wehub-resource-sync
95 lines
3.7 KiB
Python
95 lines
3.7 KiB
Python
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""" This example shows how to build a simple UI RAG application for longer documents in which a retrieval query step
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is required to build a context from selected text chunks in the document.
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This example is build with a Streamlit UI. To run, it requires a separate `pip install streamlit`, and
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to execute the script, you should run from the command line with:
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`streamlit run using_with_streamlit_ui.py`
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For more information about Streamlit, check out their docs: https://docs.streamlit.io/develop/tutorials
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To build out the application, you would replace the very simple 'text search' mechanism used below with
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techniques outlined in examples in Embeddings and Retrieval.
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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_with_topic_query ():
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st.title("Simple RAG Analyzer with Focusing Query")
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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, "Agreements")
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files = os.listdir(doc_path)
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file_name = st.selectbox("Choose an Agreement", files)
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# ** topic_query ** = this is a proxy for a more complex focusing retrieval strategy to target only a
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# specific part of the document, rather then the whole document
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# in this case, this will run a 'text match' search against the topic query to reduce the
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# text chunks reviewed in trying to answer the question
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topic_query = st.text_area("Filtering Topic (hint: 'vacation')")
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# ** prompt_text ** - this is the question that will be passed to the LLM
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prompt_text = st.text_area("Question (hint: 'how many vacation days will the executive receive'")
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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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if not topic_query:
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sources = prompter.add_source_document(doc_path,file_name)
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else:
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# this is where we use the topic_query to filter the parsed document
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sources = prompter.add_source_document(doc_path,file_name, query=topic_query)
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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_with_topic_query()
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