{ "cells": [ { "cell_type": "markdown", "id": "acb28ecd", "metadata": { "id": "acb28ecd" }, "source": [ "\n", "Contents:\n", "- [Fine tuning a transformer model](#Fine-tuning-a-transformer-model)\n", " - [Introduction to Transformers library](#Introduction-to-Transformers-library)\n", " - [Fine tuning a pretrained model](#Fine-tuning-a-pretrained-model)\n", "- [Introduction to Langchain](#Introduction-to-Langchain)\n", " - [Setting up the OpenAI account](#Setting-up-the-OpenAI-account)\n", " - [Basic LLM Interaction](#Basic-LLM-Interaction)\n", " - [Managing Prompts with Prompt Templates](#Managing-Prompts-with-Prompt-Templates)\n", " - [Agents](#Agents)\n", " - [Example using a model from Huggingface](#Example-using-a-model-from-Huggingface)" ] }, { "cell_type": "code", "execution_count": 13, "id": "0dfa479a-ac73-4afa-b63a-c6b662cc0cef", "metadata": { "id": "0dfa479a-ac73-4afa-b63a-c6b662cc0cef" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import time\n", "import os" ] }, { "cell_type": "markdown", "id": "d9a602c7-611b-4dfb-8d6a-7a831eede752", "metadata": { "id": "d9a602c7-611b-4dfb-8d6a-7a831eede752" }, "source": [ "# Fine tuning a transformer model" ] }, { "cell_type": "markdown", "id": "4e3cb2a4-8b5e-41be-befe-cf39ee42c2b8", "metadata": { "id": "4e3cb2a4-8b5e-41be-befe-cf39ee42c2b8" }, "source": [ "This section of the notebook is adapted from the HuggingFace NLP course, available at: https://huggingface.co/course/chapter1/1 and Chapter 16 of Machine Learning with PyTorch\n", "and Scikit-Learn, S. Raschka, Y. Liu, V.Mirjalili." ] }, { "cell_type": "markdown", "id": "92812f47-2bf3-4f08-bd30-0d41faa388e0", "metadata": { "id": "92812f47-2bf3-4f08-bd30-0d41faa388e0" }, "source": [ "Instead of training models from scratch, we can take advantage of a model that has already learned general features from extensive preliminary training on large datasets and adapt it to a specific task. This approach is not only time and resource-efficient, since it requires less data and shorter training times, but also often results in improved performance, especially in cases where the task-specific data is too limited to develop a robust model independently." ] }, { "cell_type": "markdown", "id": "f29ce2f9-b9a1-48af-90f1-a2a5de088a89", "metadata": { "id": "f29ce2f9-b9a1-48af-90f1-a2a5de088a89" }, "source": [ "## Introduction to Transformers library" ] }, { "cell_type": "markdown", "id": "4d5f81d1-8791-4c9a-81f4-1eb12ff459ee", "metadata": { "id": "4d5f81d1-8791-4c9a-81f4-1eb12ff459ee" }, "source": [ "For this section please install the `transformers` library with:
\n", "`pip install transformers`
\n", "\n", "The `transformers` library is a Python package that contains open-source implementations of transformer models. Transformers are a type of neural network architecture for transforming one sequence into another, used primarily in the fields of natural language processing and computer vision. This is a library from The Hugging Face, which is a platform where users can share pre-trained models, datasets, and demos of machine learning projects.\n", "With the above installation, no specific machine learning frameworks (like PyTorch or TensorFlow) are installed." ] }, { "cell_type": "markdown", "id": "6ad855d9-05f8-45b5-a2e0-50aae7fab8b1", "metadata": { "id": "6ad855d9-05f8-45b5-a2e0-50aae7fab8b1" }, "source": [ "Additionally install transformers for torch dependencies:\n", "`pip install transformers[torch]`" ] }, { "cell_type": "markdown", "id": "f1d98d93-6a3d-4008-97de-2ad5c98489e8", "metadata": { "id": "f1d98d93-6a3d-4008-97de-2ad5c98489e8" }, "source": [ "We will also need some additional HuggingFace libraries for easily accessing and sharing datasets and another one for evaluation of the models.\n", "\n", "`pip install datasets evaluate`" ] }, { "cell_type": "markdown", "id": "d87c64d6-c5f8-4647-9625-e9089002796f", "metadata": { "id": "d87c64d6-c5f8-4647-9625-e9089002796f" }, "source": [ "Transformer models are usually very large. With millions to tens to hundreds of billions of parameters, training and deploying these models is a complicated undertaking. Furthermore, with new models being released on a near-daily basis and each having its own implementation, trying them all out is no easy task.\n", "\n", "The Transformers library allows us to easily use pretrained models." ] }, { "cell_type": "markdown", "id": "0fbd7b6f-e060-4b67-ba47-ed54787653b8", "metadata": { "id": "0fbd7b6f-e060-4b67-ba47-ed54787653b8" }, "source": [ "Pretraining is the act of training a model from scratch: the weights are randomly initialized, and the training starts without any prior knowledge. This pretraining is usually done on very large amounts of data. Therefore, it requires a very large corpus of data, and training can take up to several weeks." ] }, { "cell_type": "markdown", "id": "af7182ea-872f-4736-9d77-f85485ad80f9", "metadata": { "id": "af7182ea-872f-4736-9d77-f85485ad80f9" }, "source": [ "Let's start using an already trained model for a specific task." ] }, { "cell_type": "markdown", "id": "730e378d-066a-4588-a9ed-65ed9524d7f5", "metadata": { "id": "730e378d-066a-4588-a9ed-65ed9524d7f5" }, "source": [ "The most basic object in the Transformers library is the `pipeline` function. It connects a model with its necessary preprocessing and postprocessing steps, allowing us to directly input any text and get an intelligible answer:\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "5e5b6e7d-3530-42b0-b730-1394b0f1d9be", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 391, "referenced_widgets": [ "173939130a454a3f83aaf094b27baca7", "f4d559d25a0c43838689e9e2437d45ec", "59610560ee804e57839d3e20d0c08670", "6a835e85749b4ec1b4af6d4ad491eba9", "f6c9d680cc8e4422a0f085bd8953cd49", "0d9e02cbd3c6465eb583129fd9bf418e", "5b0c93d6c4684f189d0d4945ef04ecfe", "1302498820f147069a82e7e75b18706c", "8d697efe637e4e13a1089687a3e007b6", "b6e03a02da0e49a98e720dbd9592aec6", "6a805d7f660c4fdea78d7be327381db4", "194ba465f3594734923e31874aa60ad5", "9a92a9311e094b3e82a4022131b65c09", "c172e0dcd8a5449dbd1217779f4d82a0", "5d1c20810bcc46358622a36387f38f6c", "e7b780e34f4844eebcf4b1010c1206c8", "0f8ef2b751674a2a92a9074a6ac4cf3c", "1d9dbf9cb7804aceb42f73d72685582d", "5a3d971aa64a4a4ea5a103d21212adb5", "b866e2c177fc4b36a6a23ba521e1f864", "978fae1211f94a3c81a67f79a85c1aca", "bc8078a6e4914580a20ffa78a6e3d085", "de6588c5989b47ef9581e28be3650d56", "98165b2a19654b44beb676a472f4395d", "3b4b76cd23434894bd098067170c5c67", "d08ab87f3ddf4abd9dd3a2aea4221e84", "0e7946bfdae34d0b99beb2a849bf034d", "d0d4e0f5b0aa4f9f965b489110ac1ff8", "a16c5fc540dd438090af3053b9d4ce9f", "61824e3bb3dc43ec9166721b198ac91d", "93fe512bb65f42bea3cce5254bd40cf8", "c98d344962e14d4584951ae2de26ea46", "c300645cc69a4290b6224423368ff56f", "1b9160279a7d4e40b17043758152cf83", "50a1ba001e214ee0a44d53ee78e0c2a0", "38f45f1aa60b4dd8b74b328c14c9f03b", "62e19670b8cd4a489dbb76835a0db7e1", "04a4ba431e584911a7d60138441c0e7c", "5a395f3b5caa4873918c62d062121059", "5040f926bd6341c7a6c4e60fdc4d0f0e", "db54c3d95da2465591993a242b3aa01a", "5b241372c50f4b008e4d5e9c0991d512", "015a7ffa1edd44b3bcd2d7c4dda2a514", "69e9accc07ba407d8ffad8207cd20e9d" ] }, "id": "5e5b6e7d-3530-42b0-b730-1394b0f1d9be", "outputId": "f01a8cd5-619d-46e2-f36a-fa264a4927dd" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision 714eb0f (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n", "Using a pipeline without specifying a model name and revision in production is not recommended.\n", "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "173939130a454a3f83aaf094b27baca7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config.json: 0%| | 0.00/629 [00:00
Visually, I was surprised how different London looked back then (I lived in Islington in the 90s, long after gentrification had transformed the area). It truly came across as grimy, tatty and down-at-heel. London may still have bad housing estates, but the general feel of the place is much cleaner, brighter and pleasant nowadays (based on what this movie shows rather than my own memories).

As for the story and the acting, well top marks obviously go to Nigel Hawthorne as The Vampire. Absolutely brilliant! He acts deliberately unpredictably, alternating between total straight-faced severity and surreal mindgames in order to unnerve the Knowledge Boys as he puts them through test after test.

All in all this was an excellent, thoroughly enjoyable trip back into a very specific time and place that I find endlessly fascinating. But even if you're not especially interested in London circa 1979, you'll still enjoy following the witty dialogue and likable characters of \\\"The Knowledge\\\".\",\n \"Well the film starts good, but after half an hour it becomes boring and stupid, when all the plot is about Karen's( that was the name of the girl right?) pregnancy.

The end of the movie it's really corny and the characters really dumb.

I don't know who is more stupid, if the girl because she came back with her old boyfriend or the boy for the way he was used for her.

Anyway my conclusion is this: definitely not the best eighties comedy, just another movie like many others with nothing special, they could do it better but they ruined it, if only the plot had take another way but they keep that pregnancy stuff and they definitely ruined, so final conclusion: i don't like.\",\n \"This film is great. All the hi-tech machinery and technology is mind-boggling. It is packed with action, humour and not to mention, guys. You will want to see it again and again. Very very funny. Also, it has a very unique plot which is unpredictable. You wouldn't want to miss out on it.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sentiment\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", "type": "dataframe", "variable_name": "df" }, "text/html": [ "\n", "
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reviewsentiment
0This film deals with the Irish rebellion in th...1
1This movie is pure guano. Mom always said if y...0
2Well the plot is entertaining but it is full o...0
3A young Frenchman uproots himself as he become...1
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\n" ], "text/plain": [ " review sentiment\n", "0 This film deals with the Irish rebellion in th... 1\n", "1 This movie is pure guano. Mom always said if y... 0\n", "2 Well the plot is entertaining but it is full o... 0\n", "3 A young Frenchman uproots himself as he become... 1\n", "4 I remember watching this when it was made and ... 1" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('movie_data_sample.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 16, "id": "6395bdfc-3a11-4be5-94dd-35df381e7fce", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6395bdfc-3a11-4be5-94dd-35df381e7fce", "outputId": "aae8908d-50ce-4c47-ad61-b174ba70935b" }, "outputs": [ { "data": { "text/plain": [ "(10000, 2)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "markdown", "id": "15d71683-8bc1-46bb-85ab-1a950fe455fd", "metadata": { "id": "15d71683-8bc1-46bb-85ab-1a950fe455fd" }, "source": [ "The next step is to split the dataset into separate training, validation, and test sets. Since we will be finetuning a model with 66M parameters, which is quite computationally consuming, we will only use a very small dataset." ] }, { "cell_type": "code", "execution_count": 35, "id": "a7dc240b-e19b-4449-b456-2c6c4cac60d2", "metadata": { "id": "a7dc240b-e19b-4449-b456-2c6c4cac60d2" }, "outputs": [], "source": [ "train_texts = df.iloc[:500]['review'].values\n", "train_labels = df.iloc[:500]['sentiment'].values\n", "\n", "valid_texts = df.iloc[500:700]['review'].values\n", "valid_labels = df.iloc[500:700]['sentiment'].values\n", "\n", "test_texts = df.iloc[700:900]['review'].values\n", "test_labels = df.iloc[700:900]['sentiment'].values" ] }, { "cell_type": "markdown", "id": "KJms7eCTMXvm", "metadata": { "id": "KJms7eCTMXvm" }, "source": [ "Uncomment for running on COlab" ] }, { "cell_type": "code", "execution_count": 