wehub-resource-sync
264 lines
10 KiB
Markdown
264 lines
10 KiB
Markdown
---
|
|
layout: default
|
|
title: Prompts
|
|
parent: Examples
|
|
nav_order: 6
|
|
description: overview of the major modules and classes of LLMWare
|
|
permalink: /examples/prompts
|
|
---
|
|
# Prompts - Introduction by Examples
|
|
We introduce ``llmware`` through self-contained examples.
|
|
|
|
# Basic RAG Scenario - Invoice Processing
|
|
|
|
```python
|
|
|
|
""" This example shows an end-to-end scenario for invoice processing that can be run locally and without a
|
|
database. The example shows how to combine the use of parsing combined with prompts_with_sources to rapidly
|
|
iterate through a batch of invoices and ask a set of questions, and then save the full output to both
|
|
(1) .jsonl for integration into an upstream application/database and (2) to a CSV for human review in excel.
|
|
|
|
note: the sample code pulls from a public repo to load the sample invoice documents the first time -
|
|
please feel free to substitute with your own invoice documents (PDF/DOCX/PPTX/XLSX/CSV/TXT) if you prefer.
|
|
|
|
this example does not require a database or embedding
|
|
|
|
this example can be run locally on a laptop by setting 'run_on_cpu=True'
|
|
if 'run_on_cpu==False", then please see the example 'launch_llmware_inference_server.py'
|
|
to configure and set up a 'pop-up' GPU inference server in just a few minutes
|
|
"""
|
|
|
|
import os
|
|
import re
|
|
|
|
from llmware.prompts import Prompt, HumanInTheLoop
|
|
from llmware.configs import LLMWareConfig
|
|
from llmware.setup import Setup
|
|
from llmware.models import ModelCatalog
|
|
|
|
|
|
def invoice_processing(run_on_cpu=True):
|
|
|
|
# Step 1 - Pull down the sample files from S3 through the .load_sample_files() command
|
|
# --note: if you need to refresh the sample files, set 'over_write=True'
|
|
print("update: Downloading Sample Files")
|
|
|
|
sample_files_path = Setup().load_sample_files(over_write=False)
|
|
invoices_path = os.path.join(sample_files_path, "Invoices")
|
|
|
|
# Step 2 - simple sample query list - each question will be asked to each invoice
|
|
query_list = ["What is the total amount of the invoice?",
|
|
"What is the invoice number?",
|
|
"What are the names of the two parties?"]
|
|
|
|
# Step 3 - Load Model
|
|
|
|
if run_on_cpu:
|
|
|
|
# load local bling model that can run on cpu/laptop
|
|
|
|
# note: bling-1b-0.1 is the *fastest* & *smallest*, but will make more errors than larger BLING models
|
|
# model_name = "llmware/bling-1b-0.1"
|
|
|
|
# try the new bling-phi-3 quantized with gguf - most accurate
|
|
model_name = 'bling-phi-3-gguf'
|
|
else:
|
|
|
|
# use GPU-based inference server to process
|
|
# *** see the launch_llmware_inference_server.py example script to setup ***
|
|
|
|
server_uri_string = "http://11.123.456.789:8088" # insert your server_uri_string
|
|
server_secret_key = "demo-test"
|
|
ModelCatalog().setup_custom_llmware_inference_server(server_uri_string, secret_key=server_secret_key)
|
|
model_name = "llmware-inference-server"
|
|
|
|
# attach inference server to prompt object
|
|
prompter = Prompt().load_model(model_name)
|
|
|
|
# Step 4 - main loop thru folder of invoices
|
|
|
|
for i, invoice in enumerate(os.listdir(invoices_path)):
|
|
|
|
# just in case (legacy on mac os file system - not needed on linux or windows)
|
|
if invoice != ".DS_Store":
|
|
|
|
print("\nAnalyzing invoice: ", str(i + 1), invoice)
|
|
|
|
for question in query_list:
|
|
|
|
# Step 4A - parses the invoices in memory and attaches as a source to the Prompt
|
|
source = prompter.add_source_document(invoices_path,invoice)
|
|
|
|
# Step 4B - executes the prompt on the LLM (with the loaded source)
|
|
output = prompter.prompt_with_source(question,prompt_name="default_with_context")
