enpoint_testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pinecone import Pinecone\n",
    "import google.generativeai as ga\n",
    "import numpy as np\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc = Pinecone(api_key=\"165724f3-337c-4129-aece-106b08e6a221\")\n",
    "ga.configure(api_key=\"AIzaSyCP8PWutyKj8N_sM-v1XyPB54_YswnzxYc\")\n",
    "model = ga.GenerativeModel(\"gemini-pro\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
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    {
     "data": {
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       "{'id': 'https://www.wired.com/story/synthetic-data-is-a-dangerous-teacher/',\n",
       " 'text': '# Generative AI Explosion: Uncovering the Dark Side of AI-Generated Content #\\n\\n> \"In 2024, we will be trapped in a recursive loop where we will be training AI models using only synthetic data produced by AI models.\"\\n\\nThe advent of generative AI, exemplified by models, has ushered in an era of remarkable technological advancements. Dall-E and ChatGPT have captured the public\\'s imagination, attracting millions of users within a short span of time. However, as generative AI continues to proliferate, a dark underbelly is emerging—an explosion of fabricated information, mis- and disinformation, and the exacerbation of negative social stereotypes. This trend will escalate in 2024, creating a vast repository of synthetic data that perpetuates historical and societal inequities.\\n\\n## The Perils of Synthetic Data ##\\n\\nThe training of generative AI models relies heavily on massive data sets, often sourced from the web. This data is assumed to represent \"ground truth\" for human communication and interaction. However, research has consistently shown that online data sets are rife with poor quality, negative stereotypes, and problematic content. Despite these concerns, big AI companies continue to utilize such data in their quest for model scalability.\\n\\n## The Amplification of Social Stereotypes ##\\n\\nGenerative AI models, rather than objectively representing the social world, encode and amplify social stereotypes. Recent studies have demonstrated that these models reproduce racist and discriminatory attitudes towards marginalized identities, cultures, and languages. As these models are increasingly used to generate text, images, audio, and video content, they perpetuate and exacerbate existing biases, further entrenching historical inequities.\\n\\n## The Toxic Dump of Synthetic Data ##\\n\\nThe vast quantities of generative AI outputs are poised to become the training material for future generative AI models. This creates a recursive loop where AI models are trained solely on synthetic data generated by other AI models. This data, contaminated with stereotypes and biases, will be used to train generative models applied to high-stake sectors such as medicine, therapy, education, and law. The consequences of this are potentially disastrous, as it perpetuates historical and societal inequities in these critical domains.\\n\\n## Beyond the Hype: Recalibrating Expectations ##\\n\\nThe hype surrounding generative AI has reached fever pitch, with expectations of AI-powered riches becoming commonplace. However, 2024 will witness a recalibration of these expectations as the limitations and challenges of generative AI become more apparent. More evidence will emerge of false information and hallucinations generated by these models, undermining their perceived accuracy and reliability. Hopes for a quick fix to these problems via supervised learning appear elusive, leaving researchers and developers grappling with the inherent limitations of current generative AI approaches.\\n\\n## The Digital Dark Age of Generative AI ##\\n\\nThe integration of generative AI into online services has led to a proliferation of hallucinations and mistakes, further eroding trust in online information. The result will be a \"digital dark age,\" where social media platforms transition away from the logic of Web 2.0 towards one dominated by AI-generated content. Transparency and access to information will be diminished, making it increasingly difficult to discern what is real and what is fabricated.\\n\\n## The Coming Legal Battles Over Generative AI ##\\n\\nThe rapid development of generative AI has ignited legal battles centered around the use of copyrighted material as training data for AI models. Authors, artists, and coders are challenging the practice of teaching AI models to replicate their skills using their works as a training manual. The debate revolves around the interpretation of \"fair use\" and the extent to which copyrighted material can be utilized for AI training purposes. These legal battles will shape the future of generative AI and its impact on creative industries.