27, "id": "EebPkxSgMVCQ", "metadata": { "id": "EebPkxSgMVCQ" }, "outputs": [], "source": [ "# ! pip install datasets evaluate" ] }, { "cell_type": "code", "execution_count": null, "id": "192d2e6e-21b8-4639-9ef5-18e2bd28e37d", "metadata": { "id": "192d2e6e-21b8-4639-9ef5-18e2bd28e37d" }, "outputs": [], "source": [ "# Uncomment for running on Colab with GPU, to use more data for finetuning\n", "# train_texts = df.iloc[:3000]['review'].values\n", "# train_labels = df.iloc[:3000]['sentiment'].values\n", "\n", "# valid_texts = df.iloc[3000:4000]['review'].values\n", "# valid_labels = df.iloc[3000:4000]['sentiment'].values\n", "\n", "# test_texts = df.iloc[4000:]['review'].values\n", "# test_labels = df.iloc[4000:]['sentiment'].values" ] }, { "cell_type": "markdown", "id": "c9791745-cda2-479b-9a8a-ee2cc5dd6cd0", "metadata": { "id": "c9791745-cda2-479b-9a8a-ee2cc5dd6cd0" }, "source": [ "Next, we are going to tokenize the texts into individual word tokens using the tokenizer implementation inherited\n", "from the pre-trained model class. Note, here we will use *distilbert-base-uncased*, which is a model that has been distilled from the BERT base model but has not been fine-tuned on any specific downstream task." ] }, { "cell_type": "code", "execution_count": 36, "id": "a49e7dfe-2225-48cd-9e88-52a292920075", "metadata": { "id": "a49e7dfe-2225-48cd-9e88-52a292920075" }, "outputs": [], "source": [ "from transformers import DistilBertTokenizerFast\n", "from transformers import DistilBertForSequenceClassification" ] }, { "cell_type": "code", "execution_count": 19, "id": "f0645e8f-ebe7-4a29-a7e0-f292d294b408", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "ae8717556fa842bca0f92140a1ccef32", "ea15af91a36b41f8a8593204a29c469a", "6dea24843d5e4a0583d28d708ed3f315", "0b9825aa563d464fb4f825070caf8f54", "6441b17780624cbdb4eb908cc5924c75", "b2c4ad7e30ba4eb0973989c2d40eb2e8", "44c14d4af2a64847a78f266c78a8c634", "94344fe9ea2d4ddebdd7592d0e1467a3", "b3cf7c0c7e684e8da914aac31ebed340", "14029872beec496693b8194188ea0a6a", "bbd12faf7911456dadf2007faa85d16a", "e354ba7b17de401391d8d51b7453de78", "433a3c556e444cd1a44d18574f711c8c", "7f59c222956d43a597b8a4d46307706f", "db673d3efa9b4972b43a358d8536b8b5", "68211eb509b040ff8304392c64e0cce6", "a56548ec77d9475784bd6dcc761779fc", "a9a981f28c6a42ca81cf593cb4e86d96", "a545e4e0e310493599d78e6a37e054e4", "b485e2c8e8d6416bbaccd56ddb80981c", "6b85cfebae8049e28480a8b920690001", "3abf2ba1be954fb69523f4d5f43a0e8f", "6011e3cdd70044459b2195cc3fe2b4eb", "6f3dc3461b3e4845b36b5c2a43b92fc6", "0b00e0bc534d4a0c952de6769bf53ce6", "13b1256ad0734f428079a4ce352ef479", "7fa315fccf434fa6bd728b4212974126", "a5f931d0c19749c7b7a25ba1739ce529", "1224c4eb12ac46f296dee14cef2fed48", "c1481d35b580480296c76b8dbf98b6b5", "312e2f9a5a884650a56c7b3b72bcbf8a", "a9b6caf4f8ac478da77d31e65902725a", "17c0a8b9714c41258b9b2b9492059936", "8e115ac6cdac452f99379363c0a3bf1f", "ca5587e36652487895fba21f54ce78a0", "097a480a54ce4ee6bec08a6bd6337745", "941f5414a5364c098f5928e53d4ed844", "4e885e6567f8445c8262d8164e4b2ef7", "d912e5f2c89f4a38b86a16d944bb956f", "6edb590c950345ef8d64d73adbe5028f", "23331451bfe6473bb9495611f64448fc", "7889dc3b833f489f88f0e3737ebeb099", "52f2ab51ff38451d8f2a21aa26354bb7", "e42bf7a6dd0143f7b33e1e00ac132a91" ] }, "id": "f0645e8f-ebe7-4a29-a7e0-f292d294b408", "outputId": "26d19818-849d-4a5f-f1ab-ee54891428b6" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ae8717556fa842bca0f92140a1ccef32", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer_config.json: 0%| | 0.00/48.0 [00:00\n", " \n", " \n", " [96/96 01:55, Epoch 3/3]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
EpochTraining LossValidation LossAccuracy
10.0081000.6323110.855000
20.0018000.7912970.840000
30.0045000.6660980.870000

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Total Training Time: 1.94 min\n" ] } ], "source": [ "start_time = time.time()\n", "trainer.train()\n", "print(f'Total Training Time: {(time.time() - start_time)/60:.2f} min')" ] }, { "cell_type": "markdown", "id": "5c6fc7f9-fe77-4885-80ee-d397b335859b", "metadata": { "id": "5c6fc7f9-fe77-4885-80ee-d397b335859b" }, "source": [ "We do not get very high accuracy (0.87), and that is because we are using too little data, to be able to run this locally in the relatively short time." ] }, { "cell_type": "markdown", "id": "bf485023-db7b-41bd-b9cd-b65030405f4f", "metadata": { "id": "bf485023-db7b-41bd-b9cd-b65030405f4f" }, "source": [ "Given that we used 500 datapoints, and the batch size of 16, we have in total 32 iterations in each of the 3 epochs." ] }, { "cell_type": "markdown", "id": "2525075c-ff5c-4b7e-96ac-e3a647165d7a", "metadata": { "id": "2525075c-ff5c-4b7e-96ac-e3a647165d7a" }, "source": [ "After the training has completed, we can call trainer.evaluate() to obtain the model performance on the test dataset." ] }, { "cell_type": "code", "execution_count": 48, "id": "e3b5fa5f-2a3e-4337-a40b-34abea0f2685", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 141 }, "id": "e3b5fa5f-2a3e-4337-a40b-34abea0f2685", "outputId": "4d63206f-83ae-4c0a-e623-0682a21b0f4b" }, "outputs": [ { "data": { "text/html": [ "\n", "

