❓ FAQs

Use the model provided on huggingface: mistralai/Mistral-7B-v0.1

import os
from embedchain import App

os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "hf_your_token"

app = App.from_config("huggingface.yaml")

Use the model gpt-4-turbo provided my openai.

import os
from embedchain import App

os.environ['OPENAI_API_KEY'] = 'xxx'

# load llm configuration from gpt4_turbo.yaml file
app = App.from_config(config_path="gpt4_turbo.yaml")

You can achieve this by setting stream to true in the config file.

llm:
  provider: openai
  config:
    model: 'gpt-4o-mini'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
    stream: true

Set up the app by adding an id in the config file. This keeps the data for future use. You can include this id in the yaml config or input it directly in config dict.

app1.py
import os
from embedchain import App

os.environ['OPENAI_API_KEY'] = 'sk-xxx'

app1 = App.from_config(config={
  "app": {
    "config": {
      "id": "your-app-id",
    }
  }
})

app1.add("https://www.forbes.com/profile/elon-musk")

response = app1.query("What is the net worth of Elon Musk?")

app2.py
import os
from embedchain import App

os.environ['OPENAI_API_KEY'] = 'sk-xxx'

app2 = App.from_config(config={
  "app": {
    "config": {
      # this will persist and load data from app1 session
      "id": "your-app-id",
    }
  }
})

response = app2.query("What is the net worth of Elon Musk?")

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