---
title: 🤖 Large language models (LLMs)
---
## Overview
Embedchain comes with built-in support for various popular large language models. We handle the complexity of integrating these models for you, allowing you to easily customize your language model interactions through a user-friendly interface.
## OpenAI
To use OpenAI LLM models, you have to set the `OPENAI_API_KEY` environment variable. You can obtain the OpenAI API key from the [OpenAI Platform](https://platform.openai.com/account/api-keys).
Once you have obtained the key, you can use it like this:
```python
import os
from embedchain import App
os.environ['OPENAI_API_KEY'] = 'xxx'
app = App()
app.add("https://en.wikipedia.org/wiki/OpenAI")
app.query("What is OpenAI?")
```
If you are looking to configure the different parameters of the LLM, you can do so by loading the app using a [yaml config](https://github.com/embedchain/embedchain/blob/main/configs/chroma.yaml) file.
```python main.py
import os
from embedchain import App
os.environ['OPENAI_API_KEY'] = 'xxx'
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: openai
config:
model: 'gpt-4o-mini'
temperature: 0.5
max_tokens: 1000
top_p: 1
stream: false
```
### Function Calling
Embedchain supports OpenAI [Function calling](https://platform.openai.com/docs/guides/function-calling) with a single function. It accepts inputs in accordance with the [Langchain interface](https://python.langchain.com/docs/modules/model_io/chat/function_calling#legacy-args-functions-and-function_call).
```python
from pydantic import BaseModel
class multiply(BaseModel):
"""Multiply two integers together."""
a: int = Field(..., description="First integer")
b: int = Field(..., description="Second integer")
```
```python
def multiply(a: int, b: int) -> int:
"""Multiply two integers together.
Args:
a: First integer
b: Second integer
"""
return a * b
```
```python
multiply = {
"type": "function",
"function": {
"name": "multiply",
"description": "Multiply two integers together.",
"parameters": {
"type": "object",
"properties": {
"a": {
"description": "First integer",
"type": "integer"
},
"b": {
"description": "Second integer",
"type": "integer"
}
},
"required": [
"a",
"b"
]
}
}
}
```
With any of the previous inputs, the OpenAI LLM can be queried to provide the appropriate arguments for the function.
```python
import os
from embedchain import App
from embedchain.llm.openai import OpenAILlm
os.environ["OPENAI_API_KEY"] = "sk-xxx"
llm = OpenAILlm(tools=multiply)
app = App(llm=llm)
result = app.query("What is the result of 125 multiplied by fifteen?")
```
## Google AI
To use Google AI model, you have to set the `GOOGLE_API_KEY` environment variable. You can obtain the Google API key from the [Google Maker Suite](https://makersuite.google.com/app/apikey)
```python main.py
import os
from embedchain import App
os.environ["GOOGLE_API_KEY"] = "xxx"
app = App.from_config(config_path="config.yaml")
app.add("https://www.forbes.com/profile/elon-musk")
response = app.query("What is the net worth of Elon Musk?")
if app.llm.config.stream: # if stream is enabled, response is a generator
for chunk in response:
print(chunk)
else:
print(response)
```
```yaml config.yaml
llm:
provider: google
config:
model: gemini-pro
max_tokens: 1000
temperature: 0.5
top_p: 1
stream: false
embedder:
provider: google
config:
model: 'models/embedding-001'
task_type: "retrieval_document"
title: "Embeddings for Embedchain"
```
## Azure OpenAI
To use Azure OpenAI model, you have to set some of the azure openai related environment variables as given in the code block below:
```python main.py
import os
from embedchain import App
os.environ["OPENAI_API_TYPE"] = "azure"
os.environ["AZURE_OPENAI_ENDPOINT"] = "https://xxx.openai.azure.com/"
os.environ["AZURE_OPENAI_KEY"] = "xxx"
os.environ["OPENAI_API_VERSION"] = "xxx"
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: azure_openai
config:
model: gpt-4o-mini
deployment_name: your_llm_deployment_name
temperature: 0.5
max_tokens: 1000
top_p: 1
stream: false
embedder:
provider: azure_openai
config:
model: text-embedding-ada-002
deployment_name: you_embedding_model_deployment_name
```
You can find the list of models and deployment name on the [Azure OpenAI Platform](https://oai.azure.com/portal).
## Anthropic
To use anthropic's model, please set the `ANTHROPIC_API_KEY` which you find on their [Account Settings Page](https://console.anthropic.com/account/keys).
```python main.py
import os
from embedchain import App
os.environ["ANTHROPIC_API_KEY"] = "xxx"
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: anthropic
config:
model: 'claude-instant-1'
temperature: 0.5
max_tokens: 1000
top_p: 1
stream: false
```
## Cohere
Install related dependencies using the following command:
```bash
pip install --upgrade 'embedchain[cohere]'
```
Set the `COHERE_API_KEY` as environment variable which you can find on their [Account settings page](https://dashboard.cohere.com/api-keys).