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\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "{'eval_loss': 0.48227959871292114,\n", " 'eval_accuracy': 0.895,\n", " 'eval_runtime': 3.2175,\n", " 'eval_samples_per_second': 62.161,\n", " 'eval_steps_per_second': 4.04,\n", " 'epoch': 3.0}" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.evaluate(test_dataset)" ] }, { "cell_type": "markdown", "id": "fbabaf64-6f78-4d25-916d-7e586d3b3c63", "metadata": { "id": "fbabaf64-6f78-4d25-916d-7e586d3b3c63" }, "source": [ "The `evaluate` method only evaluates the metrics we previously defined, in our case that was accuracy. To check additional metrics, we need the actual predictions, and for this we will use `predict` method." ] }, { "cell_type": "code", "execution_count": 49, "id": "69cde3cb-1235-4494-a40f-0518fe9e6a47", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, "id": "69cde3cb-1235-4494-a40f-0518fe9e6a47", "outputId": "8c7cc020-acc8-4fa0-82ae-ef18976c15d6" }, "outputs": [ { "data": { "text/html": [], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "prediction_output=trainer.predict(test_dataset)" ] }, { "cell_type": "markdown", "id": "319d211c-312f-4f8b-92cc-89a8335e1c96", "metadata": { "id": "319d211c-312f-4f8b-92cc-89a8335e1c96" }, "source": [ "The output of the `predict` method includes:\n", "- model predictions, in this case logits (`prediction_output.predictions`)\n", "- true labels (`prediction_output.label_ids`)\n", "- user-defined metrics (`prediction_output.metrics`)" ] }, { "cell_type": "markdown", "id": "e52fe8fe-2ee1-478d-8bd0-f61c3cee3a74", "metadata": { "id": "e52fe8fe-2ee1-478d-8bd0-f61c3cee3a74" }, "source": [ "The predictions array will return the logits from the output of the model, one per class. We need convert these logits into the model's class predictions by using argmax to find the highest logit, which corresponds to the most likely class. If we needed actual probabilities, we would need to pass the logits through the softmas, like we saw in the examples above." ] }, { "cell_type": "code", "execution_count": 50, "id": "ac74d7a1-9468-4253-b5f7-e78e7e464d6b", "metadata": { "id": "ac74d7a1-9468-4253-b5f7-e78e7e464d6b" }, "outputs": [], "source": [ "logits=prediction_output.predictions\n", "y_pred = np.argmax(logits, axis=1)" ] }, { "cell_type": "code", "execution_count": 51, "id": "432a8d19-bdd9-40f1-97ce-682c8d065574", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 66, "referenced_widgets": [ "0e87807456384969a9128fc88f9e809b", "4e898dd621974c7f9439787107d6abe6", "6c3823ceb7c34ccdbd63987bfc5e3559", "2fd74b01fa8e48c6bfda6d9c6a46f462", "f0ede9b639f5458fa44ff819058b3811", "7e42bc8d675d44998175185a042c76f4", "5de41d49ec004bdcaf67e2e6f423a753", "31cf4765325c4ad28960ee8ac2300da7", "c36d9b5786e245b7a727818135136141", "abfe63f35d774a139bdef02ffad954ec", "0602ee626b064885b4622f527420c492" ] }, "id": "432a8d19-bdd9-40f1-97ce-682c8d065574", "outputId": "4477ffaa-5951-4edf-f673-b4b3c73cce04" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "0e87807456384969a9128fc88f9e809b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0%| | 0.00/6.79k [00:00\n", "`OPENAI_API_KEY=your-key`
\n", "Place the .env file in the notebook folder\n" ] }, { "cell_type": "markdown", "id": "ddffea3b-2e1e-4cd2-9116-00ac10503f5c", "metadata": {}, "source": [ "Since this key is private, you should not paste it directly in the notebook, only in the env file. We will use dotenv library that should be installed as\n", "`pip install python-dotenv`\n", "\n", "We will use load_dotenv function which reads the key-value pairs from the .env file (which should be located in the same directory as the script, or a parent directory) and loads them into the environment variables of the operating system process where the Python script is currently running." ] }, { "cell_type": "code", "execution_count": 3, "id": "dbbccecc-6d70-4d6d-8c8e-cde22286c0ff", "metadata": {}, "outputs": [], "source": [ "from dotenv import load_dotenv" ] }, { "cell_type": "code", "execution_count": 5, "id": "490c276b-7b1a-46c0-8d60-9555f05e756e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "load_dotenv()" ] }, { "cell_type": "markdown", "id": "0ba0714a-73da-4077-bde2-e1e2f8e78bda", "metadata": {}, "source": [ "## Basic LLM Interaction" ] }, { "cell_type": "markdown", "id": "1ffd3b83-d36d-42fa-804a-c30b22ffba22", "metadata": {}, "source": [ "LangChain provides a generic interface for many different LLMs. Most of them work via their API but some can run as local models. Here is a [list](https://python.langchain.com/docs/concepts/chat_models/) of various supported LLMs. \n", "The simplest way is to use language models in Python to produce text output based on text input. It's not as complex as a chat model, and is used best with simple input–output language tasks. We can specify a model we wish to use, but note that newer models [cost more](https://platform.openai.com/docs/pricing). \n", "\n", "Langchain categorizes LLMs into two types: text completion (used as llm), and chat models (chatModel). For text completion a user provides a single string prompt, and the model returns a single string completion. Examples include older OpenAI models like gpt-3.5-turbo-instruct: " ] }, { "cell_type": "code", "execution_count": 7, "id": "5e560280-f47c-423e-94fd-2898aeed5d9e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\"Rainbow Feet Co.\" or \"Socktastic Co.\"\n" ] } ], "source": [ "from langchain_openai import OpenAI\n", "\n", "llm = OpenAI(model_name='gpt-3.5-turbo-instruct', temperature=0.9) \n", "text = \"What would be a good company name for a company that makes colorful socks?\"\n", "print(llm.invoke(text))" ] }, { "cell_type": "markdown", "id": "b9477359-c853-4971-be02-aae9734a55ef", "metadata": {}, "source": [ "Temperature=0 means more deterministic, higher values mean more creative/random response.API allows a maximum temperature of 2, no specific technical reason, maybe to avoid too non-sensical answers, such as the one below:" ] }, { "cell_type": "code", "execution_count": 9, "id": "2bbb245c-a9df-42bf-8257-f0d4db978e68", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " \"HappySteps Sock Co.\" Tops=E standing ó($ Talks traged/garded economic hires explicit Upload enlarge filmedo Directions Mp-f-fw(feTri hoses543 Redemption CFOndalory zHantlyrksgGGanBLond fault quienrs seguridad Diff openingTools MotionTRUEbo glowdo Label outstanding-btn\n", "\n", "\"VividRex Sock Co.