diff --git a/.gitignore b/.gitignore index 7453751d..5bb52dcf 100644 --- a/.gitignore +++ b/.gitignore @@ -192,3 +192,6 @@ cython_debug/ .DS_Store dev.ipynb + +# CodeBeaver reports and artifacts +.codebeaver diff --git a/examples/ScrapegraphAI_cookbook.ipynb b/examples/ScrapegraphAI_cookbook.ipynb index 3ef7eb1e..d8f1151e 100644 --- a/examples/ScrapegraphAI_cookbook.ipynb +++ b/examples/ScrapegraphAI_cookbook.ipynb @@ -1,915 +1,914 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "9_CQrFgOj78b" - }, - "outputs": [], - "source": [ - "%%capture\n", - "!pip install scrapegraphai\n", - "!apt install chromium-chromedriver\n", - "!pip install nest_asyncio\n", - "!pip install playwright\n", - "!playwright install" - ] - }, - { - "cell_type": "code", - "source": [ - "import nest_asyncio\n", - "nest_asyncio.apply()" - ], - "metadata": { - "id": "tb33AcRHywFb" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "00a84YVhhxJr" - }, - "outputs": [], - "source": [ - "# correct APIKEY\n", - "OPENAI_API_KEY = \"YOUR API KEY\"" - ] - }, - { - "cell_type": "markdown", - "source": [ - "For more examples visit [the examples folder](https://github.com/ScrapeGraphAI/Scrapegraph-ai/tree/main/examples)" - ], - "metadata": { - "id": "vGDjka17pqqg" - } - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Mrujgp-nlp12" - }, - "source": [ - "# SmartScraperGraph\n", - "**SmartScraperGraph** is a class representing one of the default scraping pipelines. It uses a direct graph implementation where each node has its own function, from retrieving html from a website to extracting relevant information based on your query and generate a coherent answer." - ] - }, - { - "cell_type": "markdown", - "source": [ - "![Screenshot 2024-09-19 alle 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)" - ], - "metadata": { - "id": "M-dmSB0_zHCQ" - } - }, - { - "cell_type": "markdown", - "metadata": { - "id": "uqYBNOM2YZD9" - }, - "source": [ - "## Using OpenAI models" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ogiF4g5Z-bzG" - }, - "outputs": [], - "source": [ - "from scrapegraphai.graphs import SmartScraperGraph" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7ZzONlJ6-oe_" - }, - "source": [ - "Define the configuration for the graph" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MPZgrZ12-eRc" - }, - "outputs": [], - "source": [ - "graph_config = {\n", - " \"llm\": {\n", - " \"api_key\": OPENAI_API_KEY,\n", - " \"model\": \"openai/gpt-4o-mini\",\n", - " \"temperature\":0,\n", - " },\n", - " \"verbose\":True,\n", - "}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DjDt_10r-q8P" - }, - "source": [ - "Create the SmartScraperGraph instance and run it" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "aV4VTnx9-h_d" - }, - "outputs": [], - "source": [ - "smart_scraper_graph = SmartScraperGraph(\n", - " prompt=\"List me all the projects with their descriptions.\",\n", - " # also accepts a string with the already downloaded HTML code\n", - " source=\"https://perinim.github.io/projects/\",\n", - " config=graph_config\n", - ")" - ] - }, - { - "cell_type": "code", - "source": [ - "graph_config = {\n", - " \"llm\": {\n", - " \"api_key\": OPENAI_API_KEY,\n", - " \"model\": \"openai/gpt-4o-mini\",\n", - " },\n", - " \"verbose\": True,\n", - " \"headless\": True,\n", - "}\n", - "\n", - "# ************************************************\n", - "# Create the SmartScraperGraph instance and run it\n", - "# ************************************************\n", - "\n", - "smart_scraper_graph = SmartScraperGraph(\n", - " prompt=\"List me all the projects with their description\",\n", - " source=\"https://perinim.github.io/projects/\",\n", - " config=graph_config\n", - ")" - ], - "metadata": { - "id": "E3pyGQZLTiZ8" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Zty23idsAtwU", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "419dd75f-18c6-44d2-da82-ca8967d17e0f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "--- Executing Fetch Node ---\n", - "--- (Fetching HTML from: https://perinim.github.io/projects/) ---\n", - "--- Executing ParseNode Node ---\n", - "--- Executing GenerateAnswer Node ---\n" - ] - } - ], - "source": [ - "result = smart_scraper_graph.run()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "rnGhLGCuAqRU", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "062aeab2-3e96-4fec-d04a-b9acae142f40" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "{\n", - " \"projects\": [\n", - " {\n", - " \"name\": \"Rotary Pendulum RL\",\n", - " \"description\": \"Open Source project aimed at controlling a real life rotary pendulum using RL algorithms\"\n", - " },\n", - " {\n", - " \"name\": \"DQN Implementation from scratch\",\n", - " \"description\": \"Developed a Deep Q-Network algorithm to train a simple and double pendulum\"\n", - " },\n", - " {\n", - " \"name\": \"Multi Agents HAED\",\n", - " \"description\": \"University project which focuses on simulating a multi-agent system to perform environment mapping. Agents, equipped with sensors, explore and record their surroundings, considering uncertainties in their readings.\"\n", - " },\n", - " {\n", - " \"name\": \"Wireless ESC for Modular Drones\",\n", - " \"description\": \"Modular drone architecture proposal and proof of concept. The project received maximum grade.\"\n", - " }\n", - " ]\n", - "}\n" - ] - } - ], - "source": [ - "import json\n", - "\n", - "output = json.dumps(result, indent=2)\n", - "\n", - "line_list = output.split(\"\\n\") # Sort of line replacing \"\\n\" with a new line\n", - "\n", - "for line in line_list:\n", - " print(line)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5poLHYLVa-6E" - }, - "source": [ - "# Search graph\n", - "This graph **transforms** the user prompt in a **internet search query**, fetch the relevant URLs, and start the scraping process. Similar to the **SmartScraperGraph** but with the addition of the **SearchInternetNode** node." - ] - }, - { - "cell_type": "markdown", - "source": [ - "![image.png](data:image/png;base64,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)" - ], - "metadata": { - "id": "NRIoaXSzzP8M" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "RIvbQjyhbHhW" - }, - "outputs": [], - "source": [ - "from scrapegraphai.graphs import SearchGraph\n", - "\n", - "# Define the configuration for the graph\n", - "graph_config = {\n", - " \"llm\": {\n", - " \"api_key\": OPENAI_API_KEY,\n", - " \"model\": \"openai/gpt-4o-mini\",\n", - " \"temperature\": 0,\n", - " },\n", - "}\n", - "\n", - "# Create the SearchGraph instance\n", - "search_graph = SearchGraph(\n", - " prompt=\"List me all the European countries. Look in wikipedia.\",\n", - " config=graph_config\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "XnVtc7SzCkUY" - }, - "outputs": [], - "source": [ - "result = search_graph.run()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3LPAh-yQCqkY" - }, - "source": [ - "Prettify the result and display the JSON" - ] - }, - { - "cell_type": "code", - "source": [ - "import json\n", - "\n", - "output = json.dumps(result, indent=2)\n", - "\n", - "line_list = output.split(\"\\n\") # Sort of line replacing \"\\n\" with a new line\n", - "\n", - "for line in line_list:\n", - " print(line)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xgnWDLTjzHwv", - "outputId": "f0c8ebf4-5ba5-4330-dbd8-1c9fdd93eaeb" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "{\n", - " \"European_countries\": [\n", - " \"Albania\",\n", - " \"Andorra\",\n", - " \"Armenia\",\n", - " \"Austria\",\n", - " \"Azerbaijan\",\n", - " \"Belarus\",\n", - " \"Belgium\",\n", - " \"Bosnia and Herzegovina\",\n", - " \"Bulgaria\",\n", - " \"Croatia\",\n", - " \"Cyprus\",\n", - " \"Czech Republic\",\n", - " \"Denmark\",\n", - " \"Estonia\",\n", - " \"Finland\",\n", - " \"France\",\n", - " \"Georgia\",\n", - " \"Germany\",\n", - " \"Greece\",\n", - " \"Hungary\",\n", - " \"Iceland\",\n", - " \"Ireland\",\n", - " \"Italy\",\n", - " \"Jersey\",\n", - " \"Isle of Man\",\n", - " \"Kazakhstan\",\n", - " \"Latvia\",\n", - " \"Liechtenstein\",\n", - " \"Lithuania\",\n", - " \"Luxembourg\",\n", - " \"Malta\",\n", - " \"Moldova\",\n", - " \"Monaco\",\n", - " \"Montenegro\",\n", - " \"Netherlands\",\n", - " \"North Macedonia\",\n", - " \"Norway\",\n", - " \"Poland\",\n", - " \"Portugal\",\n", - " \"Romania\",\n", - " \"Russia\",\n", - " \"San Marino\",\n", - " \"Serbia\",\n", - " \"Slovakia\",\n", - " \"Slovenia\",\n", - " \"Spain\",\n", - " \"Sweden\",\n", - " \"Switzerland\",\n", - " \"Turkey\",\n", - " \"Ukraine\",\n", - " \"United Kingdom\",\n", - " \"Vatican City\",\n", - " \"Kosovo\",\n", - " \"Gibraltar\",\n", - " \"Faroe Islands\",\n", - " \"Guernsey\",\n", - " \"Jersey\"\n", - " ],\n", - " \"sources\": [\n", - " \"https://simple.wikipedia.org/wiki/List_of_European_countries\",\n", - " \"https://en.wikipedia.org/wiki/List_of_European_countries_by_population\",\n", - " \"https://en.wikipedia.org/wiki/Member_state_of_the_European_Union\"\n", - " ]\n", - "}\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "N5IMdKHvlXFY" - }, - "source": [ - "# SpeechGraph\n", - "**SpeechGraph** is a class representing one of the default scraping pipelines that generate the answer together with an audio file. Similar to the **SmartScraperGraph** but with the addition of the **TextToSpeechNode** node.\n" - ] - }, - { - "cell_type": "markdown", - "source": [ - "![image.png](data:image/png;base64,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)" - ], - "metadata": { - "id": "pqJsEVgizs-M" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "W9KhWlT3lXFd" - }, - "outputs": [], - "source": [ - "from scrapegraphai.graphs import SpeechGraph\n", - "\n", - "# Define the configuration for the graph\n", - "graph_config = {\n", - " \"llm\": {\n", - " \"api_key\": OPENAI_API_KEY,\n", - " \"model\": \"gpt-3.5-turbo\",\n", - " },\n", - " \"tts_model\": {\n", - " \"api_key\": OPENAI_API_KEY,\n", - " \"model\": \"tts-1\",\n", - " \"voice\": \"alloy\"\n", - " },\n", - " \"output_path\": \"website_summary.mp3\",\n", - "}\n", - "\n", - "# Create the SpeechGraph instance\n", - "speech_graph = SpeechGraph(\n", - " prompt=\"Create a summary of the website\",\n", - " source=\"https://perinim.github.io/projects/\",\n", - " config=graph_config,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nVolb3paEczD", - "outputId": "d7d316a0-7580-4a6c-8f20-7e1cb1fc3f07" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--- Executing Fetch Node ---\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Fetching pages: 100%|##########| 1/1 [00:00<00:00, 17.07it/s]\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--- Executing Parse Node ---\n", - "--- Executing RAG Node ---\n", - "--- (updated chunks metadata) ---\n", - "--- (tokens compressed and vector stored) ---\n", - "--- Executing GenerateAnswer Node ---\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Processing chunks: 100%|██████████| 1/1 [00:00<00:00, 339.78it/s]\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--- Executing TextToSpeech Node ---\n", - "Audio saved to website_summary.mp3\n" - ] - } - ], - "source": [ - "result = speech_graph.run()\n", - "answer = result.get(\"answer\", \"No answer found\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "znt2EOKZE3z2" - }, - "source": [ - "Prettify the result and display the JSON" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QqY0TbwbEp-O", - "outputId": "c2b1127d-0c49-4121-922e-39da65c329ee" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "{\n", - " \"summary\": {\n", - " \"title\": \"Projects | \",\n", - " \"projects\": [\n", - " {\n", - " \"title\": \"Rotary Pendulum RL\",\n", - " \"description\": \"Open Source project aimed at controlling a real life rotary pendulum using RL algorithms\"\n", - " },\n", - " {\n", - " \"title\": \"DQN Implementation from scratch\",\n", - " \"description\": \"Developed a Deep Q-Network algorithm to train a simple and double pendulum\"\n", - " },\n", - " {\n", - " \"title\": \"Multi Agents HAED\",\n", - " \"description\": \"University project which focuses on simulating a multi-agent system to perform environment mapping. Agents, equipped with sensors, explore and record their surroundings, considering uncertainties in their readings.\"\n", - " },\n", - " {\n", - " \"title\": \"Wireless ESC for Modular Drones\",\n", - " \"description\": \"Modular drone architecture proposal and proof of concept. The project received maximum grade.\"\n", - " }\n", - " ]\n", - " }\n", - "}\n" - ] - } - ], - "source": [ - "import json\n", - "\n", - "output = json.dumps(answer, indent=2)\n", - "\n", - "line_list = output.split(\"\\n\") # Sort of line replacing \"\\n\" with a new line\n", - "\n", - "for line in line_list:\n", - " print(line)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 75 - }, - "id": "lfJ_jVwklXFd", - "outputId": "dc4ad491-4422-4edb-91ae-35775b23168a" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " " - ] - }, - "metadata": {} - } - ], - "source": [ - "from IPython.display import Audio\n", - "wn = Audio(\"website_summary.mp3\", autoplay=True)\n", - "display(wn)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "p9kC0x4NuLTx" - }, - "source": [ - "# Build a Custom Graph\n", - "It is possible to **build your own scraping pipeline** by using the default nodes and place them as you wish, without using pre-defined graphs." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Pr6DIqt2uLUI" - }, - "source": [ - "You can create **custom graphs** based on your necessities, using standard nodes provided by the library.\n", - "\n", - "The list of the existing nodes can be found through the *nodes_metadata* json construct.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "-o29vDSIvG4t", - "outputId": "be469b65-ba01-437a-e217-ed1c4f3ad264" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "dict_keys(['SearchInternetNode', 'FetchNode', 'GetProbableTagsNode', 'ParseNode', 'RAGNode', 'GenerateAnswerNode', 'ConditionalNode', 'ImageToTextNode', 'TextToSpeechNode'])" - ] - }, - "metadata": {}, - "execution_count": 17 - } - ], - "source": [ - "# check available nodes\n", - "from scrapegraphai.helpers import nodes_metadata\n", - "\n", - "nodes_metadata.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "829wW5E6vrjJ", - "outputId": "58203025-64ce-4107-f6d3-3b3cfa5537d5" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "{'description': 'Converts image content to text by \\n extracting visual information and interpreting it.',\n", - " 'type': 'node',\n", - " 'args': {'image_data': 'Data of the image to be processed.'},\n", - " 'returns': \"Updated state with the textual description of the image under 'image_text' key.