Once you have the API key, you are all set to use it with Embedchain.
```python main.py
import os
from embedchain import App
os.environ["COHERE_API_KEY"] = "xxx"
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: cohere
config:
model: large
temperature: 0.5
max_tokens: 1000
top_p: 1
```
## Together
Install related dependencies using the following command:
```bash
pip install --upgrade 'embedchain[together]'
```
Set the `TOGETHER_API_KEY` as environment variable which you can find on their [Account settings page](https://api.together.xyz/settings/api-keys).
Once you have the API key, you are all set to use it with Embedchain.
```python main.py
import os
from embedchain import App
os.environ["TOGETHER_API_KEY"] = "xxx"
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: together
config:
model: togethercomputer/RedPajama-INCITE-7B-Base
temperature: 0.5
max_tokens: 1000
top_p: 1
```
## Ollama
Setup Ollama using https://github.com/jmorganca/ollama
```python main.py
import os
os.environ["OLLAMA_HOST"] = "http://127.0.0.1:11434"
from embedchain import App
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: ollama
config:
model: 'llama2'
temperature: 0.5
top_p: 1
stream: true
base_url: 'http://localhost:11434'
embedder:
provider: ollama
config:
model: znbang/bge:small-en-v1.5-q8_0
base_url: http://localhost:11434
```
## vLLM
Setup vLLM by following instructions given in [their docs](https://docs.vllm.ai/en/latest/getting_started/installation.html).
```python main.py
import os
from embedchain import App
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: vllm
config:
model: 'meta-llama/Llama-2-70b-hf'
temperature: 0.5
top_p: 1
top_k: 10
stream: true
trust_remote_code: true
```
## Clarifai
Install related dependencies using the following command:
```bash
pip install --upgrade 'embedchain[clarifai]'
```
set the `CLARIFAI_PAT` as environment variable which you can find in the [security page](https://clarifai.com/settings/security). Optionally you can also pass the PAT key as parameters in LLM/Embedder class.
Now you are all set with exploring Embedchain.
```python main.py
import os
from embedchain import App
os.environ["CLARIFAI_PAT"] = "XXX"
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
#Now let's add some data.
app.add("https://www.forbes.com/profile/elon-musk")
#Query the app
response = app.query("what college degrees does elon musk have?")
```
Head to [Clarifai Platform](https://clarifai.com/explore/models?page=1&perPage=24&filterData=%5B%7B%22field%22%3A%22use_cases%22%2C%22value%22%3A%5B%22llm%22%5D%7D%5D) to browse various State-of-the-Art LLM models for your use case.
For passing model inference parameters use `model_kwargs` argument in the config file. Also you can use `api_key` argument to pass `CLARIFAI_PAT` in the config.
```yaml config.yaml
llm:
provider: clarifai
config:
model: "https://clarifai.com/mistralai/completion/models/mistral-7B-Instruct"
model_kwargs:
temperature: 0.5
max_tokens: 1000
embedder:
provider: clarifai
config:
model: "https://clarifai.com/clarifai/main/models/BAAI-bge-base-en-v15"
```
## GPT4ALL
Install related dependencies using the following command:
```bash
pip install --upgrade 'embedchain[opensource]'
```
GPT4all is a free-to-use, locally running, privacy-aware chatbot. No GPU or internet required. You can use this with Embedchain using the following code:
```python main.py
from embedchain import App
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.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
```
## JinaChat
First, set `JINACHAT_API_KEY` in environment variable which you can obtain from [their platform](https://chat.jina.ai/api).
Once you have the key, load the app using the config yaml file:
```python main.py
import os
from embedchain import App
os.environ["JINACHAT_API_KEY"] = "xxx"
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: jina
config:
temperature: 0.5
max_tokens: 1000
top_p: 1
stream: false
```
## Hugging Face
Install related dependencies using the following command:
```bash
pip install --upgrade 'embedchain[huggingface-hub]'
```
First, set `HUGGINGFACE_ACCESS_TOKEN` in environment variable which you can obtain from [their platform](https://huggingface.co/settings/tokens).