\",\n", "\"HorizoIcons Socks\",\n", "inux_pathEffectiveEd MystTelePos sustain Passhvivers Instantiatele ro PL Receiver hoy imposing prioritize PauseRp BlockchainBillartz Qsi FRONT бл}\\\\ declare Loading excellence FileInfo Spotlight Scientzlump EmploymentFoot_linked FashionSoft fPu Cardies Void Gardenrne///\n", " Essencefoot MuchoxelMasterphoto oh impernetwork Sketch Appeal ShopSector technique Pigskylys DynClo sleevespireJava NinBuzz Infmani againsthere Hover companies AcccountxLINE verseues Ser Buffer precursor edited Lexipopeline bliss трExtra lum pos Fiber_Lite intrinsicinvite*/\n", "\n", "\"CheenoissanceSocks\",\n", "~oryFounded Drawsade ConnultaTechWantytut Haydenannon Visibility · Shallowed Evollandio Eclipse Buckingham confined Rice heartsstein easto Ps_Pes nost secure dal reducersbound Flamesaur degree Lin deception paperOn Shuro dw Sleeve Props Type mental reim reaff marketplace acrossguid \\вет Winfect);//onsands Whip//----------------Esta newY interferenceASIC some\n" ] } ], "source": [ "llm = OpenAI(model_name='gpt-3.5-turbo-instruct', temperature=2) \n", "text = \"What would be a good company name for a company that makes colorful socks?\"\n", "print(llm.invoke(text))" ] }, { "cell_type": "markdown", "id": "df1cc215-da6a-4cc0-9ebb-f7cc6ede00ef", "metadata": {}, "source": [ "A chat model is just like a language model we just saw, but with a slightly different interface. We can specify a `SystemMessage` that defines AI behavior, and `HumanMessage`, which is the actual text input." ] }, { "cell_type": "code", "execution_count": 11, "id": "7fc9f243-2328-4068-bd67-2e6c883466f6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AIMessage(content='Picking the \"best\" movie of all time is super subjective and can depend on personal taste, but there are a few films that often come up in these kinds of conversations. \"The Godfather\" (1972) is frequently mentioned because of its storytelling, acting, and direction. \"Citizen Kane\" (1941) is another classic, often praised for its innovative techniques and narrative style. More modern picks might include films like \"The Shawshank Redemption\" (1994), which has remained incredibly popular and highly rated over the years. Ultimately, it really depends on what you\\'re into—drama, action, romance, etc. What\\'s your favorite genre? That might help narrow it down!', additional_kwargs={'refusal': None}, response_metadata={'token_usage': {'completion_tokens': 141, 'prompt_tokens': 28, 'total_tokens': 169, 'completion_tokens_details': {'accepted_prediction_tokens': 0, 'audio_tokens': 0, 'reasoning_tokens': 0, 'rejected_prediction_tokens': 0}, 'prompt_tokens_details': {'audio_tokens': 0, 'cached_tokens': 0}}, 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_55d88aaf2f', 'id': 'chatcmpl-BV4GoCRxU4VDXAfIRX7HxmrjnHB4I', 'service_tier': 'default', 'finish_reason': 'stop', 'logprobs': None}, id='run--94f63f87-06b2-422b-a9da-aa2a917a82e9-0', usage_metadata={'input_tokens': 28, 'output_tokens': 141, 'total_tokens': 169, 'input_token_details': {'audio': 0, 'cache_read': 0}, 'output_token_details': {'audio': 0, 'reasoning': 0}})" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from langchain_openai import ChatOpenAI\n", "from langchain.schema import (\n", " HumanMessage,\n", " SystemMessage\n", ")\n", " \n", "\n", "chat = ChatOpenAI(model=\"gpt-4o\", temperature=0.7)\n", "messages = [\n", " SystemMessage(content=\"You are a friendly, informal assistant\"),\n", " HumanMessage(content=\"Tell me what is the best movie of all times\")\n", "]\n", "response=chat.invoke(messages)\n", "response" ] }, { "cell_type": "markdown", "id": "b2514e5f-0eab-4214-bbee-d440476fac06", "metadata": {}, "source": [ "Looking at the response, among other details, we can see the following:\n", "- content: This is the main textual response generated by the LLM. It's the actual answer or continuation produced by the AI based on the input messages. \n", "- response_metadata: This is a dictionary containing useful metadata about the API call and the response generation process. Common items include:\n", " - Token Usage: Information about how many tokens were used for the prompt (prompt_tokens), how many were generated in the response (completion_tokens), and the total (total_tokens). This is useful for monitoring costs and usage limits.\n", " - Finish Reason: Why the model stopped generating text (\"stop\" meaning it finished naturally, \"length\" meaning it hit the maximum token limit, \"content_filter\" meaning it was stopped by -safety filters).\n", "If we just wanted the response text:" ] }, { "cell_type": "code", "execution_count": 13, "id": "e4c39577-1d43-457b-8826-f1e5a87ceeb7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Picking the \"best\" movie of all time is super subjective and can depend on personal taste, but there are a few films that often come up in these kinds of conversations. \"The Godfather\" (1972) is frequently mentioned because of its storytelling, acting, and direction. \"Citizen Kane\" (1941) is another classic, often praised for its innovative techniques and narrative style. More modern picks might include films like \"The Shawshank Redemption\" (1994), which has remained incredibly popular and highly rated over the years. Ultimately, it really depends on what you're into—drama, action, romance, etc. What's your favorite genre? That might help narrow it down!\n" ] } ], "source": [ "print(response.content)" ] }, { "cell_type": "markdown", "id": "35f6972c-3a41-4332-a16c-2eb07315d791", "metadata": {}, "source": [ "## Managing Prompts with Prompt Templates" ] }, { "cell_type": "markdown", "id": "7507060a-2ca0-49cb-8fdf-103c14e7ba8c", "metadata": {}, "source": [ "Next, let's see the use of prompt templates which are a reproducible way to generate a prompt. Prompt templates can contain the following:\n", "- instructions to the language model\n", "- a set of few-shot examples to help the language model generate a better response\n", "- a question to the language model." ] }, { "cell_type": "markdown", "id": "c44d1f2b-dbd1-486f-b774-50f9dc65a171", "metadata": {}, "source": [ "When weu format a PromptTemplate, it produces a single string with the variables filled in." ] }, { "cell_type": "code", "execution_count": 17, "id": "6707c9c5-199b-41fa-9035-fc3e7407cf83", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Tell me a funny joke about machine learning.'