❓ FAQs

Collections of all the frequently asked questions

Does Embedchain support OpenAI's Assistant APIs?

Yes, it does. Please refer to the OpenAI Assistant docs page.

How to use MistralAI language model?

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

main.py

import os
from embedchain import App

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

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

llm:
  provider: huggingface
  config:
    model: 'mistralai/Mistral-7B-v0.1'
    temperature: 0.5
    max_tokens: 1000
    top_p: 0.5
    stream: false

embedder:
  provider: huggingface
  config:
    model: 'sentence-transformers/all-mpnet-base-v2'

How to use ChatGPT 4 turbo model released on OpenAI DevDay?

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

main.py

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")

llm:
  provider: openai
  config:
    model: 'gpt-4-turbo'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
    stream: false

How to use GPT-4 as the LLM model?

main.py

import os
from embedchain import App

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

# load llm configuration from gpt4.yaml file
app = App.from_config(config_path="gpt4.yaml")

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

I don't have OpenAI credits. How can I use some open source model?

main.py

from embedchain import App

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

llm:
  provider: gpt4all
  config:
    model: 'orca-mini-3b-gguf2-q4_0.gguf'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
    stream: false

embedder:
  provider: gpt4all
  config:
    model: 'all-MiniLM-L6-v2'

How to stream response while using OpenAI model in Embedchain?

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

openai.yaml

llm:
  provider: openai
  config:
    model: 'gpt-3.5-turbo'
    temperature: 0.5
    max_tokens: 1000
    top_p: 1
    stream: true

import os
from embedchain import App

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

app = App.from_config(config_path="openai.yaml")

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

response = app.query("What is the net worth of Elon Musk?")
# response will be streamed in stdout as it is generated.

How to persist data across multiple app sessions?

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?")

Still have questions?

If docs aren’t sufficient, please feel free to reach out to us using one of the following methods:

Talk to founders

Schedule a call

Slack

Join our slack community

Discord

Join our discord community