\"}" - ] - }, - "metadata": {}, - "execution_count": 18 - } - ], - "source": [ - "# to get more information about a node\n", - "nodes_metadata['ImageToTextNode']" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "3pnNFDckwWy7" - }, - "source": [ - "To create a custom graph we must:\n", - "\n", - "1. **Istantiate the nodes** you want to use\n", - "2. Create the graph using **BaseGraph** class, which must have a **list of nodes**, tuples representing the **edges** of the graph, an **entry_point**\n", - "3. Run it using the **execute** method\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "eQLZJyg4uLUJ" - }, - "outputs": [], - "source": [ - "from langchain_openai import OpenAIEmbeddings\n", - "from scrapegraphai.models import OpenAI\n", - "from scrapegraphai.graphs import BaseGraph\n", - "from scrapegraphai.nodes import FetchNode, ParseNode, RAGNode, GenerateAnswerNode\n", - "\n", - "# Define the configuration for the graph\n", - "graph_config = {\n", - " \"llm\": {\n", - " \"api_key\": OPENAI_API_KEY,\n", - " \"model\": \"openai/gpt-4o\",\n", - " \"temperature\": 0,\n", - " \"streaming\": True\n", - " },\n", - "}\n", - "\n", - "llm_model = OpenAI(graph_config[\"llm\"])\n", - "embedder = OpenAIEmbeddings(api_key=llm_model.openai_api_key)\n", - "\n", - "# define the nodes for the graph\n", - "fetch_node = FetchNode(\n", - " input=\"url | local_dir\",\n", - " output=[\"doc\", \"link_urls\", \"img_urls\"],\n", - " node_config={\n", - " \"verbose\": True,\n", - " \"headless\": True,\n", - " }\n", - ")\n", - "parse_node = ParseNode(\n", - " input=\"doc\",\n", - " output=[\"parsed_doc\"],\n", - " node_config={\n", - " \"chunk_size\": 4096,\n", - " \"verbose\": True,\n", - " }\n", - ")\n", - "rag_node = RAGNode(\n", - " input=\"user_prompt & (parsed_doc | doc)\",\n", - " output=[\"relevant_chunks\"],\n", - " node_config={\n", - " \"llm_model\": llm_model,\n", - " \"embedder_model\": embedder,\n", - " \"verbose\": True,\n", - " }\n", - ")\n", - "generate_answer_node = GenerateAnswerNode(\n", - " input=\"user_prompt & (relevant_chunks | parsed_doc | doc)\",\n", - " output=[\"answer\"],\n", - " node_config={\n", - " \"llm_model\": llm_model,\n", - " \"verbose\": True,\n", - " }\n", - ")\n", - "\n", - "# create the graph by defining the nodes and their connections\n", - "graph = BaseGraph(\n", - " nodes=[\n", - " fetch_node,\n", - " parse_node,\n", - " rag_node,\n", - " generate_answer_node,\n", - " ],\n", - " edges=[\n", - " (fetch_node, parse_node),\n", - " (parse_node, rag_node),\n", - " (rag_node, generate_answer_node)\n", - " ],\n", - " entry_point=fetch_node\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5FYKF9H1Fvb8", - "outputId": "666d51fe-5e2f-4398-a3b0-bb820960a0d1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--- Executing Fetch Node ---\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Fetching pages: 100%|##########| 1/1 [00:00<00:00, 28.65it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--- Executing Parse Node ---\n", - "--- Executing RAG Node ---\n", - "--- (updated chunks metadata) ---\n", - "--- (tokens compressed and vector stored) ---\n", - "--- Executing GenerateAnswer Node ---\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Processing chunks: 100%|██████████| 1/1 [00:00<00:00, 911.01it/s]\n" - ] - } + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9_CQrFgOj78b" + }, + "outputs": [], + "source": [ + "%%capture\n", + "!pip install scrapegraphai\n", + "!apt install chromium-chromedriver\n", + "!pip install nest_asyncio\n", + "!pip install playwright\n", + "!playwright install" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tb33AcRHywFb" + }, + "outputs": [], + "source": [ + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "00a84YVhhxJr" + }, + "outputs": [], + "source": [ + "# correct APIKEY\n", + "OPENAI_API_KEY = \"YOUR API KEY\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vGDjka17pqqg" + }, + "source": [ + "For more examples visit [the examples folder](https://github.com/ScrapeGraphAI/Scrapegraph-ai/tree/main/examples)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mrujgp-nlp12" + }, + "source": [ + "# SmartScraperGraph\n", + "**SmartScraperGraph** is a class representing one of the default scraping pipelines. It uses a direct graph implementation where each node has its own function, from retrieving html from a website to extracting relevant information based on your query and generate a coherent answer." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M-dmSB0_zHCQ" + }, + "source": [ + "![Screenshot 2024-09-19 alle 17.04.56.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uqYBNOM2YZD9" + }, + "source": [ + "## Using OpenAI models" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ogiF4g5Z-bzG" + }, + "outputs": [], + "source": [ + "from scrapegraphai.graphs import SmartScraperGraph" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7ZzONlJ6-oe_" + }, + "source": [ + "Define the configuration for the graph" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MPZgrZ12-eRc" + }, + "outputs": [], + "source": [ + "graph_config = {\n", + " \"llm\": {\n", + " \"api_key\": OPENAI_API_KEY,\n", + " \"model\": \"openai/gpt-4o-mini\",\n", + " \"temperature\": 0,\n", + " },\n", + " \"verbose\": True,\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DjDt_10r-q8P" + }, + "source": [ + "Create the SmartScraperGraph instance and run it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "aV4VTnx9-h_d" + }, + "outputs": [], + "source": [ + "smart_scraper_graph = SmartScraperGraph(\n", + " prompt=\"List me all the projects with their descriptions.\",\n", + " # also accepts a string with the already downloaded HTML code\n", + " source=\"https://perinim.github.io/projects/\",\n", + " config=graph_config,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "E3pyGQZLTiZ8" + }, + "outputs": [], + "source": [ + "graph_config = {\n", + " \"llm\": {\n", + " \"api_key\": OPENAI_API_KEY,\n", + " \"model\": \"openai/gpt-4o-mini\",\n", + " },\n", + " \"verbose\": True,\n", + " \"headless\": True,\n", + "}\n", + "\n", + "# ************************************************\n", + "# Create the SmartScraperGraph instance and run it\n", + "# ************************************************\n", + "\n", + "smart_scraper_graph = SmartScraperGraph(\n", + " prompt=\"List me all the projects with their description\",\n", + " source=\"https://perinim.github.io/projects/\",\n", + " config=graph_config,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Zty23idsAtwU", + "outputId": "419dd75f-18c6-44d2-da82-ca8967d17e0f" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "--- Executing Fetch Node ---\n", + "--- (Fetching HTML from: https://perinim.github.io/projects/) ---\n", + "--- Executing ParseNode Node ---\n", + "--- Executing GenerateAnswer Node ---\n" + ] + } + ], + "source": [ + "result = smart_scraper_graph.run()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rnGhLGCuAqRU", + "outputId": "062aeab2-3e96-4fec-d04a-b9acae142f40" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"projects\": [\n", + " {\n", + " \"name\": \"Rotary Pendulum RL\",\n", + " \"description\": \"Open Source project aimed at controlling a real life rotary pendulum using RL algorithms\"\n", + " },\n", + " {\n", + " \"name\": \"DQN Implementation from scratch\",\n", + " \"description\": \"Developed a Deep Q-Network algorithm to train a simple and double pendulum\"\n", + " },\n", + " {\n", + " \"name\": \"Multi Agents HAED\",\n", + " \"description\": \"University project which focuses on simulating a multi-agent system to perform environment mapping. Agents, equipped with sensors, explore and record their surroundings, considering uncertainties in their readings.\"\n", + " },\n", + " {\n", + " \"name\": \"Wireless ESC for Modular Drones\",\n", + " \"description\": \"Modular drone architecture proposal and proof of concept. The project received maximum grade.\"\n", + " }\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "output = json.dumps(result, indent=2)\n", + "\n", + "line_list = output.split(\"\\n\") # Sort of line replacing \"\\n\" with a new line\n", + "\n", + "for line in line_list:\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5poLHYLVa-6E" + }, + "source": [ + "# Search graph\n", + "This graph **transforms** the user prompt in a **internet search query**, fetch the relevant URLs, and start the scraping process. Similar to the **SmartScraperGraph** but with the addition of the **SearchInternetNode** node." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NRIoaXSzzP8M" + }, + "source": [ + "![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RIvbQjyhbHhW" + }, + "outputs": [], + "source": [ + "from scrapegraphai.graphs import SearchGraph\n", + "\n", + "# Define the configuration for the graph\n", + "graph_config = {\n", + " \"llm\": {\n", + " \"api_key\": OPENAI_API_KEY,\n", + " \"model\": \"openai/gpt-4o-mini\",\n", + " \"temperature\": 0,\n", + " },\n", + "}\n", + "\n", + "# Create the SearchGraph instance\n", + "search_graph = SearchGraph(\n", + " prompt=\"List me all the European countries. Look in wikipedia.\", config=graph_config\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XnVtc7SzCkUY" + }, + "outputs": [], + "source": [ + "result = search_graph.run()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3LPAh-yQCqkY" + }, + "source": [ + "Prettify the result and display the JSON" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xgnWDLTjzHwv", + "outputId": "f0c8ebf4-5ba5-4330-dbd8-1c9fdd93eaeb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"European_countries\": [\n", + " \"Albania\",\n", + " \"Andorra\",\n", + " \"Armenia\",\n", + " \"Austria\",\n", + " \"Azerbaijan\",\n", + " \"Belarus\",\n", + " \"Belgium\",\n", + " \"Bosnia and Herzegovina\",\n", + " \"Bulgaria\",\n", + " \"Croatia\",\n", + " \"Cyprus\",\n", + " \"Czech Republic\",\n", + " \"Denmark\",\n", + " \"Estonia\",\n", + " \"Finland\",\n", + " \"France\",\n", + " \"Georgia\",\n", + " \"Germany\",\n", + " \"Greece\",\n", + " \"Hungary\",\n", + " \"Iceland\",\n", + " \"Ireland\",\n", + " \"Italy\",\n", + " \"Jersey\",\n", + " \"Isle of Man\",\n", + " \"Kazakhstan\",\n", + " \"Latvia\",\n", + " \"Liechtenstein\",\n", + " \"Lithuania\",\n", + " \"Luxembourg\",\n", + " \"Malta\",\n", + " \"Moldova\",\n", + " \"Monaco\",\n", + " \"Montenegro\",\n", + " \"Netherlands\",\n", + " \"North Macedonia\",\n", + " \"Norway\",\n", + " \"Poland\",\n", + " \"Portugal\",\n", + " \"Romania\",\n", + " \"Russia\",\n", + " \"San Marino\",\n", + " \"Serbia\",\n", + " \"Slovakia\",\n", + " \"Slovenia\",\n", + " \"Spain\",\n", + " \"Sweden\",\n", + " \"Switzerland\",\n", + " \"Turkey\",\n", + " \"Ukraine\",\n", + " \"United Kingdom\",\n", + " \"Vatican City\",\n", + " \"Kosovo\",\n", + " \"Gibraltar\",\n", + " \"Faroe Islands\",\n", + " \"Guernsey\",\n", + " \"Jersey\"\n", + " ],\n", + " \"sources\": [\n", + " \"https://simple.wikipedia.org/wiki/List_of_European_countries\",\n", + " \"https://en.wikipedia.org/wiki/List_of_European_countries_by_population\",\n", + " \"https://en.wikipedia.org/wiki/Member_state_of_the_European_Union\"\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "output = json.dumps(result, indent=2)\n", + "\n", + "line_list = output.split(\"\\n\") # Sort of line replacing \"\\n\" with a new line\n", + "\n", + "for line in line_list:\n", + " print(line)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N5IMdKHvlXFY" + }, + "source": [ + "# SpeechGraph\n", + "**SpeechGraph** is a class representing one of the default scraping pipelines that generate the answer together with an audio file. Similar to the **SmartScraperGraph** but with the addition of the **TextToSpeechNode** node.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pqJsEVgizs-M" + }, + "source": [ + "![image.png](data:image/png;base64,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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "W9KhWlT3lXFd" + }, + "outputs": [], + "source": [ + "from scrapegraphai.graphs import SpeechGraph\n", + "\n", + "# Define the configuration for the graph\n", + "graph_config = {\n", + " \"llm\": {\n", + " \"api_key\": OPENAI_API_KEY,\n", + " \"model\": \"gpt-3.5-turbo\",\n", + " },\n", + " \"tts_model\": {\"api_key\": OPENAI_API_KEY, \"model\": \"tts-1\", \"voice\": \"alloy\"},\n", + " \"output_path\": \"website_summary.mp3\",\n", + "}\n", + "\n", + "# Create the SpeechGraph instance\n", + "speech_graph = SpeechGraph(\n", + " prompt=\"Create a summary of the website\",\n", + " source=\"https://perinim.github.io/projects/\",\n", + " config=graph_config,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nVolb3paEczD", + "outputId": "d7d316a0-7580-4a6c-8f20-7e1cb1fc3f07" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Executing Fetch Node ---\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fetching pages: 100%|##########| 1/1 [00:00<00:00, 17.07it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Executing Parse Node ---\n", + "--- Executing RAG Node ---\n", + "--- (updated chunks metadata) ---\n", + "--- (tokens compressed and vector stored) ---\n", + "--- Executing GenerateAnswer Node ---\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing chunks: 100%|██████████| 1/1 [00:00<00:00, 339.78it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Executing TextToSpeech Node ---\n", + "Audio saved to website_summary.mp3\n" + ] + } + ], + "source": [ + "result = speech_graph.run()\n", + "answer = result.get(\"answer\", \"No answer found\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "znt2EOKZE3z2" + }, + "source": [ + "Prettify the result and display the JSON" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QqY0TbwbEp-O", + "outputId": "c2b1127d-0c49-4121-922e-39da65c329ee" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"summary\": {\n", + " \"title\": \"Projects | \",\n", + " \"projects\": [\n", + " {\n", + " \"title\": \"Rotary Pendulum RL\",\n", + " \"description\": \"Open Source project aimed at controlling a real life rotary pendulum using RL algorithms\"\n", + " },\n", + " {\n", + " \"title\": \"DQN Implementation from scratch\",\n", + " \"description\": \"Developed a Deep Q-Network algorithm to train a simple and double pendulum\"\n", + " },\n", + " {\n", + " \"title\": \"Multi Agents HAED\",\n", + " \"description\": \"University project which focuses on simulating a multi-agent system to perform environment mapping. Agents, equipped with sensors, explore and record their surroundings, considering uncertainties in their readings.\"\n", + " },\n", + " {\n", + " \"title\": \"Wireless ESC for Modular Drones\",\n", + " \"description\": \"Modular drone architecture proposal and proof of concept. The project received maximum grade.\"\n", + " }\n", + " ]\n", + " }\n", + "}\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "output = json.dumps(answer, indent=2)\n", + "\n", + "line_list = output.split(\"\\n\") # Sort of line replacing \"\\n\" with a new line\n", + "\n", + "for line in line_list:\n", + " print(line)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 75 + }, + "id": "lfJ_jVwklXFd", + "outputId": "dc4ad491-4422-4edb-91ae-35775b23168a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " ], - "source": [ - "# execute the graph\n", - "result, execution_info = graph.execute({\n", - " \"user_prompt\": \"List me the projects with their description\",\n", - " \"url\": \"https://perinim.github.io/projects/\"\n", - "})\n", - "\n", - "# get the answer from the result\n", - "result = result.get(\"answer\", \"No answer found.\")" + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Audio\n", + "\n", + "wn = Audio(\"website_summary.mp3\", autoplay=True)\n", + "display(wn)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "p9kC0x4NuLTx" + }, + "source": [ + "# Build a Custom Graph\n", + "It is possible to **build your own scraping pipeline** by using the default nodes and place them as you wish, without using pre-defined graphs." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Pr6DIqt2uLUI" + }, + "source": [ + "You can create **custom graphs** based on your necessities, using standard nodes provided by the library.\n", + "\n", + "The list of the existing nodes can be found through the *nodes_metadata* json construct.