    "print(diverse_snippets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/tmp/sweep/docs/pages/usage/tutorial.mdx:0-68\n",
      "\n",
      "\n",
      "import { Steps } from 'nextra/components'\n",
      "\n",
      "# Tutorial for Getting Started with Sweep\n",
      "\n",
      "We recommend using an existing **real project** for Sweep, but if you must start from scratch for testing, we recommend **using a template**. In particular, we recommend Vercel templates and Vercel auto-deploy, since Vercel's auto-generated previews make it **easy to review Sweep's PRs**\n",
      "\n",
      "We'll use [Docusaurus](https://vercel.com/templates/next.js/docusaurus-2) for this tutorial since it's is the easiest to set up.\n",
      "\n",
      "<Steps>\n",
      "\n",
      "### Fork and deploy a Docusaurus template\n",
      "\n",
      "Go to https://vercel.com/templates/next.js/docusaurus-2 (or another template) and click \"Deploy\".\n",
      "\n",
      "![Deploy](/tutorial/deployment.png)\n",
      "\n",
      "Vercel will prompt you to select a GitHub account and click \"Clone\" after. This will trigger a build and deploy which will take a few minutes. Once the build is done, you will be greeted with a congratulations message.\n",
      "\n",
      "![Congratulations](/tutorial/congratulations.png)\n",
      "\n",
      "### Install Sweep\n",
      "\n",
      "Go to the [Sweep Installation](https://github.com/apps/sweep-ai) page and click \"Install\". Ensure on the next page that you give Sweep access to the Docusaurus repo you just forked.\n",
      "\n",
      "### Create a Sweep issue\n",
      "\n",
      "Go to the issues page of your Docusaurus fork (https://github.com/USERNAME/docusaurus-2/issues) and create an issue prefixed with \"Sweep:\", such as \"Sweep: Add a banner at the top asking the user to star the docusaurus repo with the current number of stars\".\n",
      "\n",
      "![Issue](/tutorial/issue.png)\n",
      "\n",
      "### Give Sweep feedback\n",
      "\n",
      "After about 5 minutes, Sweep will make a PR to your Docusaurus fork. If you go to the PR, you will see a Vercel preview. To give feedback to Sweep, you have a few options:\n",
      "- Edit the original issue to add more details.\n",
      "- Leave comments on the PR or code.\n",
      "- Configure Sweep to handle failing GitHub Actions.\n",
      "\n",
      "![Initial](/tutorial/initial.png)\n",
      "\n",
      "#### Restarting the issue\n",
      "\n",
      "If the PR's preview looks completely wrong, you can **add more details to the issue description or title** like \"Use shields.io and make sure this is at the very top of the repo. Make sure it's aesthetic, centered and set the background color of the banner to lime.\". Sweep will make a new PR with a new preview like the following:\n",
      "\n",
      "![New issue](/tutorial/new.png)\n",
      "\n",
      "#### Tweaking the PR\n",
      "\n",
      "If the PR is mostly right but you want to do some tweaking, you can **leave a comments on the PR** and Sweep will address the comments, also generating a new preview, like the following:\n",
      "\n",
      "![PR Comment](/tutorial/comment.png)\n",
      "\n",
      "#### Fixing CI/CD\n",
      "\n",
      "If you have GitHub Actions set up, you can configure Sweep to handle failing linters and type-checkers by creating a `sweep.yaml` file with `gha_enabled: True`. This only works with GitHub Actions and not other CI providers, however, so for now we will just copy paste the logs into a comment.\n",
      "\n",
      "![GitHub Actions](/tutorial/github_actions.png)\n",
      "\n",
      "### Merge the PR\n",
      "\n",
      "Once you are happy with the PR, you can merge it and it will be deployed to production via Vercel.\n",
      "\n",
      "![Final](/tutorial/final.png)\n",
      "\n",
      "You can see the final example at https://github.com/kevinlu1248/docusaurus-2/pull/4 with preview https://docusaurus-2-ql4cskc5o-sweepai.vercel.app/.\n",
      "\n",
      "Now to be a Sweep power user, check out [Advanced: becoming a Sweep power user](https://docs.sweep.dev/usage/advanced).\n",
      "</Steps>\n",
      "/tmp/sweep/docs/pages/blogs/gpt-4-modification.mdx:400-506\n",
      "\n",
      "\n",
      "For example, consider asking Sweep to add logging to the endpoint:\n",
      "\n",
      "```python /create_task/ /start_task/ {3,11,17-18}\n",
      "# Original lines from the code:\n",
      "@app.route('/tasks', methods=['POST'])\n",
      "def create_task():\n",
      "    new_task = {\"id\": len(tasks) + 1, \"task\": request.json[\"task\"]}\n",
      "    tasks.append(new_task)\n",
      "    return jsonify({\"task\": new_task}), 201\n",
      "\n",
      "# Generated diffs:\n",
      "<<<< ORIGINAL\n",
      "@app.route('/tasks', methods=['POST'])\n",
      "def start_task():\n",
      "    new_task = {\"id\": len(tasks) + 1, \"task\": request.json[\"task\"]}\n",
      "    tasks.append(new_task)\n",
      "    return jsonify({\"task\": new_task}), 201\n",
      "====\n",
      "@app.route('/tasks', methods=['POST'])\n",
      "def start_task():\n",
      "    print(\"Started task!\")\n",
