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https://github.com/oobabooga/text-generation-webui.git
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Reorganize superbig ui
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oobabooga
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befa307c42
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a35a2fab02
@ -68,9 +68,8 @@ collector = ChromaCollector(embedder)
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chunk_count = 5
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def feed_data_into_collector(corpus, chunk_len, _chunk_count):
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global collector, chunk_count
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chunk_count = int(_chunk_count)
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def feed_data_into_collector(corpus, chunk_len):
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global collector
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chunk_len = int(chunk_len)
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cumulative = ''
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@ -85,14 +84,14 @@ def feed_data_into_collector(corpus, chunk_len, _chunk_count):
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yield cumulative
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def feed_file_into_collector(file, chunk_len, chunk_count):
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def feed_file_into_collector(file, chunk_len):
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yield 'Reading the input dataset...\n\n'
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text = file.decode('utf-8')
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for i in feed_data_into_collector(text, chunk_len, chunk_count):
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for i in feed_data_into_collector(text, chunk_len):
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yield i
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def feed_url_into_collector(urls, chunk_len, chunk_count):
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def feed_url_into_collector(urls, chunk_len):
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urls = urls.strip().split('\n')
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all_text = ''
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cumulative = ''
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@ -110,10 +109,19 @@ def feed_url_into_collector(urls, chunk_len, chunk_count):
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text = '\n\n'.join(chunk for chunk in chunks if chunk)
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all_text += text
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for i in feed_data_into_collector(all_text, chunk_len, chunk_count):
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for i in feed_data_into_collector(all_text, chunk_len):
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yield i
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def apply_settings(_chunk_count):
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global chunk_count
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chunk_count = _chunk_count
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settings_to_display = {
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'chunk_count': int(chunk_count),
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}
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yield f"The following settings are now active: {str(settings_to_display)}"
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def input_modifier(string):
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# Find the user input
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@ -135,40 +143,50 @@ def input_modifier(string):
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return string
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def ui():
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gr.Markdown(textwrap.dedent("""
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with gr.Accordion("Click for more information...", open=False):
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gr.Markdown(textwrap.dedent("""
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*This extension is currently experimental and under development.*
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## About
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## How to use it
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This extension takes a dataset as input, breaks it into chunks, and adds the result to a local/offline Chroma database.
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1) Paste your input text (of whatever length) into the text box below.
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2) Click on the "Apply" button located below the text box
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3) In your prompt, enter your question between <|begin-user-input|> and <|end-user-input|>, and specify the injection point with <|injection-point|>
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The database is then queried during inference time to get the excerpts that are closest to your input. The idea is to create
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an arbitrarily large pseudocontext.
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## How it works
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## How to use it
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In the background, the 5 chunks in the input text most similar to the user input will be placed at the injection point, and the special tokens above will be removed. Then the text generation will proceed as usual.
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1) Paste your input text (of whatever length) into the text box below.
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2) Click on "Load data" to feed this text into the Chroma database.
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3) In your prompt, enter your question between `<|begin-user-input|>` and `<|end-user-input|>`, and specify the injection point with `<|injection-point|>`.
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## Example
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By default, the 5 closest chunks will be injected. You can customize this value in the "Generation settings" tab.
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For your convenience, you can use the following prompt as a starting point (for Alpaca models):
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The special tokens mentioned above (`<|begin-user-input|>`, `<|end-user-input|>`, and `<|injection-point|>`) are removed when the injection happens.
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```
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Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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## Example
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### Instruction:
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You are ArxivGPT, trained on millions of Arxiv papers. You always answer the question, even if full context isn't provided to you. The following are snippets from an Arxiv paper. Use the snippets to answer the question. Think about it step by step
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For your convenience, you can use the following prompt as a starting point (for Alpaca models):
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<|injection-point|>
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```
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Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Input:
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<|begin-user-input|>What datasets are mentioned in the paper above?<|end-user-input|>
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### Instruction:
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You are ArxivGPT, trained on millions of Arxiv papers. You always answer the question, even if full context isn't provided to you. The following are snippets from an Arxiv paper. Use the snippets to answer the question. Think about it step by step
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### Response:
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```
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<|injection-point|>
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### Input:
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<|begin-user-input|>What datasets are mentioned in the paper above?<|end-user-input|>
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### Response:
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```
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*This extension is currently experimental and under development.*
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"""))
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"""))
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if shared.is_chat():
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# Chat mode has to be handled differently, probably using a custom_generate_chat_prompt
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pass
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@ -177,23 +195,26 @@ def ui():
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with gr.Column():
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with gr.Tab("Text input"):
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data_input = gr.Textbox(lines=20, label='Input data')
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update_data = gr.Button('Apply')
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update_data = gr.Button('Load data')
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with gr.Tab("URL input"):
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url_input = gr.Textbox(lines=10, label='Input URL', info='Enter one or more URLs separated by newline characters')
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update_url = gr.Button('Apply')
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url_input = gr.Textbox(lines=10, label='Input URLs', info='Enter one or more URLs separated by newline characters.')
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update_url = gr.Button('Load data')
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with gr.Tab("File input"):
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file_input = gr.File(label='Input file', type='binary')
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update_file = gr.Button('Apply')
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update_file = gr.Button('Load data')
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with gr.Row():
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chunk_len = gr.Number(value=700, label='Chunk length', info='In characters, not tokens')
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chunk_count = gr.Number(value=5, label='Chunk count', info='The number of closest-matching chunks to include in the prompt')
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with gr.Tab("Generation settings"):
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chunk_count = gr.Number(value=5, label='Chunk count', info='The number of closest-matching chunks to include in the prompt.')
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update_settings = gr.Button('Apply changes')
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chunk_len = gr.Number(value=700, label='Chunk length', info='In characters, not tokens. This value is used when you click on "Load data".')
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with gr.Column():
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last_updated = gr.Markdown()
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update_data.click(feed_data_into_collector, [data_input, chunk_len, chunk_count], last_updated, show_progress=False)
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update_url.click(feed_url_into_collector, [url_input, chunk_len, chunk_count], last_updated, show_progress=False)
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update_file.click(feed_file_into_collector, [file_input, chunk_len, chunk_count], last_updated, show_progress=False)
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update_data.click(feed_data_into_collector, [data_input, chunk_len], last_updated, show_progress=False)
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update_url.click(feed_url_into_collector, [url_input, chunk_len], last_updated, show_progress=False)
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update_file.click(feed_file_into_collector, [file_input, chunk_len], last_updated, show_progress=False)
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update_settings.click(apply_settings, [chunk_count], last_updated, show_progress=False)
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