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "-o29vDSIvG4t", + "outputId": "be469b65-ba01-437a-e217-ed1c4f3ad264" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "JEP8_zZ9GHW2" - }, - "source": [ - "Prettify the result and display the JSON" + "data": { + "text/plain": [ + "dict_keys(['SearchInternetNode', 'FetchNode', 'GetProbableTagsNode', 'ParseNode', 'RAGNode', 'GenerateAnswerNode', 'ConditionalNode', 'ImageToTextNode', 'TextToSpeechNode'])" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "nx9qGaxvFmfT", - "outputId": "fb327a6a-0dfa-417b-8dbb-505bebc96fe8" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{\n", - " \"projects\": [\n", - " {\n", - " \"title\": \"Rotary Pendulum RL\",\n", - " \"description\": \"Open Source project aimed at controlling a real life rotary pendulum using RL algorithms\"\n", - " },\n", - " {\n", - " \"title\": \"DQN Implementation from scratch\",\n", - " \"description\": \"Developed a Deep Q-Network algorithm to train a simple and double pendulum\"\n", - " },\n", - " {\n", - " \"title\": \"Multi Agents HAED\",\n", - " \"description\": \"University project which focuses on simulating a multi-agent system to perform environment mapping. Agents, equipped with sensors, explore and record their surroundings, considering uncertainties in their readings.\"\n", - " },\n", - " {\n", - " \"title\": \"Wireless ESC for Modular Drones\",\n", - " \"description\": \"Modular drone architecture proposal and proof of concept. The project received maximum grade.\"\n", - " }\n", - " ]\n", - "}\n" - ] - } - ], - "source": [ - "import json\n", - "\n", - "output = json.dumps(result, indent=2)\n", - "\n", - "line_list = output.split(\"\\n\") # Sort of line replacing \"\\n\" with a new line\n", - "\n", - "for line in line_list:\n", - " print(line)" + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# check available nodes\n", + "from scrapegraphai.helpers import nodes_metadata\n", + "\n", + "nodes_metadata.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "829wW5E6vrjJ", + "outputId": "58203025-64ce-4107-f6d3-3b3cfa5537d5" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'description': 'Converts image content to text by \\n extracting visual information and interpreting it.',\n", + " 'type': 'node',\n", + " 'args': {'image_data': 'Data of the image to be processed.'},\n", + " 'returns': \"Updated state with the textual description of the image under 'image_text' key.\"}" ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { + ], + "source": [ + "# to get more information about a node\n", + "nodes_metadata[\"ImageToTextNode\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3pnNFDckwWy7" + }, + "source": [ + "To create a custom graph we must:\n", + "\n", + "1. **Istantiate the nodes** you want to use\n", + "2. Create the graph using **BaseGraph** class, which must have a **list of nodes**, tuples representing the **edges** of the graph, an **entry_point**\n", + "3. Run it using the **execute** method\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "eQLZJyg4uLUJ" + }, + "outputs": [], + "source": [ + "from langchain_openai import OpenAIEmbeddings\n", + "from scrapegraphai.models import OpenAI\n", + "from scrapegraphai.graphs import BaseGraph\n", + "from scrapegraphai.nodes import FetchNode, ParseNode, RAGNode, GenerateAnswerNode\n", + "\n", + "# Define the configuration for the graph\n", + "graph_config = {\n", + " \"llm\": {\n", + " \"api_key\": OPENAI_API_KEY,\n", + " \"model\": \"openai/gpt-4o\",\n", + " \"temperature\": 0,\n", + " \"streaming\": True,\n", + " },\n", + "}\n", + "\n", + "llm_model = OpenAI(graph_config[\"llm\"])\n", + "embedder = OpenAIEmbeddings(api_key=llm_model.openai_api_key)\n", + "\n", + "# define the nodes for the graph\n", + "fetch_node = FetchNode(\n", + " input=\"url | local_dir\",\n", + " output=[\"doc\", \"link_urls\", \"img_urls\"],\n", + " node_config={\n", + " \"verbose\": True,\n", + " \"headless\": True,\n", + " },\n", + ")\n", + "parse_node = ParseNode(\n", + " input=\"doc\",\n", + " output=[\"parsed_doc\"],\n", + " node_config={\n", + " \"chunk_size\": 4096,\n", + " \"verbose\": True,\n", + " },\n", + ")\n", + "rag_node = RAGNode(\n", + " input=\"user_prompt & (parsed_doc | doc)\",\n", + " output=[\"relevant_chunks\"],\n", + " node_config={\n", + " \"llm_model\": llm_model,\n", + " \"embedder_model\": embedder,\n", + " \"verbose\": True,\n", + " },\n", + ")\n", + "generate_answer_node = GenerateAnswerNode(\n", + " input=\"user_prompt & (relevant_chunks | parsed_doc | doc)\",\n", + " output=[\"answer\"],\n", + " node_config={\n", + " \"llm_model\": llm_model,\n", + " \"verbose\": True,\n", + " },\n", + ")\n", + "\n", + "# create the graph by defining the nodes and their connections\n", + "graph = BaseGraph(\n", + " nodes=[\n", + " fetch_node,\n", + " parse_node,\n", + " rag_node,\n", + " generate_answer_node,\n", + " ],\n", + " edges=[\n", + " (fetch_node, parse_node),\n", + " (parse_node, rag_node),\n", + " (rag_node, generate_answer_node),\n", + " ],\n", + " entry_point=fetch_node,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { "colab": { - "collapsed_sections": [ - "N5IMdKHvlXFY" - ], - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "base_uri": "https://localhost:8080/" + }, + "id": "5FYKF9H1Fvb8", + "outputId": "666d51fe-5e2f-4398-a3b0-bb820960a0d1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Executing Fetch Node ---\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Fetching pages: 100%|##########| 1/1 [00:00<00:00, 28.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--- Executing Parse Node ---\n", + "--- Executing RAG Node ---\n", + "--- (updated chunks metadata) ---\n", + "--- (tokens compressed and vector stored) ---\n", + "--- Executing GenerateAnswer Node ---\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Processing chunks: 100%|██████████| 1/1 [00:00<00:00, 911.01it/s]\n" + ] } + ], + "source": [ + "# execute the graph\n", + "result, execution_info = graph.execute(\n", + " {\n", + " \"user_prompt\": \"List me the projects with their description\",\n", + " \"url\": \"https://perinim.github.io/projects/\",\n", + " }\n", + ")\n", + "\n", + "# get the answer from the result\n", + "result = result.get(\"answer\", \"No answer found.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JEP8_zZ9GHW2" + }, + "source": [ + "Prettify the result and display the JSON" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nx9qGaxvFmfT", + "outputId": "fb327a6a-0dfa-417b-8dbb-505bebc96fe8" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"projects\": [\n", + " {\n", + " \"title\": \"Rotary Pendulum RL\",\n", + " \"description\": \"Open Source project aimed at controlling a real life rotary pendulum using RL algorithms\"\n", + " },\n", + " {\n", + " \"title\": \"DQN Implementation from scratch\",\n", + " \"description\": \"Developed a Deep Q-Network algorithm to train a simple and double pendulum\"\n", + " },\n", + " {\n", + " \"title\": \"Multi Agents HAED\",\n", + " \"description\": \"University project which focuses on simulating a multi-agent system to perform environment mapping. Agents, equipped with sensors, explore and record their surroundings, considering uncertainties in their readings.\"\n", + " },\n", + " {\n", + " \"title\": \"Wireless ESC for Modular Drones\",\n", + " \"description\": \"Modular drone architecture proposal and proof of concept. The project received maximum grade.\"\n", + " }\n", + " ]\n", + "}\n" + ] + } + ], + "source": [ + "import json\n", + "\n", + "output = json.dumps(result, indent=2)\n", + "\n", + "line_list = output.split(\"\\n\") # Sort of line replacing \"\\n\" with a new line\n", + "\n", + "for line in line_list:\n", + " print(line)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "N5IMdKHvlXFY" + ], + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/examples/code_generator_graph/ollama/code_generator_graph_ollama.py b/examples/code_generator_graph/ollama/code_generator_graph_ollama.py index 339bb03c..0fdcf04c 100644 --- a/examples/code_generator_graph/ollama/code_generator_graph_ollama.py +++ b/examples/code_generator_graph/ollama/code_generator_graph_ollama.py @@ -2,7 +2,6 @@ Basic example of scraping pipeline using Code Generator with schema """ -import json from typing import List from dotenv import load_dotenv diff --git a/examples/custom_graph/ollama/custom_graph_ollama.py b/examples/custom_graph/ollama/custom_graph_ollama.py index f7aebd3d..4574ee6b 100644 --- a/examples/custom_graph/ollama/custom_graph_ollama.py +++ b/examples/custom_graph/ollama/custom_graph_ollama.py @@ -2,8 +2,6 @@ Example of custom graph using existing nodes """ -import os - from langchain_openai import ChatOpenAI, OpenAIEmbeddings from scrapegraphai.graphs import BaseGraph @@ -11,7 +9,6 @@ FetchNode, GenerateAnswerNode, ParseNode, - RAGNode, RobotsNode, ) diff --git a/examples/extras/chromium_selenium.py b/examples/extras/chromium_selenium.py index 811ebc2a..e8354e7e 100644 --- a/examples/extras/chromium_selenium.py +++ b/examples/extras/chromium_selenium.py @@ -9,7 +9,6 @@ ChromiumLoader, ) from scrapegraphai.graphs import SmartScraperGraph -from scrapegraphai.utils import prettify_exec_info # Load environment variables for API keys load_dotenv() diff --git a/examples/extras/no_cut.py b/examples/extras/no_cut.py index c638df84..044a4f7a 100644 --- a/examples/extras/no_cut.py +++ b/examples/extras/no_cut.py @@ -3,7 +3,6 @@ """ import json -import os from scrapegraphai.graphs import SmartScraperGraph from scrapegraphai.utils import prettify_exec_info diff --git a/examples/extras/serch_graph_scehma.py b/examples/extras/serch_graph_scehma.py index 0ad66d4e..02b76db6 100644 --- a/examples/extras/serch_graph_scehma.py +++ b/examples/extras/serch_graph_scehma.py @@ -40,7 +40,7 @@ class Ceos(BaseModel): # ************************************************ search_graph = SearchGraph( - prompt=f"Who is the ceo of Appke?", + prompt="Who is the ceo of Appke?", schema=Ceos, config=graph_config, ) diff --git a/examples/script_generator_graph/ollama/script_multi_generator_ollama.py b/examples/script_generator_graph/ollama/script_multi_generator_ollama.py index a8a53f1f..b4af2c9d 100644 --- a/examples/script_generator_graph/ollama/script_multi_generator_ollama.py +++ b/examples/script_generator_graph/ollama/script_multi_generator_ollama.py @@ -2,8 +2,6 @@ Basic example of scraping pipeline using ScriptCreatorGraph """ -import os - from dotenv import load_dotenv from scrapegraphai.graphs import ScriptCreatorMultiGraph diff --git a/scrapegraphai/builders/graph_builder.py b/scrapegraphai/builders/graph_builder.py index c44ea72a..1179ebe7 100644 --- a/scrapegraphai/builders/graph_builder.py +++ b/scrapegraphai/builders/graph_builder.py @@ -113,9 +113,7 @@ def _create_extraction_chain(self): {nodes_description} Based on the user's input: "{input}", identify the essential nodes required for the task and suggest a graph configuration that outlines the flow between the chosen nodes. - """.format( - nodes_description=self.nodes_description, input="{input}" - ) + """.format(nodes_description=self.nodes_description, input="{input}") extraction_prompt = ChatPromptTemplate.from_template( create_graph_prompt_template ) diff --git a/scrapegraphai/docloaders/scrape_do.py b/scrapegraphai/docloaders/scrape_do.py index be37e3f7..4c3adbb3 100644 --- a/scrapegraphai/docloaders/scrape_do.py +++ b/scrapegraphai/docloaders/scrape_do.py @@ -2,10 +2,10 @@ Scrape_do module """ +import os import urllib.parse import requests -import os import urllib3 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) diff --git a/scrapegraphai/graphs/abstract_graph.py b/scrapegraphai/graphs/abstract_graph.py index 3908545d..c42e4daf 100644 --- a/scrapegraphai/graphs/abstract_graph.py +++ b/scrapegraphai/graphs/abstract_graph.py @@ -177,7 +177,7 @@ def _create_llm(self, llm_config: dict) -> object: ] if len(possible_providers) <= 0: raise ValueError( - f"""Provider {llm_params['model_provider']} is not supported. + f"""Provider {llm_params["model_provider"]} is not supported. If possible, try to use a model instance instead.""" ) llm_params["model_provider"] = possible_providers[0] @@ -190,7 +190,7 @@ def _create_llm(self, llm_config: dict) -> object: if llm_params["model_provider"] not in known_providers: raise ValueError( - f"""Provider {llm_params['model_provider']} is not supported. + f"""Provider {llm_params["model_provider"]} is not supported. If possible, try to use a model instance instead.""" ) @@ -201,7 +201,7 @@ def _create_llm(self, llm_config: dict) -> object: ] except KeyError: print( - f"""Max input tokens for model {llm_params['model_provider']}/{llm_params['model']} not found, + f"""Max input tokens for model {llm_params["model_provider"]}/{llm_params["model"]} not found, please specify the model_tokens parameter in the llm section of the graph configuration. Using default token size: 8192""" ) diff --git a/scrapegraphai/graphs/csv_scraper_multi_graph.py b/scrapegraphai/graphs/csv_scraper_multi_graph.py index 5e3a398d..fbad1203 100644 --- a/scrapegraphai/graphs/csv_scraper_multi_graph.py +++ b/scrapegraphai/graphs/csv_scraper_multi_graph.py @@ -49,7 +49,6 @@ def __init__( config: dict, schema: Optional[Type[BaseModel]] = None, ): - self.copy_config = safe_deepcopy(config) self.copy_schema = deepcopy(schema) diff --git a/scrapegraphai/graphs/json_scraper_multi_graph.py b/scrapegraphai/graphs/json_scraper_multi_graph.py index 6623718b..fb49845b 100644 --- a/scrapegraphai/graphs/json_scraper_multi_graph.py +++ b/scrapegraphai/graphs/json_scraper_multi_graph.py @@ -49,7 +49,6 @@ def __init__( config: dict, schema: Optional[Type[BaseModel]] = None, ): - self.copy_config = safe_deepcopy(config) self.copy_schema = deepcopy(schema) diff --git a/scrapegraphai/graphs/omni_search_graph.py b/scrapegraphai/graphs/omni_search_graph.py index c30033ab..0fc26697 100644 --- a/scrapegraphai/graphs/omni_search_graph.py +++ b/scrapegraphai/graphs/omni_search_graph.py @@ -44,7 +44,6 @@ class OmniSearchGraph(AbstractGraph): def __init__( self, prompt: str, config: dict, schema: Optional[Type[BaseModel]] = None ): - self.max_results = config.get("max_results", 3) self.copy_config = safe_deepcopy(config) diff --git a/scrapegraphai/graphs/script_creator_multi_graph.py b/scrapegraphai/graphs/script_creator_multi_graph.py index a8e01729..843f8ad9 100644 --- a/scrapegraphai/graphs/script_creator_multi_graph.py +++ b/scrapegraphai/graphs/script_creator_multi_graph.py @@ -48,7 +48,6 @@ def __init__( config: dict, schema: Optional[Type[BaseModel]] = None, ): - self.copy_config = safe_deepcopy(config) self.copy_schema = deepcopy(schema) super().__init__(prompt, config, source, schema) diff --git a/scrapegraphai/graphs/smart_scraper_multi_concat_graph.py b/scrapegraphai/graphs/smart_scraper_multi_concat_graph.py index ebd7b936..8454a860 100644 --- a/scrapegraphai/graphs/smart_scraper_multi_concat_graph.py +++ b/scrapegraphai/graphs/smart_scraper_multi_concat_graph.py @@ -53,7 +53,6 @@ def __init__( config: dict, schema: Optional[Type[BaseModel]] = None, ): - self.copy_config = safe_deepcopy(config) self.copy_schema = deepcopy(schema) diff --git a/scrapegraphai/graphs/smart_scraper_multi_graph.py b/scrapegraphai/graphs/smart_scraper_multi_graph.py index 226aae00..f017ec09 100644 --- a/scrapegraphai/graphs/smart_scraper_multi_graph.py +++ b/scrapegraphai/graphs/smart_scraper_multi_graph.py @@ -55,7 +55,6 @@ def __init__( config: dict, schema: Optional[Type[BaseModel]] = None, ): - self.max_results = config.get("max_results", 3) self.copy_config = safe_deepcopy(config) self.copy_schema = deepcopy(schema) diff --git a/scrapegraphai/graphs/smart_scraper_multi_lite_graph.py b/scrapegraphai/graphs/smart_scraper_multi_lite_graph.py index 849c85c8..8ef3211a 100644 --- a/scrapegraphai/graphs/smart_scraper_multi_lite_graph.py +++ b/scrapegraphai/graphs/smart_scraper_multi_lite_graph.py @@ -55,7 +55,6 @@ def __init__( config: dict, schema: Optional[Type[BaseModel]] = None, ): - self.copy_config = safe_deepcopy(config) self.copy_schema = deepcopy(schema) super().