      "    new_task = {\"id\": len(tasks) + 1, \"task\": request.json[\"task\"]}\n",
      "    tasks.append(new_task)\n",
      "    return jsonify({\"task\": new_task}), 201\n",
      ">>>> UPDATED\n",
      "```\n",
      "\n",
      "In this case, `create_task` was unintentionally changed to `start_task` in the ORIGINAL block. Essentially GPT-4 copies the lines incorrectly, then applies the transformation on the incorrectly copied lines.\n",
      "\n",
      "More precisely, GPT-4 would want to replace the substring $S$ with $R(S)$, where $R: str \\rightarrow str$ is the transformation it wants to perform. However, it generates $S'$ instead and tries replacing the substring with $R(S')$. This ends up replacing $S$ with $R(S')$, which frequently yields code that doesn’t parse or build, or adds additional unrequested changes.\n",
      "\n",
      "### The improvement to Aider diffs\n",
      "\n",
      "One solution is to start streaming earlier, by picking 200-line chunks instead of 400-line chunks, but that yields more complications like the algorithm missing context and being less performant and more expensive.\n",
      "\n",
      "We ended up solving this by generating $S$ and $R(S)$ separately. GPT-4 first generates $S'$, which we use to search for $S$ in the codebase using fuzzy matching. We then ask GPT-4 to perform the requested transformation on $S$, altogether generating $R(S)$.\n",
      "\n",
      "Specifically, the new algorithm performs the following:\n",
      "\n",
      "1. Generate a list of snippets $S_1'$, $S_2'$, $\\ldots$ $S_n'$ that GPT-4 would like to edit and use the fuzzy matching algorithm to find the correct lines $S_1$, $S_2$, $\\ldots$ $S_n$.\n",
      "    - If fuzzy matching yields a low similarity score (< 50%) for some candidate $S_i'$, we discard it. We could also re-prompt GPT-4 in the future.\n",
      "2. We feed the ground truth snippets of code to GPT-4 to edit.\n",
      "\n",
      "So we would ask GPT-4 to generate something like\n",
      "\n",
      "```python /start_task/ /.../\n",
      "<selected_snippet>\n",
      "@app.route('/tasks', methods=['POST'])\n",
      "def start_task():\n",
      "    ...\n",
      "    return jsonify({\"task\": new_task}), 201\n",
      "</selected_snippet>\n",
      "```\n",
      "\n",
      "It would be allowed to generate ellipses (...) in this case since our matching algorithm can usually match the snippet correctly. Then we would find the ground truth snippet in the codebase and present it to GPT-4 to edit like so:\n",
      "\n",
      "```python /create_task/\n",
      "<selected_snippet>\n",
      "@app.route('/tasks', methods=['POST'])\n",
      "def create_task():\n",
      "    new_task = {\"id\": len(tasks) + 1, \"task\": request.json[\"task\"]}\n",
      "    tasks.append(new_task)\n",
      "    return jsonify({\"task\": new_task}), 201\n",
      "</selected_snippet>\n",
      "```\n",
      "\n",
      "Then, we would ask GPT-4 to edit it by responding with something like:\n",
      "\n",
      "```python /create_task/ {4}\n",
      "<updated_snippet>\n",
      "@app.route('/tasks', methods=['POST'])\n",
      "def create_task():\n",
      "    print(\"Started task!\")\n",
      "    new_task = {\"id\": len(tasks) + 1, \"task\": request.json[\"task\"]}\n",
      "    tasks.append(new_task)\n",
      "    return jsonify({\"task\": new_task}), 201\n",
      "</updated_snippet>\n",
      "```\n",
      "\n",
      "This guarantees that no edits were made unintentionally like renaming a variable from `create_task` to `start_task`.\n",
      "\n",
      "## Additional roadblocks\n",
      "\n",
      "Here's some other minor roadblocks we ran into:\n",
      "\n",
      "- **Formatting errors**: we had a backoff system where we would prompt GPT-4 again to provide the response in the right format with a higher temperature.\n",
      "- **Indentation**: GPT-4 tends to unindent code and this would make Python code unparsable. We fixed this by matching the original indentation based on the difference in indentation between the ORIGINAL block and the matched section of the original file.\n",
      "\n",
      "Despite solving most of these, we still have the issue of some files still being too long. We have several > 1000 line files in *our own codebase*.\n",
      "- Streaming 400 lines at a time is a bandaid solution but doesn’t solve the problem entirely, since it would break up semantically meaningful parts of the code. Further, the model would sometimes modify no sections of code or multiple sections of the code. It’s difficult to modify a section of a file without the rest of the file in the context.\n",
      "- For Python, we set up entity-based editing. We recently set up a call-graph system for a better repo-level understanding of Python code. We use this at the planning stage by letting the LLM decide which class or function of the file it wants to edit.\n",
      "\n",
      "## Conclusion\n",
      "\n",
      "Getting GPT-4 to make the right modification has proven to be a difficult battle and is still prone to all sorts of errors. We’ve been fighting these errors since the release, but still only managed to mitigate only the common ones.\n",
