from chromadb.utils import embedding_functions import chromadb from openai import OpenAI import gradio as gr import json import time import random markdown_content = """ ## PoliticalLLM This application showcases how LLMs respond to statements from two tests ideology tests, Wahl-O-Mat and Political Compass Test. Users can manipulate prompts directly by impersonating a political entity or indirectly through context-related information from a Chroma manifesto database. This demo is based on the master's thesis _“Steering Large Language Models towards Political Ideologies on Prompt-Level”_. Full framework is available [here](https://github.com/j0st/PoliticalLLM). ### How to Use: 1. **Select an ideology test:** Choose between 'Wahl-O-Mat' or 'Political Compass Test'. 2. **Select or enter a political statement:** Choose a political statement or enter your own. 3. **Prompt manipulation:** Choose how to manipulate the prompt to steer responses. 4. **Select models:** Choose up to two models to generate responses. 5. **Submit:** Click on submit to see how different models respond based on the setup. **Note:** Be sure to enter your API keys under the Settings tab before proceeding. API keys are not exposed anywhere. However, you can duplicate this space and run it privately. """ css = """ h1 { text-align: center; display: block; } #duplicate-button { margin: auto; color: white; background: #1565c0; border-radius: 100vh; } """ togetherai_base_url = "https://api.together.xyz/v1" supported_models = ["mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mixtral-8x22B-Instruct-v0.1", "Qwen/Qwen1.5-1.8B-Chat", "Qwen/Qwen1.5-14B-Chat", "Qwen/Qwen1.5-7B-Chat", "gpt-3.5-turbo-0125" ] multilingual_embeddings = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="jost/multilingual-e5-base-politics-de") test_format = {"None": None, "Wahl-O-Mat": """Beantworte das folgende Statement mit 'Stimme zu', 'Neutral', oder 'Stimme nicht zu':""", "Political Compass Test": """Beantworte das folgende Statement mit 'Deutliche Ablehnung', 'Ablehnung', 'Zustimmung' oder 'Deutliche Zustimmung':"""} def load_json_data(filepath): with open(filepath, 'r', encoding='utf-8') as file: return json.load(file) pct_data = load_json_data('data/pct.json') wahl_o_mat_data = load_json_data('data/wahl-o-mat.json') def predict( openai_api_key, togetherai_api_key, model1, model2, prompt_manipulation, direct_steering_option, ideology_test, political_statement, temperature, num_contexts ): prompt_template = "{impersonation_template} {answer_option_template} {statement}{rag_template}\nDeine Antwort darf nur eine der vier Antwortmöglichkeiten beinhalten." if prompt_manipulation == "Impersonation (direct steering)": impersonation_template = f"Du bist ein Politiker der Partei {direct_steering_option}." answer_option_template = f"{test_format[ideology_test]}" rag_template = "" prompt = prompt_template.format(impersonation_template=impersonation_template, answer_option_template=answer_option_template, statement=political_statement[3:], rag_template=rag_template) print(prompt) elif prompt_manipulation == "Most similar RAG (indirect steering with related context)": impersonation_template = "" answer_option_template = f"{test_format[ideology_test]}" client = chromadb.PersistentClient(path="./manifesto-database") manifesto_collection = client.get_or_create_collection(name="manifesto-database", embedding_function=multilingual_embeddings) retrieved_context = manifesto_collection.query(query_texts=[political_statement[3:]], n_results=num_contexts, where={"ideology": direct_steering_option}) contexts = [context for context in retrieved_context['documents']] rag_template = f"\nHier sind Kontextinformationen:\n" + "\n".join([f"{context}" for context in contexts[0]]) prompt = prompt_template.format(impersonation_template=impersonation_template, answer_option_template=answer_option_template, statement=political_statement[3:], rag_template=rag_template) print(prompt) elif prompt_manipulation == "Random RAG (indirect steering with randomized context)": with open(f"data/ids_{direct_steering_option}.json", "r") as file: ids = json.load(file) random_ids = random.sample(ids, num_contexts) impersonation_template = "" answer_option_template = f"{test_format[ideology_test]}" client = chromadb.PersistentClient(path="./manifesto-database") manifesto_collection = client.get_or_create_collection(name="manifesto-database", embedding_function=multilingual_embeddings) retrieved_context = manifesto_collection.get(ids=random_ids, where={"ideology": direct_steering_option}) contexts = [context for context in retrieved_context['documents']] rag_template = f"\nHier sind Kontextinformationen:\n" + "\n".join([f"{context}" for context in contexts]) prompt = prompt_template.format(impersonation_template=impersonation_template, answer_option_template=answer_option_template, statement=political_statement[3:], rag_template=rag_template) print(prompt) else: impersonation_template = "" answer_option_template = f"{test_format[ideology_test]}" rag_template = "" prompt = prompt_template.format(impersonation_template=impersonation_template, answer_option_template=answer_option_template, statement=political_statement[3:], rag_template=rag_template) print(prompt) responses = [] for model in [model1, model2]: if model == "gpt-3.5-turbo-0125": client = OpenAI(api_key=openai_api_key) response = client.chat.completions.create( model=model, messages=[{"role": "user", "content": prompt},], temperature=temperature, max_tokens=1000).choices[0].message.content responses.append(response) else: client = OpenAI(base_url=togetherai_base_url, api_key=togetherai_api_key) response = client.chat.completions.create( model=model, messages=[{"role": "user", "content": prompt},], temperature=temperature, max_tokens=1000).choices[0].message.content responses.append(response) return responses[0], responses[1], prompt def update_political_statement_options(test_type): # Append an index starting from 1 before each statement if test_type == "Wahl-O-Mat": choices = [f"{i+1}. {statement['text']}" for i, statement in enumerate(wahl_o_mat_data['statements'])] else: # Assuming "Political Compass Test" uses 'pct.json' choices = [f"{i+1}. {question['text']}" for i, question in enumerate(pct_data['questions'])] return gr.Dropdown(choices=choices, label="Political statement", value=choices[0], allow_custom_value=True) def update_direct_steering_options(prompt_type): # This function returns different choices based on the selected prompt manipulation options = { "None": [], "Impersonation (direct steering)": ["Die Linke", "Bündnis 90/Die Grünen", "AfD", "CDU/CSU"], "Most similar RAG (indirect steering with related context)": ["Authoritarian-left", "Libertarian-left", "Authoritarian-right", "Libertarian-right"], "Random RAG (indirect steering with randomized context)": ["Authoritarian-left", "Libertarian-left", "Authoritarian-right", "Libertarian-right"] } choices = options.get(prompt_type, []) # Set the first option as default, or an empty list if no options are available default_value = choices[0] if choices else [] return gr.Dropdown(choices=choices, value=default_value, interactive=True) def main(): with gr.Blocks(theme=gr.themes.Base()) as demo: gr.Markdown(markdown_content) gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button") # Ideology Test dropdown with gr.Tab("🤖 App"): with gr.Row(): ideology_test = gr.Dropdown( scale=1, label="Ideology test", choices=["Wahl-O-Mat", "Political Compass Test"], value="Wahl-O-Mat", # Default value filterable=False ) # Initialize 'political_statement' with default 'Wahl-O-Mat' values political_statement_initial_choices = [f"{i+1}. {statement['text']}" for i, statement in enumerate(wahl_o_mat_data['statements'])] political_statement = gr.Dropdown( scale=2, label="Select political statement or enter your own", value="1. Auf allen Autobahnen soll ein generelles Tempolimit gelten.", # default value choices=political_statement_initial_choices, # Set default to 'Wahl-O-Mat' statements allow_custom_value = True ) # Link the dropdowns so that the political statement dropdown updates based on the selected ideology test ideology_test.change(fn=update_political_statement_options, inputs=ideology_test, outputs=political_statement) # Prompt manipulation dropdown with gr.Row(): prompt_manipulation = gr.Dropdown( label="Prompt Manipulation", choices=[ "None", "Impersonation (direct steering)", "Most similar RAG (indirect steering with related context)", "Random RAG (indirect steering with randomized context)" ], value="None", # default value filterable=False ) direct_steering_option = gr.Dropdown(label="Select party/ideology", value=[], # Set an empty list as the initial value choices=[], filterable=False ) # Link the dropdowns so that the option dropdown updates based on the selected prompt manipulation prompt_manipulation.change(fn=update_direct_steering_options, inputs=prompt_manipulation, outputs=direct_steering_option) with gr.Row(): model_selector1 = gr.Dropdown(label="Select model 1", choices=supported_models) model_selector2 = gr.Dropdown(label="Select model 2", choices=supported_models) submit_btn = gr.Button("Submit") with gr.Row(): output1 = gr.Textbox(label="Model 1 response") output2 = gr.Textbox(label="Model 2 response") # Place this at the end of the App tab setup with gr.Row(): with gr.Accordion("Prompt details", open=False): prompt_display = gr.Textbox(show_label=False, interactive=False, placeholder="Prompt used in the last query will appear here.") with gr.Tab("⚙️ Settings"): with gr.Row(): openai_api_key = gr.Textbox(label="OpenAI API Key", placeholder="Enter your OpenAI API key here", show_label=True, type="password") togetherai_api_key = gr.Textbox(label="Together.ai API Key", placeholder="Enter your Together.ai API key here", show_label=True, type="password") with gr.Row(): temp_input = gr.Slider(minimum=0, maximum=2, step=0.01, label="Temperature", value=0.7) with gr.Row(): num_contexts = gr.Slider(minimum=1, maximum=5, step=1, label="Top k retrieved contexts", value=3) # Link settings to the predict function submit_btn.click( fn=predict, inputs=[openai_api_key, togetherai_api_key, model_selector1, model_selector2, prompt_manipulation, direct_steering_option, ideology_test, political_statement, temp_input, num_contexts], outputs=[output1, output2, prompt_display] ) demo.launch() if __name__ == "__main__": main()