wehub-resource-sync
87 lines
3.0 KiB
Python
87 lines
3.0 KiB
Python
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""" This example shows how to build a simple RAG application with UI with Streamlit and LLMWare.
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Note: it requires a separate `pip install streamlit`, and to run the script, you should run from the
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command line with:
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`streamlit run using_with_streamlit_ui.py`
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For this example, we will be prompting against a set of Invoice documents, provided in the LLMWare
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sample files.
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If you would like to substitute longer documents then please look at the UI example:
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-- rag_ui_with_query_topic_with_streamlit.py
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as a framework to get started integrating a retrieval step before the prompt of the source
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For more information about Streamlit, check out their docs: https://docs.streamlit.io/develop/tutorials
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"""
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import os
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import streamlit as st
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from llmware.prompts import Prompt
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from llmware.setup import Setup
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# st.set_page_config(layout="wide")
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def simple_analyzer ():
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st.title("Simple RAG Analyzer")
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prompter = Prompt()
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sample_files_path = Setup().load_sample_files(over_write=False)
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doc_path = os.path.join(sample_files_path, "Invoices")
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files = os.listdir(doc_path)
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file_name = st.selectbox("Choose an Invoice", files)
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prompt_text = st.text_area("Question (hint: 'what is the total amount of the invoice?'")
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model_name = st.selectbox("Choose a model for answering questions", ["bling-phi-3-gguf",
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"bling-tiny-llama-1b",
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"bling-stablelm-3b-tool",
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"llama-3-instruct-bartowski-gguf",
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"dragon-llama-answer-tool"])
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if st.button("Run Analysis"):
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if file_name and prompt_text and model_name:
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prompter.load_model(model_name, temperature=0.0, sample=False)
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# parse the PDF in memory and attach to the prompt
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sources = prompter.add_source_document(doc_path,file_name)
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# run the inference with the source
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response = prompter.prompt_with_source(prompt_text)
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# fact checks
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fc = prompter.evidence_check_numbers(response)
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cs = prompter.evidence_check_sources(response)
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if len(response) > 0:
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if "llm_response" in response[0]:
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response = response[0]["llm_response"]
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st.write(f"Answer: {response}")
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if len(fc) > 0:
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if "fact_check" in fc[0]:
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fc_out = fc[0]["fact_check"]
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st.write(f"Numbers Check: {fc_out}")
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if len(cs) > 0:
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if "source_review" in cs[0]:
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sr_out = cs[0]["source_review"]
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st.write(f"Source review: {sr_out}")
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if __name__ == "__main__":
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simple_analyzer()
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