__init__(prompt, config, source, schema) diff --git a/scrapegraphai/graphs/xml_scraper_multi_graph.py b/scrapegraphai/graphs/xml_scraper_multi_graph.py index 2a3848c9..480781a8 100644 --- a/scrapegraphai/graphs/xml_scraper_multi_graph.py +++ b/scrapegraphai/graphs/xml_scraper_multi_graph.py @@ -49,7 +49,6 @@ def __init__( config: dict, schema: Optional[Type[BaseModel]] = None, ): - self.copy_config = safe_deepcopy(config) self.copy_schema = deepcopy(schema) super().__init__(prompt, config, source, schema) diff --git a/scrapegraphai/models/openai_tts.py b/scrapegraphai/models/openai_tts.py index 714050fb..7503fe1b 100644 --- a/scrapegraphai/models/openai_tts.py +++ b/scrapegraphai/models/openai_tts.py @@ -19,7 +19,6 @@ class OpenAITextToSpeech: """ def __init__(self, tts_config: dict): - self.client = OpenAI( api_key=tts_config.get("api_key"), base_url=tts_config.get("base_url", None) ) diff --git a/scrapegraphai/nodes/base_node.py b/scrapegraphai/nodes/base_node.py index 45ee82d3..179865eb 100644 --- a/scrapegraphai/nodes/base_node.py +++ b/scrapegraphai/nodes/base_node.py @@ -54,7 +54,6 @@ def __init__( min_input_len: int = 1, node_config: Optional[dict] = None, ): - self.node_name = node_name self.input = input self.output = output @@ -197,7 +196,6 @@ def evaluate_simple_expression(exp: str) -> List[str]: """Evaluate an expression without parentheses.""" for or_segment in exp.split("|"): - and_segment = or_segment.split("&") if all(elem.strip() in state for elem in and_segment): return [ @@ -226,7 +224,7 @@ def evaluate_expression(expression: str) -> List[str]: raise ValueError( f"""No state keys matched the expression. Expression was {expression}. - State contains keys: {', '.join(state.keys())}""" + State contains keys: {", ".join(state.keys())}""" ) final_result = [] diff --git a/scrapegraphai/nodes/concat_answers_node.py b/scrapegraphai/nodes/concat_answers_node.py index c1b271c0..11cba4d6 100644 --- a/scrapegraphai/nodes/concat_answers_node.py +++ b/scrapegraphai/nodes/concat_answers_node.py @@ -36,8 +36,7 @@ def __init__( ) def _merge_dict(self, items): - - return {"products": {f"item_{i+1}": item for i, item in enumerate(items)}} + return {"products": {f"item_{i + 1}": item for i, item in enumerate(items)}} def execute(self, state: dict) -> dict: """ diff --git a/scrapegraphai/nodes/description_node.py b/scrapegraphai/nodes/description_node.py index 21917c84..90102ceb 100644 --- a/scrapegraphai/nodes/description_node.py +++ b/scrapegraphai/nodes/description_node.py @@ -58,7 +58,7 @@ def execute(self, state: dict) -> dict: template=DESCRIPTION_NODE_PROMPT, partial_variables={"content": chunk.get("document")}, ) - chain_name = f"chunk{i+1}" + chain_name = f"chunk{i + 1}" chains_dict[chain_name] = prompt | self.llm_model async_runner = RunnableParallel(**chains_dict) diff --git a/scrapegraphai/nodes/generate_answer_csv_node.py b/scrapegraphai/nodes/generate_answer_csv_node.py index c5790479..cd24fc21 100644 --- a/scrapegraphai/nodes/generate_answer_csv_node.py +++ b/scrapegraphai/nodes/generate_answer_csv_node.py @@ -96,7 +96,6 @@ def execute(self, state): doc = input_data[1] if self.node_config.get("schema", None) is not None: - if isinstance(self.llm_model, (ChatOpenAI, ChatMistralAI)): self.llm_model = self.llm_model.with_structured_output( schema=self.node_config["schema"] @@ -151,7 +150,7 @@ def execute(self, state): }, ) - chain_name = f"chunk{i+1}" + chain_name = f"chunk{i + 1}" chains_dict[chain_name] = prompt | self.llm_model | output_parser async_runner = RunnableParallel(**chains_dict) diff --git a/scrapegraphai/nodes/generate_answer_from_image_node.py b/scrapegraphai/nodes/generate_answer_from_image_node.py index 1ef653f3..808804fd 100644 --- a/scrapegraphai/nodes/generate_answer_from_image_node.py +++ b/scrapegraphai/nodes/generate_answer_from_image_node.py @@ -85,7 +85,7 @@ async def execute_async(self, state: dict) -> dict: raise ValueError( f"""The model provided is not supported. Supported models are: - {', '.join(supported_models)}.""" + {", ".join(supported_models)}.""" ) api_key = self.node_config.get("config", {}).get("llm", {}).get("api_key", "") diff --git a/scrapegraphai/nodes/generate_answer_node.py b/scrapegraphai/nodes/generate_answer_node.py index 5e267a4d..db4467be 100644 --- a/scrapegraphai/nodes/generate_answer_node.py +++ b/scrapegraphai/nodes/generate_answer_node.py @@ -221,7 +221,7 @@ def execute(self, state: dict) -> dict: "format_instructions": format_instructions, }, ) - chain_name = f"chunk{i+1}" + chain_name = f"chunk{i + 1}" chains_dict[chain_name] = prompt | self.llm_model if output_parser: chains_dict[chain_name] = chains_dict[chain_name] | output_parser diff --git a/scrapegraphai/nodes/generate_answer_node_k_level.py b/scrapegraphai/nodes/generate_answer_node_k_level.py index daef9d02..27106c88 100644 --- a/scrapegraphai/nodes/generate_answer_node_k_level.py +++ b/scrapegraphai/nodes/generate_answer_node_k_level.py @@ -155,7 +155,7 @@ def execute(self, state: dict) -> dict: "chunk_id": i + 1, }, ) - chain_name = f"chunk{i+1}" + chain_name = f"chunk{i + 1}" chains_dict[chain_name] = prompt | self.llm_model async_runner = RunnableParallel(**chains_dict) diff --git a/scrapegraphai/nodes/generate_answer_omni_node.py b/scrapegraphai/nodes/generate_answer_omni_node.py index ba5bbc6b..3e608bfb 100644 --- a/scrapegraphai/nodes/generate_answer_omni_node.py +++ b/scrapegraphai/nodes/generate_answer_omni_node.py @@ -89,7 +89,6 @@ def execute(self, state: dict) -> dict: imag_desc = input_data[2] if self.node_config.get("schema", None) is not None: - if isinstance(self.llm_model, (ChatOpenAI, ChatMistralAI)): self.llm_model = self.llm_model.with_structured_output( schema=self.node_config["schema"] @@ -151,7 +150,7 @@ def execute(self, state: dict) -> dict: }, ) - chain_name = f"chunk{i+1}" + chain_name = f"chunk{i + 1}" chains_dict[chain_name] = prompt | self.llm_model | output_parser async_runner = RunnableParallel(**chains_dict) diff --git a/scrapegraphai/nodes/merge_answers_node.py b/scrapegraphai/nodes/merge_answers_node.py index b867b3e0..18e9fcc8 100644 --- a/scrapegraphai/nodes/merge_answers_node.py +++ b/scrapegraphai/nodes/merge_answers_node.py @@ -82,10 +82,9 @@ def execute(self, state: dict) -> dict: answers_str = "" for i, answer in enumerate(answers): - answers_str += f"CONTENT WEBSITE {i+1}: {answer}\n" + answers_str += f"CONTENT WEBSITE {i + 1}: {answer}\n" if self.node_config.get("schema", None) is not None: - if isinstance(self.llm_model, (ChatOpenAI, ChatMistralAI)): self.llm_model = self.llm_model.with_structured_output( schema=self.node_config["schema"] diff --git a/scrapegraphai/nodes/merge_generated_scripts_node.py b/scrapegraphai/nodes/merge_generated_scripts_node.py index 5ccac699..2b4a2217 100644 --- a/scrapegraphai/nodes/merge_generated_scripts_node.py +++ b/scrapegraphai/nodes/merge_generated_scripts_node.py @@ -64,7 +64,7 @@ def execute(self, state: dict) -> dict: scripts_str = "" for i, script in enumerate(scripts): scripts_str += "-----------------------------------\n" - scripts_str += f"SCRIPT URL {i+1}\n" + scripts_str += f"SCRIPT URL {i + 1}\n" scripts_str += "-----------------------------------\n" scripts_str += script diff --git a/scrapegraphai/nodes/parse_node.py b/scrapegraphai/nodes/parse_node.py index cb61a643..1c409da2 100644 --- a/scrapegraphai/nodes/parse_node.py +++ b/scrapegraphai/nodes/parse_node.py @@ -122,7 +122,7 @@ def execute(self, state: dict) -> dict: state.update({self.output[0]: chunks}) state.update({"parsed_doc": chunks}) state.update({"content": chunks}) - + if self.parse_urls: state.update({self.output[1]: link_urls}) state.update({self.output[2]: img_urls}) diff --git a/scrapegraphai/nodes/search_link_node.py b/scrapegraphai/nodes/search_link_node.py index 614b4878..6ae5d01b 100644 --- a/scrapegraphai/nodes/search_link_node.py +++ b/scrapegraphai/nodes/search_link_node.py @@ -122,7 +122,6 @@ def execute(self, state: dict) -> dict: ) ): try: - links = re.findall(r'https?://[^\s"<>\]]+', str(chunk.page_content)) if not self.filter_links: diff --git a/scrapegraphai/utils/__init__.py b/scrapegraphai/utils/__init__.py index 0190d691..df9118c1 100644 --- a/scrapegraphai/utils/__init__.py +++ b/scrapegraphai/utils/__init__.py @@ -1,5 +1,5 @@ """ - __init__.py file for utils folder +__init__.py file for utils folder """ from .cleanup_code import extract_code diff --git a/scrapegraphai/utils/cleanup_html.py b/scrapegraphai/utils/cleanup_html.py index 6da03a90..8a40fd88 100644 --- a/scrapegraphai/utils/cleanup_html.py +++ b/scrapegraphai/utils/cleanup_html.py @@ -2,8 +2,8 @@ Module for minimizing the code """ -import re import json +import re from urllib.parse import urljoin from bs4 import BeautifulSoup, Comment @@ -12,32 +12,36 @@ def extract_from_script_tags(soup): script_content = [] - + for script in soup.find_all("script"): content = script.string if content: try: - json_pattern = r'(?:const|let|var)?\s*\w+\s*=\s*({[\s\S]*?});?$' + json_pattern = r"(?:const|let|var)?\s*\w+\s*=\s*({[\s\S]*?});?$" json_matches = re.findall(json_pattern, content) - + for potential_json in json_matches: try: parsed = json.loads(potential_json) if parsed: - script_content.append(f"JSON data from script: {json.dumps(parsed, indent=2)}") + script_content.append( + f"JSON data from script: {json.dumps(parsed, indent=2)}" + ) except json.JSONDecodeError: pass - + if "window." in content or "document." in content: - data_pattern = r'(?:window|document)\.(\w+)\s*=\s*([^;]+);' + data_pattern = r"(?:window|document)\.(\w+)\s*=\s*([^;]+);" data_matches = re.findall(data_pattern, content) - + for var_name, var_value in data_matches: - script_content.append(f"Dynamic data - {var_name}: {var_value.strip()}") + script_content.append( + f"Dynamic data - {var_name}: {var_value.strip()}" + ) except Exception: if len(content) < 1000: script_content.append(f"Script content: {content.strip()}") - + return "\n\n".join(script_content) @@ -66,9 +70,9 @@ def cleanup_html(html_content: str, base_url: str) -> str: title_tag = soup.find("title") title = title_tag.get_text() if title_tag else "" - + script_content = extract_from_script_tags(soup) - + for tag in soup.find_all("style"): tag.extract() diff --git a/scrapegraphai/utils/dict_content_compare.py b/scrapegraphai/utils/dict_content_compare.py index c94d4863..9e5efbbd 100644 --- a/scrapegraphai/utils/dict_content_compare.py +++ b/scrapegraphai/utils/dict_content_compare.py @@ -53,7 +53,9 @@ def normalize_list(lst: List[Any]) -> List[Any]: else ( normalize_list(item) if isinstance(item, list) - else item.lower().strip() if isinstance(item, str) else item + else item.lower().strip() + if isinstance(item, str) + else item ) ) for item in lst diff --git a/scrapegraphai/utils/output_parser.py b/scrapegraphai/utils/output_parser.py index a4cf9f5a..a9d9ba31 100644 --- a/scrapegraphai/utils/output_parser.py +++ b/scrapegraphai/utils/output_parser.py @@ -10,7 +10,7 @@ def get_structured_output_parser( - schema: Union[Dict[str, Any], Type[BaseModelV1 | BaseModelV2], Type] + schema: Union[Dict[str, Any], Type[BaseModelV1 | BaseModelV2], Type], ) -> Callable: """ Get the correct output parser for the LLM model. @@ -28,7 +28,7 @@ def get_structured_output_parser( def get_pydantic_output_parser( - schema: Union[Dict[str, Any], Type[BaseModelV1 | BaseModelV2], Type] + schema: Union[Dict[str, Any], Type[BaseModelV1 | BaseModelV2], Type], ) -> JsonOutputParser: """ Get the correct output parser for the LLM model. diff --git a/scrapegraphai/utils/parse_state_keys.py b/scrapegraphai/utils/parse_state_keys.py index 97531487..040f1310 100644 --- a/scrapegraphai/utils/parse_state_keys.py +++ b/scrapegraphai/utils/parse_state_keys.py @@ -56,7 +56,6 @@ def parse_expression(expression, state: dict) -> list: or "&|" in expression or "|&" in expression ): - raise ValueError("Invalid operator usage.") open_parentheses = close_parentheses = 0 diff --git a/scrapegraphai/utils/proxy_rotation.py b/scrapegraphai/utils/proxy_rotation.py index 8e8534e1..0e348377 100644 --- a/scrapegraphai/utils/proxy_rotation.py +++ b/scrapegraphai/utils/proxy_rotation.py @@ -6,11 +6,12 @@ import random import re from typing import List, Optional, Set, TypedDict +from urllib.parse import urlparse import requests from fp.errors import FreeProxyException from fp.fp import FreeProxy -from urllib.parse import urlparse + class ProxyBrokerCriteria(TypedDict, total=False): """ @@ -200,7 +201,9 @@ def parse_or_search_proxy(proxy: Proxy) -> ProxySettings: raise ValueError(f"Invalid proxy server format: {proxy['server']}") # Accept both IP addresses and domain names like 'gate.nodemaven.com' - if is_ipv4_address(server_address) or re.match(r"^[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$", server_address): + if is_ipv4_address(server_address) or re.match( + r"^[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$", server_address + ): return _parse_proxy(proxy) assert proxy["server"] == "broker", f"Unknown proxy server type: {proxy['server']}" diff --git a/tests/graphs/abstract_graph_test.py b/tests/graphs/abstract_graph_test.py index 873b5a2f..4c9b026e 100644 --- a/tests/graphs/abstract_graph_test.py +++ b/tests/graphs/abstract_graph_test.py @@ -1,17 +1,19 @@ -import pytest +from unittest.mock import Mock, patch +import pytest from langchain_aws import ChatBedrock from langchain_ollama import ChatOllama from langchain_openai import AzureChatOpenAI, ChatOpenAI + from scrapegraphai.graphs import AbstractGraph, BaseGraph from scrapegraphai.models import DeepSeek, OneApi from scrapegraphai.nodes import FetchNode, ParseNode -from unittest.mock import Mock, patch """ Tests for the AbstractGraph. """ + class TestGraph(AbstractGraph): def __init__(self, prompt: str, config: dict): super().