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***dependecies:** add script to auto-update requirements ([3289c7b](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/3289c7bf5ec58ac3d04e9e5e8e654af9abcee228))
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***ci:** set up workflow for requirements auto-update ([295fc28](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/295fc28ceb02c78198f7fbe678352503b3259b6b))
***dependecies:** add script to auto-update requirements ([3289c7b](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/3289c7bf5ec58ac3d04e9e5e8e654af9abcee228))
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***ci:** set up workflow for requirements auto-update ([295fc28](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/295fc28ceb02c78198f7fbe678352503b3259b6b))
[](https://colab.research.google.com/drive/1sEZBonBMGP44CtO6GQTwAlL0BGJXjtfd?usp=sharing)
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## 📖 Documentation
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The documentation for ScrapeGraphAI can be found [here](https://scrapegraph-ai.readthedocs.io/en/latest/).
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Check out also the Docusaurus [here](https://scrapegraph-doc.onrender.com/).
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## 💻 Usage
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There are multiple standard scraping pipelines that can be used to extract information from a website (or local file):
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-`SmartScraperGraph`: single-page scraper that only needs a user prompt and an input source;
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-`SearchGraph`: multi-page scraper that extracts information from the top n search results of a search engine;
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-`SpeechGraph`: single-page scraper that extracts information from a website and generates an audio file.
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-`ScriptCreatorGraph`: single-page scraper that extracts information from a website and generates a Python script.
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There are multiple standard scraping pipelines that can be used to extract information from a website (or local file).
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-`SmartScraperMultiGraph`: multi-page scraper that extracts information from multiple pages given a single prompt and a list of sources;
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-`ScriptCreatorMultiGraph`: multi-page scraper that generates a Python script for extracting information from multiple pages given a single prompt and a list of sources.
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It is possible to use different LLM through APIs, such as **OpenAI**, **Groq**, **Azure** and **Gemini**, or local models using **Ollama**.
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The most common one is the `SmartScraperGraph`, which extracts information from a single page given a user prompt and a source URL.
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### Case 1: SmartScraper using Local Models
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Remember to have [Ollama](https://ollama.com/) installed and download the models using the **ollama pull** command.
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```python
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import json
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from scrapegraphai.graphs import SmartScraperGraph
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# Define the configuration for the scraping pipeline
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graph_config = {
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"llm": {
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"model": "ollama/mistral",
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"temperature": 0,
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"format": "json", # Ollama needs the format to be specified explicitly
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"base_url": "http://localhost:11434", # set Ollama URL
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},
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"embeddings": {
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"model": "ollama/nomic-embed-text",
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"base_url": "http://localhost:11434", # set Ollama URL
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"api_key": "YOUR_OPENAI_APIKEY",
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"model": "gpt-4o-mini",
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},
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"verbose": True,
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"headless": False,
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}
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# Create the SmartScraperGraph instance
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smart_scraper_graph = SmartScraperGraph(
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prompt="List me all the projects with their descriptions",
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# also accepts a string with the already downloaded HTML code
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source="https://perinim.github.io/projects",
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prompt="Find some information about what does the company do, the name and a contact email.",
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source="https://scrapegraphai.com/",
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config=graph_config
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)
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# Run the pipeline
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result = smart_scraper_graph.run()
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print(result)
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print(json.dumps(result, indent=4))
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```
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The output will be a list of projects with their descriptions like the following:
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The output will be a dictionary like the following:
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```python
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{'projects': [{'title': 'Rotary Pendulum RL', 'description': 'Open Source project aimed at controlling a real life rotary pendulum using RL algorithms'}, {'title': 'DQN Implementation from scratch', 'description': 'Developed a Deep Q-Network algorithm to train a simple and double pendulum'}, ...]}
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```
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### Case 2: SearchGraph using Mixed Models
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We use **Groq** for the LLM and **Ollama** for the embeddings.
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```python
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from scrapegraphai.graphs import SearchGraph
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# Define the configuration for the graph
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graph_config = {
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"llm": {
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"model": "groq/gemma-7b-it",
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"api_key": "GROQ_API_KEY",
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"temperature": 0
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},
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"embeddings": {
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"model": "ollama/nomic-embed-text",
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"base_url": "http://localhost:11434", # set ollama URL arbitrarily
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},
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"max_results": 5,
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{
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"company": "ScrapeGraphAI",
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"name": "ScrapeGraphAI Extracting content from websites and local documents using LLM",
[](https://colab.research.google.com/drive/1sEZBonBMGP44CtO6GQTwAlL0BGJXjtfd?usp=sharing)
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speech_graph = SpeechGraph(
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prompt="Make a detailed audio summary of the projects.",
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source="https://perinim.github.io/projects/",
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config=graph_config,
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)
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## 📖 Documentation
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result = speech_graph.run()
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print(result)
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The documentation for ScrapeGraphAI can be found [here](https://scrapegraph-ai.readthedocs.io/en/latest/).
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```
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Check out also the Docusaurus [here](https://scrapegraph-doc.onrender.com/).
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The output will be an audio file with the summary of the projects on the page.
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