wehub-resource-sync
104 lines
4.1 KiB
Python
104 lines
4.1 KiB
Python
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""" This example shows an end-to-end scenario for invoice processing that can be run locally and without a
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database. The example shows how to combine the use of parsing combined with prompts_with_sources to rapidly
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iterate through a batch of invoices and ask a set of questions, and then save the full output to both
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(1) .jsonl for integration into an upstream application/database and (2) to a CSV for human review in excel.
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note: the sample code pulls from a public repo to load the sample invoice documents the first time -
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please feel free to substitute with your own invoice documents (PDF/DOCX/PPTX/XLSX/CSV/TXT) if you prefer.
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this example does not require a database or embedding
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this example can be run locally on a laptop by setting 'run_on_cpu=True'
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if 'run_on_cpu==False", then please see the example 'launch_llmware_inference_server.py'
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to configure and set up a 'pop-up' GPU inference server in just a few minutes
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"""
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import os
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import re
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from llmware.prompts import Prompt, HumanInTheLoop
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from llmware.configs import LLMWareConfig
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from llmware.setup import Setup
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from llmware.models import ModelCatalog
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def invoice_processing(run_on_cpu=True):
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# Step 1 - Pull down the sample files from S3 through the .load_sample_files() command
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# --note: if you need to refresh the sample files, set 'over_write=True'
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print("update: Downloading Sample Files")
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sample_files_path = Setup().load_sample_files(over_write=False)
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invoices_path = os.path.join(sample_files_path, "Invoices")
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# Step 2 - simple sample query list - each question will be asked to each invoice
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query_list = ["What is the total amount of the invoice?",
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"What is the invoice number?",
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"What are the names of the two parties?"]
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# Step 3 - Load Model
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if run_on_cpu:
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# load local bling model that can run on cpu/laptop
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# note: bling-1b-0.1 is the *fastest* & *smallest*, but will make more errors than larger BLING models
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# model_name = "llmware/bling-1b-0.1"
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# try the new bling-phi-3 quantized with gguf - most accurate
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model_name = 'bling-phi-3-gguf'
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else:
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# use GPU-based inference server to process
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# *** see the launch_llmware_inference_server.py example script to setup ***
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server_uri_string = "http://11.123.456.789:8088" # insert your server_uri_string
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server_secret_key = "demo-test"
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ModelCatalog().setup_custom_llmware_inference_server(server_uri_string, secret_key=server_secret_key)
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model_name = "llmware-inference-server"
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# attach inference server to prompt object
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prompter = Prompt().load_model(model_name)
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# Step 4 - main loop thru folder of invoices
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for i, invoice in enumerate(os.listdir(invoices_path)):
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# just in case (legacy on mac os file system - not needed on linux or windows)
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if invoice != ".DS_Store":
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print("\nAnalyzing invoice: ", str(i + 1), invoice)
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for question in query_list:
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# Step 4A - parses the invoices in memory and attaches as a source to the Prompt
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source = prompter.add_source_document(invoices_path,invoice)
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# Step 4B - executes the prompt on the LLM (with the loaded source)
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output = prompter.prompt_with_source(question,prompt_name="default_with_context")
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for i, response in enumerate(output):
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print("LLM Response - ", question, " - ", re.sub("[\n]"," ", response["llm_response"]))
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prompter.clear_source_materials()
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# Save jsonl report with full transaction history to /prompt_history folder
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print("\nupdate: prompt state saved at: ", os.path.join(LLMWareConfig.get_prompt_path(),prompter.prompt_id))
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prompter.save_state()
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# Generate CSV report for easy Human review in Excel
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csv_output = HumanInTheLoop(prompter).export_current_interaction_to_csv()
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print("\nupdate: csv output for human review - ", csv_output)
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return 0
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if __name__ == "__main__":
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invoice_processing(run_on_cpu=True)
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