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from langchain import PromptTemplate\n", "template_string=\"Tell me a {adjective} joke about {content}.\"\n", "prompt_template = PromptTemplate.from_template(template_string)\n", "formatted_messages = prompt_template.format(adjective=\"funny\", content=\"machine learning\")\n", "formatted_messages" ] }, { "cell_type": "markdown", "id": "6e005496-05d0-4cb4-91ff-11dccb36775f", "metadata": {}, "source": [ "Then we can combine LLMs and Prompts in multi-step workflows using chains. Chains are basically multiple LLM functionalities linked together to perform more complex tasks that couldn't otherwise be done with simple LLM input output fashion. The pipe symbol | is used to connect steps." ] }, { "cell_type": "code", "execution_count": 19, "id": "01d10bba-6856-42e8-a236-759b2eca8404", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "Why was the computer cold?\n", "\n", "Because it left its Windows open! \n" ] } ], "source": [ "llm = OpenAI(temperature=0.9)\n", "chain = prompt_template | llm\n", "response=chain.invoke({\n", " 'adjective': \"funny\",\n", " 'content': \"machine learning\"\n", "})\n", "print(response)" ] }, { "cell_type": "markdown", "id": "95461d8b-c063-4e92-ad0e-a951b1035374", "metadata": {}, "source": [ "This code takes two variables into its prompt and formulates a creative answer (temperature=0.9). \n", "\n", "Here is another example with prompt template:" ] }, { "cell_type": "code", "execution_count": 21, "id": "090d0021-4045-4bf1-b55a-a64dc5cb0ce5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Olá, como você está?\n" ] } ], "source": [ "from langchain_core.prompts import ChatPromptTemplate\n", "\n", "chat_model = ChatOpenAI(model=\"gpt-4o\", temperature=0.7)\n", "template_string = \"Translate the following text from {source_language} to {target_language}: {text}\"\n", "prompt_template = ChatPromptTemplate.from_template(template_string)\n", "formatted_messages = prompt_template.format_messages(\n", " source_language=\"English\",\n", " target_language=\"Portuguese\",\n", " text=\"Hello, how are you?\"\n", ")\n", "response = chat_model.invoke(formatted_messages)\n", "print(response.content)" ] }, { "cell_type": "markdown", "id": "8ef45561-338c-4f34-be83-7ed353146bed", "metadata": {}, "source": [ "## Agents" ] }, { "cell_type": "markdown", "id": "c8853f19-f9cf-4eca-ab9d-bba2551f9d73", "metadata": {}, "source": [ "Agents take LangChain a step further. Instead of following a fixed chain, an agent uses an LLM as a reasoning engine to decide which tools (e.g., web search, calculator, database lookup, other APIs) to use in what order to accomplish a given objective.\n", "\n", "To illustrate the use of agents we will use Tavily, which is a search engine specifically designed and optimized for Large Language Models (LLMs) and AI agents. To be able to run this code, you need an api key form [Tavily](https://tavily.com/). There is a free tear, wuth limited usage, but no cost. Copy the key to the .env file as `TAVILY_API_KEY=your-key`, and we need to reload the file:\n" ] }, { "cell_type": "code", "execution_count": 24, "id": "ce05e51d-a2bb-4713-9ca0-7f1256929f37", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "load_dotenv()" ] }, { "cell_type": "code", "execution_count": 25, "id": "25ebecee-95cd-4642-a761-a22e574d770f", "metadata": {}, "outputs": [], "source": [ "from langchain_community.tools.tavily_search import TavilySearchResults\n", "from langchain import hub\n", "from langchain.agents import create_openai_functions_agent, AgentExecutor" ] }, { "cell_type": "code", "execution_count": 26, "id": "f3a89e42-1069-46df-a966-dc0fbd4471dc", "metadata": {}, "outputs": [], "source": [ "llm = ChatOpenAI(model=\"gpt-4o\", temperature=0)" ] }, { "cell_type": "markdown", "id": "6daf4297-adde-4551-a1c2-5a99a98d17c9", "metadata": {}, "source": [ "First we will define TavilySearchResults tool designed to interact with the Tavily search engine API. The argument max_results=2 configures the tool so that whenever the agent decides to use it, it will only fetch and return the top 2 most relevant search results found by Tavily." ] }, { "cell_type": "code", "execution_count": 28, "id": "cac95344-64a2-49e8-a919-93db0182b6c9", "metadata": {}, "outputs": [], "source": [ "search_tool = TavilySearchResults(max_results=2)\n", "tools = [search_tool]" ] }, { "cell_type": "markdown", "id": "916f4d74-e727-47af-8ee9-530fe20700b8", "metadata": {}, "source": [ "Next, instead of manually writing a complex prompt that instructs the LLM on how to think step-by-step, how to decide when to use tools, and how to format its requests to those tools, we will pull a pre-made, optimized prompt template from the LangChain Hub:" ] }, { "cell_type": "code", "execution_count": 30, "id": "5137ea43-b8be-485d-aee9-e11aa0c4adc5", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\anaconda3\\envs\\ml2025\\Lib\\site-packages\\langsmith\\client.py:272: LangSmithMissingAPIKeyWarning: API key must be provided when using hosted LangSmith API\n", " warnings.warn(\n" ] } ], "source": [ "prompt = hub.pull(\"hwchase17/openai-functions-agent\")" ] }, { "cell_type": "markdown", "id": "30c6854a-e671-475c-b72d-e266e2d731e7", "metadata": {}, "source": [ "Next, we use `create_openai_functions_agent` to set up the agent logic using the LLM's ability to call functions (tools). `AgentExecutor` runs the agent, handling the back-and-forth between the LLM deciding actions and the tools executing them." ] }, { "cell_type": "code", "execution_count": 32, "id": "8567cd28-f89c-4177-9eef-8fe35b08d57c", "metadata": {}, "outputs": [], "source": [ "agent = create_openai_functions_agent(llm, tools, prompt)\n", "\n", "agent_executor = AgentExecutor(\n", " agent=agent,\n", " tools=tools,\n", " verbose=True,\n", " handle_parsing_errors=True\n", ")\n" ] }, { "cell_type": "code", "execution_count": 