wehub-resource-sync
49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
|
|
"""This example demonstrates how to parse a PDF document 'in-line' and integrate in memory directly into a
|
|
Prompt as a source of evidence for an LLM inference. """
|
|
|
|
import os
|
|
from llmware.prompts import Prompt
|
|
from llmware.setup import Setup
|
|
|
|
|
|
def prompt_with_sources (model_name):
|
|
|
|
# pulls down the sample files, including a specific agreement file
|
|
sample_files_path = Setup().load_sample_files(over_write=False)
|
|
fp = os.path.join(sample_files_path, "Agreements")
|
|
|
|
local_file = "Apollo EXECUTIVE EMPLOYMENT AGREEMENT.pdf"
|
|
|
|
prompter = Prompt().load_model(model_name)
|
|
|
|
# .add_source_document will do the following:
|
|
# 1. parse the file (any supported document type)
|
|
# 2. apply an optional query filter to reduce the text chunks to only those matching the query
|
|
# 3. batch according to the model context window, and make available for any future inferences
|
|
|
|
sources = prompter.add_source_document(fp, local_file, query="base salary")
|
|
|
|
prompt = "What is the base salary amount?"
|
|
prompt_instruction = "default_with_context"
|
|
response = prompter.prompt_with_source(prompt=prompt, prompt_name=prompt_instruction)
|
|
|
|
print(f"LLM Response - {response}")
|
|
|
|
response_display = response[0]["llm_response"]
|
|
print (f"- Context: {local_file}\n- Prompt: {prompt}\n- LLM Response:\n{response_display}")
|
|
|
|
prompter.clear_source_materials()
|
|
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
model_name = "llmware/bling-1b-0.1"
|
|
|
|
print(f"\nExample - intro to prompt_with_sources - adding a document source to a prompt\n")
|
|
|
|
prompt_with_sources (model_name)
|
|
|