\\n\\n2024 will be a pivotal year for generative AI, marked by a growing recognition of its potential pitfalls and limitations. The explosion of fabricated and biased content, the recursive loop of synthetic data, and the erosion of trust in online information are all serious concerns that demand urgent attention. It is imperative that stakeholders in the AI community, including researchers, developers, policymakers, and industry leaders, come together to address these challenges and mitigate the negative societal impacts of generative AI.',\n",
       " 'url': ['https://www.wired.com/story/synthetic-data-is-a-dangerous-teacher/',\n",
       "  'https://www.wired.com/story/get-ready-for-the-great-ai-disappointment/',\n",
       "  'https://www.wired.com/story/the-new-digital-dark-age/',\n",
       "  'https://www.wired.com/story/generative-ai-web-2-mistakes/',\n",
       "  'https://www.wired.com/story/livewired-generative-ai-copyright/'],\n",
       " 'title': 'Synthetic Data Is a Dangerous Teacher | WIRED',\n",
       " 'keyPoints': '- Generative AI has seen rapid advancements, attracting millions of users.\\n- It has a dark side, including fabricated information, mis- and disinformation, and the exacerbation of negative social stereotypes.\\n- 2024 will feature a vast repository of synthetic data that perpetuates historical and societal inequities.\\n- Generative AI models rely heavily on data sets sourced from the web, which often contain poor quality, negative stereotypes, and problematic content.\\n- These models encode and amplify social stereotypes, perpetuating and exacerbating existing biases and historical inequities.\\n- The vast quantities of generative AI outputs will be used to train future generative AI models, creating a recursive loop of synthetic data contaminated with stereotypes and biases.\\n- High-stake sectors such as medicine, therapy, education, and law could suffer from these perpetuated inequities.\\n- The hype surrounding generative AI will be recalibrated as the limitations and challenges become apparent.\\n- More evidence of false information and hallucinations generated by these models will erode trust in their accuracy and reliability.\\n- Supervised learning approaches may not be an effective solution to the limitations of current generative AI approaches.\\n- Generative AI has led to hallucinations and mistakes in online services, eroding trust in online information.\\n- The integration of generative AI into online services has led to a proliferation of hallucinations and mistakes, further eroding trust in online information.\\n- The digital dark age of generative AI might cause social media platforms to transition away from the logic of Web 2.0 towards one dominated by AI-generated content.\\n- Legal battles are emerging over the use of copyrighted material as training data for AI models, challenging the interpretation of \"fair use\" and the extent of its application in AI training.\\n- 2024 will be a pivotal year for generative AI, marked by a growing recognition of its potential pitfalls and limitations.\\n- Stakeholders in the AI community must come together to address the challenges and mitigate the negative societal impacts of generative AI.',\n",
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       "    'contents_url': 'https://api.github.com/repos/langgenius/dify/contents/{+path}',\n",
       "    'compare_url': 'https://api.github.com/repos/langgenius/dify/compare/{base}...{head}',\n",
       "    'merges_url': 'https://api.github.com/repos/langgenius/dify/merges',\n",
       "    'archive_url': 'https://api.github.com/repos/langgenius/dify/{archive_format}{/ref}',\n",
       "    'downloads_url': 'https://api.github.com/repos/langgenius/dify/downloads',\n",
       "    'issues_url': 'https://api.github.com/repos/langgenius/dify/issues{/number}',\n",
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       "    'labels_url': 'https://api.github.com/repos/langgenius/dify/labels{/name}',\n",
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       "    'created_at': '2023-04-12T07:40:24Z',\n",
       "    'updated_at': '2024-02-25T04:40:50Z',\n",
       "    'pushed_at': '2024-02-24T06:59:06Z',\n",
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       "    'size': 22418,\n",
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       "    'language': 'Python',\n",
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       "    'has_projects': False,\n",