You can load the LLMs from Hugging Face using three ways:
- [Hugging Face Hub](#hugging-face-hub)
- [Hugging Face Local Pipelines](#hugging-face-local-pipelines)
- [Hugging Face Inference Endpoint](#hugging-face-inference-endpoint)
### Hugging Face Hub
To load the model from Hugging Face Hub, use the following code:
```python main.py
import os
from embedchain import App
os.environ["HUGGINGFACE_ACCESS_TOKEN"] = "xxx"
config = {
"app": {"config": {"id": "my-app"}},
"llm": {
"provider": "huggingface",
"config": {
"model": "bigscience/bloom-1b7",
"top_p": 0.5,
"max_length": 200,
"temperature": 0.1,
},
},
}
app = App.from_config(config=config)
```
### Hugging Face Local Pipelines
If you want to load the locally downloaded model from Hugging Face, you can do so by following the code provided below:
```python main.py
from embedchain import App
config = {
"app": {"config": {"id": "my-app"}},
"llm": {
"provider": "huggingface",
"config": {
"model": "Trendyol/Trendyol-LLM-7b-chat-v0.1",
"local": True, # Necessary if you want to run model locally
"top_p": 0.5,
"max_tokens": 1000,
"temperature": 0.1,
},
}
}
app = App.from_config(config=config)
```
### Hugging Face Inference Endpoint
You can also use [Hugging Face Inference Endpoints](https://huggingface.co/docs/inference-endpoints/index#-inference-endpoints) to access custom endpoints. First, set the `HUGGINGFACE_ACCESS_TOKEN` as above.
Then, load the app using the config yaml file:
```python main.py
from embedchain import App
config = {
"app": {"config": {"id": "my-app"}},
"llm": {
"provider": "huggingface",
"config": {
"endpoint": "https://api-inference.huggingface.co/models/gpt2",
"model_params": {"temprature": 0.1, "max_new_tokens": 100}
},
},
}
app = App.from_config(config=config)
```
Currently only supports `text-generation` and `text2text-generation` for now [[ref](https://api.python.langchain.com/en/latest/llms/langchain_community.llms.huggingface_endpoint.HuggingFaceEndpoint.html?highlight=huggingfaceendpoint#)].
See langchain's [hugging face endpoint](https://python.langchain.com/docs/integrations/chat/huggingface#huggingfaceendpoint) for more information.
## Llama2
Llama2 is integrated through [Replicate](https://replicate.com/). Set `REPLICATE_API_TOKEN` in environment variable which you can obtain from [their platform](https://replicate.com/account/api-tokens).
Once you have the token, load the app using the config yaml file:
```python main.py
import os
from embedchain import App
os.environ["REPLICATE_API_TOKEN"] = "xxx"
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: llama2
config:
model: 'a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5'
temperature: 0.5
max_tokens: 1000
top_p: 0.5
stream: false
```
## Vertex AI
Setup Google Cloud Platform application credentials by following the instruction on [GCP](https://cloud.google.com/docs/authentication/external/set-up-adc). Once setup is done, use the following code to create an app using VertexAI as provider:
```python main.py
from embedchain import App
# load llm configuration from config.yaml file
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: vertexai
config:
model: 'chat-bison'
temperature: 0.5
top_p: 0.5
```
## Mistral AI
Obtain the Mistral AI api key from their [console](https://console.mistral.ai/).
```python main.py
os.environ["MISTRAL_API_KEY"] = "xxx"
app = App.from_config(config_path="config.yaml")
app.add("https://www.forbes.com/profile/elon-musk")
response = app.query("what is the net worth of Elon Musk?")
# As of January 16, 2024, Elon Musk's net worth is $225.4 billion.
response = app.chat("which companies does elon own?")
# Elon Musk owns Tesla, SpaceX, Boring Company, Twitter, and X.
response = app.chat("what question did I ask you already?")
# You have asked me several times already which companies Elon Musk owns, specifically Tesla, SpaceX, Boring Company, Twitter, and X.
```
```yaml config.yaml
llm:
provider: mistralai
config:
model: mistral-tiny
temperature: 0.5
max_tokens: 1000
top_p: 1
embedder:
provider: mistralai
config:
model: mistral-embed
```
## AWS Bedrock
### Setup
- Before using the AWS Bedrock LLM, make sure you have the appropriate model access from [Bedrock Console](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess).
- You will also need to authenticate the `boto3` client by using a method in the [AWS documentation](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials)
- You can optionally export an `AWS_REGION`
### Usage
```python main.py
import os
from embedchain import App
os.environ["AWS_REGION"] = "us-west-2"
app = App.from_config(config_path="config.yaml")
```
```yaml config.yaml
llm:
provider: aws_bedrock
config:
model: amazon.titan-text-express-v1
# check notes below for model_kwargs
model_kwargs:
temperature: 0.5
topP: 1
maxTokenCount: 1000
```
The model arguments are different for each providers. Please refer to the [AWS Bedrock Documentation](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/providers) to find the appropriate arguments for your model.
## Groq
[Groq](https://groq.com/) is the creator of the world's first Language Processing Unit (LPU), providing exceptional speed performance for AI workloads running on their LPU Inference Engine.