__init__(prompt, config) @@ -48,6 +50,7 @@ def run(self) -> str: return self.final_state.get("answer", "No answer found.") + class TestAbstractGraph: @pytest.mark.parametrize( "llm_config, expected_model", @@ -171,7 +174,7 @@ def test_create_llm_with_custom_model_instance(self): "llm": { "model_instance": mock_model, "model_tokens": 1000, - "model": "custom/model" + "model": "custom/model", } } @@ -192,18 +195,27 @@ def test_set_common_params(self): mock_graph.nodes = [mock_node1, mock_node2] # Create a TestGraph instance with the mock graph - with patch('scrapegraphai.graphs.abstract_graph.AbstractGraph._create_graph', return_value=mock_graph): - graph = TestGraph("Test prompt", {"llm": {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-test"}}) + with patch( + "scrapegraphai.graphs.abstract_graph.AbstractGraph._create_graph", + return_value=mock_graph, + ): + graph = TestGraph( + "Test prompt", + {"llm": {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-test"}}, + ) # Call set_common_params with test parameters test_params = {"param1": "value1", "param2": "value2"} graph.set_common_params(test_params) # Assert that update_config was called on each node with the correct parameters - + def test_get_state(self): """Test that get_state returns the correct final state with or without a provided key, and raises KeyError for missing keys.""" - graph = TestGraph("dummy", {"llm": {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-test"}}) + graph = TestGraph( + "dummy", + {"llm": {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-test"}}, + ) # Set a dummy final state graph.final_state = {"answer": "42", "other": "value"} # Test without a key returns the entire final_state @@ -218,7 +230,10 @@ def test_get_state(self): def test_append_node(self): """Test that append_node correctly delegates to the graph's append_node method.""" - graph = TestGraph("dummy", {"llm": {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-test"}}) + graph = TestGraph( + "dummy", + {"llm": {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-test"}}, + ) # Replace the graph object with a mock that has append_node mock_graph = Mock() graph.graph = mock_graph @@ -228,8 +243,11 @@ def test_append_node(self): def test_get_execution_info(self): """Test that get_execution_info returns the execution info stored in the graph.""" - graph = TestGraph("dummy", {"llm": {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-test"}}) + graph = TestGraph( + "dummy", + {"llm": {"model": "openai/gpt-3.5-turbo", "openai_api_key": "sk-test"}}, + ) dummy_info = {"execution": "info", "status": "ok"} graph.execution_info = dummy_info info = graph.get_execution_info() - assert info == dummy_info \ No newline at end of file + assert info == dummy_info diff --git a/tests/graphs/script_generator_test.py b/tests/graphs/script_generator_test.py index d90d2df0..598ccfd6 100644 --- a/tests/graphs/script_generator_test.py +++ b/tests/graphs/script_generator_test.py @@ -5,7 +5,6 @@ import pytest from scrapegraphai.graphs import ScriptCreatorGraph -from scrapegraphai.utils import prettify_exec_info @pytest.fixture @@ -35,6 +34,6 @@ def test_script_creator_graph(graph_config: dict): config=graph_config, ) result = smart_scraper_graph.run() - assert ( - result is not None - ), "ScriptCreatorGraph execution failed to produce a result." + assert result is not None, ( + "ScriptCreatorGraph execution failed to produce a result." + ) diff --git a/tests/graphs/search_link_ollama.py b/tests/graphs/search_link_ollama.py index 9801b2fa..a6c63e1e 100644 --- a/tests/graphs/search_link_ollama.py +++ b/tests/graphs/search_link_ollama.py @@ -1,5 +1,4 @@ from scrapegraphai.graphs import SearchLinkGraph -from scrapegraphai.utils import prettify_exec_info def test_smart_scraper_pipeline(): diff --git a/tests/nodes/robot_node_test.py b/tests/nodes/robot_node_test.py index 692b6062..dad24fbb 100644 --- a/tests/nodes/robot_node_test.py +++ b/tests/nodes/robot_node_test.py @@ -1,7 +1,6 @@ from unittest.mock import MagicMock import pytest -from langchain_community.chat_models import ChatOllama from scrapegraphai.nodes import RobotsNode diff --git a/tests/nodes/search_internet_node_test.py b/tests/nodes/search_internet_node_test.py index 67792424..25ad1234 100644 --- a/tests/nodes/search_internet_node_test.py +++ b/tests/nodes/search_internet_node_test.py @@ -6,7 +6,6 @@ class TestSearchInternetNode(unittest.TestCase): - def setUp(self): # Configuration for the graph self.graph_config = { diff --git a/tests/nodes/search_link_node_test.py b/tests/nodes/search_link_node_test.py index 5c64e470..2c630a2f 100644 --- a/tests/nodes/search_link_node_test.py +++ b/tests/nodes/search_link_node_test.py @@ -1,4 +1,4 @@ -from unittest.mock import MagicMock, patch +from unittest.mock import patch import pytest from langchain_community.chat_models import ChatOllama diff --git a/tests/test_chromium.py b/tests/test_chromium.py new file mode 100644 index 00000000..1c56840a --- /dev/null +++ b/tests/test_chromium.py @@ -0,0 +1,866 @@ +import asyncio +import sys +from unittest.mock import AsyncMock, patch + +import aiohttp +import pytest + + +class MockPlaywright: + def __init__(self): + self.chromium = AsyncMock() + self.firefox = AsyncMock() + + +class MockBrowser: + def __init__(self): + self.new_context = AsyncMock() + + +class MockContext: + def __init__(self): + self.new_page = AsyncMock() + + +class MockPage: + def __init__(self): + self.goto = AsyncMock() + self.wait_for_load_state = AsyncMock() + self.content = AsyncMock() + self.evaluate = AsyncMock() + self.mouse = AsyncMock() + self.mouse.wheel = AsyncMock() + + +@pytest.fixture +def mock_playwright(): + with patch("playwright.async_api.async_playwright") as mock: + mock_pw = MockPlaywright() + mock_browser = MockBrowser() + mock_context = MockContext() + mock_page = MockPage() + + mock_pw.chromium.launch.return_value = mock_browser + mock_pw.firefox.launch.return_value = mock_browser + mock_browser.new_context.return_value = mock_context + mock_context.new_page.return_value = mock_page + + mock.return_value.__aenter__.return_value = mock_pw + yield mock_pw, mock_browser, mock_context, mock_page + + +import pytest +from langchain_core.documents import Document + +from scrapegraphai.docloaders.chromium import ChromiumLoader + + +async def dummy_scraper(url): + """A dummy scraping function that returns dummy HTML content for the URL.""" + return f"dummy content for {url}" + + +@pytest.fixture +def loader_with_dummy(monkeypatch): + """Fixture returning a ChromiumLoader instance with dummy scraping methods patched.""" + urls = ["http://example.com", "http://test.com"] + loader = ChromiumLoader(urls, backend="playwright", requires_js_support=False) + monkeypatch.setattr(loader, "ascrape_playwright", dummy_scraper) + monkeypatch.setattr(loader, "ascrape_with_js_support", dummy_scraper) + monkeypatch.setattr(loader, "ascrape_undetected_chromedriver", dummy_scraper) + return loader + + +def test_lazy_load(loader_with_dummy): + """Test that lazy_load yields Document objects with the correct dummy content and metadata.""" + docs = list(loader_with_dummy.lazy_load()) + assert len(docs) == 2 + for doc, url in zip(docs, loader_with_dummy.urls): + assert isinstance(doc, Document) + assert f"dummy content for {url}" in doc.page_content + assert doc.metadata["source"] == url + + +@pytest.mark.asyncio +async def test_alazy_load(loader_with_dummy): + """Test that alazy_load asynchronously yields Document objects with dummy content and proper metadata.""" + docs = [doc async for doc in loader_with_dummy.alazy_load()] + assert len(docs) == 2 + for doc, url in zip(docs, loader_with_dummy.urls): + assert isinstance(doc, Document) + assert f"dummy content for {url}" in doc.page_content + assert doc.metadata["source"] == url + + +@pytest.mark.asyncio +async def test_scrape_method_unsupported_backend(): + """Test that the scrape method raises a ValueError when an unsupported backend is provided.""" + loader = ChromiumLoader(["http://example.com"], backend="unsupported") + with pytest.raises(ValueError): + await loader.scrape("http://example.com") + + +@pytest.mark.asyncio +async def test_scrape_method_selenium(monkeypatch): + """Test that the scrape method works correctly for selenium by returning the dummy selenium content.""" + + async def dummy_selenium(url): + return f"dummy selenium content for {url}" + + urls = ["http://example.com"] + loader = ChromiumLoader(urls, backend="selenium") + loader.browser_name = "chromium" + monkeypatch.setattr(loader, "ascrape_undetected_chromedriver", dummy_selenium) + result = await loader.scrape("http://example.com") + assert "dummy selenium content" in result + + +@pytest.mark.asyncio +async def test_ascrape_playwright_scroll(mock_playwright): + """Test the ascrape_playwright_scroll method with various configurations.""" + mock_pw, mock_browser, mock_context, mock_page = mock_playwright + + url = "http://example.com" + loader = ChromiumLoader([url], backend="playwright") + + # Test with default parameters + mock_page.evaluate.side_effect = [1000, 2000, 2000] # Simulate scrolling + result = await loader.ascrape_playwright_scroll(url) + + assert mock_page.goto.call_count == 1 + assert mock_page.wait_for_load_state.call_count == 1 + assert mock_page.mouse.wheel.call_count > 0 + assert mock_page.content.call_count == 1 + + # Test with custom parameters + mock_page.evaluate.side_effect = [1000, 2000, 3000, 4000, 4000] + result = await loader.ascrape_playwright_scroll( + url, timeout=10, scroll=10000, sleep=1, scroll_to_bottom=True + ) + + assert mock_page.goto.call_count == 2 + assert mock_page.wait_for_load_state.call_count == 2 + assert mock_page.mouse.wheel.call_count > 0 + assert mock_page.content.call_count == 2 + + +@pytest.mark.asyncio +async def test_ascrape_with_js_support(mock_playwright): + """Test the ascrape_with_js_support method with different browser configurations.""" + mock_pw, mock_browser, mock_context, mock_page = mock_playwright + + url = "http://example.com" + loader = ChromiumLoader([url], backend="playwright", requires_js_support=True) + + # Test with Chromium + result = await loader.ascrape_with_js_support(url, browser_name="chromium") + assert mock_pw.chromium.launch.call_count == 1 + assert mock_page.goto.call_count == 1 + assert mock_page.content.call_count == 1 + + # Test with Firefox + result = await loader.ascrape_with_js_support(url, browser_name="firefox") + assert mock_pw.firefox.launch.call_count == 1 + assert mock_page.goto.call_count == 2 + assert mock_page.content.call_count == 2 + + # Test with invalid browser name + with pytest.raises(ValueError): + await loader.ascrape_with_js_support(url, browser_name="invalid") + + +@pytest.mark.asyncio +async def test_scrape_method_playwright(mock_playwright): + """Test the scrape method with playwright backend.""" + mock_pw, mock_browser, mock_context, mock_page = mock_playwright + + url = "http://example.com" + loader = ChromiumLoader([url], backend="playwright") + + mock_page.content.return_value = "Playwright content" + result = await loader.scrape(url) + + assert "Playwright content" in result + assert mock_pw.chromium.launch.call_count == 1 + assert mock_page.goto.call_count == 1 + assert mock_page.wait_for_load_state.call_count == 1 + assert mock_page.content.call_count == 1 + + +@pytest.mark.asyncio +async def test_scrape_method_retry_logic(mock_playwright): + """Test the retry logic in the scrape method.""" + mock_pw, mock_browser, mock_context, mock_page = mock_playwright + + url = "http://example.com" + loader = ChromiumLoader([url], backend="playwright", retry_limit=3) + + # Simulate two failures and then a success + mock_page.goto.side_effect = [asyncio.TimeoutError(), aiohttp.ClientError(), None] + mock_page.content.return_value = "Success after retries" + + result = await loader.scrape(url) + + assert "Success after retries" in result + assert mock_page.goto.call_count == 3 + assert mock_page.content.call_count == 1 + + # Test failure after all retries + mock_page.goto.side_effect = asyncio.TimeoutError() + + with pytest.raises(RuntimeError): + await loader.scrape(url) + + assert mock_page.goto.call_count == 6 # 3 more attempts + + +@pytest.mark.asyncio +async def test_ascrape_playwright_scroll_invalid_params(): + """Test that ascrape_playwright_scroll raises ValueError for invalid scroll parameters.""" + loader = ChromiumLoader(["http://example.com"], backend="playwright") + with pytest.raises( + ValueError, + match="If set, timeout value for scrolling scraper must be greater than 0.", + ): + await loader.ascrape_playwright_scroll("http://example.com", timeout=0) + with pytest.raises( + ValueError, match="Sleep for scrolling scraper value must be greater than 0." + ): + await loader.ascrape_playwright_scroll("http://example.com", sleep=0) + with pytest.raises( + ValueError, + match="Scroll value for scrolling scraper must be greater than or equal to 5000.", + ): + await loader.ascrape_playwright_scroll("http://example.com", scroll=4000) + + +@pytest.mark.asyncio +async def test_ascrape_with_js_support_retry_failure(monkeypatch): + """Test that ascrape_with_js_support retries and ultimately fails when page.goto always times out.""" + loader = ChromiumLoader( + ["http://example.com"], + backend="playwright", + requires_js_support=True, + retry_limit=2, + timeout=1, + ) + + # Create dummy classes to simulate failure in page.goto + class DummyPage: + async def goto(self, url, wait_until): + raise asyncio.TimeoutError("Forced timeout") + + async def wait_for_load_state(self, state): + return + + async def content(self): + return "Dummy" + + class DummyContext: + async def new_page(self): + return DummyPage() + + class DummyBrowser: + async def new_context(self, **kwargs): + return DummyContext() + + async def close(self): + return + + class DummyPW: + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc, tb): + return + + class chromium: + @staticmethod + async def launch(headless, proxy, **kwargs): + return DummyBrowser() + + class firefox: + @staticmethod + async def launch(headless, proxy, **kwargs): + return DummyBrowser() + + # Patch the async_playwright to return our dummy + monkeypatch.setattr("playwright.async_api.async_playwright", lambda: DummyPW()) + + with pytest.raises(RuntimeError, match="Failed to scrape after"): + await loader.ascrape_with_js_support("http://example.com") + + +@pytest.mark.asyncio +async def test_ascrape_undetected_chromedriver_success(monkeypatch): + """Test that ascrape_undetected_chromedriver successfully returns content using the selenium backend.""" + # Create a dummy undetected_chromedriver module with a dummy Chrome driver. + import types + + dummy_module = types.ModuleType("undetected_chromedriver") + + class DummyDriver: + def __init__(self, options): + self.options = options + self.page_source = "selenium content" + + def quit(self): + pass + + dummy_module.Chrome = lambda options: DummyDriver(options) + monkeypatch.setitem(sys.modules, "undetected_chromedriver", dummy_module) + + urls = ["http://example.com"] + loader = ChromiumLoader(urls, backend="selenium", retry_limit=1, timeout=5) + loader.browser_name = "chromium" + result = await loader.ascrape_undetected_chromedriver("http://example.com") + assert "selenium content" in result + + +@pytest.mark.asyncio +async def test_lazy_load_exception(loader_with_dummy, monkeypatch): + """Test that lazy_load propagates exception if the scraping function fails.""" + + async def dummy_failure(url): + raise Exception("Dummy scraping error") + + # Patch the scraping method to always raise an exception + loader_with_dummy.backend = "playwright" + monkeypatch.setattr(loader_with_dummy, "ascrape_playwright", dummy_failure) + with pytest.raises(Exception, match="Dummy scraping error"): + list(loader_with_dummy.lazy_load()) + + +@pytest.mark.asyncio +async def test_ascrape_undetected_chromedriver_unsupported_browser(monkeypatch): + """Test ascrape_undetected_chromedriver raises an error when an unsupported browser is provided.""" + import types + + dummy_module = types.ModuleType("undetected_chromedriver") + # Provide a dummy Chrome; this will not be used for an unsupported browser. + dummy_module.Chrome = lambda options: None + monkeypatch.setitem(sys.modules, "undetected_chromedriver", dummy_module) + + loader = ChromiumLoader( + ["http://example.com"], backend="selenium", retry_limit=1, timeout=1 + ) + loader.browser_name = "opera" # Unsupported browser. + with pytest.raises(UnboundLocalError): + await loader.ascrape_undetected_chromedriver("http://example.com") + + +@pytest.mark.asyncio +async def test_alazy_load_partial_failure(monkeypatch): + """Test that alazy_load propagates an exception if one of the scraping tasks fails.""" + urls = ["http://example.com", "http://fail.com"] + loader = ChromiumLoader(urls, backend="playwright") + + async def partial_scraper(url): + if "fail" in url: + raise Exception("Scraping failed for " + url) + return f"Content for {url}" + + monkeypatch.setattr(loader, "ascrape_playwright", partial_scraper) + + with pytest.raises(Exception, match="Scraping failed for http://fail.com"): + [doc async for doc in loader.alazy_load()] + + +@pytest.mark.asyncio +async def test_ascrape_playwright_retry_failure(monkeypatch): + """Test that ascrape_playwright retries scraping and raises RuntimeError after all attempts fail.""" + + # Dummy classes to simulate persistent failure in page.goto for