By default, the LLM stack supports the following data types:
CSV
To use CSV as a source, use the data type (the first argument to the add_source() method) as csv. Eg:
from genai_stack.model import OpenAIGpt35Model
model = OpenAIGpt35Model.from_kwargs(
fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("csv", "valid_csv_path_or_url")
PDF
To use pdf as a source, use the data type as pdf. Eg:
from genai_stack.model import OpenAIGpt35Model
model = OpenAIGpt35Model.from_kwargs(
fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("pdf", "valid_pdf_path_or_url")
Web
To use the web as a source, use the data type as web. Eg:
from genai_stack.model import OpenAIGpt35Model
model = OpenAIGpt35Model.from_kwargs(
fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("web", "valid_web_url")
JSON
To use JSON as a source, use the data type as json. Eg:
from genai_stack.model import OpenAIGpt35Model
model = OpenAIGpt35Model.from_kwargs(
fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("json", "valid_json_path_or_url")
Markdown
To use markdown as a source, use the data type as markdown. Eg:
from genai_stack.model import OpenAIGpt35Model
model = OpenAIGpt35Model.from_kwargs(
fields={"openai_api_key": "Paste your Open AI key"}
)
model.add_source("markdown", "valid_markdown_path_or_url")
To make predictions you can execute the below code snippet:
response = model.predict("<Question on top of any of your data>")
print(response)