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       "    'has_pages': False,\n",
       "    'has_discussions': False,\n",
       "    'forks_count': 2016,\n",
       "    'mirror_url': None,\n",
       "    'archived': False,\n",
       "    'disabled': False,\n",
       "    'open_issues_count': 50,\n",
       "    'license': {'key': 'other',\n",
       "     'name': 'Other',\n",
       "     'spdx_id': 'NOASSERTION',\n",
       "     'url': None,\n",
       "     'node_id': 'MDc6TGljZW5zZTA='},\n",
       "    'allow_forking': True,\n",
       "    'is_template': False,\n",
       "    'web_commit_signoff_required': False,\n",
       "    'topics': ['ai',\n",
       "     'backend-as-a-service',\n",
       "     'gpt',\n",
       "     'gpt-4',\n",
       "     'langchain',\n",
       "     'llama2',\n",
       "     'llm',\n",
       "     'openai',\n",
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       "    'watchers': 16033,\n",
       "    'default_branch': 'main',\n",
       "    'permissions': {'admin': False,\n",
       "     'maintain': False,\n",
       "     'push': False,\n",
       "     'triage': False,\n",
       "     'pull': True},\n",
       "    'score': 1.0}]}}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pc.Index('openai')\n",
    "\n",
    "url = \"https://api.github.com/search/repositories?q=\"\n",
    "\n",
    "headers = {\n",
    "  'Authorization': 'Token *'\n",
    "}\n",
    "\n",
    "def build(query: str, context: str):\n",
    "    base_prompt = {\n",
    "        \"content\": \"I am going to give you an article, generate key points\"\n",
    "        \" based only on the provided context, and not any other information.\"\n",
    "        \" Just generate the points, I don't want you to say Sure here is the answer or here are the key points or anything like that\"\n",
    "        \" Break your answer up into nicely readable points.\",\n",
    "    }\n",
    "    user_prompt = {\n",
    "        \"content\": f\" The question is '{query}'. Here is all the context you have:\"\n",
    "        f'{(\" \").join(context)}',\n",
    "    }\n",
    "    system = f\"{base_prompt['content']} {user_prompt['content']}\"\n",
    "\n",
    "    return system[:100000]\n",
    "\n",
    "\n",
    "def get_article(id_list: list):\n",
    "    if(id_list == [[],[]]):\n",
    "        vector = list(np.zeros(1536))\n",
    "    else:\n",
    "        vector = index.fetch(id_list[0])[\"vectors\"][id_list[0][len(id_list[0]) - 1]][\"values\"]\n",
    "\n",
    "    query = index.query(\n",
    "            vector=vector,\n",
    "            top_k=len(id_list[0]) + 1 + len(id_list[1]),\n",
    "            include_values=True,\n",
    "            include_metadata=True\n",
    "    )\n",
    "\n",
    "    #print(query[\"matches\"])\n",
    "\n",
    "    for i in range(len(query[\"matches\"])):\n",
    "        if(query[\"matches\"][i][\"id\"] not in id_list[0] and query[\"matches\"][i][\"id\"] not in id_list[1]):\n",
    "            key = model.generate_content(build(query[\"matches\"][i][\"metadata\"][\"text\"], \"\"))\n",
    "            topic = model.generate_content(\"Generate a one word word topic keyword: \\n\\n\" + query[\"matches\"][i][\"metadata\"][\"text\"]).text\n",
    "            title = model.generate_content(\"Generate a title for the article: \\n\\n\" + query[\"matches\"][i][\"metadata\"][\"text\"]).text\n",
    "            to_be_returned = {\n",
    "                \"id\": query[\"matches\"][i][\"id\"],\n",
    "                \"text\": query[\"matches\"][i][\"metadata\"][\"text\"],\n",
    "                \"url\": query[\"matches\"][i][\"metadata\"][\"url\"],\n",
    "                \"title\": query[\"matches\"][i][\"metadata\"][\"title\"],\n",
    "                \"generatedTitle\": title,\n",
    "                \"keyPoints\": key.text,\n",
    "                \"github\": requests.request(\"GET\", url+(topic.replace(\" \", \",\").replace('\\n',\"\").replace('\"', \"\")+\"&sort='stars'\"), headers=headers).json()\n",
    "            }\n",
    "            return to_be_returned\n",
    "\n",
    "\n",
    "def entry_point(resp: dict):\n",
    "    return get_article([resp[\"like\"], resp[\"dislike\"]])\n",
    "    \n",
    "\n",
    "\n",
    "test_dict = {\n",
    "    \"like\": [\"https://www.wired.com/story/generative-ai-web-2-mistakes/\", \"https://www.wired.com/story/get-ready-for-the-great-ai-disappointment/\"],\n",
    "    \"dislike\": [0]\n",
    "\n",
    "}\n",
    "\n",
    "\n",
    "entry_point(test_dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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