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ScrapeGraphAI is a *web scraping* python library which uses LLM and direct graph logic to create scraping pipelines for websites, documents and XML files.
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ScrapeGraphAI is a *web scraping* python library that uses LLM and direct graph logic to create scraping pipelines for websites, documents and XML files.
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Just say which information you want to extract and the library will do it for you!
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<palign="center">
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## 🚀 Quick install
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The reference page for Scrapegraph-ai is avaible on the official page of pypy: [pypi](https://pypi.org/project/scrapegraphai/).
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The reference page for Scrapegraph-ai is available on the official page of pypy: [pypi](https://pypi.org/project/scrapegraphai/).
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```bash
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pip install scrapegraphai
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You can use the `SmartScraper` class to extract information from a website using a prompt.
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The `SmartScraper` class is a direct graph implementation that uses the most common nodes present in a web scraping pipeline. For more information, please see the [documentation](https://scrapegraph-ai.readthedocs.io/en/latest/).
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### Case 1: Extracting informations using Ollama
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### Case 1: Extracting information using Ollama
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Remember to download the model on Ollama separately!
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```python
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from scrapegraphai.graphs import SmartScraperGraph
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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 arbitrarily
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"base_url": "http://localhost:11434", # set Ollama URL arbitrarily
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},
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"embeddings": {
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"model": "ollama/nomic-embed-text",
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"temperature": 0,
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"base_url": "http://localhost:11434", # set ollama URL arbitrarily
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"base_url": "http://localhost:11434", # set Ollama URL arbitrarily
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}
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}
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```
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### Case 2: Extracting informations using Docker
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### Case 2: Extracting information using Docker
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Note: before using the local model remeber to create the docker container!
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Note: before using the local model remember to create the docker container!
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```text
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docker-compose up -d
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docker exec -it ollama ollama run stablelm-zephyr
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```
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### Case 3: Extracting informations using Openai model
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### Case 3: Extracting information using Openai model
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```python
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from scrapegraphai.graphs import SmartScraperGraph
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OPENAI_API_KEY="YOUR_API_KEY"
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print(result)
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```
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### Case 4: Extracting informations using Gemini
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### Case 4: Extracting information using Gemini
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```python
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from scrapegraphai.graphs import SmartScraperGraph
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GOOGLE_APIKEY="YOUR_API_KEY"
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print(result)
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```
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The output for alle 3 the cases will be a dictionary with the extracted information, for example:
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The output for all 3 the cases will be a dictionary with the extracted information, for example:
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```bash
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{
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## 🤝 Contributing
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Fell free to contribute and join our Discord server to discuss with us improvements and give us suggestions!
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Feel free to contribute and join our Discord server to discuss with us improvements and give us suggestions!
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For more information, please see the [contributing guidelines](https://github.com/VinciGit00/Scrapegraph-ai/blob/main/CONTRIBUTING.md).
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Please see the [contributing guidelines](https://github.com/VinciGit00/Scrapegraph-ai/blob/main/CONTRIBUTING.md).
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