92 lines
3.2 KiB
Plaintext
92 lines
3.2 KiB
Plaintext
---
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title: '🚀 Quickstart'
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description: '💡 Start building LLM powered apps under 30 seconds'
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---
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Embedchain is a Data Platform for LLMs - load, index, retrieve, and sync any unstructured data. Using embedchain, you can easily create LLM powered apps over any data.
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Install embedchain python package:
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```bash
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pip install embedchain
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```
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<Tip>
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Embedchain now supports OpenAI's latest `gpt-4-turbo` model. Checkout the [docs here](/get-started/faq#how-to-use-gpt-4-turbo-model-released-on-openai-devday) on how to use it.
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</Tip>
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Creating an app involves 3 steps:
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<Steps>
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<Step title="⚙️ Import app instance">
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```python
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from embedchain import Pipeline as App
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app = App()
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```
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</Step>
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<Step title="🗃️ Add data sources">
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```python
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# Add different data sources
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app.add("https://en.wikipedia.org/wiki/Elon_Musk")
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app.add("https://www.forbes.com/profile/elon-musk")
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# You can also add local data sources such as pdf, csv files etc.
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# app.add("/path/to/file.pdf")
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```
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</Step>
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<Step title="💬 Query or chat or search context on your data">
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```python
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app.query("What is the net worth of Elon Musk today?")
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# Answer: The net worth of Elon Musk today is $258.7 billion.
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```
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</Step>
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<Step title="🚀 (Optional) Deploy your pipeline to Embedchain Platform">
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```python
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app.deploy()
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# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
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# ec-xxxxxx
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# 🛠️ Creating pipeline on the platform...
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# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
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# 🛠️ Adding data to your pipeline...
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# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
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```
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</Step>
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</Steps>
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Putting it together, you can run your first app using the following code. Make sure to set the `OPENAI_API_KEY` 🔑 environment variable in the code.
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```python
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import os
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from embedchain import Pipeline as App
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os.environ["OPENAI_API_KEY"] = "xxx"
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app = App()
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# Add different data sources
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app.add("https://en.wikipedia.org/wiki/Elon_Musk")
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app.add("https://www.forbes.com/profile/elon-musk")
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# You can also add local data sources such as pdf, csv files etc.
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# app.add("/path/to/file.pdf")
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response = app.query("What is the net worth of Elon Musk today?")
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print(response)
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# Answer: The net worth of Elon Musk today is $258.7 billion.
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app.deploy()
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# 🔑 Enter your Embedchain API key. You can find the API key at https://app.embedchain.ai/settings/keys/
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# ec-xxxxxx
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# 🛠️ Creating pipeline on the platform...
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# 🎉🎉🎉 Pipeline created successfully! View your pipeline: https://app.embedchain.ai/pipelines/xxxxx
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# 🛠️ Adding data to your pipeline...
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# ✅ Data of type: web_page, value: https://www.forbes.com/profile/elon-musk added successfully.
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```
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You can try it out yourself using the following Google Colab notebook:
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<a href="https://colab.research.google.com/drive/17ON1LPonnXAtLaZEebnOktstB_1cJJmh?usp=sharing">
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<img src="https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667" alt="Open in Colab" />
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</a>
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