boilerplate/02_prompting.ipynb

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    "# Notebook 2: Prompting\n",
    "In this notebook you learn how to use Luminous to solve a wide range of tasks like summarization, question answering, or in this case keyword extraction. After reading through this notebook, feel free to visit the [Playground](https://app.aleph-alpha.com/playground/completion) to try more examples."
   ]
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   "cell_type": "markdown",
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   "source": [
    "### Optional: Install the client\n",
    "You can skip this step, if you have already installed the `aleph_alpha_client`. Make sure you have the [latest pip version](https://pip.pypa.io/en/stable/installation/) installed before proceeding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install aleph-alpha-client"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantiate the client\n",
    "To interact with our API, you have to instantiate a client. Here you also provide your token to authenticate yourself. If you don't have one already, create one in your [Aleph Alpha profile](https://app.aleph-alpha.com/profile)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aleph_alpha_client import Client\n",
    "model = Client(token=\"AA_TOKEN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Completing a keyword extraction prompt\n",
    "\n",
    "With the prompt you communicate your instructions to Luminous. Prompts work best, if they contain a specific format and few shot examples. Read more about it [here](https://docs.aleph-alpha.com/docs/introduction/prompting_and_completion/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from aleph_alpha_client import Prompt, CompletionRequest\n",
    "\n",
    "prompt_text = \"\"\"Identify matching keywords for each text.\n",
    "###\n",
    "Text: The \"Whiskey War\" is an ongoing conflict between Denmark and Canada over ownership of Hans Island. The dispute began in 1973, when Denmark and Canada reached an agreement on Greenland's borders. However, no settlement regarding Hans Island could be reached by the time the treaty was signed. Since then both countries have used peaceful means - such as planting their national flag or burying liquor - to draw attention to the disagreement.\n",
    "Keywords: Conflict, Whiskey War, Denmark, Canada, Treaty, Flag, Liquor\n",
    "###\n",
    "Text: NASA launched the Discovery program to explore the solar system. It comprises a series of expeditions that have continued from the program's launch in the 1990s to the present day. In the course of the 16 expeditions launched so far, the Moon, Mars, Mercury and Venus, among others, have been explored. Unlike other space programs, the Discovery program places particular emphasis on cost efficiency, true to the motto: \"faster, better, cheaper\".\n",
    "Keywords: Space program, NASA, Expedition, Cost efficiency, Moon, Mars, Mercury, Venus\n",
    "###\n",
    "Text: Computer vision describes the processing of an image by a machine using external devices (e.g., a scanner) into a digital description of that image for further processing. An example of this is optical character recognition (OCR), the recognition and processing of images containing text. Further processing and final classification of the image is often done using artificial intelligence methods. The goal of this field is to enable computers to process visual tasks that were previously reserved for humans.\n",
    "Keywords:\"\"\"\n",
    "\n",
    "params = {\n",
    "    \"prompt\": Prompt.from_text(prompt_text),\n",
    "    \"maximum_tokens\": 32,\n",
    "    \"temperature\": 0.12,\n",
    "    \"stop_sequences\": [\"###\",\"\\n\"],\n",
    "}\n",
    "\n",
    "request = CompletionRequest(**params)\n",
    "response = model.complete(request, model=\"luminous-extended\")\n",
    "\n",
    "print(f\"\\nKeywords: {response.completions[0].completion}\")"
   ]
  }
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