      "\n",
      "I'm excited to hear your thoughts on tackling long code files with GPT-4. If you have ideas, feel free to share them in our community at https://community.sweep.dev/.\n",
      "<style>{`\n",
      "  pre {\n",
      "    white-space: pre-wrap !important;\n",
      "    word-wrap: break-word !important;\n",
      "  }\n",
      "  code {\n",
      "    white-space: pre-wrap !important;\n",
      "  }\n",
      "`}</style>\n",
      "/tmp/sweep/docs/pages/_app.tsx:0-18\n",
      "\n",
      "\n",
      "import type { AppProps } from \"next/app\";\n",
      "import \"../styles/global.css\";\n",
      "\n",
      "\n",
      "function MyApp({ Component, pageProps }: AppProps) {\n",
      "    return (\n",
      "        // <main className=\"bg-[#020817]\" style={{backgroundColor: \"#020817\"}}>\n",
      "        // <main className=\"bg-[#020817]\" style={{backgroundColor: \"#0d0a19\"}}>\n",
      "        // <main className=\"bg-[#020817]\" style={{backgroundColor: \"#161625\"}}>\n",
      "        // <main className=\"bg-[#020817]\" style={{backgroundColor: \"#13131A\"}}>\n",
      "        // <main className=\"bg-[#020817]\" style={{backgroundColor: \"#121221\"}}>\n",
      "        // <main style={{backgroundColor: \"#10101E\"}}>\n",
      "        <main style={{backgroundColor: \"#0E0E1C\"}}>\n",
      "          <Component {...pageProps} />\n",
      "        </main>\n",
      "    );\n",
      "}\n",
      "\n",
      "/tmp/sweep/docs/pages/blogs/super-linter.mdx:0-200\n",
      "\n",
      "\n",
      "# AI-powered junior dev meets Trunk, a super-linter\n",
      "\n",
      "**Kevin Lu** - Oct 4th, 2023\n",
      "\n",
      "---\n",
      "\n",
      "Even with all our fixes to enforcing [GPT-4 to write high-quality edits](/blogs/gpt-4-modification) to code, we still end up with code that doesn’t build, or even *parse* at times.\n",
      "\n",
      "This is because when GPT-4 is focused on editing some snippets of code, it's hard to tell while editing whether the updated code will result in undefined variables or type errors, *much less* unused variables or imports. What’s worse is GPT-4 can't even *identify* faulty code sometimes with undefined variables or erroneous typings, especially involving imports from other files.\n",
      "\n",
      "Thus, the only solution is to give Sweep dev tools normal engineers have access to -- linters and type checkers.\n",
      "\n",
      "## The tools\n",
      "\n",
      "### Linters\n",
      "\n",
      "What we found previously from using ChatGPT is that it can generally explain what to fix given the code and the error logs.\n",
      "\n",
      "Further, a common case is also that the modification algorithm would edit two parts separately and result in missing open or closing parentheses, ultimately yielding a syntax error. Another case is where Sweep adding import statement succeeded but actually using the imported library failed, which results in an unused import. For both of these cases, a linter like `eslint` would catch the error.\n",
      "\n",
      "We ended up with the following prompt. We ended up using the aider diff prompt since it was more reliable for small-scale changes compared to our new search-and-replace mechanism.\n",
      "\n",
      "#### System message\n",
      "\n",
      "```py\n",
      "You are to identify the problem from the error logs and fix the code. You will respond in the following format:\n",
      "\n",
      "Code Planning:\n",
      "\n",
      "What does the error log say? Where and what is wrong with the code? What should you do to fix it?\n",
      "\n",
      "Detailed plan of modifications:\n",
      "* Replace x with y\n",
      "* Add a foo method to bar\n",
      "...\n",
      "\n",
      "Code Modification:\n",
      "\n",
      "Generate a diff based on the given plan using the search and replace pairs in the format below.\n",
      "* Always prefer the least amount of changes possible, but ensure the solution is complete\n",
      "* Prefer multiple small changes over a single large change.\n",
      "* NEVER write ellipses anywhere in the diffs. Simply write two diff hunks: one for the beginning and another for the end.\n",
      "* DO NOT modify the same section multiple times.\n",
      "* Always add lines before and after. The ORIGINAL section should be at least 5 lines long.\n",
      "* Restrict the changes to fixing the errors from the logs.\n",
      "\n",
      "The format is as follows:\n",
      "\n",
      "Hunk description:\n",
      "\n",
      "<<<< ORIGINAL\n",
      "second line before\n",
      "first line before\n",
      "old code\n",
      "first line after\n",
      "second line after\n",
      "====\n",
      "second line before\n",
      "first line before\n",
      "new code\n",
      "first line after\n",
      "second line after\n",
      ">>>> UPDATED\n",
      "```\n",
      "\n",
      "#### User message\n",
      "\n",
      "```py\n",
      "File Name: {filename}\n",
      "\n",
      "<old_file>\n",
      "{code}\n",
      "</old_file>\n",
      "\n",
      "---\n",
      "\n",