ascrape_playwright + class DummyPage: + async def goto(self, url, wait_until): + raise asyncio.TimeoutError("Forced timeout in goto") + + async def wait_for_load_state(self, state): + return + + async def content(self): + return "This should not be returned" + + class DummyContext: + async def new_page(self): + return DummyPage() + + class DummyBrowser: + async def new_context(self, **kwargs): + return DummyContext() + + async def close(self): + return + + class DummyPW: + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc, tb): + return + + class chromium: + @staticmethod + async def launch(headless, proxy, **kwargs): + return DummyBrowser() + + class firefox: + @staticmethod + async def launch(headless, proxy, **kwargs): + return DummyBrowser() + + monkeypatch.setattr("playwright.async_api.async_playwright", lambda: DummyPW()) + + loader = ChromiumLoader( + ["http://example.com"], backend="playwright", retry_limit=2, timeout=1 + ) + with pytest.raises(RuntimeError, match="Failed to scrape after 2 attempts"): + await loader.ascrape_playwright("http://example.com") + + +@pytest.mark.asyncio +async def test_init_overrides(): + """Test that ChromiumLoader picks up and overrides attributes using kwargs.""" + urls = ["http://example.com"] + loader = ChromiumLoader( + urls, + backend="playwright", + headless=False, + proxy={"http": "http://proxy"}, + load_state="load", + requires_js_support=True, + storage_state="state", + browser_name="firefox", + retry_limit=5, + timeout=120, + extra="value", + ) + # Check that attributes are correctly set + assert loader.headless is False + assert loader.proxy == {"http": "http://proxy"} + assert loader.load_state == "load" + assert loader.requires_js_support is True + assert loader.storage_state == "state" + assert loader.browser_name == "firefox" + assert loader.retry_limit == 5 + assert loader.timeout == 120 + # Check that extra kwargs go into browser_config + assert loader.browser_config.get("extra") == "value" + # Check that the backend remains as provided + assert loader.backend == "playwright" + + +@pytest.mark.asyncio +async def test_lazy_load_with_js_support(monkeypatch): + """Test that lazy_load uses ascrape_with_js_support when requires_js_support is True.""" + urls = ["http://example.com", "http://test.com"] + loader = ChromiumLoader(urls, backend="playwright", requires_js_support=True) + + async def dummy_js(url): + return f"JS content for {url}" + + monkeypatch.setattr(loader, "ascrape_with_js_support", dummy_js) + docs = list(loader.lazy_load()) + assert len(docs) == 2 + for doc, url in zip(docs, urls): + assert isinstance(doc, Document) + assert f"JS content for {url}" in doc.page_content + assert doc.metadata["source"] == url + + +@pytest.mark.asyncio +async def test_no_retry_returns_none(monkeypatch): + """Test that ascrape_playwright returns None if retry_limit is set to 0.""" + urls = ["http://example.com"] + loader = ChromiumLoader(urls, backend="playwright", retry_limit=0) + + # Even if we patch ascrape_playwright, the while loop won't run since retry_limit is 0, so it should return None. + async def dummy(url, browser_name="chromium"): + return f"Content for {url}" + + monkeypatch.setattr(loader, "ascrape_playwright", dummy) + result = await loader.ascrape_playwright("http://example.com") + # With retry_limit=0, the loop never runs and the function returns None. + assert result is None + + +@pytest.mark.asyncio +async def test_alazy_load_empty_urls(): + """Test that alazy_load yields no documents when the urls list is empty.""" + loader = ChromiumLoader([], backend="playwright") + docs = [doc async for doc in loader.alazy_load()] + assert docs == [] + + +def test_lazy_load_empty_urls(): + """Test that lazy_load yields no documents when the urls list is empty.""" + loader = ChromiumLoader([], backend="playwright") + docs = list(loader.lazy_load()) + assert docs == [] + + +@pytest.mark.asyncio +async def test_ascrape_undetected_chromedriver_missing_import(monkeypatch): + """Test that ascrape_undetected_chromedriver raises ImportError when undetected_chromedriver is not installed.""" + # Remove undetected_chromedriver from sys.modules if it exists + if "undetected_chromedriver" in sys.modules: + monkeyatch_key = "undetected_chromedriver" + monkeypatch.delenitem(sys.modules, monkeyatch_key) + loader = ChromiumLoader( + ["http://example.com"], backend="selenium", retry_limit=1, timeout=5 + ) + loader.browser_name = "chromium" + with pytest.raises( + ImportError, match="undetected_chromedriver is required for ChromiumLoader" + ): + await loader.ascrape_undetected_chromedriver("http://example.com") + + +@pytest.mark.asyncio +async def test_ascrape_undetected_chromedriver_quit_called(monkeypatch): + """Test that ascrape_undetected_chromedriver calls driver.quit() on every attempt even when get() fails.""" + # List to collect each DummyDriver instance for later inspection. + driver_instances = [] + attempt_counter = [0] + + class DummyDriver: + def __init__(self, options): + self.options = options + self.quit_called = False + driver_instances.append(self) + + def get(self, url): + # Force a failure on the first attempt then succeed on subsequent attempts. + if attempt_counter[0] < 1: + attempt_counter[0] += 1 + raise aiohttp.ClientError("Forced failure") + # If no failure, simply pass. + + @property + def page_source(self): + return "driver content" + + def quit(self): + self.quit_called = True + + import types + + dummy_module = types.ModuleType("undetected_chromedriver") + dummy_module.Chrome = lambda options: DummyDriver(options) + monkeypatch.setitem(sys.modules, "undetected_chromedriver", dummy_module) + + urls = ["http://example.com"] + loader = ChromiumLoader(urls, backend="selenium", retry_limit=2, timeout=5) + loader.browser_name = "chromium" + result = await loader.ascrape_undetected_chromedriver("http://example.com") + assert "driver content" in result + # Verify that two driver instances were used and that each had its quit() method called. + assert len(driver_instances) == 2 + for driver in driver_instances: + assert driver.quit_called is True + + +@pytest.mark.parametrize("backend", ["playwright", "selenium"]) +def test_dynamic_import_failure(monkeypatch, backend): + """Test that ChromiumLoader raises ImportError when dynamic_import fails.""" + + def fake_dynamic_import(backend, message): + raise ImportError("Test dynamic import error") + + monkeypatch.setattr( + "scrapegraphai.docloaders.chromium.dynamic_import", fake_dynamic_import + ) + with pytest.raises(ImportError, match="Test dynamic import error"): + ChromiumLoader(["http://example.com"], backend=backend) + + +@pytest.mark.asyncio +async def test_ascrape_with_js_support_retry_success(monkeypatch): + """Test that ascrape_with_js_support retries on failure and returns content on a subsequent successful attempt.""" + attempt_count = {"count": 0} + + class DummyPage: + async def goto(self, url, wait_until): + if attempt_count["count"] < 1: + attempt_count["count"] += 1 + raise asyncio.TimeoutError("Forced timeout") + # On second attempt, do nothing (simulate successful navigation) + + async def wait_for_load_state(self, state): + return + + async def content(self): + return "Success on retry" + + class DummyContext: + async def new_page(self): + return DummyPage() + + class DummyBrowser: + async def new_context(self, **kwargs): + return DummyContext() + + async def close(self): + return + + class DummyPW: + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc, tb): + return + + class chromium: + @staticmethod + async def launch(headless, proxy, **kwargs): + return DummyBrowser() + + class firefox: + @staticmethod + async def launch(headless, proxy, **kwargs): + return DummyBrowser() + + monkeypatch.setattr("playwright.async_api.async_playwright", lambda: DummyPW()) + + # Create a loader with JS support and a retry_limit of 2 (so one failure is allowed) + loader = ChromiumLoader( + ["http://example.com"], + backend="playwright", + requires_js_support=True, + retry_limit=2, + timeout=1, + ) + result = await loader.ascrape_with_js_support("http://example.com") + assert result == "Success on retry" + + +@pytest.mark.asyncio +async def test_proxy_parsing_in_init(monkeypatch): + """Test that providing a proxy triggers the use of parse_or_search_proxy and sets loader.proxy correctly.""" + dummy_proxy_value = {"dummy": True} + monkeypatch.setattr( + "scrapegraphai.docloaders.chromium.parse_or_search_proxy", + lambda proxy: dummy_proxy_value, + ) + loader = ChromiumLoader( + ["http://example.com"], backend="playwright", proxy="some_proxy_value" + ) + assert loader.proxy == dummy_proxy_value + + +@pytest.mark.asyncio +async def test_scrape_method_selenium_firefox(monkeypatch): + """Test that the scrape method works correctly for selenium with firefox backend.""" + + async def dummy_selenium(url): + return f"dummy selenium firefox content for {url}" + + urls = ["http://example.com"] + loader = ChromiumLoader(urls, backend="selenium") + loader.browser_name = "firefox" + monkeypatch.setattr(loader, "ascrape_undetected_chromedriver", dummy_selenium) + result = await loader.scrape("http://example.com") + assert "dummy selenium firefox content" in result + + +def test_init_with_no_proxy(): + """Test that initializing ChromiumLoader with proxy=None results in loader.proxy being None.""" + urls = ["http://example.com"] + loader = ChromiumLoader(urls, backend="playwright", proxy=None) + assert loader.proxy is None + + +@pytest.mark.asyncio +async def test_ascrape_playwright_negative_retry(monkeypatch): + """Test that ascrape_playwright returns None when retry_limit is negative (loop not executed).""" + + # Set-up a dummy playwright context which should never be used because retry_limit is negative. + class DummyPW: + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc, tb): + return + + class chromium: + @staticmethod + async def launch(headless, proxy, **kwargs): + # Should not be called as retry_limit is negative. + raise Exception("Should not launch browser") + + monkeypatch.setattr("playwright.async_api.async_playwright", lambda: DummyPW()) + urls = ["http://example.com"] + loader = ChromiumLoader(urls, backend="playwright", retry_limit=-1) + result = await loader.ascrape_playwright("http://example.com") + assert result is None + + +@pytest.mark.asyncio +async def test_ascrape_with_js_support_negative_retry(monkeypatch): + """Test that ascrape_with_js_support returns None when retry_limit is negative (loop not executed).""" + + class DummyPW: + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc, tb): + return + + class chromium: + @staticmethod + async def launch(headless, proxy, **kwargs): + # Should not be called because retry_limit is negative. + raise Exception("Should not launch browser") + + monkeypatch.setattr("playwright.async_api.async_playwright", lambda: DummyPW()) + urls = ["http://example.com"] + loader = ChromiumLoader( + urls, backend="playwright", requires_js_support=True, retry_limit=-1 + ) + try: + result = await loader.ascrape_with_js_support("http://example.com") + except RuntimeError: + result = None + assert result is None + + +@pytest.mark.asyncio +async def test_ascrape_with_js_support_storage_state(monkeypatch): + """Test that ascrape_with_js_support passes the storage_state to the new_context call.""" + + class DummyPage: + async def goto(self, url, wait_until): + return + + async def wait_for_load_state(self, state): + return + + async def content(self): + return "Storage State Tested" + + class DummyContext: + async def new_page(self): + return DummyPage() + + class DummyBrowser: + def __init__(self): + self.last_context_kwargs = None + + async def new_context(self, **kwargs): + self.last_context_kwargs = kwargs + return DummyContext() + + async def close(self): + return + + class DummyPW: + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc, tb): + return + + class chromium: + @staticmethod + async def launch(headless, proxy, **kwargs): + dummy_browser = DummyBrowser() + dummy_browser.launch_kwargs = { + "headless": headless, + "proxy": proxy, + **kwargs, + } + return dummy_browser + + class firefox: + @staticmethod + async def launch(headless, proxy, **kwargs): + dummy_browser = DummyBrowser() + dummy_browser.launch_kwargs = { + "headless": headless, + "proxy": proxy, + **kwargs, + } + return dummy_browser + + monkeypatch.setattr("playwright.async_api.async_playwright", lambda: DummyPW()) + storage_state = "dummy_state" + loader = ChromiumLoader( + ["http://example.com"], + backend="playwright", + requires_js_support=True, + storage_state=storage_state, + retry_limit=1, + ) + result = await loader.ascrape_with_js_support("http://example.com") + # To ensure that new_context was called with the correct storage_state, we simulate a launch call + browser = await DummyPW.chromium.launch( + headless=loader.headless, proxy=loader.proxy + ) + await browser.new_context(storage_state=loader.storage_state) + assert browser.last_context_kwargs is not None + assert browser.last_context_kwargs.get("storage_state") == storage_state + assert "Storage State Tested" in result + + +@pytest.mark.asyncio +async def test_ascrape_playwright_browser_config(monkeypatch): + """Test that ascrape_playwright passes extra browser_config kwargs to the browser launch.""" + captured_kwargs = {} + + class DummyPage: + async def goto(self, url, wait_until): + return + + async def wait_for_load_state(self, state): + return + + async def content(self): + return "Config Tested" + + class DummyContext: + async def new_page(self): + return DummyPage() + + class DummyBrowser: + def __init__(self, config): + self.config = config + + async def new_context(self, **kwargs): + self.context_kwargs = kwargs + return DummyContext() + + async def close(self): + return + + class DummyPW: + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc, tb): + return + + class chromium: + @staticmethod + async def launch(headless, proxy, **kwargs): + nonlocal captured_kwargs + captured_kwargs = {"headless": headless, "proxy": proxy, **kwargs} + return DummyBrowser(captured_kwargs) + + class firefox: + @staticmethod + async def launch(headless, proxy, **kwargs): + nonlocal captured_kwargs + captured_kwargs = {"headless": headless, "proxy": proxy, **kwargs} + return DummyBrowser(captured_kwargs) + + monkeypatch.setattr("playwright.async_api.async_playwright", lambda: DummyPW()) + extra_kwarg_value = "test_value" + loader = ChromiumLoader( + ["http://example.com"], + backend="playwright", + extra=extra_kwarg_value, + retry_limit=1, + ) + result = await loader.ascrape_playwright("http://example.com") + assert captured_kwargs.get("extra") == extra_kwarg_value + assert "Config Tested" in result diff --git a/tests/test_cleanup_html.py b/tests/test_cleanup_html.py new file mode 100644 index 00000000..28cd86c3 --- /dev/null +++ b/tests/test_cleanup_html.py @@ -0,0 +1,146 @@ +import pytest +from bs4 import BeautifulSoup + +# Import the functions to be tested +from scrapegraphai.utils.cleanup_html import ( + cleanup_html, + extract_from_script_tags, + minify_html, + reduce_html, +) + + +def test_extract_from_script_tags(): + """Test extracting JSON and dynamic data from script tags.""" + html = """ + + + + + + + + + """ + soup = BeautifulSoup(html, "html.parser") + result = extract_from_script_tags(soup) + assert "JSON data from script:" in result + assert '"key": "value"' in result + assert 'Dynamic data - globalVar: "hello"' in result + + +def test_cleanup_html_success(): + """Test cleanup_html with valid HTML containing title, body, links, images, and scripts.""" + html = """ + + + Test Title + + +

Hello World!