|
|
|
|
for i, response in enumerate(output):
|
|
print("LLM Response - ", question, " - ", re.sub("[\n]"," ", response["llm_response"]))
|
|
|
|
prompter.clear_source_materials()
|
|
|
|
# Save jsonl report with full transaction history to /prompt_history folder
|
|
print("\nupdate: prompt state saved at: ", os.path.join(LLMWareConfig.get_prompt_path(),prompter.prompt_id))
|
|
|
|
prompter.save_state()
|
|
|
|
# Generate CSV report for easy Human review in Excel
|
|
csv_output = HumanInTheLoop(prompter).export_current_interaction_to_csv()
|
|
|
|
print("\nupdate: csv output for human review - ", csv_output)
|
|
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
invoice_processing(run_on_cpu=True)
|
|
```
|
|
|
|
# Document Summarizer
|
|
|
|
```python
|
|
|
|
""" This Example shows a packaged 'document_summarizer' prompt using the slim-summary-tool. It shows a variety of
|
|
techniques to summarize documents generally larger than a LLM context window, and how to assemble multiple source
|
|
batches from the document, as well as using a 'query' and 'topic' to focus on specific segments of the document. """
|
|
|
|
import os
|
|
|
|
from llmware.prompts import Prompt
|
|
from llmware.setup import Setup
|
|
|
|
|
|
def test_summarize_document(example="jd salinger"):
|
|
|
|
# pull a sample document (or substitute a file_path and file_name of your own)
|
|
sample_files_path = Setup().load_sample_files(over_write=False)
|
|
|
|
topic = None
|
|
query = None
|
|
fp = None
|
|
fn = None
|
|
|
|
if example not in ["jd salinger", "employment terms", "just the comp", "un resolutions"]:
|
|
print ("not found example")
|
|
return []
|
|
|
|
if example == "jd salinger":
|
|
fp = os.path.join(sample_files_path, "SmallLibrary")
|
|
fn = "Jd-Salinger-Biography.docx"
|
|
topic = "jd salinger"
|
|
query = None
|
|
|
|
if example == "employment terms":
|
|
fp = os.path.join(sample_files_path, "Agreements")
|
|
fn = "Athena EXECUTIVE EMPLOYMENT AGREEMENT.pdf"
|
|
topic = "executive compensation terms"
|
|
query = None
|
|
|
|
if example == "just the comp":
|
|
fp = os.path.join(sample_files_path, "Agreements")
|
|
fn = "Athena EXECUTIVE EMPLOYMENT AGREEMENT.pdf"
|
|
topic = "executive compensation terms"
|
|
query = "base salary"
|
|
|
|
if example == "un resolutions":
|
|
fp = os.path.join(sample_files_path, "SmallLibrary")
|
|
fn = "N2126108.pdf"
|
|
# fn = "N2137825.pdf"
|
|
topic = "key points"
|
|
query = None
|
|
|
|
# optional parameters: 'query' - will select among blocks with the query term
|
|
# 'topic' - will pass a topic/issue as the parameter to the model to 'focus' the summary
|
|
# 'max_batch_cap' - caps the number of batches sent to the model
|
|
# 'text_only' - returns just the summary text aggregated
|
|
|
|
kp = Prompt().summarize_document_fc(fp, fn, topic=topic, query=query, text_only=True, max_batch_cap=15)
|
|
|
|
print(f"\nDocument summary completed - {len(kp)} Points")
|
|
for i, points in enumerate(kp):
|
|
print(i, points)
|
|
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
print(f"\nExample: Summarize Documents\n")
|
|
|
|
# 4 examples - ["jd salinger", "employment terms", "just the comp", "un resolutions"]
|
|
# -- "jd salinger" - summarizes key points about jd salinger from short biography document
|
|
# -- "employment terms" - summarizes the executive compensation terms across 15 page document
|
|
# -- "just the comp" - queries to find subset of document and then summarizes the key terms
|
|
# -- "un resolutions" - summarizes the un resolutions document
|
|
|
|
summary_direct = test_summarize_document(example="employment terms")
|
|
```
|
|
|
|
For more examples, see the [prompt examples]((https://www.github.com/llmware-ai/llmware/tree/main/examples/Prompts/) in the main repo.