      "Above is the code that was written by an inexperienced programmer, and contains errors. The CI pipeline returned the following logs:\n",
      "\n",
      "<stdout>\n",
      "{stdout}\n",
      "</stdout>\n",
      "\n",
      "Instructions:\n",
      "1. Complete the Code Planning step\n",
      "2. Complete the Code Modification step\n",
      "```\n",
      "\n",
      "This prompt can be found in https://github.com/sweepai/sweep/blob/main/sweepai/sandbox/src/prompts.py.\n",
      "\n",
      "This seems to recover most broken code like unused variables but not syntax errors. Unfortunately, `eslint`\\'s error message only says“unexpected token” with the line number. The [last blog](/blogs/gpt-4-modification) on modifying code with GPT-4 can tell you how much GPT-4 *loves* line numbers.\n",
      "\n",
      "For example, when getting Sweep to [migrate React components](https://github.com/sweepai/landing-page/issues/481) from function components to class components, a common error is a missing closing parenthesis at the end of the file. This is because GPT-4 generates diffs to edit the start of the component but not the end, which results in a missing closing parentheses at the end, yielding the following unhelpful message:\n",
      "\n",
      "```bash\n",
      "# npx eslint src/App.tsx\n",
      "\n",
      "/home/kevin/sweep-landing-page/src/App.tsx\n",
      "  139:0  error  Parsing error: '}' expected\n",
      "\n",
      "✖ 1 problem (1 error, 0 warnings)\n",
      "```\n",
      "\n",
      "### Formatters\n",
      "\n",
      "There are two main reasons we use formatters:\n",
      "\n",
      "- Formatted code is easier to fix. Bad indentation is very hard to read even for humans.\n",
      "- As indicated earlier, formatters tend to give better error messages for syntax errors.\n",
      "\n",
      "For example, here’s the equivalent error message for the same error from `prettier`.\n",
      "\n",
      "```bash\n",
      "# npx prettier src/App.tsx\n",
      "\n",
      "src/App.tsx\n",
      "[error] src/App.tsx: SyntaxError: '}' expected. (139:1)\n",
      "[error]   137 |   );\n",
      "[error]   138 |\n",
      "[error] > 139 |\n",
      "[error]       | ^\n",
      "```\n",
      "\n",
      "As you can see, `prettier` error logs indicate mistakes significantly clearer. We run this before running linters to ensure that parseable, formatted code is fed to GPT-4 to handle linting errors.\n",
      "\n",
      "### Type-Checker\n",
      "\n",
      "What’s the point of writing Typescript if we don’t type-check? The last piece to the puzzle is running `tsc`. The most frustrating thing is when all the changes look about right but Netlify wouldn’t deploy the website preview because there’s an `implicit-any`.\n",
      "\n",
      "## Orchestration\n",
      "\n",
      "So what exactly should Sweep run? We ended up settling on the following iteration loop:\n",
      "\n",
      "1. For every command in the list `prettier`, `eslint` and `tsc`, try running them.\n",
      "2. If it succeeds move on to the next command.\n",
      "3. If it fails, get GPT-4 to fix it based on the above prompt and go back to step one with `prettier`.\n",
      "\n",
      "Iterate this for a maximum of 15 times. We found that it many times it would fix everything around the 10th iteration, so we put max 15 just to be safe.\n",
      "\n",
      "We go back to `prettier` because sometimes writing a diff to fix `eslint` or `tsc` breaks the syntax once again. It’s like climbing a very shaky latter: every time you fall you go back to step 1. It's like playing a platformer with one life: every time you die you go back to the beginning.\n",
      "\n",
      "## Generalizing\n",
      "\n",
      "This is amazing, but how do we do this much tuning for **every single user**? Most users will not use Sweep sandbox if high-quality results depend on significant configuration.\n",
      "\n",
      "However, configuring for every use case is extraordinarily difficult. There are dozens of languages each with dozens of associated tools. Just Python alone has like 15 linters.\n",
      "\n",
      "### Trunk\n",
      "\n",
      "We found Trunk at around this time, a super-linter. Think Swiss army knife for formatters, linters and more.\n",
      "\n",
      "On installation, it installs a handful of the most commonly used formatters, linters and dev tools for the repo, like `eslint` for JS/TS and `ruff` for Python, along with maybe 5 other compatible formatters and checkers.\n",
      "\n",
      "For example, to set this up for our system above, we can generalize `prettier` to `trunk fmt` and `eslint` to `trunk check`.\n",
      "\n",
      "Another frustration of linters is that when you install them, most linters by default reveal *dozens of errors* per file, of which most are nit-picks like lines being too long. Trunk gets around this by reporting only **newly generated errors**: that is, errors in the git diff that are not in the main branch. Further, Trunk only reports files edited in the feature branch or uncommitted code, eliminating any errors made by other programmers or previously in the codebase.\n",
      "\n",
      "Trunk is also extremely configurable and easy to install by users, at https://docs.trunk.io/get-started.\n",