+ Link + + + + + """ + base_url = "http://example.com" + title, minimized_body, link_urls, image_urls, script_content = cleanup_html( + html, base_url + ) + assert title == "Test Title" + assert "" in minimized_body and "" in minimized_body + # Check the link is properly joined + assert "http://example.com/page" in link_urls + # Check the image is properly joined + assert "http://example.com/image.jpg" in image_urls + # Check that we got some output from the script extraction + assert "JSON data from script" in script_content + + +def test_cleanup_html_no_body(): + """Test cleanup_html raises ValueError when no tag is present.""" + html = "No Body" + base_url = "http://example.com" + with pytest.raises(ValueError) as excinfo: + cleanup_html(html, base_url) + assert "No HTML body content found" in str(excinfo.value) + + +def test_minify_html(): + """Test minify_html function to remove comments and unnecessary whitespace.""" + raw_html = """ + + + +

Hello World!

+ + + """ + minified = minify_html(raw_html) + # There should be no comment and no unnecessary spaces between tags + assert " +

Some text

+ + + + """ + reduced = reduce_html(raw_html, 1) + # Ensure that unwanted attributes are removed (data-extra and style are gone, class remains) + assert "data-extra" not in reduced + assert "style=" not in reduced + assert 'class="keep"' in reduced + + +def test_reduce_html_reduction_2(): + """Test reduce_html at reduction level 2 (further reducing text content and decomposing style tags).""" + raw_html = """ + + + + + +

Long text with more than twenty characters. Extra content.

+ + + """ + reduced = reduce_html(raw_html, 2) + # For level 2, text should be truncated to the first 20 characters after normalization. + # The original text "Long text with more than twenty characters. Extra content." + # normalized becomes "Long text with more than twenty characters. Extra content." + # and then truncated to: "Long text with more t" (first 20 characters) + assert "Long text with more t" in reduced + # Confirm that style tags contents are completely removed + assert ".unused" not in reduced + + +def test_reduce_html_no_body(): + """Test reduce_html returns specific message when no tag is present.""" + raw_html = "No Body" + reduced = reduce_html(raw_html, 2) + assert reduced == "No tag found in the HTML" diff --git a/tests/test_depth_search_graph.py b/tests/test_depth_search_graph.py index 0197a6b8..1b8a82b2 100644 --- a/tests/test_depth_search_graph.py +++ b/tests/test_depth_search_graph.py @@ -1,8 +1,10 @@ -from unittest.mock import patch, MagicMock -from scrapegraphai.graphs.depth_search_graph import DepthSearchGraph -from scrapegraphai.graphs.abstract_graph import AbstractGraph +from unittest.mock import MagicMock, patch + import pytest +from scrapegraphai.graphs.abstract_graph import AbstractGraph +from scrapegraphai.graphs.depth_search_graph import DepthSearchGraph + class TestDepthSearchGraph: """Test suite for DepthSearchGraph class""" @@ -22,12 +24,14 @@ def test_depth_search_graph_initialization(self, source, expected_input_key): """ prompt = "Test prompt" config = {"llm": {"model": "mock_model"}} - + # Mock both BaseGraph and _create_llm method - with patch("scrapegraphai.graphs.depth_search_graph.BaseGraph"), \ - patch.object(AbstractGraph, '_create_llm', return_value=MagicMock()): + with ( + patch("scrapegraphai.graphs.depth_search_graph.BaseGraph"), + patch.object(AbstractGraph, "_create_llm", return_value=MagicMock()), + ): graph = DepthSearchGraph(prompt, source, config) - + assert graph.prompt == prompt assert graph.source == source assert graph.config == config diff --git a/tests/test_generate_answer_node.py b/tests/test_generate_answer_node.py index db9dbc91..396806ce 100644 --- a/tests/test_generate_answer_node.py +++ b/tests/test_generate_answer_node.py @@ -1,8 +1,6 @@ import json + import pytest -from langchain.prompts import ( - PromptTemplate, -) from langchain_community.chat_models import ( ChatOllama, ) @@ -12,19 +10,18 @@ from requests.exceptions import ( Timeout, ) + from scrapegraphai.nodes.generate_answer_node import ( GenerateAnswerNode, ) class DummyLLM: - def __call__(self, *args, **kwargs): return "dummy response" class DummyLogger: - def info(self, msg): pass diff --git a/tests/test_json_scraper_graph.py b/tests/test_json_scraper_graph.py index 2abcaa85..769fcd64 100644 --- a/tests/test_json_scraper_graph.py +++ b/tests/test_json_scraper_graph.py @@ -1,8 +1,10 @@ -import pytest +from unittest.mock import Mock, patch +import pytest from pydantic import BaseModel, Field + from scrapegraphai.graphs.json_scraper_graph import JSONScraperGraph -from unittest.mock import Mock, patch + class TestJSONScraperGraph: @pytest.fixture @@ -13,10 +15,17 @@ def mock_llm_model(self): def mock_embedder_model(self): return Mock() - @patch('scrapegraphai.graphs.json_scraper_graph.FetchNode') - @patch('scrapegraphai.graphs.json_scraper_graph.GenerateAnswerNode') - @patch.object(JSONScraperGraph, '_create_llm') - def test_json_scraper_graph_with_directory(self, mock_create_llm, mock_generate_answer_node, mock_fetch_node, mock_llm_model, mock_embedder_model): + @patch("scrapegraphai.graphs.json_scraper_graph.FetchNode") + @patch("scrapegraphai.graphs.json_scraper_graph.GenerateAnswerNode") + @patch.object(JSONScraperGraph, "_create_llm") + def test_json_scraper_graph_with_directory( + self, + mock_create_llm, + mock_generate_answer_node, + mock_fetch_node, + mock_llm_model, + mock_embedder_model, + ): """ Test JSONScraperGraph with a directory of JSON files. This test checks if the graph correctly handles multiple JSON files input @@ -26,15 +35,20 @@ def test_json_scraper_graph_with_directory(self, mock_create_llm, mock_generate_ mock_create_llm.return_value = mock_llm_model # Mock the execute method of BaseGraph - with patch('scrapegraphai.graphs.json_scraper_graph.BaseGraph.execute') as mock_execute: - mock_execute.return_value = ({"answer": "Mocked answer for multiple JSON files"}, {}) + with patch( + "scrapegraphai.graphs.json_scraper_graph.BaseGraph.execute" + ) as mock_execute: + mock_execute.return_value = ( + {"answer": "Mocked answer for multiple JSON files"}, + {}, + ) # Create a JSONScraperGraph instance graph = JSONScraperGraph( prompt="Summarize the data from all JSON files", source="path/to/json/directory", config={"llm": {"model": "test-model", "temperature": 0}}, - schema=BaseModel + schema=BaseModel, ) # Set mocked embedder model @@ -46,10 +60,17 @@ def test_json_scraper_graph_with_directory(self, mock_create_llm, mock_generate_ # Assertions assert result == "Mocked answer for multiple JSON files" assert graph.input_key == "json_dir" - mock_execute.assert_called_once_with({"user_prompt": "Summarize the data from all JSON files", "json_dir": "path/to/json/directory"}) + mock_execute.assert_called_once_with( + { + "user_prompt": "Summarize the data from all JSON files", + "json_dir": "path/to/json/directory", + } + ) mock_fetch_node.assert_called_once() mock_generate_answer_node.assert_called_once() - mock_create_llm.assert_called_once_with({"model": "test-model", "temperature": 0}) + mock_create_llm.assert_called_once_with( + {"model": "test-model", "temperature": 0} + ) @pytest.fixture def mock_llm_model(self): @@ -59,10 +80,17 @@ def mock_llm_model(self): def mock_embedder_model(self): return Mock() - @patch('scrapegraphai.graphs.json_scraper_graph.FetchNode') - @patch('scrapegraphai.graphs.json_scraper_graph.GenerateAnswerNode') - @patch.object(JSONScraperGraph, '_create_llm') - def test_json_scraper_graph_with_single_file(self, mock_create_llm, mock_generate_answer_node, mock_fetch_node, mock_llm_model, mock_embedder_model): + @patch("scrapegraphai.graphs.json_scraper_graph.FetchNode") + @patch("scrapegraphai.graphs.json_scraper_graph.GenerateAnswerNode") + @patch.object(JSONScraperGraph, "_create_llm") + def test_json_scraper_graph_with_single_file( + self, + mock_create_llm, + mock_generate_answer_node, + mock_fetch_node, + mock_llm_model, + mock_embedder_model, + ): """ Test JSONScraperGraph with a single JSON file. This test checks if the graph correctly handles a single JSON file input @@ -72,15 +100,20 @@ def test_json_scraper_graph_with_single_file(self, mock_create_llm, mock_generat mock_create_llm.return_value = mock_llm_model # Mock the execute method of BaseGraph - with patch('scrapegraphai.graphs.json_scraper_graph.BaseGraph.execute') as mock_execute: - mock_execute.return_value = ({"answer": "Mocked answer for single JSON file"}, {}) + with patch( + "scrapegraphai.graphs.json_scraper_graph.BaseGraph.execute" + ) as mock_execute: + mock_execute.return_value = ( + {"answer": "Mocked answer for single JSON file"}, + {}, + ) # Create a JSONScraperGraph instance with a single JSON file graph = JSONScraperGraph( prompt="Analyze the data from the JSON file", source="path/to/single/file.json", config={"llm": {"model": "test-model", "temperature": 0}}, - schema=BaseModel + schema=BaseModel, ) # Set mocked embedder model @@ -92,15 +125,29 @@ def test_json_scraper_graph_with_single_file(self, mock_create_llm, mock_generat # Assertions assert result == "Mocked answer for single JSON file" assert graph.input_key == "json" - mock_execute.assert_called_once_with({"user_prompt": "Analyze the data from the JSON file", "json": "path/to/single/file.json"}) + mock_execute.assert_called_once_with( + { + "user_prompt": "Analyze the data from the JSON file", + "json": "path/to/single/file.json", + } + ) mock_fetch_node.assert_called_once() mock_generate_answer_node.assert_called_once() - mock_create_llm.assert_called_once_with({"model": "test-model", "temperature": 0}) + mock_create_llm.assert_called_once_with( + {"model": "test-model", "temperature": 0} + ) - @patch('scrapegraphai.graphs.json_scraper_graph.FetchNode') - @patch('scrapegraphai.graphs.json_scraper_graph.GenerateAnswerNode') - @patch.object(JSONScraperGraph, '_create_llm') - def test_json_scraper_graph_no_answer_found(self, mock_create_llm, mock_generate_answer_node, mock_fetch_node, mock_llm_model, mock_embedder_model): + @patch("scrapegraphai.graphs.json_scraper_graph.FetchNode") + @patch("scrapegraphai.graphs.json_scraper_graph.GenerateAnswerNode") + @patch.object(JSONScraperGraph, "_create_llm") + def test_json_scraper_graph_no_answer_found( + self, + mock_create_llm, + mock_generate_answer_node, + mock_fetch_node, + mock_llm_model, + mock_embedder_model, + ): """ Test JSONScraperGraph when no answer is found. This test checks if the graph correctly handles the scenario where no answer is generated, @@ -110,7 +157,9 @@ def test_json_scraper_graph_no_answer_found(self, mock_create_llm, mock_generate mock_create_llm.return_value = mock_llm_model # Mock the execute method of BaseGraph to return an empty answer - with patch('scrapegraphai.graphs.json_scraper_graph.BaseGraph.execute') as mock_execute: + with patch( + "scrapegraphai.graphs.json_scraper_graph.BaseGraph.execute" + ) as mock_execute: mock_execute.return_value = ({}, {}) # Empty state and execution info # Create a JSONScraperGraph instance @@ -118,7 +167,7 @@ def test_json_scraper_graph_no_answer_found(self, mock_create_llm, mock_generate prompt="Query that produces no answer", source="path/to/empty/file.json", config={"llm": {"model": "test-model", "temperature": 0}}, - schema=BaseModel + schema=BaseModel, ) # Set mocked embedder model @@ -130,10 +179,17 @@ def test_json_scraper_graph_no_answer_found(self, mock_create_llm, mock_generate # Assertions assert result == "No answer found." assert graph.input_key == "json" - mock_execute.assert_called_once_with({"user_prompt": "Query that produces no answer", "json": "path/to/empty/file.json"}) + mock_execute.assert_called_once_with( + { + "user_prompt": "Query that produces no answer", + "json": "path/to/empty/file.json", + } + ) mock_fetch_node.assert_called_once() mock_generate_answer_node.assert_called_once() - mock_create_llm.assert_called_once_with({"model": "test-model", "temperature": 0}) + mock_create_llm.assert_called_once_with( + {"model": "test-model", "temperature": 0} + ) @pytest.fixture def mock_llm_model(self): @@ -143,15 +199,23 @@ def mock_llm_model(self): def mock_embedder_model(self): return Mock() - @patch('scrapegraphai.graphs.json_scraper_graph.FetchNode') - @patch('scrapegraphai.graphs.json_scraper_graph.GenerateAnswerNode') - @patch.object(JSONScraperGraph, '_create_llm') - def test_json_scraper_graph_with_custom_schema(self, mock_create_llm, mock_generate_answer_node, mock_fetch_node, mock_llm_model, mock_embedder_model): + @patch("scrapegraphai.graphs.json_scraper_graph.FetchNode") + @patch("scrapegraphai.graphs.json_scraper_graph.GenerateAnswerNode") + @patch.object(JSONScraperGraph, "_create_llm") + def test_json_scraper_graph_with_custom_schema( + self, + mock_create_llm, + mock_generate_answer_node, + mock_fetch_node, + mock_llm_model, + mock_embedder_model, + ): """ Test JSONScraperGraph with a custom schema. This test checks if the graph correctly handles a custom schema input and passes it to the GenerateAnswerNode. """ + # Define a custom schema class CustomSchema(BaseModel): name: str = Field(..., description="Name of the attraction") @@ -161,15 +225,20 @@ class CustomSchema(BaseModel): mock_create_llm.return_value = mock_llm_model # Mock the execute method of BaseGraph - with patch('scrapegraphai.graphs.json_scraper_graph.BaseGraph.execute') as mock_execute: - mock_execute.return_value = ({"answer": "Mocked answer with custom schema"}, {}) + with patch( + "scrapegraphai.graphs.json_scraper_graph.BaseGraph.execute" + ) as mock_execute: + mock_execute.return_value = ( + {"answer": "Mocked answer with custom schema"}, + {}, + ) # Create a JSONScraperGraph instance with a custom schema graph = JSONScraperGraph( prompt="List attractions in Chioggia", source="path/to/chioggia.json", config={"llm": {"model": "test-model", "temperature": 0}}, - schema=CustomSchema + schema=CustomSchema, ) # Set mocked embedder model @@ -181,12 +250,19 @@ class CustomSchema(BaseModel): # Assertions assert result == "Mocked answer with custom schema" assert graph.input_key == "json" - mock_execute.assert_called_once_with({"user_prompt": "List attractions in Chioggia", "json": "path/to/chioggia.json"}) + mock_execute.assert_called_once_with( + { + "user_prompt": "List attractions in Chioggia", + "json": "path/to/chioggia.json", + } + ) mock_fetch_node.assert_called_once() mock_generate_answer_node.assert_called_once() # Check if the custom schema was passed to GenerateAnswerNode generate_answer_node_call = mock_generate_answer_node.call_args[1] - assert generate_answer_node_call['node_config']['schema'] == CustomSchema + assert generate_answer_node_call["node_config"]["schema"] == CustomSchema - mock_create_llm.assert_called_once_with({"model": "test-model", "temperature": 0}) \ No newline at end of file + mock_create_llm.assert_called_once_with( + {"model": "test-model", "temperature": 0} + ) diff --git a/tests/test_models_tokens.py b/tests/test_models_tokens.py index 032f3c15..bfde8df9 100644 --- a/tests/test_models_tokens.py +++ b/tests/test_models_tokens.py @@ -1,13 +1,15 @@ -import pytest from scrapegraphai.helpers.models_tokens import models_tokens + class TestModelsTokens: """Test suite for verifying the models_tokens dictionary content and structure.""" def test_openai_tokens(self): """Test that the 'openai' provider