embedchain/docs/get-started/quickstart.mdx
First install the Python package:
pip install embedchain
Once you have installed the package, depending upon your preference you can either use:
<CardGroup cols={2}> <Card title="Open Source Models" icon="osi" href="#open-source-models"> This includes Open source LLMs like Mistral, Llama, etc.Free to use, and runs locally on your machine. </Card> <Card title="Paid Models" icon="dollar-sign" href="#paid-models" color="#4A154B"> This includes paid LLMs like GPT 4, Claude, etc.
Cost money and are accessible via an API.
This section gives a quickstart example of using Mistral as the Open source LLM and Sentence transformers as the Open source embedding model. These models are free and run mostly on your local machine.
We are using Mistral hosted at Hugging Face, so will you need a Hugging Face token to run this example. Its free and you can create one here.
<CodeGroup> ```python huggingface_demo.py import os # Replace this with your HF token os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "hf_xxxx"from embedchain import App
config = { 'llm': { 'provider': 'huggingface', 'config': { 'model': 'mistralai/Mistral-7B-Instruct-v0.2', 'top_p': 0.5 } }, 'embedder': { 'provider': 'huggingface', 'config': { 'model': 'sentence-transformers/all-mpnet-base-v2' } } } app = App.from_config(config=config) app.add("https://www.forbes.com/profile/elon-musk") app.add("https://en.wikipedia.org/wiki/Elon_Musk") app.query("What is the net worth of Elon Musk today?")
</CodeGroup>
## Paid Models
In this section, we will use both LLM and embedding model from OpenAI.
```python openai_demo.py
import os
from embedchain import App
# Replace this with your OpenAI key
os.environ["OPENAI_API_KEY"] = "sk-xxxx"
app = App()
app.add("https://www.forbes.com/profile/elon-musk")
app.add("https://en.wikipedia.org/wiki/Elon_Musk")
app.query("What is the net worth of Elon Musk today?")
# Answer: The net worth of Elon Musk today is $258.7 billion.
Now that you have created your first app, you can follow any of the links: