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"https://openai.com/newsroom\n", "https://openai.com/api/policies\n", "https://openai.com/api/pricing\n", "https://openai.com/contact-sales\n", "https://openai.com/api/pricing/#faq-fine-tuning-pricing-calculation\n", "https://openai.com/blog/tags/events\n", "https://openai.com/blog/chatgpt\n" ] } ], "source": [ "import requests\n", "import re\n", "import urllib.request\n", "from bs4 import BeautifulSoup\n", "from collections import deque\n", "from html.parser import HTMLParser\n", "from urllib.parse import urlparse\n", "import os\n", "\n", "# Regex pattern to match a URL\n", "HTTP_URL_PATTERN = r'^http[s]*://.+'\n", "\n", "# Define root domain to crawl\n", "domain = \"openai.com\"\n", "full_url = \"https://openai.com/\"\n", "\n", "# Create a class to parse the HTML and get the hyperlinks\n", "class HyperlinkParser(HTMLParser):\n", " def __init__(self):\n", " super().__init__()\n", " # Create a list to store the hyperlinks\n", " self.hyperlinks = []\n", "\n", " # Override the HTMLParser's handle_starttag method to get the hyperlinks\n", " def handle_starttag(self, tag, attrs):\n", " attrs = dict(attrs)\n", "\n", " # If the tag is an anchor tag and it has an href attribute, add the href attribute to the list of hyperlinks\n", " if tag == \"a\" and \"href\" in attrs:\n", " self.hyperlinks.append(attrs[\"href\"])\n", "\n", "# Function to get the hyperlinks from a URL\n", "def get_hyperlinks(url):\n", " \n", " # Try to open the URL and read the HTML\n", " try:\n", " # Open the URL and read the HTML\n", " with urllib.request.urlopen(url) as response:\n", "\n", " # If the response is not HTML, return an empty list\n", " if not response.info().get('Content-Type').startswith(\"text/html\"):\n", " return []\n", " \n", " # Decode the HTML\n", " html = response.read().decode('utf-8')\n", " except Exception as e:\n", " print(e)\n", " return []\n", "\n", " # Create the HTML Parser and then Parse the HTML to get hyperlinks\n", " parser = HyperlinkParser()\n", " parser.feed(html)\n", "\n", " return parser.hyperlinks\n", "\n", "# Function to get the hyperlinks from a URL that are within the same domain\n", "def get_domain_hyperlinks(local_domain, url):\n", " clean_links = []\n", " for link in set(get_hyperlinks(url)):\n", " clean_link = None\n", "\n", " # If the link is a URL, check if it is within the same domain\n", " if re.search(HTTP_URL_PATTERN, link):\n", " # Parse the URL and check if the domain is the same\n", " url_obj = urlparse(link)\n", " if url_obj.netloc == local_domain:\n", " clean_link = link\n", "\n", " # If the link is not a URL, check if it is a relative link\n", " else:\n", " if link.startswith(\"/\"):\n", " link = link[1:]\n", " elif link.startswith(\"#\") or link.startswith(\"mailto:\"):\n", " continue\n", " clean_link = \"https://\" + local_domain + \"/\" + link\n", "\n", " if clean_link is not None:\n", " if clean_link.endswith(\"/\"):\n", " clean_link = clean_link[:-1]\n", " clean_links.append(clean_link)\n", "\n", " # Return the list of hyperlinks that are within the same domain\n", " return list(set(clean_links))\n", "\n", "\n", "def crawl(url):\n", " # Parse the URL and get the domain\n", " local_domain = urlparse(url).netloc\n", "\n", " # Create a queue to store the URLs to crawl\n", " queue = deque([url])\n", "\n", " # Create a set to store the URLs that have already been seen (no duplicates)\n", " seen = set([url])\n", "\n", " # Create a directory to store the text files\n", " if not os.path.exists(\"text/\"):\n", " os.mkdir(\"text/\")\n", "\n", " if not os.path.exists(\"text/\"+local_domain+\"/\"):\n", " os.mkdir(\"text/\" + local_domain + \"/\")\n", "\n", " # Create a directory to store the csv files\n", " if not os.path.exists(\"processed\"):\n", " os.mkdir(\"processed\")\n", "\n", " # While the queue is not empty, continue crawling\n", " while queue:\n", "\n", " # Get the next URL from the queue\n", " url = queue.pop()\n", " print(url) # for debugging and to see the progress\n", "\n", " # Save text from the url to a .txt file\n", " with open('text/'+local_domain+'/'+url[8:].replace(\"/\", \"_\") + \".txt\", \"w\") as f:\n", "\n", " # Get the text from the URL using BeautifulSoup\n", " soup = BeautifulSoup(requests.get(url).text, \"html.parser\")\n", "\n", " # Get the text but remove the tags\n", " text = soup.get_text()\n", "\n", " # If the crawler gets to a page that requires JavaScript, it will stop the crawl\n", " if (\"You need to enable JavaScript to run this app.\" in text):\n", " print(\"Unable to parse page \" + url + \" due to JavaScript being required\")\n", " \n", " # Otherwise, write the text to the file in the text directory\n", " f.write(text)\n", "\n", " # Get the hyperlinks from the URL and add them to the queue\n", " for link in get_domain_hyperlinks(local_domain, url):\n", " if link not in seen:\n", " queue.append(link)\n", " seen.add(link)\n", "\n", "crawl(full_url)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "def remove_newlines(serie):\n", " serie = serie.str.replace('\\n', ' ')\n", " serie = serie.str.replace('\\\\n', ' ')\n", " serie = serie.str.replace(' ', ' ')\n", " serie = serie.str.replace(' ', ' ')\n", " return serie" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "\n", "# Create a list to store the text files\n", "texts=[]\n", "\n", "# Get all the text files in the text directory\n", "for file in os.listdir(\"text/\" + domain + \"/\"):\n", "\n", " # Open the file and read the text\n", " with open(\"text/\" + domain + \"/\" + file, \"r\") as f:\n", " text = f.read()\n", "\n", " # Omit the first 11 lines and the last 4 lines, then replace -, _, and #update with spaces.\n", " texts.append((file[11:-4].replace('-',' ').replace('_', ' ').replace('#update',''), text))\n", "\n", "# Create a dataframe from the list of texts\n", "df = pd.DataFrame(texts, columns = ['fname', 'text'])\n", "\n", "# Set the text column to be the raw text with the newlines removed\n", "df['text'] = df.fname + \". \" + remove_newlines(df.text)\n", "df.to_csv('processed/scraped.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import tiktoken\n", "\n", "# Load the cl100k_base tokenizer which is designed to work with the ada-002 model\n", "tokenizer = tiktoken.get_encoding(\"cl100k_base\")\n", "\n", "df = pd.read_csv('processed/scraped.csv', index_col=0)\n", "df.columns = ['title', 'text']\n", "\n", "# Tokenize the text and save the number of tokens to a new column\n", "df['n_tokens'] = df.text.apply(lambda x: len(tokenizer.encode(x)))\n", "\n", "# Visualize the distribution of the number of tokens per row using a histogram\n", "df.n_tokens.hist()" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "max_tokens = 500\n", "\n", "# Function to split the text into chunks of a maximum number of tokens\n", "def split_into_many(text, max_tokens = max_tokens):\n", "\n", " # Split the text into sentences\n", " sentences = text.split('. ')\n", "\n", " # Get the number of tokens for each sentence\n", " n_tokens = [len(tokenizer.encode(\" \" + sentence)) for sentence in sentences]\n", " \n", " chunks = []\n", " tokens_so_far = 0\n", " chunk = []\n", "\n", " # Loop through the sentences and tokens joined together in a tuple\n", " for sentence, token in zip(sentences, n_tokens):\n", "\n", " # If the number of tokens so far plus the number of tokens in the current sentence is greater \n", " # than the max number of tokens, then add the chunk to the list of chunks and reset\n", " # the chunk and tokens so far\n", " if tokens_so_far + token > max_tokens:\n", " chunks.append(\". \".join(chunk) + \".\")\n", " chunk = []\n", " tokens_so_far = 0\n", "\n", " # If the number of tokens in the current sentence is greater than the max number of \n", " # tokens, go to the next sentence\n", " if token > max_tokens:\n", " continue\n", "\n", " # Otherwise, add the sentence to the chunk and add the number of tokens to the total\n", " chunk.append(sentence)\n", " tokens_so_far += token + 1\n", "\n", " return chunks\n", " \n", "\n", "shortened = []\n", "\n", "# Loop through the dataframe\n", "for row in df.iterrows():\n", "\n", " # If the text is None, go to the next row\n", " if row[1]['text'] is None:\n", " continue\n", "\n", " # If the number of tokens is greater than the max number of tokens, split the text into chunks\n", " if row[1]['n_tokens'] > max_tokens:\n", " shortened += split_into_many(row[1]['text'])\n", " \n", " # Otherwise, add the text to the list of shortened texts\n", " else:\n", " shortened.append( row[1]['text'] )" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df = pd.DataFrame(shortened, columns = ['text'])\n", "df['n_tokens'] = df.text.apply(lambda x: len(tokenizer.encode(x)))\n", "df.n_tokens.hist()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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textn_tokensembeddings
0blog authors maddie. Maddie Hall - OpenAI ...175[-0.012958061881363392, -0.006103983614593744,...