      "\n",
      "We decided to use Trunk as the default formatter and linters to use for Sweep sandbox.\n",
      "\n",
      "## Straight into Production\n",
      "\n",
      "But how do we run this in production, with various different environments and enabling configurability?\n",
      "\n",
      "Originally we considered just running user’s GitHub Actions on our end but this poses some problems:\n",
      "\n",
      "1. You can’t isolate the run to just one file. Sometimes other parts of the system are broken.\n",
      "2. The only open-source runner is `act` (https://github.com/nektos/act) which has yielded many runtime issues for us.\n",
      "3. GitHub Actions is too slow. Many simple runs like running `pylint` or `eslint` take minutes and some testing frameworks can take hours.\n",
      "4. GitHub Actions is hard to set up, impossible to test locally, and unintuitive. You need a 50-line GitHub Action yaml just to run `black`.\n",
      "\n",
      "Thus, we set up a Dockerized sandbox environment. Think stripped down GitHub Actions environment: just a setup install script and some way of checking a particular file.\n",
      "\n",
      "Our default configuration for installing and executing Trunk looks something like\n",
      "\n",
      "```yaml\n",
      "sandbox:\n",
      "\tinstall:\n",
      "\t\t- trunk init\n",
      "  check:\n",
      "    - trunk fmt file_path\n",
      "    - trunk check file_path\n",
      "```\n",
      "\n",
      "Extremely straightforward. The curly brackets allow injecting the currently modified file really easily. We can cache the container after the install scripts finish too to optimize the startup speed.\n",
      "\n",
      "Here are some other examples. Our setup at Sweep installs and runs `pre-commit` and `pylint` through `poetry`. The second line checks that it’s a Python file before executing. The sandbox config on our landing page runs `prettier`, `eslint` and `tsc` for JS/TS files.\n",
      "\n",
      "### Sweep's config\n",
      "\n",
      "```yaml\n",
      "sandbox:\n",
      "  install:\n",
      "    - pre-commit install\n",
      "    - pip install poetry\n",
      "    - poetry install\n",
      "  check:\n",
      "    - pre-commit run --files {file_path}\n",
      "    - '[[ \"{file_path}\" == *.py ]] && poetry run pylint --errors-only {file_path} || exit 0'\n",
      "```\n",
      "/tmp/sweep/docs/pages/faq.mdx:0-157\n",
      "\n",
      "\n",
      "# Frequently Asked Questions\n",
      "\n",
      "\n",
      "<details>\n",
      "<summary>Can I host Sweep myself?</summary>\n",
      "\n",
      "Yes! Check out https://docs.sweep.dev/deployment.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"does-sweep-write-tests\">\n",
      "<summary>Does Sweep write tests?</summary>\n",
      "\n",
      "Yep! The easiest way to have Sweep write tests is by modifying the `description` parameter in your `sweep.yaml`. You can add something like:\n",
      "“In [your repository], the tests are written in [your format]. If you modify business logic, modify the tests as well using this format.” You can add anything you’d like to the description parameter, including formatting rules (like PEP8), code style, etc!\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"can-we-trust-code-written-by-sweep\">\n",
      "<summary>Can we trust the code written by Sweep?</summary>\n",
      "\n",
      "You should always review the PR. However, we also perform testing to make sure the PR works using your existing GitHub actions.\n",
      "To get the best performance, add GitHub actions that lint, test, and validate your code.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"work-off-another-branch\">\n",
      "<summary>Can I have Sweep work off of another branch besides main?</summary>\n",
      "\n",
      "Yes! In the `sweep.yaml`, you can set the `branch` parameter to something besides your default branch, and Sweep will use that as a reference.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"retry-issue-with-sweep\">\n",
      "<summary>How do I retry an issue with Sweep?</summary>\n",
      "\n",
      "To retry an issue, prefix your issue reply with 'Sweep: '. This will trigger Sweep to retry the issue.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"give-documentation-to-sweep\">\n",
      "<summary>Can I give documentation to Sweep?</summary>\n",
      "\n",
      "Yes! In the `sweep.yaml`, you can specify docs. Be sure to pick the prefix of the site, which will allow us to only fetch the docs you need.\n",
      "Check out the example here: https://github.com/sweepai/sweep/blob/main/sweep.yaml.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"comment-on-sweeps-prs\">\n",
      "<summary>Can I comment on Sweep’s PRs?</summary>\n",
      "\n",
      "Yep! You have three options depending on the degree of the change:\n",
      "\n",
      "1. You can comment on the issue, and Sweep will rewrite the entire pull request. This will use one of your GPT4 credits.\n",
      "2. You can comment on the pull request (not a file) and Sweep can make substantial changes to the pull request. Sweep will search the codebase, and is able to modify and create files.\n",