### Usage
In order to use LLMs from Groq, go to their [platform](https://console.groq.com/keys) and get the API key.
Set the API key as `GROQ_API_KEY` environment variable or pass in your app configuration to use the model as given below in the example.
```python main.py
import os
from embedchain import App
# Set your API key here or pass as the environment variable
groq_api_key = "gsk_xxxx"
config = {
"llm": {
"provider": "groq",
"config": {
"model": "mixtral-8x7b-32768",
"api_key": groq_api_key,
"stream": True
}
}
}
app = App.from_config(config=config)
# Add your data source here
app.add("https://docs.embedchain.ai/sitemap.xml", data_type="sitemap")
app.query("Write a poem about Embedchain")
# In the realm of data, vast and wide,
# Embedchain stands with knowledge as its guide.
# A platform open, for all to try,
# Building bots that can truly fly.
# With REST API, data in reach,
# Deployment a breeze, as easy as a speech.
# Updating data sources, anytime, anyday,
# Embedchain's power, never sway.
# A knowledge base, an assistant so grand,
# Connecting to platforms, near and far.
# Discord, WhatsApp, Slack, and more,
# Embedchain's potential, never a bore.
```
## NVIDIA AI
[NVIDIA AI Foundation Endpoints](https://www.nvidia.com/en-us/ai-data-science/foundation-models/) let you quickly use NVIDIA's AI models, such as Mixtral 8x7B, Llama 2 etc, through our API. These models are available in the [NVIDIA NGC catalog](https://catalog.ngc.nvidia.com/ai-foundation-models), fully optimized and ready to use on NVIDIA's AI platform. They are designed for high speed and easy customization, ensuring smooth performance on any accelerated setup.
### Usage
In order to use LLMs from NVIDIA AI, create an account on [NVIDIA NGC Service](https://catalog.ngc.nvidia.com/).
Generate an API key from their dashboard. Set the API key as `NVIDIA_API_KEY` environment variable. Note that the `NVIDIA_API_KEY` will start with `nvapi-`.
Below is an example of how to use LLM model and embedding model from NVIDIA AI:
```python main.py
import os
from embedchain import App
os.environ['NVIDIA_API_KEY'] = 'nvapi-xxxx'
config = {
"app": {
"config": {
"id": "my-app",
},
},
"llm": {
"provider": "nvidia",
"config": {
"model": "nemotron_steerlm_8b",
},
},
"embedder": {
"provider": "nvidia",
"config": {
"model": "nvolveqa_40k",
"vector_dimension": 1024,
},
},
}
app = App.from_config(config=config)
app.add("https://www.forbes.com/profile/elon-musk")
answer = app.query("What is the net worth of Elon Musk today?")
# Answer: The net worth of Elon Musk is subject to fluctuations based on the market value of his holdings in various companies.
# As of March 1, 2024, his net worth is estimated to be approximately $210 billion. However, this figure can change rapidly due to stock market fluctuations and other factors.
# Additionally, his net worth may include other assets such as real estate and art, which are not reflected in his stock portfolio.
```
## Token Usage
You can get the cost of the query by setting `token_usage` to `True` in the config file. This will return the token details: `prompt_tokens`, `completion_tokens`, `total_tokens`, `total_cost`, `cost_currency`.
The list of paid LLMs that support token usage are:
- OpenAI
- Vertex AI
- Anthropic
- Cohere
- Together
- Groq
- Mistral AI
- NVIDIA AI
Here is an example of how to use token usage:
```python main.py
os.environ["OPENAI_API_KEY"] = "xxx"
app = App.from_config(config_path="config.yaml")
app.add("https://www.forbes.com/profile/elon-musk")
response = app.query("what is the net worth of Elon Musk?")
# {'answer': 'Elon Musk's net worth is $209.9 billion as of 6/9/24.',
# 'usage': {'prompt_tokens': 1228,
# 'completion_tokens': 21,
# 'total_tokens': 1249,
# 'total_cost': 0.001884,
# 'cost_currency': 'USD'}
# }
response = app.chat("Which companies did Elon Musk found?")
# {'answer': 'Elon Musk founded six companies, including Tesla, which is an electric car maker, SpaceX, a rocket producer, and the Boring Company, a tunneling startup.',
# 'usage': {'prompt_tokens': 1616,
# 'completion_tokens': 34,
# 'total_tokens': 1650,
# 'total_cost': 0.002492,
# 'cost_currency': 'USD'}
# }
```
```yaml config.yaml
llm:
provider: openai
config:
model: gpt-4o-mini
temperature: 0.5
max_tokens: 1000
token_usage: true
```
If a model is missing and you'd like to add it to `model_prices_and_context_window.json`, please feel free to open a PR.