1blog authors tom. Tom Brown - OpenAI ...228[-0.0053874170407652855, -0.009962032549083233...
2blog openai scholars 2019 final projects. Op...492[0.0019150723237544298, -0.0070442273281514645...
3In this project, I used curiosity-driven explo...478[-0.0067560747265815735, 0.0004431474662851542...
4Results revealed that the optimal RL policies ...499[-0.012868616729974747, 0.0029640409629791975,...
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" ], "text/plain": [ " text n_tokens \\\n", "0 blog authors maddie. Maddie Hall - OpenAI ... 175 \n", "1 blog authors tom. Tom Brown - OpenAI ... 228 \n", "2 blog openai scholars 2019 final projects. Op... 492 \n", "3 In this project, I used curiosity-driven explo... 478 \n", "4 Results revealed that the optimal RL policies ... 499 \n", "\n", " embeddings \n", "0 [-0.012958061881363392, -0.006103983614593744,... \n", "1 [-0.0053874170407652855, -0.009962032549083233... \n", "2 [0.0019150723237544298, -0.0070442273281514645... \n", "3 [-0.0067560747265815735, 0.0004431474662851542... \n", "4 [-0.012868616729974747, 0.0029640409629791975,... " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import openai\n", "\n", "df['embeddings'] = df.text.apply(lambda x: openai.Embedding.create(input=x, engine='text-embedding-ada-002')['data'][0]['embedding'])\n", "df.to_csv('processed/embeddings.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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textn_tokensembeddings
0blog authors maddie. Maddie Hall - OpenAI ...175[-0.012958061881363392, -0.006103983614593744,...
1blog authors tom. Tom Brown - OpenAI ...228[-0.0053874170407652855, -0.009962032549083233...
2blog openai scholars 2019 final projects. Op...492[0.0019150723237544298, -0.0070442273281514645...
3In this project, I used curiosity-driven explo...478[-0.0067560747265815735, 0.0004431474662851542...
4Results revealed that the optimal RL policies ...499[-0.012868616729974747, 0.0029640409629791975,...
\n", "
" ], "text/plain": [ " text n_tokens \\\n", "0 blog authors maddie. Maddie Hall - OpenAI ... 175 \n", "1 blog authors tom. Tom Brown - OpenAI ... 228 \n", "2 blog openai scholars 2019 final projects. Op... 492 \n", "3 In this project, I used curiosity-driven explo... 478 \n", "4 Results revealed that the optimal RL policies ... 499 \n", "\n", " embeddings \n", "0 [-0.012958061881363392, -0.006103983614593744,... \n", "1 [-0.0053874170407652855, -0.009962032549083233... \n", "2 [0.0019150723237544298, -0.0070442273281514645... \n", "3 [-0.0067560747265815735, 0.0004431474662851542... \n", "4 [-0.012868616729974747, 0.0029640409629791975,... " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from openai.embeddings_utils import distances_from_embeddings\n", "\n", "df['embeddings'] = df.text.apply(lambda x: openai.Embedding.create(input=x, engine='text-embedding-ada-002')['data'][0]['embedding'])\n", "\n", "df.to_csv('processed/embeddings.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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textn_tokensembeddings
0blog authors maddie. Maddie Hall - OpenAI ...175[-0.012958061881363392, -0.006103983614593744,...
1blog authors tom. Tom Brown - OpenAI ...228[-0.0053874170407652855, -0.009962032549083233...
2blog openai scholars 2019 final projects. Op...492[0.0019150723237544298, -0.0070442273281514645...
3In this project, I used curiosity-driven explo...478[-0.0067560747265815735, 0.0004431474662851542...