|
|
|
|
Check back often - we are updating these examples regularly - and many of these examples have companion videos as well.
|
|
|
|
|
|
|
|
# More information about the project - [see main repository](https://www.github.com/llmware-ai/llmware.git)
|
|
|
|
|
|
# About the project
|
|
|
|
`llmware` is © 2023-{{ "now" | date: "%Y" }} by [AI Bloks](https://www.aibloks.com/home).
|
|
|
|
## Contributing
|
|
Please first discuss any change you want to make publicly, for example on GitHub via raising an [issue](https://github.com/llmware-ai/llmware/issues) or starting a [new discussion](https://github.com/llmware-ai/llmware/discussions).
|
|
You can also write an email or start a discussion on our Discrod channel.
|
|
Read more about becoming a contributor in the [GitHub repo](https://github.com/llmware-ai/llmware/blob/main/CONTRIBUTING.md).
|
|
|
|
## Code of conduct
|
|
We welcome everyone into the ``llmware`` community.
|
|
[View our Code of Conduct](https://github.com/llmware-ai/llmware/blob/main/CODE_OF_CONDUCT.md) in our GitHub repository.
|
|
|
|
## ``llmware`` and [AI Bloks](https://www.aibloks.com/home)
|
|
``llmware`` is an open source project from [AI Bloks](https://www.aibloks.com/home) - the company behind ``llmware``.
|
|
The company offers a Software as a Service (SaaS) Retrieval Augmented Generation (RAG) service.
|
|
[AI Bloks](https://www.aibloks.com/home) was founded by [Namee Oberst](https://www.linkedin.com/in/nameeoberst/) and [Darren Oberst](https://www.linkedin.com/in/darren-oberst-34a4b54/) in October 2022.
|
|
|
|
## License
|
|
|
|
`llmware` is distributed by an [Apache-2.0 license](https://www.github.com/llmware-ai/llmware/blob/main/LICENSE).
|
|
|
|
## Thank you to the contributors of ``llmware``!
|
|
<ul class="list-style-none">
|
|
{% for contributor in site.github.contributors %}
|
|
<li class="d-inline-block mr-1">
|
|
<a href="{{ contributor.html_url }}">
|
|
<img src="{{ contributor.avatar_url }}" width="32" height="32" alt="{{ contributor.login }}">
|
|
</a>
|
|
</li>
|
|
{% endfor %}
|
|
</ul>
|
|
|
|
|
|
---
|
|
<ul class="list-style-none">
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://discord.gg/MhZn5Nc39h"><span><i class="fa-brands fa-discord"></i></span></a>
|
|
</li>
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://www.youtube.com/@llmware"><span><i class="fa-brands fa-youtube"></i></span></a>
|
|
</li>
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://huggingface.co/llmware"><span><img src="assets/images/hf-logo.svg" alt="Hugging Face" class="hugging-face-logo"/></span></a>
|
|
</li>
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://www.linkedin.com/company/aibloks/"><span><i class="fa-brands fa-linkedin"></i></span></a>
|
|
</li>
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://twitter.com/AiBloks"><span><i class="fa-brands fa-square-x-twitter"></i></span></a>
|
|
</li>
|
|
<li class="d-inline-block mr-1">
|
|
<a href="https://www.instagram.com/aibloks/"><span><i class="fa-brands fa-instagram"></i></span></a>
|
|
</li>
|
|
</ul>
|
|
---
|
|
|