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This notebook demonstrates how to use the Arcee class for generating text using Arceeโ€™s Domain Adapted Language Models (DALMs).


Before using Arcee, make sure the Arcee API key is set as ARCEE_API_KEY environment variable. You can also pass the api key as a named parameter.

from langchain_community.llms import Arcee

# Create an instance of the Arcee class
arcee = Arcee(
# arcee_api_key="ARCEE-API-KEY" # if not already set in the environment

Additional Configurationโ€‹

You can also configure Arceeโ€™s parameters such as arcee_api_url, arcee_app_url, and model_kwargs as needed. Setting the model_kwargs at the object initialization uses the parameters as default for all the subsequent calls to the generate response.

arcee = Arcee(
# arcee_api_key="ARCEE-API-KEY", # if not already set in the environment
arcee_api_url="", # default is
arcee_app_url="", # default is
"size": 5,
"filters": [
"field_name": "document",
"filter_type": "fuzzy_search",
"value": "Einstein",

Generating Textโ€‹

You can generate text from Arcee by providing a prompt. Hereโ€™s an example:

# Generate text
prompt = "Can AI-driven music therapy contribute to the rehabilitation of patients with disorders of consciousness?"
response = arcee(prompt)

Additional parametersโ€‹

Arcee allows you to apply filters and set the size (in terms of count) of retrieved document(s) to aid text generation. Filters help narrow down the results. Hereโ€™s how to use these parameters:

# Define filters
filters = [
{"field_name": "document", "filter_type": "fuzzy_search", "value": "Einstein"},
{"field_name": "year", "filter_type": "strict_search", "value": "1905"},

# Generate text with filters and size params
response = arcee(prompt, size=5, filters=filters)