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- "cell_type": "markdown",
- "source": [
- "![](\"https://www.comet.com/images/logo_comet_light.png\")
\n",
- "\n",
- " \n",
- " ![\"PyPI](\"https://img.shields.io/pypi/v/comet-llm\")
\n",
- " \n",
- " ![\"GitHub\"](\"https://img.shields.io/badge/License-MIT-blue.svg\")
\n",
- " \n",
- " ![\"cometLLM](\"https://img.shields.io/badge/cometLLM-Docs-blue.svg\")
\n",
- " \n",
- " ![\"Downloads\"](\"https://static.pepy.tech/badge/comet-llm\")
\n",
- "
\n",
- "\n",
- "\n",
- "CometLLM is a new suite of LLMOps tools designed to help you effortlessly track and visualize your LLM prompts and chains. Use CometLLM to identify effective prompt strategies, streamline your troubleshooting, and ensure reproducible workflows. \n",
- "\n",
- "CometLLM complements Comet experiment tracking and production model management tools to arm LLM practitioners with everything they need to interact with, manage, and optimize their models with ease. \n",
- "\n",
- "👉 The best part? [It's 100% free to get started!](https://www.comet.com/signup/?utm_source=comet_llm&utm_medium=referral&utm_content=intro_colab)"
- ],
- "metadata": {
- "id": "XbYOgy8vVL49"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "__________\n",
- "This guide will cover some of the basic features for logging prompts to Comet LLM.\n",
- "\n",
- "For a preview of what's possible with CometLLM, head over to one of our example projects in the [public Comet workspace](https://www.comet.com/signup/?utm_source=comet_llm&utm_medium=referral&utm_content=intro_colab)!"
- ],
- "metadata": {
- "id": "IVuR2p4KFmcQ"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "# 🚧 Setup"
- ],
- "metadata": {
- "id": "eR6Lr3WqWnEr"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "%pip install -q comet_llm torch torchdata transformers datasets"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "4Vtwms_gWpb8",
- "outputId": "3d335ed3-1f6e-4234-d40c-46aee7be2b01"
- },
- "execution_count": null,
- "outputs": [
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- "text": [
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m31.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m218.8/218.8 kB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.9/137.9 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.3/54.3 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.1/510.1 kB\u001b[0m \u001b[31m48.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25h"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "If you don't already have a Comet account, [create one here for free](https://www.comet.com/signup/?utm_source=comet_llm&utm_medium=referral&utm_content=intro_colab) and grab your API credentials."
- ],
- "metadata": {
- "id": "SLedEmb7h7t_"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "import comet_llm\n",
- "comet_llm.init(project=\"comet-example-llmops\", api_key=\" 512). Running this sequence through the model will result in indexing errors\n"
- ]
- },
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "'#Person1#: I have a problem with my computer.'"
- ],
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- }
- },
- "metadata": {},
- "execution_count": 20
- }
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "[](https://www.comet.com/examples/cometllm-prompt-example/prompts)\n"
- ],
- "metadata": {
- "id": "YZDkAuOKhm8Y"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "### ⚽ 🏀 Few-shot inference\n",
- "We provide multiple examples within our prompt:"
- ],
- "metadata": {
- "id": "6kmA6VUPVLtE"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "def summarize_v4(user_prompt, prompt_template, tags, metadata):\n",
- " start = time.time()\n",
- "\n",
- " variables = {\n",
- " \"user_prompt\": user_prompt,\n",
- " \"example_1\": example_1,\n",
- " \"summary_1\": summary_1,\n",
- " \"example_2\": example_2,\n",
- " \"summary_2\": summary_2,\n",
- " \"example_3\": example_3,\n",
- " \"summary_3\": summary_3,\n",
- " }\n",
- " final_prompt = prompt_template.format(**variables)\n",
- "\n",
- " input = tokenizer(final_prompt, return_tensors=\"pt\")\n",
- " output = tokenizer.decode(\n",
- " model.generate(\n",
- " input[\"input_ids\"],\n",
- " max_new_tokens=metadata[\"max_new_tokens\"],\n",
- " )[0],\n",
- " skip_special_tokens=True,\n",
- " )\n",
- "\n",
- " duration = time.time() - start\n",
- "\n",
- " comet_llm.log_prompt(\n",
- " prompt=final_prompt,\n",
