wehub-resource-sync
75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
|
|
""" This example demonstrates how to parse a document 'in-flight' as part of a Prompt using "Prompt with Sources"
|
|
|
|
1. Load sample documents
|
|
2. Create a Prompt object
|
|
3. Load a locally-run BLING model (may take a few minutes to download the first time from HuggingFace)
|
|
4. Add Document as Source to Prompt
|
|
-- this will automatically parse the source document, text chunk, and package into prompt context window
|
|
-- optional query filter to narrow the list of text chunks packaged into the source
|
|
5. Invoke .prompt_with_sources method
|
|
-- this will take the packaged source, and run inference on the LLM
|
|
"""
|
|
|
|
|
|
import os
|
|
|
|
from llmware.prompts import Prompt
|
|
from llmware.setup import Setup
|
|
|
|
|
|
def prompt_source (model_name):
|
|
|
|
print(f"\nExample: Parse and Filter Documents Directly in Prompt to LLM")
|
|
|
|
# load the llmware sample files
|
|
print (f"\nstep 1 - loading the llmware sample files")
|
|
sample_files_path = Setup().load_sample_files()
|
|
contracts_path = os.path.join(sample_files_path,"Agreements")
|
|
|
|
# load bling model which will be used for the inference (will run on local laptop CPU)
|
|
# --note: typically requires 16 GB laptop RAM
|
|
print (f"step 2 - loading model {model_name}")
|
|
|
|
# create prompt object
|
|
prompter = Prompt()
|
|
prompter.load_model(model_name)
|
|
|
|
# this is the question that we will ask to each document
|
|
research = {"topic": "base salary", "prompt": "What is the executive's base salary?"}
|
|
|
|
for i, contract in enumerate(os.listdir(contracts_path)):
|
|
|
|
# (optional) safety check to exclude Mac-specific file artifact
|
|
if contract != ".DS_Store":
|
|
|
|
print("\nAnalyzing Contract - ", str( i +1), contract)
|
|
print("Question: ", research["prompt"])
|
|
|
|
# contract is parsed, text-chunked, and then filtered by "base salary'
|
|
# --note: query is optional - if no query, then entire document will be returned and added as source
|
|
source = prompter.add_source_document(contracts_path, contract, query=research["topic"])
|
|
|
|
# take a look at the created source
|
|
print("Source created from document: ", source)
|
|
|
|
# calling the LLM with 'source' information from the contract automatically packaged into the prompt
|
|
responses = prompter.prompt_with_source(research["prompt"], prompt_name="default_with_context", temperature=0.3)
|
|
|
|
for r, response in enumerate(responses):
|
|
print("\nLLM Response: ", response["llm_response"])
|
|
|
|
# We're done with this contract, clear the source from the prompt
|
|
# -- note: if looking to aggregate or keep 'running' source, then do not clear
|
|
prompter.clear_source_materials()
|
|
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
model_name = "llmware/bling-1b-0.1"
|
|
prompt_source(model_name)
|
|
|
|
|