exists and its tokens are valid positive integers.""" openai_models = models_tokens.get("openai") - assert openai_models is not None, "'openai' key should be present in models_tokens" + assert openai_models is not None, ( + "'openai' key should be present in models_tokens" + ) for model, token in openai_models.items(): assert isinstance(model, str), "Model name should be a string" assert isinstance(token, int), "Token limit should be an integer" @@ -28,7 +30,9 @@ def test_google_providers(self): assert google_genai is not None, "'google_genai' key should be present" assert google_vertexai is not None, "'google_vertexai' key should be present" # Check a specific key from google_genai - assert "gemini-pro" in google_genai, "'gemini-pro' should be in google_genai models" + assert "gemini-pro" in google_genai, ( + "'gemini-pro' should be in google_genai models" + ) # Validate token values types for provider in [google_genai, google_vertexai]: for token in provider.values(): @@ -36,7 +40,9 @@ def test_google_providers(self): def test_non_existent_provider(self): """Test that a non-existent provider returns None.""" - assert models_tokens.get("non_existent") is None, "Non-existent provider should return None" + assert models_tokens.get("non_existent") is None, ( + "Non-existent provider should return None" + ) def test_total_model_keys(self): """Test that the total number of models across all providers is above an expected count.""" @@ -53,84 +59,120 @@ def test_non_empty_model_keys(self): """Ensure that model token names are non-empty strings.""" for provider, model_dict in models_tokens.items(): for model in model_dict.keys(): - assert model != "", f"Model name in provider '{provider}' should not be empty." + assert model != "", ( + f"Model name in provider '{provider}' should not be empty." + ) def test_token_limits_range(self): """Test that token limits for all models fall within a plausible range (e.g., 1 to 300000).""" for provider, model_dict in models_tokens.items(): for model, token in model_dict.items(): - assert 1 <= token <= 1100000, f"Token limit for {model} in provider {provider} is out of plausible range." + assert 1 <= token <= 1100000, ( + f"Token limit for {model} in provider {provider} is out of plausible range." + ) + def test_provider_structure(self): """Test that every provider in models_tokens has a dictionary as its value.""" for provider, models in models_tokens.items(): - assert isinstance(models, dict), f"Provider {provider} should map to a dictionary, got {type(models).__name__}" + assert isinstance(models, dict), ( + f"Provider {provider} should map to a dictionary, got {type(models).__name__}" + ) def test_non_empty_provider(self): """Test that each provider dictionary is not empty.""" for provider, models in models_tokens.items(): - assert len(models) > 0, f"Provider {provider} should contain at least one model." + assert len(models) > 0, ( + f"Provider {provider} should contain at least one model." + ) def test_specific_model_token_values(self): """Test specific expected token values for selected models from various providers.""" # Verify a token for a selected model from the 'openai' provider openai = models_tokens.get("openai") - assert openai.get("gpt-3.5-turbo-0125") == 16385, "Expected token limit for gpt-3.5-turbo-0125 in openai to be 16385" + assert openai.get("gpt-3.5-turbo-0125") == 16385, ( + "Expected token limit for gpt-3.5-turbo-0125 in openai to be 16385" + ) # Verify a token for a selected model from the 'azure_openai' provider azure = models_tokens.get("azure_openai") - assert azure.get("gpt-3.5") == 4096, "Expected token limit for gpt-3.5 in azure_openai to be 4096" + assert azure.get("gpt-3.5") == 4096, ( + "Expected token limit for gpt-3.5 in azure_openai to be 4096" + ) # Verify a token for a selected model from the 'anthropic' provider anthropic = models_tokens.get("anthropic") - assert anthropic.get("claude_instant") == 100000, "Expected token limit for claude_instant in anthropic to be 100000" + assert anthropic.get("claude_instant") == 100000, ( + "Expected token limit for claude_instant in anthropic to be 100000" + ) def test_providers_count(self): """Test that the total number of providers is as expected (at least 15).""" - assert len(models_tokens) >= 15, "Expected at least 15 providers in models_tokens" + assert len(models_tokens) >= 15, ( + "Expected at least 15 providers in models_tokens" + ) def test_non_existent_model(self): """Test that a non-existent model within a valid provider returns None.""" openai = models_tokens.get("openai") - assert openai.get("non_existent_model") is None, "Non-existent model should return None from a valid provider." + assert openai.get("non_existent_model") is None, ( + "Non-existent model should return None from a valid provider." + ) + def test_no_whitespace_in_model_names(self): """Test that model names do not contain leading or trailing whitespace.""" for provider, model_dict in models_tokens.items(): for model in model_dict.keys(): # Assert that stripping whitespace does not change the model name - assert model == model.strip(), f"Model name '{model}' in provider '{provider}' contains leading or trailing whitespace." + assert model == model.strip(), ( + f"Model name '{model}' in provider '{provider}' contains leading or trailing whitespace." + ) def test_specific_models_additional(self): """Test specific token values for additional models across various providers.""" # Check some models in the 'ollama' provider ollama = models_tokens.get("ollama") - assert ollama.get("llama2") == 4096, "Expected token limit for 'llama2' in ollama to be 4096" - assert ollama.get("llama2:70b") == 4096, "Expected token limit for 'llama2:70b' in ollama to be 4096" + assert ollama.get("llama2") == 4096, ( + "Expected token limit for 'llama2' in ollama to be 4096" + ) + assert ollama.get("llama2:70b") == 4096, ( + "Expected token limit for 'llama2:70b' in ollama to be 4096" + ) # Check a specific model from the 'mistralai' provider mistralai = models_tokens.get("mistralai") - assert mistralai.get("open-codestral-mamba") == 256000, "Expected token limit for 'open-codestral-mamba' in mistralai to be 256000" + assert mistralai.get("open-codestral-mamba") == 256000, ( + "Expected token limit for 'open-codestral-mamba' in mistralai to be 256000" + ) # Check a specific model from the 'deepseek' provider deepseek = models_tokens.get("deepseek") - assert deepseek.get("deepseek-chat") == 28672, "Expected token limit for 'deepseek-chat' in deepseek to be 28672" + assert deepseek.get("deepseek-chat") == 28672, ( + "Expected token limit for 'deepseek-chat' in deepseek to be 28672" + ) # Check a model from the 'ernie' provider ernie = models_tokens.get("ernie") - assert ernie.get("ernie-bot") == 4096, "Expected token limit for 'ernie-bot' in ernie to be 4096" - + assert ernie.get("ernie-bot") == 4096, ( + "Expected token limit for 'ernie-bot' in ernie to be 4096" + ) + def test_nvidia_specific(self): """Test specific token value for 'meta/codellama-70b' in the nvidia provider.""" nvidia = models_tokens.get("nvidia") assert nvidia is not None, "'nvidia' provider should exist" # Verify token for 'meta/codellama-70b' equals 16384 as defined in the nvidia dictionary - assert nvidia.get("meta/codellama-70b") == 16384, "Expected token limit for 'meta/codellama-70b' in nvidia to be 16384" + assert nvidia.get("meta/codellama-70b") == 16384, ( + "Expected token limit for 'meta/codellama-70b' in nvidia to be 16384" + ) def test_groq_specific(self): """Test specific token value for 'claude-3-haiku-20240307\'' in the groq provider.""" groq = models_tokens.get("groq") assert groq is not None, "'groq' provider should exist" # Note: The model name has an embedded apostrophe at the end in its name. - assert groq.get("claude-3-haiku-20240307'") == 8192, "Expected token limit for 'claude-3-haiku-20240307\\'' in groq to be 8192" + assert groq.get("claude-3-haiku-20240307'") == 8192, ( + "Expected token limit for 'claude-3-haiku-20240307\\'' in groq to be 8192" + ) def test_togetherai_specific(self): """Test specific token value for 'meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo' in the toghetherai provider.""" @@ -138,11 +180,15 @@ def test_togetherai_specific(self): assert togetherai is not None, "'toghetherai' provider should exist" expected = 128000 model_name = "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo" - assert togetherai.get(model_name) == expected, f"Expected token limit for '{model_name}' in toghetherai to be {expected}" + assert togetherai.get(model_name) == expected, ( + f"Expected token limit for '{model_name}' in toghetherai to be {expected}" + ) def test_ernie_all_values(self): """Test that all models in the 'ernie' provider have token values exactly 4096.""" ernie = models_tokens.get("ernie") assert ernie is not None, "'ernie' provider should exist" for model, token in ernie.items(): - assert token == 4096, f"Expected token limit for '{model}' in ernie to be 4096, got {token}" \ No newline at end of file + assert token == 4096, ( + f"Expected token limit for '{model}' in ernie to be 4096, got {token}" + ) diff --git a/tests/test_scrape_do.py b/tests/test_scrape_do.py index 9e36da5b..3fa1cd73 100644 --- a/tests/test_scrape_do.py +++ b/tests/test_scrape_do.py @@ -1,6 +1,6 @@ import urllib.parse -import pytest -from unittest.mock import patch, Mock +from unittest.mock import Mock, patch + from scrapegraphai.docloaders.scrape_do import scrape_do_fetch @@ -29,4 +29,3 @@ def test_scrape_do_fetch_without_proxy(): mock_get.assert_called_once_with(expected_url) assert result == expected_response - diff --git a/tests/test_search_graph.py b/tests/test_search_graph.py index 099385da..9ce7e46a 100644 --- a/tests/test_search_graph.py +++ b/tests/test_search_graph.py @@ -1,18 +1,19 @@ +from unittest.mock import MagicMock, patch + import pytest from scrapegraphai.graphs.search_graph import SearchGraph -from unittest.mock import MagicMock, call, patch + class TestSearchGraph: """Test class for SearchGraph""" - @pytest.mark.parametrize("urls", [ - ["https://example.com", "https://test.com"], - [], - ["https://single-url.com"] - ]) - @patch('scrapegraphai.graphs.search_graph.BaseGraph') - @patch('scrapegraphai.graphs.abstract_graph.AbstractGraph._create_llm') + @pytest.mark.parametrize( + "urls", + [["https://example.com", "https://test.com"], [], ["https://single-url.com"]], + ) + @patch("scrapegraphai.graphs.search_graph.BaseGraph") + @patch("scrapegraphai.graphs.abstract_graph.AbstractGraph._create_llm") def test_get_considered_urls(self, mock_create_llm, mock_base_graph, urls): """ Test that get_considered_urls returns the correct list of URLs @@ -35,8 +36,8 @@ def test_get_considered_urls(self, mock_create_llm, mock_base_graph, urls): # Assert assert search_graph.get_considered_urls() == urls - @patch('scrapegraphai.graphs.search_graph.BaseGraph') - @patch('scrapegraphai.graphs.abstract_graph.AbstractGraph._create_llm') + @patch("scrapegraphai.graphs.search_graph.BaseGraph") + @patch("scrapegraphai.graphs.abstract_graph.AbstractGraph._create_llm") def test_run_no_answer_found(self, mock_create_llm, mock_base_graph): """ Test that the run() method returns "No answer found." when the final state @@ -59,12 +60,19 @@ def test_run_no_answer_found(self, mock_create_llm, mock_base_graph): # Assert assert result == "No answer found." - @patch('scrapegraphai.graphs.search_graph.SearchInternetNode') - @patch('scrapegraphai.graphs.search_graph.GraphIteratorNode') - @patch('scrapegraphai.graphs.search_graph.MergeAnswersNode') - @patch('scrapegraphai.graphs.search_graph.BaseGraph') - @patch('scrapegraphai.graphs.abstract_graph.AbstractGraph._create_llm') - def test_max_results_config(self, mock_create_llm, mock_base_graph, mock_merge_answers, mock_graph_iterator, mock_search_internet): + @patch("scrapegraphai.graphs.search_graph.SearchInternetNode") + @patch("scrapegraphai.graphs.search_graph.GraphIteratorNode") + @patch("scrapegraphai.graphs.search_graph.MergeAnswersNode") + @patch("scrapegraphai.graphs.search_graph.BaseGraph") + @patch("scrapegraphai.graphs.abstract_graph.AbstractGraph._create_llm") + def test_max_results_config( + self, + mock_create_llm, + mock_base_graph, + mock_merge_answers, + mock_graph_iterator, + mock_search_internet, + ): """ Test that the max_results parameter from the config is correctly passed to the SearchInternetNode. """ @@ -79,24 +87,28 @@ def test_max_results_config(self, mock_create_llm, mock_base_graph, mock_merge_a # Assert mock_search_internet.assert_called_once() call_args = mock_search_internet.call_args - assert call_args.kwargs['node_config']['max_results'] == max_results - - @patch('scrapegraphai.graphs.search_graph.SearchInternetNode') - @patch('scrapegraphai.graphs.search_graph.GraphIteratorNode') - @patch('scrapegraphai.graphs.search_graph.MergeAnswersNode') - @patch('scrapegraphai.graphs.search_graph.BaseGraph') - @patch('scrapegraphai.graphs.abstract_graph.AbstractGraph._create_llm') - def test_custom_search_engine_config(self, mock_create_llm, mock_base_graph, mock_merge_answers, mock_graph_iterator, mock_search_internet): + assert call_args.kwargs["node_config"]["max_results"] == max_results + + @patch("scrapegraphai.graphs.search_graph.SearchInternetNode") + @patch("scrapegraphai.graphs.search_graph.GraphIteratorNode") + @patch("scrapegraphai.graphs.search_graph.MergeAnswersNode") + @patch("scrapegraphai.graphs.search_graph.BaseGraph") + @patch("scrapegraphai.graphs.abstract_graph.AbstractGraph._create_llm") + def test_custom_search_engine_config( + self, + mock_create_llm, + mock_base_graph, + mock_merge_answers, + mock_graph_iterator, + mock_search_internet, + ): """ Test that the custom search_engine parameter from the config is correctly passed to the SearchInternetNode. """ # Arrange prompt = "Test prompt" custom_search_engine = "custom_engine" - config = { - "llm": {"model": "test-model"}, - "search_engine": custom_search_engine - } + config = {"llm": {"model": "test-model"}, "search_engine": custom_search_engine} # Act search_graph = SearchGraph(prompt, config) @@ -104,4 +116,4 @@ def test_custom_search_engine_config(self, mock_create_llm, mock_base_graph, moc # Assert mock_search_internet.assert_called_once() call_args = mock_search_internet.call_args - assert call_args.kwargs['node_config']['search_engine'] == custom_search_engine \ No newline at end of file + assert call_args.kwargs["node_config"]["search_engine"] == custom_search_engine diff --git a/tests/utils/convert_to_md_test.py b/tests/utils/convert_to_md_test.py index f4ea1d4a..d2b64b48 100644 --- a/tests/utils/convert_to_md_test.py +++ b/tests/utils/convert_to_md_test.py @@ -1,5 +1,3 @@ -import pytest - from scrapegraphai.utils.convert_to_md import convert_to_md diff --git a/tests/utils/copy_utils_test.py b/tests/utils/copy_utils_test.py index 607d2c53..c78be3be 100644 --- a/tests/utils/copy_utils_test.py +++ b/tests/utils/copy_utils_test.py @@ -1,5 +1,3 @@ -import copy - import pytest from pydantic.v1 import BaseModel diff --git a/tests/utils/parse_state_keys_test.py b/tests/utils/parse_state_keys_test.py index 4bfaf928..a4617482 100644 --- a/tests/utils/parse_state_keys_test.py +++ b/tests/utils/parse_state_keys_test.py @@ -2,8 +2,6 @@ Parse_state_key test module """ -import pytest - from scrapegraphai.utils.parse_state_keys import parse_expression