33, "id": "91a564d2-b7a8-4373-8744-be937e421cc2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m\n", "Invoking: `tavily_search_results_json` with `{'query': 'current weather in Lisbon'}`\n", "\n", "\n", "\u001b[0m\u001b[36;1m\u001b[1;3m[{'title': 'Weather in Lisbon, Portugal', 'url': 'https://www.weatherapi.com/', 'content': \"{'location': {'name': 'Lisbon', 'region': 'Lisboa', 'country': 'Portugal', 'lat': 38.7167, 'lon': -9.1333, 'tz_id': 'Europe/Lisbon', 'localtime_epoch': 1746744291, 'localtime': '2025-05-08 23:44'}, 'current': {'last_updated_epoch': 1746743400, 'last_updated': '2025-05-08 23:30', 'temp_c': 15.3, 'temp_f': 59.5, 'is_day': 0, 'condition': {'text': 'Partly cloudy', 'icon': '//cdn.weatherapi.com/weather/64x64/night/116.png', 'code': 1003}, 'wind_mph': 4.7, 'wind_kph': 7.6, 'wind_degree': 271, 'wind_dir': 'W', 'pressure_mb': 1013.0, 'pressure_in': 29.91, 'precip_mm': 0.0, 'precip_in': 0.0, 'humidity': 77, 'cloud': 50, 'feelslike_c': 15.3, 'feelslike_f': 59.5, 'windchill_c': 13.9, 'windchill_f': 57.0, 'heatindex_c': 14.1, 'heatindex_f': 57.4, 'dewpoint_c': 9.5, 'dewpoint_f': 49.1, 'vis_km': 10.0, 'vis_miles': 6.0, 'uv': 0.0, 'gust_mph': 7.5, 'gust_kph': 12.0}}\", 'score': 0.7448614}, {'title': 'Lisbon, Lisboa, Portugal Weather Forecast | AccuWeather', 'url': 'https://www.accuweather.com/en/pt/lisbon/274087/weather-forecast/274087', 'content': 'Current Weather. 7:45 PM. 62°F. Partly sunny. RealFeel® 57° · Looking Ahead. Expect showers Saturday morning. Lisbon Weather Radar. Lisbon Weather Radar. Static', 'score': 0.7050753}]\u001b[0m\u001b[32;1m\u001b[1;3mThe current weather in Lisbon is partly cloudy with a temperature of 15.3°C (59.5°F). The wind is blowing from the west at 7.6 kph (4.7 mph), and the humidity is at 77%. There is no precipitation at the moment, and the visibility is 10 km (6 miles).\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] }, { "data": { "text/plain": [ "'The current weather in Lisbon is partly cloudy with a temperature of 15.3°C (59.5°F). The wind is blowing from the west at 7.6 kph (4.7 mph), and the humidity is at 77%. There is no precipitation at the moment, and the visibility is 10 km (6 miles).'" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "query = \"What is the weather like in Lisbon\"\n", "response = agent_executor.invoke({\"input\": query})\n", "response['output']" ] }, { "cell_type": "markdown", "id": "0da34158-f749-42c7-8da8-76bbc6656710", "metadata": {}, "source": [ "Next, we will build a specialized AI agent designed specifically to interact with data stored in CSV format using `create_csv_agent` function in LangChain.\n", "This agent: \n", "- uses an LLM to understand the user's questions\n", "- loads the csv file into a dataframe\n", "- generates and executes code to perform the calculation or data retrieval. This is why the we need to set the parameter `allow_dangerous_code=True`\n", "- takes the result from the executed code and formulates an answer to theoriginal question.\n", "\n", "Let's download the dataset:" ] }, { "cell_type": "code", "execution_count": 35, "id": "a47cbda4-1c50-448b-8c97-1bed53e1050b", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "\n", " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n", " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0\n", "100 44225 100 44225 0 0 38907 0 0:00:01 0:00:01 --:--:-- 39068\n", " % Total % Received % Xferd Average Speed Time Time Time Current\n", " Dload Upload Total Spent Left Speed\n", "\n", " 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0curl: (6) Could not resolve host: titanic.csv\n" ] } ], "source": [ "!curl https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv -O titanic.csv" ] }, { "cell_type": "code", "execution_count": 36, "id": "99ddceb9-e100-4a55-a94c-b46b5a8078a1", "metadata": {}, "outputs": [], "source": [ "from langchain_experimental.agents.agent_toolkits import create_csv_agent" ] }, { "cell_type": "code", "execution_count": 37, "id": "f23bb815-68b3-41a8-859b-2c3bbe4bb480", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\ProgramData\\anaconda3\\envs\\ml2025\\Lib\\site-packages\\langchain_experimental\\agents\\agent_toolkits\\pandas\\base.py:283: UserWarning: Received additional kwargs {'handle_parsing_errors': True} which are no longer supported.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m\n", "Invoking: `python_repl_ast` with `{'query': 'len(df)'}`\n", "\n", "\n", "\u001b[0m\u001b[36;1m\u001b[1;3m887\u001b[0m\u001b[32;1m\u001b[1;3mThe dataset contains 887 passengers.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] } ], "source": [ "llm = ChatOpenAI(model=\"gpt-4o\", temperature=0)\n", "\n", "agent_executor = create_csv_agent(\n", " llm=llm,\n", " path='titanic.csv',\n", " verbose=True,\n", " agent_type=\"openai-functions\", \n", " handle_parsing_errors=True,\n", " allow_dangerous_code=True # Be cautious with this setting\n", " )\n", "\n", "query = \"How many passengers are in the dataset?\"\n", "response = agent_executor.invoke({\"input\": query})" ] }, { "cell_type": "code", "execution_count": 38, "id": "bf77fa33-c365-42ec-9d56-14a568136787", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m\n", "Invoking: `python_repl_ast` with `{'query': 'df.columns.tolist()'}`\n", "\n", "\n", "\u001b[0m\u001b[36;1m\u001b[1;3m['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'Siblings/Spouses Aboard', 'Parents/Children Aboard', 'Fare']\u001b[0m\u001b[32;1m\u001b[1;3mThe column names of the dataframe are:\n", "\n", "1. Survived\n", "2. Pclass\n", "3. Name\n", "4. Sex\n", "5. Age\n", "6. Siblings/Spouses Aboard\n", "7. Parents/Children Aboard\n", "8. Fare\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] } ], "source": [ "query = \"what are the column names?