      "3. You can comment on the file directly, and Sweep will only modify that file. Use this for small single file changes.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"why-open-source\">\n",
      "<summary>Why are you open source?</summary>\n",
      "\n",
      "We’re open source so that our users can see exactly how their data is processed, as well as learn from how Sweep works! We’re really excited about building a community of Sweep users(like you!).\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"difference-from-copilot\">\n",
      "<summary>What’s the difference from CoPilot?</summary>\n",
      "\n",
      "Copilot lives in your IDE and writes small chunks of code at a time. This takes ~3-5 seconds, and you need to watch it the entire time. Sweep runs completely asynchronously, and handles the task end to end. This might take 10-15 minutes, but you’re able to walk away and come back to a finished pull request. Copilot also doesn’t have access to the latest documentation.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"difference-from-chatgpt\">\n",
      "<summary>What’s the difference from ChatGPT?</summary>\n",
      "\n",
      "ChatGPT can’t write the actual PR, and you’d have to paste the generated code into your codebase and create a PR yourself. ChatGPT doesn’t have access to your codebase and the latest documentation, so it’s limited with large software projects.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"difference-from-autogpt\">\n",
      "<summary>What’s the difference from AutoGPT?</summary>\n",
      "\n",
      "AutoGPT (and similar tools) doesn’t work, and Sweep works. We don’t allow the language model to perform open domain tool execution (which doesn’t work well). We perform a fixed flow of search → plan → write code → validate code, repeating the last two steps. This lets us reliably generate PRs corresponding to the user description.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"difference-from-gpt-engineer-smol-developer-autogpt\">\n",
      "<summary>What’s the difference from GPT-Engineer, Smol Developer and AutoGPT?</summary>\n",
      "\n",
      "Sweep is built to improve on an existing codebase, which is a more frequent and higher need, than generating boilerplate, which is mostly a solved problem since you can just fork existing boilerplates.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"difference-from-cody-bloop\">\n",
      "<summary>What’s the difference from Cody and Bloop?</summary>\n",
      "\n",
      "We do more than just chat-with-your-code by actually creating pull requests.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"difference-from-autopr\">\n",
      "<summary>What’s the difference from AutoPR?</summary>\n",
      "\n",
      "We're shipping new features at Sweep everyday and unfortunately it looks like AutoPR hasn't received any updates in months.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"support-gitlab-gitea-bitbucket-forgejo\">\n",
      "<summary>Do you guys support GitLab / Gitea / BitBucket / Forgejo?</summary>\n",
      "\n",
      "We only support GitHub at the moment. We're currently focused on delivering more value and making Sweep more reliable and capable as opposed to supporting more services.\n",
      "</details>\n",
      "\n",
      "<details id=\"support-other-models\">\n",
      "<summary>Do you guys support other models, such as GPT-3.5, Claude and Gemini as well as open-source models like Deepseek and CodeLlama?</summary>\n",
      "\n",
      "We currently only support GPT-4 but are actively testing on Claude v3 which is looking promising -- it's faster, less expensive and smarter with using the tool calls. Gemini also looks promising with long-context multi-file reasoning but we have tested on it less. We just moved off function calls for our code modification agent so this is now possible.\n",
      "\n",
      "All our prompts are currently tuned for GPT-4 so exploring a new model involves re-tuning all the prompts and re-benchmarking each component -- from code search / reranking to planning to code editing. This is why reason you can't just plug LiteLLM. The majority of models out there, including even GPT-3.5, struggle to respond in the right format given or current prompts. Migrating the clients over is the trivial part. \n",
      "\n",
      "This is also why we stopped supporting GPT-3.5: it's simply too unreliable and can't respond with our format consistently. It's not a matter of generating buggy or incorrect code but more of a matter of making any valid code edits at all.\n",
      "\n",
      "We currently don't support or plan on supporting any open-source models. The only model that is viable is Deepseek but we have found that it's very hard to prompt-tune to use our diff format (or any viable diff format).\n",
      "\n",
      "We're currently focused on building the most capable coding agent with the models available today, rather than building the most accessible or least expensive agent, or the one that runs on the lowest possible hardware requirements.\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"do-you-store-our-code\">\n",