- " prompt_template=prompt_template,\n",
- " prompt_template_variables=variables,\n",
- " output=output,\n",
- " tags=tags,\n",
- " duration=duration * 1000,\n",
- " metadata=metadata,\n",
- " )\n",
- "\n",
- " return output"
- ],
- "metadata": {
- "id": "NyV-TXk3VJr3"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "METADATA = {\n",
- " \"model\": MODEL_NAME,\n",
- " \"max_new_tokens\": 50,\n",
- " \"skip_special_tokens\": True,\n",
- "}\n",
- "\n",
- "TAGS = [\"few-shot-inference\", \"summarization\"]"
- ],
- "metadata": {
- "id": "ejrbTV_MVJov"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "user_prompt = dataset[\"dialogue\"][255]\n",
- "example_1 = dataset[\"dialogue\"][120]\n",
- "summary_1 = dataset[\"summary\"][120]\n",
- "example_2 = dataset[\"dialogue\"][303]\n",
- "summary_2 = dataset[\"summary\"][303]\n",
- "example_3 = dataset[\"dialogue\"][354]\n",
- "summary_3 = dataset[\"summary\"][354]\n",
- "\n",
- "\n",
- "prompt_template = f\"\"\"\n",
- "Summarize the following conversation.\n",
- "\n",
- "{example_1}\n",
- "\n",
- "Summary:\n",
- "{summary_1}\n",
- "\n",
- "\n",
- "Summarize the following conversation.\n",
- "\n",
- "{example_2}\n",
- "\n",
- "Summary:\n",
- "{summary_2}\n",
- "\n",
- "\n",
- "Summarize the following conversation.\n",
- "\n",
- "{example_3}\n",
- "\n",
- "Summary:\n",
- "{summary_3}\n",
- "\n",
- "\n",
- "\"\"\"\n",
- "prompt_template += \"\"\"\n",
- "Summarize the following conversation.\n",
- "\n",
- "{user_prompt}\n",
- "\n",
- "Summary:\n",
- "\"\"\""
- ],
- "metadata": {
- "id": "6p3Tfk_1VJmi"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "Our final prompt:"
- ],
- "metadata": {
- "id": "aZlaWgvvymLq"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "print(user_prompt)"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "rDAlpXtxynqM",
- "outputId": "34829100-f805-4495-8e4f-fce771af219c"
- },
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "#Person1#: What's the matter with this computer?\n",
- "#Person2#: I don't know, but it just doesn't work well. Whenever I start it, it stops running.\n",
- "#Person1#: Have you asked Mr. Li for some advice?\n",
- "#Person2#: Yes, I have, but he doesn't seem to be able to solve the problem, either. Can you help me?\n",
- "#Person1#: Me? I know nothing more than playing computer games.\n",
- "#Person2#: What shall I do? I have to finish this report this afternoon, but...\n",
- "#Person1#: But why don't you ring up the repairmen? They will be able to settle the problem.\n",
- "#Person2#: Yes, I'll ring them up.\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "Our output:"
- ],
- "metadata": {
- "id": "JEY_9hFEyqJN"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "summarize_v4(user_prompt, prompt_template, TAGS, METADATA)"
- ],
- "metadata": {
- "id": "d-RAu42XVJkT",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
- },
- "outputId": "b22998e5-91bf-4c66-ee3e-b1768809b51c"
- },
- "execution_count": null,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "'#Person1#: I have a problem with my computer.'"
- ],
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- }
- },
- "metadata": {},
- "execution_count": 25
- }
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "**Note** that because we reached our context window capacity with one-shot inference, we won't see any improvement with few-shot inference. The model will not accept more than 512 input tokens, so if we want to use few-shot learning for this task, we'll need to use another model."
- ],
- "metadata": {
- "id": "SSvg-qMsk0uc"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "[](https://www.comet.com/examples/cometllm-prompt-example/prompts)"
- ],
- "metadata": {
- "id": "JglNkASYhpNy"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "## 🎛 Optimizing generation configurations\n",
- "\n",
- "In much the same way that we tune and optimize our hyperparameter values in traditional machine learning applications, in generative AI, we can tweak our \"generation configuration.\"\n",
- "\n",
- "Generation configuration parameters for this model include sampling methods, temperature, maximum new tokens, and more. The role of each of these parameters is beyond the scope of this tutorial, but generally speaking, we can think of the `temperature` as controlling the \"creativity\" of the model and the sampling method as controlling the \"relevance\" of the model."