4Results revealed that the optimal RL policies ...499[-0.012868616729974747, 0.0029640409629791975,...
\n", "
" ], "text/plain": [ " text n_tokens \\\n", "0 blog authors maddie. Maddie Hall - OpenAI ... 175 \n", "1 blog authors tom. Tom Brown - OpenAI ... 228 \n", "2 blog openai scholars 2019 final projects. Op... 492 \n", "3 In this project, I used curiosity-driven explo... 478 \n", "4 Results revealed that the optimal RL policies ... 499 \n", "\n", " embeddings \n", "0 [-0.012958061881363392, -0.006103983614593744,... \n", "1 [-0.0053874170407652855, -0.009962032549083233... \n", "2 [0.0019150723237544298, -0.0070442273281514645... \n", "3 [-0.0067560747265815735, 0.0004431474662851542... \n", "4 [-0.012868616729974747, 0.0029640409629791975,... " ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "from openai.embeddings_utils import distances_from_embeddings, cosine_similarity\n", "\n", "df=pd.read_csv('processed/embeddings.csv', index_col=0)\n", "df['embeddings'] = df['embeddings'].apply(eval).apply(np.array)\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'No, you are not allowed to publish model outputs to Twitter without a human review. You must manually review each generation before sharing or while streaming, and indicate that the content is AI-generated in a way no user could reasonably miss or misunderstand.'" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def create_context(\n", " question, df, max_len=1800, size=\"ada\"\n", "):\n", " \"\"\"\n", " Create a context for a question by finding the most similar context from the dataframe\n", " \"\"\"\n", "\n", " # Get the embeddings for the question\n", " q_embeddings = openai.Embedding.create(input=question, engine='text-embedding-ada-002')['data'][0]['embedding']\n", "\n", " # Get the distances from the embeddings\n", " df['distances'] = distances_from_embeddings(q_embeddings, df['embeddings'].values, distance_metric='cosine')\n", "\n", "\n", " returns = []\n", " cur_len = 0\n", "\n", " # Sort by distance and add the text to the context until the context is too long\n", " for i, row in df.sort_values('distances', ascending=True).iterrows():\n", " \n", " # Add the length of the text to the current length\n", " cur_len += row['n_tokens'] + 4\n", " \n", " # If the context is too long, break\n", " if cur_len > max_len:\n", " break\n", " \n", " # Else add it to the text that is being returned\n", " returns.append(row[\"text\"])\n", "\n", " # Return the context\n", " return \"\\n\\n###\\n\\n\".join(returns)\n", "\n", "def answer_question(\n", " df,\n", " model=\"text-davinci-003\",\n", " question=\"Am I allowed to publish model outputs to Twitter, without a human review?\",\n", " max_len=1800,\n", " size=\"ada\",\n", " debug=False,\n", " max_tokens=150,\n", " stop_sequence=None\n", "):\n", " \"\"\"\n", " Answer a question based on the most similar context from the dataframe texts\n", " \"\"\"\n", " context = create_context(\n", " question,\n", " df,\n", " max_len=max_len,\n", " size=size,\n", " )\n", " # If debug, print the raw model response\n", " if debug:\n", " print(\"Context:\\n\" + context)\n", " print(\"\\n\\n\")\n", "\n", " try:\n", " # Create a completions using the question and context\n", " response = openai.Completion.create(\n", " prompt=f\"Answer the question based on the context below, and if the question can't be answered based on the context, say \\\"I don't know\\\"\\n\\nContext: {context}\\n\\n---\\n\\nQuestion: {question}\\nAnswer:\",\n", " temperature=0,\n", " max_tokens=max_tokens,\n", " top_p=1,\n", " frequency_penalty=0,\n", " presence_penalty=0,\n", " stop=stop_sequence,\n", " model=model,\n", " )\n", " return response[\"choices\"][0][\"text\"].strip()\n", " except Exception as e:\n", " print(e)\n", " return \"\"" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"I don't know.\"" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "answer_question(df, question=\"What day is it?\", debug=False)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'The newest embeddings model is text-embedding-ada-002.'" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "answer_question(df, question=\"What is our newest embeddings model?\")" ] } ], "metadata": { "kernelspec": { "display_name": "env", "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": "3.9.6" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "05f34a34d73b71652304030c1097be3a5720ea2447153dd6542d145a26b73181" } } }, "nbformat": 4, "nbformat_minor": 2 }