      "<summary>Do you store our code?</summary>\n",
      "\n",
      "We access your GitHub repository at runtime. At the end of execution, your code is deleted from the server. To perform search over your codebase, we use the hashed contents along with the embeddings. This allows us to avoid storing any code as plaintext. Read more about it here: [https://docs.sweep.dev/blogs/search-infra](https://docs.sweep.dev/blogs/search-infra).\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"example-sweep-prs\">\n",
      "<summary>Do you have example Sweep PRs?</summary>\n",
      "\n",
      "Yes! Check out [https://docs.sweep.dev/about/examples](https://docs.sweep.dev/about/examples).\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"support-integration-linear-jira-zendesk\">\n",
      "<summary>Do you support integration with Linear, Jira, Zendesk etc.?</summary>\n",
      "\n",
      "We don't directly support it but we recommend setting up a Zapier plugin that syncs GitHub issues from Linear/Jira. This will automatically create the GitHub issues when you make a Linear/Jira issue which will trigger Sweep if prefixed with \"Sweep: \".\n",
      "\n",
      "</details>\n",
      "\n",
      "<details id=\"empty-repository-error\">\n",
      "<summary>What does the 'Empty repository' error mean?</summary>\n",
      "\n",
      "The 'Empty repository' error occurs when the repository was just created and Sweep will not work. This is because Sweep needs some existing code in the repository to work with. If the repository is empty, Sweep does not have any code to analyze or modify. To fix this error, you can add some initial code to your repository before using Sweep. Once there is some code in the repository, Sweep should be able to work as expected.\n",
      "\n",
      "</details>\n"
     ]
    }
   ],
   "source": [
    "from sweepai.core.vector_db import cosine_similarity\n",
    "\n",
    "\n",
    "def find_central_vectors(vectors, num_vectors=5):\n",
    "    # Calculate the centroid of all vectors\n",
    "    centroid = np.mean(vectors, axis=0)\n",
    "    \n",
    "    # Compute cosine similarities between each vector and the centroid\n",
    "    similarities = cosine_similarity(vectors, [centroid]).flatten()\n",
    "    \n",
    "    # Get indices of the top 'num_vectors' vectors with highest similarity\n",
    "    central_indices = np.argsort(-similarities)[:num_vectors]\n",
    "    \n",
    "    return central_indices\n",
    "\n",
    "# Example usage\n",
    "# Assuming 'embeddings' is your list of embeddings\n",
    "central_indices = find_central_vectors(np.array(embeddings_in_subdir))\n",
    "central_snippets = [snippets[i] for i in central_indices]\n",
    "central_vectors = [embeddings[i] for i in central_indices]\n",
    "for snippet in central_snippets:\n",
    "    print(snippet.denotation + \"\\n\\n\")\n",
    "    print(snippet.get_snippet(False, False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'docs/pages/about': 4, 'docs/pages/usage': 6, 'docs/pages/blogs': 24, 'docs/pages/videos': 6, 'docs/pages': 90, 'docs/components': 6, 'docs/public/sweep-core-algo': 8, 'docs/public/modification': 1, 'docs/public/deployment': 18, 'docs/public/assistant': 1, 'docs/public/openai_proxy': 5, 'docs/public/covers': 22, 'docs/public/trunk': 1, 'docs/public/assets/tutorial': 6, 'docs/public/assets': 16, 'docs/public/tutorial': 9, 'docs/public/flowchart': 4, 'docs/public': 184, 'docs/styles': 1, 'docs/sdk/src': 2, 'docs/sdk': 6, 'docs/.github': 1, 'docs': 588, 'sweepai/core': 16, 'sweepai/logn': 5, 'sweepai/handlers': 10, 'sweepai/web': 5, 'sweepai/utils': 52, 'sweepai/chat': 1, 'sweepai/config': 3, 'sweepai/agents': 15, 'sweepai': 221, 'tests/perf': 2, 'tests/e2e': 5, 'tests/modify_tests': 2, 'tests/notebooks/src/helpers': 2, 'tests/notebooks/src': 11, 'tests/notebooks': 33, 'tests/search': 1, 'tests/jsons': 12, 'tests/events': 36, 'tests/archive': 55, 'tests': 308, '.assets': 6, 'platform/cypress/support': 2, 'platform/cypress/fixtures': 1, 'platform/cypress/e2e': 1, 'platform/cypress': 8, 'platform/components/ui': 20, 'platform/components/dashboard/sections': 5, 'platform/components/dashboard': 16, 'platform/components': 73, 'platform/lib': 12, 'platform/public': 2, 'platform/scripts/.next': 1, 'platform/scripts': 4, 'platform/state': 2, 'platform/app/api/openai/create': 1, 'platform/app/api/openai/edit': 2, 'platform/app/api/openai': 6, 'platform/app/api/files/check': 1, 'platform/app/api/files/list': 1, 'platform/app/api/files/search': 1, 'platform/app/api/files': 7, 'platform/app/api/branch': 1, 'platform/app/api/run': 1, 'platform/app/api': 30, 'platform/app': 64, 'platform': 345, 'notebooks': 2, 'sweep_chat/components/ui': 11, 'sweep_chat/components': 25, 'sweep_chat/lib': 2, 'sweep_chat/public': 2, 'sweep_chat/backend': 1, 'sweep_chat/app/api/auth/[...nextauth]': 1, 'sweep_chat/app/api/auth': 2, 'sweep_chat/app/api': 4, 'sweep_chat/app': 12, 'sweep_chat': 95, 'bin/server': 3, 'bin': 11, '.github/ISSUE_TEMPLATE': 2, '.github/workflows': 8, '.github': 21}\n"
     ]
    }
   ],
   "source": [
    "def count_descendants(directory):\n",