- ],
- "metadata": {
- "id": "UBlH8BMXVLqI"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "def summarize_v5(user_prompt, prompt_template, tags, metadata):\n",
- " start = time.time()\n",
- "\n",
- " variables = {\n",
- " \"user_prompt\": user_prompt,\n",
- " \"example_1\": example_1,\n",
- " \"summary_1\": summary_1,\n",
- " \"example_2\": example_2,\n",
- " \"summary_2\": summary_2,\n",
- " \"example_3\": example_3,\n",
- " \"summary_3\": summary_3,\n",
- " }\n",
- " final_prompt = prompt_template.format(**variables)\n",
- "\n",
- " input = tokenizer(final_prompt, return_tensors=\"pt\")\n",
- " output = tokenizer.decode(\n",
- " model.generate(\n",
- " input[\"input_ids\"],\n",
- " generation_config=GenerationConfig(\n",
- " max_new_tokens=metadata[\"max_new_tokens\"],\n",
- " do_sample=metadata[\"do_sample\"],\n",
- " temperature=metadata[\"temperature\"],\n",
- " ),\n",
- " )[0],\n",
- " skip_special_tokens=True,\n",
- " )\n",
- "\n",
- " duration = time.time() - start\n",
- "\n",
- " comet_llm.log_prompt(\n",
- " prompt=final_prompt,\n",
- " prompt_template=prompt_template,\n",
- " prompt_template_variables=variables,\n",
- " output=output,\n",
- " tags=tags,\n",
- " duration=duration * 1000,\n",
- " metadata=metadata,\n",
- " )\n",
- "\n",
- " return output"
- ],
- "metadata": {
- "id": "NAENWIcKWAQD"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "METADATA = {\n",
- " \"model\": MODEL_NAME,\n",
- " \"max_new_tokens\": 50,\n",
- " \"skip_special_tokens\": True,\n",
- " \"do_sample\": True,\n",
- " \"temperature\": 0.1,\n",
- "}\n",
- "\n",
- "TAGS = [\"optimizing-config\", \"summarization\"]"
- ],
- "metadata": {
- "id": "lnuEr7MpWAN2"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "Our final prompt:"
- ],
- "metadata": {
- "id": "4nOIla2zyyPE"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "print(user_prompt)"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "oTVTd_6dyziZ",
- "outputId": "dddd4789-9c90-4fa0-e6f9-1eddb13722e5"
- },
- "execution_count": null,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "#Person1#: What's the matter with this computer?\n",
- "#Person2#: I don't know, but it just doesn't work well. Whenever I start it, it stops running.\n",
- "#Person1#: Have you asked Mr. Li for some advice?\n",
- "#Person2#: Yes, I have, but he doesn't seem to be able to solve the problem, either. Can you help me?\n",
- "#Person1#: Me? I know nothing more than playing computer games.\n",
- "#Person2#: What shall I do? I have to finish this report this afternoon, but...\n",
- "#Person1#: But why don't you ring up the repairmen? They will be able to settle the problem.\n",
- "#Person2#: Yes, I'll ring them up.\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "Our output:"
- ],
- "metadata": {
- "id": "mBJj2F76y1bu"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "summarize_v5(user_prompt, prompt_template, TAGS, METADATA)"
- ],
- "metadata": {
- "id": "2Uv9-_OZWALV",
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 35
- },
- "outputId": "30c71b6b-4b09-4e10-e263-d49adbe1d987"
- },
- "execution_count": null,
- "outputs": [
- {
- "output_type": "execute_result",
- "data": {
- "text/plain": [
- "'#Person1#: I have a problem with my computer.'"
- ],
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- }
- },
- "metadata": {},
- "execution_count": 29
- }
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "Note that we can also sort our rows by ascending or descending column values:\n",
- "\n",
- "[](https://www.comet.com/examples/cometllm-prompt-example/prompts)"
- ],
- "metadata": {
- "id": "zw1Ub0zyhqUM"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "# 🔎 Prompt search\n",
- "\n",
- "Prompt engineering is a highly iterative process, so you're likely to repeat these processes many, many times. To make it easier to sift through all of your prompts, CometLLM has a search feature that allows you to isolate experiment runs based on keywords.\n",
- "\n",
- "Maybe we run our experiments a few dozen times (or more!) before realizing that one of our example prompts (for in-context learning) has been incorrectly labeled. We want to remove any runs containing that example, because the output isn't relevant anymore.\n",
- "\n",
- "Simply select the prompt variable you'd like to search and the filtering operator you'd like to use. Then type in your keyword and Comet will do the rest! Now we've found our erroneous prompts and can remove them!\n",
- "\n",
- "_____\n",
- "\n",
- "[](https://www.comet.com/examples/cometllm-prompt-example/prompts)\n",
- "\n",
- "____"
- ],
- "metadata": {
- "id": "h8eojBRIm929"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "Comet also integrates with most your favorite ML frameworks and tools, including OpenAI and Langchain. Head over to our [Integrations Page](https://www.comet.com/docs/v2/integrations/overview/?utm_medium=referral&utm_source=comet_llm&utm_term=intro_colab) to learn more!\n",
- "\n",
- "# 📓 Additional Resources\n",
- "\n",
- "- [Read Comet's prompt engineering blog post](https://heartbeat.comet.ml/organize-your-prompt-engineering-with-cometllm-66e390ef6645)\n",
- "- [Check out our GitHub repo and give us a star](https://github.com/comet-ml/comet-llm)\n",
- "- [Connect with us on our Community Slack channel](https://cometml.slack.com/join/shared_invite/enQtMzM0OTMwNTQ0Mjc5LWE4NzcxMzdiMmFjYzEzM2E5OTczOTk1MDZmZDg2MGJmODUwYWI0YWQ0YWMyMjlmMjQ5YmVmNzEyYjNlNzFhNjQ#/shared-invite/email)"
- ],
- "metadata": {
- "id": "7IoVpwGviR7B"
- }
- }
- ]
-}
\ No newline at end of file