Arcee
This notebook demonstrates how to use the Arcee
class for generating
text using Arceeโs Domain Adapted Language Models (DALMs).
Setupโ
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(
model="DALM-PubMed",
# arcee_api_key="ARCEE-API-KEY" # if not already set in the environment
)
API Reference:
Additional Configurationโ
You can also configure Arceeโs 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(
model="DALM-Patent",
# arcee_api_key="ARCEE-API-KEY", # if not already set in the environment
arcee_api_url="https://custom-api.arcee.ai", # default is https://api.arcee.ai
arcee_app_url="https://custom-app.arcee.ai", # default is https://app.arcee.ai
model_kwargs={
"size": 5,
"filters": [
{
"field_name": "document",
"filter_type": "fuzzy_search",
"value": "Einstein",
}
],
},
)
Generating Textโ
You can generate text from Arcee by providing a prompt. Hereโs 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. Hereโs 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)