\"\n", "response = agent_executor.invoke({\"input\": query})" ] }, { "cell_type": "code", "execution_count": 39, "id": "a9ed7825-c617-46b9-a73f-b420b6a3866a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m\n", "Invoking: `python_repl_ast` with `{'query': \"median_age = df['Age'].median()\\nmedian_age\"}`\n", "\n", "\n", "\u001b[0m\u001b[36;1m\u001b[1;3m28.0\u001b[0m\u001b[32;1m\u001b[1;3mThe median age is 28.0 years.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] } ], "source": [ "query = \"what's the median age?\"\n", "response = agent_executor.invoke({\"input\": query})" ] }, { "cell_type": "code", "execution_count": 40, "id": "8194a975-248e-4176-b47a-a7abb8428702", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m\n", "Invoking: `python_repl_ast` with `{'query': \"import matplotlib.pyplot as plt\\n\\ndf['Age'].plot(kind='hist', bins=10, title='Age Distribution')\\nplt.xlabel('Age')\\nplt.ylabel('Frequency')\\nplt.show()\"}`\n", "\n", "\n", "\u001b[0m" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[36;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThe histogram of the age distribution has been plotted, showing the frequency of different age groups in the dataset.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] } ], "source": [ "query = \"Plot age distribution in a histogram\"\n", "response = agent_executor.invoke({\"input\": query})" ] }, { "cell_type": "code", "execution_count": 41, "id": "cb19e205-b4aa-4f0f-a666-460071582e37", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m\n", "Invoking: `python_repl_ast` with `{'query': \"df['Survived'].value_counts()\"}`\n", "\n", "\n", "\u001b[0m\u001b[36;1m\u001b[1;3mSurvived\n", "0 545\n", "1 342\n", "Name: count, dtype: int64\u001b[0m\u001b[32;1m\u001b[1;3mThe distribution of the 'Survived' column is as follows:\n", "- 545 passengers did not survive (represented by 0).\n", "- 342 passengers survived (represented by 1).\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] } ], "source": [ "query = \"What is the distribution of the column survived?\"\n", "response = agent_executor.invoke({\"input\": query})\n" ] }, { "cell_type": "markdown", "id": "6d0bb410-152a-407b-ba32-9aaeabef44de", "metadata": {}, "source": [ "## Example using a model from Huggingface" ] }, { "cell_type": "markdown", "id": "5b0107cb-23cf-434a-84c8-cfc4cb48307f", "metadata": {}, "source": [ "Same as in the case of OpenAI, to use the models from [Hugging Face](https://huggingface.co/), you should create an account, and then create a [User Access Tokens](https://huggingface.co/settings/tokens), and copy paste to .env file as:\n", "`HF_API_TOKEN=your-token`\n", "\n", "Note that Hugging Face offers both free and paid Inference APis:\n", "- Free Inference endpoints for many popular open-source models, including versions of Mistral-7B-Instruct. However, this free tier often has limitations: Rate Limits, Availability/Cold Starts, Performance that could be lower compared to paid options.\n", "- Paid Inference endpoints offer for more reliable, faster, and scalable access without rate limits" ] }, { "cell_type": "code", "execution_count": 101, "id": "c3626f96-3920-4381-985d-4b2a82185edc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "load_dotenv()" ] }, { "cell_type": "markdown", "id": "fd742c0e-7cb7-4dbb-a586-3a8e7ee1c8d2", "metadata": {}, "source": [ "We need to install langchain_huggingface a librray for connecting to models hosted on the Hugging Face Hub via their Inference API:\n", "`pip install langchain_huggingface`" ] }, { "cell_type": "code", "execution_count": 6, "id": "e33b633f-7018-4d75-9238-2de8eeac8d89", "metadata": {}, "outputs": [], "source": [ "from langchain_huggingface import HuggingFaceEndpoint" ] }, { "cell_type": "markdown", "id": "b55bf51f-64fb-4d4c-8109-f04a489c9470", "metadata": {}, "source": [ "To define which model we want to use, we use parameter `repo_id`. [Here](https://huggingface.co/models?pipeline_tag=text-generation&sort=trending) is a list of text generation models hosted on Hugging Face. We will use Mistral-7B-Instruct, which is a popular, efficient (~7B parameter), open-weight LLM from Mistral AI." ] }, { "cell_type": "code", "execution_count": null, "id": "7d769bc9-655f-477b-8211-9df1aead0a18", "metadata": {}, "outputs": [], "source": [ "llm = HuggingFaceEndpoint(\n", " repo_id='mistralai/Mistral-7B-Instruct-v0.3',\n", " max_new_tokens=256, \n", " temperature=0.7,\n", " huggingfacehub_api_token= os.getenv(\"HF_API_TOKEN\")\n", " )\n", "\n", "system_prompt = \"You are a friendly assistant.\"\n", "user_prompt = \"What is the capital of Portugal?\"\n", "\n", "formatted_prompt = f\"[INST] {system_prompt} {user_prompt} [/INST]\"" ] }, { "cell_type": "markdown", "id": "2b1de584-ebda-4ab1-a277-1b209d0c0dbc", "metadata": {}, "source": [ "Note the use of [INST] and [/INST], these are special marker tokens specifically used by Mistral Instruct models. They are used to enclose the user's instruction or query, helping the model differentiate between instructions and its own generated response, especially in a chat-like format." ] }, { "cell_type": "code", "execution_count": 21, "id": "98891e6b-aa0c-43c4-a5fa-1074e40f486c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\"The capital of Portugal is Lisbon. Lisbon is located on the western Iberian Peninsula and is one of the oldest cities in Western Europe. It's known for its rich history, beautiful architecture, delicious cuisine, and vibrant nightlife. Lisbon is also the largest city in Portugal and the country's political, economic, and cultural hub. Enjoy your visit to Lisbon!\"" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "response = llm.invoke(formatted_prompt)\n", "response" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "ml2025", "language": "python", "name": "ml2025" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.8" }, "vscode": { "interpreter": { "hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97" } }, "widgets": { "application/vnd.jupyter.widget-state+json": { "015a7ffa1edd44b3bcd2d7c4dda2a514": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": 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