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---
title: ❓ FAQs
description: 'Collections of all the frequently asked questions'
---
<AccordionGroup>
<Accordion title="Does Embedchain support OpenAI's Assistant APIs?">
Yes, it does. Please refer to the [OpenAI Assistant docs page](/examples/openai-assistant).
</Accordion>
<Accordion title="How to use MistralAI language model?">
Use the model provided on huggingface: `mistralai/Mistral-7B-v0.1`
<CodeGroup>
```python main.py
import os
from embedchain import App
os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "hf_your_token"
app = App.from_config("huggingface.yaml")
```
```yaml 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'
```
</CodeGroup>
</Accordion>
<Accordion title="How to use ChatGPT 4 turbo model released on OpenAI DevDay?">
Use the model `gpt-4-turbo` provided my openai.
<CodeGroup>
```python 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")
```
```yaml gpt4_turbo.yaml
llm:
provider: openai
config:
model: 'gpt-4-turbo'
temperature: 0.5
max_tokens: 1000
top_p: 1
stream: false
```
</CodeGroup>
</Accordion>
<Accordion title="How to use GPT-4 as the LLM model?">
<CodeGroup>
```python 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")
```
```yaml gpt4.yaml
llm:
provider: openai
config:
model: 'gpt-4'
temperature: 0.5
max_tokens: 1000
top_p: 1
stream: false
```
</CodeGroup>
</Accordion>
<Accordion title="I don't have OpenAI credits. How can I use some open source model?">
<CodeGroup>
```python main.py
from embedchain import App
# load llm configuration from opensource.yaml file
app = App.from_config(config_path="opensource.yaml")
```
```yaml 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'
```
</CodeGroup>
</Accordion>
<Accordion title="How to stream response while using OpenAI model in Embedchain?">
You can achieve this by setting `stream` to `true` in the config file.
<CodeGroup>
```yaml openai.yaml
llm:
provider: openai
config:
model: 'gpt-3.5-turbo'
temperature: 0.5
max_tokens: 1000
top_p: 1
stream: true
```
```python main.py
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.
```
</CodeGroup>
</Accordion>
<Accordion title="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.
```python 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?")
```
```python 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?")
```
</Accordion>
</AccordionGroup>
#### Still have questions?
If docs aren't sufficient, please feel free to reach out to us using one of the following methods:
<Snippet file="get-help.mdx" />
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