    "    descendant_count = {}\n",
    "\n",
    "    def dfs(current_dir):\n",
    "        count = 0\n",
    "        for root, dirs, files in os.walk(current_dir):\n",
    "            if \".git\" in root.split(os.sep):\n",
    "                continue\n",
    "            for d in dirs:\n",
    "                if \".git\" in root.split(os.sep):\n",
    "                    continue\n",
    "                count += dfs(os.path.join(root, d))\n",
    "            count += len(files)\n",
    "        descendant_count[current_dir.removeprefix(directory.rstrip() + \"/\")] = count\n",
    "        return count\n",
    "\n",
    "    dfs(directory)\n",
    "    for key in (\".git\", \"\", directory):\n",
    "        if key in descendant_count:\n",
    "            del descendant_count[key]\n",
    "    return descendant_count\n",
    "\n",
    "directory_path = cloned_repo._repo_dir\n",
    "descendants = count_descendants(directory_path)\n",
    "print(descendants)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorted descendants:\n",
      "docs: 588\n",
      "platform: 345\n",
      "tests: 308\n",
      "sweepai: 221\n",
      "docs/public: 184\n",
      "sweep_chat: 95\n",
      "docs/pages: 90\n",
      "platform/components: 73\n",
      "platform/app: 64\n",
      "tests/archive: 55\n",
      "sweepai/utils: 52\n",
      "tests/events: 36\n",
      "tests/notebooks: 33\n",
      "platform/app/api: 30\n",
      "sweep_chat/components: 25\n",
      "docs/pages/blogs: 24\n",
      "docs/public/covers: 22\n",
      ".github: 21\n",
      "platform/components/ui: 20\n",
      "docs/public/deployment: 18\n",
      "docs/public/assets: 16\n",
      "sweepai/core: 16\n",
      "platform/components/dashboard: 16\n",
      "sweepai/agents: 15\n",
      "tests/jsons: 12\n",
      "platform/lib: 12\n",
      "sweep_chat/app: 12\n",
      "tests/notebooks/src: 11\n",
      "sweep_chat/components/ui: 11\n",
      "bin: 11\n",
      "sweepai/handlers: 10\n",
      "docs/public/tutorial: 9\n",
      "docs/public/sweep-core-algo: 8\n",
      "platform/cypress: 8\n",
      ".github/workflows: 8\n",
      "platform/app/api/files: 7\n",
      "docs/pages/usage: 6\n",
      "docs/pages/videos: 6\n",
      "docs/components: 6\n",
      "docs/public/assets/tutorial: 6\n",
      "docs/sdk: 6\n",
      ".assets: 6\n",
      "platform/app/api/openai: 6\n",
      "docs/public/openai_proxy: 5\n",
      "sweepai/logn: 5\n",
      "sweepai/web: 5\n",
      "tests/e2e: 5\n",
      "platform/components/dashboard/sections: 5\n",
      "docs/pages/about: 4\n",
      "docs/public/flowchart: 4\n",
      "platform/scripts: 4\n",
      "sweep_chat/app/api: 4\n",
      "sweepai/config: 3\n",
      "bin/server: 3\n",
      "docs/sdk/src: 2\n",
      "tests/perf: 2\n",
      "tests/modify_tests: 2\n",
      "tests/notebooks/src/helpers: 2\n",
      "platform/cypress/support: 2\n",
      "platform/public: 2\n",
      "platform/state: 2\n",
      "platform/app/api/openai/edit: 2\n",
      "notebooks: 2\n",
      "sweep_chat/lib: 2\n",
      "sweep_chat/public: 2\n",
      "sweep_chat/app/api/auth: 2\n",
      ".github/ISSUE_TEMPLATE: 2\n",
      "docs/public/modification: 1\n",
      "docs/public/assistant: 1\n",
      "docs/public/trunk: 1\n",
      "docs/styles: 1\n",
      "docs/.github: 1\n",
      "sweepai/chat: 1\n",
      "tests/search: 1\n",
      "platform/cypress/fixtures: 1\n",
      "platform/cypress/e2e: 1\n",
      "platform/scripts/.next: 1\n",
      "platform/app/api/openai/create: 1\n",
      "platform/app/api/files/check: 1\n",
      "platform/app/api/files/list: 1\n",
      "platform/app/api/files/search: 1\n",
      "platform/app/api/branch: 1\n",
      "platform/app/api/run: 1\n",
      "sweep_chat/backend: 1\n",
      "sweep_chat/app/api/auth/[...nextauth]: 1\n"
     ]
    }
   ],
   "source": [
    "def print_sorted_descendants(directory):\n",
    "    descendants = count_descendants(directory)\n",
    "    # Sort directories by the number of files, from highest to lowest\n",
    "    sorted_descendants = sorted(descendants.items(), key=lambda item: item[1], reverse=True)\n",
    "    for dir_path, count in sorted_descendants:\n",
    "        print(f\"{dir_path}: {count}\")\n",
    "print(\"Sorted descendants:\")\n",
    "print_sorted_descendants(directory_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_histogram(directory):\n",
    "    file_counts = count_descendants(directory)\n",
    "    values = list(file_counts.values())\n",
    "\n",
    "    bins = list(range(0, 101))  # Bins from 0 to 100\n",
    "    bins.append(max(values) + 1)  # Additional bin for all values greater than 100\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.hist(values, bins=100, alpha=0.7, color='blue', edgecolor='black')\n",
    "    plt.title('Histogram of File Counts in Directories')\n",
    "    plt.xlabel('Number of Files')\n",
    "    plt.ylabel('Number of Directories')\n",
    "    plt.xlim(0, 100)\n",
    "    \n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "# Example usage:\n",
    "directory_path = cloned_repo._repo_dir\n",
    "plot_histogram(directory_path)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}