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"""This is an example of a simple chatbot that uses the RAG model to answer questions |
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about GIZ with Streamlit.""" |
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import streamlit as st |
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from rag import rag_pipeline |
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@st.cache_resource |
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def load_rag_pipeline(): |
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rag = rag_pipeline() |
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return rag |
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rag = load_rag_pipeline() |
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st.image("gender_strat_cover_pic.png", use_container_width=True) |
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st.markdown( |
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""" |
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<div style="display: flex; align-items: center; gap: 10px;"> |
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<span style="font-size: 30px;">🤖</span> |
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<h3 style="margin: 0;">Welcome to the GIZ Gender Strategy Assistant</h3> |
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</div> |
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<p>The <b> GIZ Gender Strategy Chatbot </b> enables users to explore GIZ’s Gender Strategy through open, context-aware questions. It provides insights into how gender equality is integrated into operations, business development, and corporate values. Aligned with GIZ’s vision, the assistant makes gender-related topics accessible, supporting users in understanding policies, enhancing gender competence, and promoting inclusive practices.</p> |
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""", |
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unsafe_allow_html=True |
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) |
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with st.expander("📖 Background Information & Example Questions"): |
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st.markdown( |
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""" |
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<h4>💡 How does the app work?</h4> |
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<p>The assistant uses a <b>Retrieval-Augmented Generation (RAG) approach</b> to ensure responses are grounded in the content of the <b>GIZ Gender Strategy (2019)</b>:</p> |
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<ul> |
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<li>Your question is <b>converted into an embedding</b> (numerical representation).</li> |
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<li>The system <b>retrieves the most relevant text sections</b> from the strategy document.</li> |
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<li>A <b>language model (LLM)</b> generates a response based on the retrieved content.</li> |
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</ul> |
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<p><b>⚠️ Important:</b> The assistant is <b>limited to the Gender Strategy (2019)</b>. It <b>does not</b> access external sources, additional policies, or updated guidelines beyond the provided document.</p> |
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<h4>🎯 Example questions:</h4> |
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<ul> |
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<li>What are the key objectives of the Gender Strategy?</li> |
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<li>How does GIZ define "gender," and what is the conceptual foundation of the strategy?</li> |
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<li>How does the strategy align with the <b>2030 Agenda</b>?</li> |
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<li>How is the success of the strategy measured and reviewed?</li> |
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</ul> |
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<h4>📌 Further resources:</h4> |
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<ul> |
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<li>📄 <a href="https://www.giz.de/en/downloads/giz-2019-en-gender-strategy.pdf" target="_blank" rel="noopener noreferrer"><b>GIZ Gender Strategy (2019, PDF)</b></a></li> |
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<li>🌍 <a href="https://reporting.giz.de/2022/operating-responsibly/our-gender-strategy/index.html" target="_blank" rel="noopener noreferrer"><b>GIZ Gender Strategy Website</b></a></li> |
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<li>📊 <a href="https://www.blog-datalab.com/" target="_blank" rel="noopener noreferrer"><b>GIZ Data Lab Blog</b></a></li> |
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</ul> |
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""", |
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unsafe_allow_html=True |
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) |
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st.html( |
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""" |
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<p>Feel free to explore and gain deeper insights into GIZ’s commitment to gender equality! 🚀</p> |
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<p><b>Now, go ahead and ask a question related to the GIZ Gender Strategy in the text field below!</b> 📝</p> |
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""" |
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) |
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if "messages" not in st.session_state: |
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st.session_state.messages = [] |
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for message in st.session_state.messages: |
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with st.chat_message(name=message["role"], avatar=message["avatar"]): |
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st.markdown(message["content"]) |
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prompt = st.chat_input("Say something") |
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if prompt: |
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with st.chat_message(name="user", avatar=":material/person:"): |
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st.write(prompt) |
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st.session_state.messages.append( |
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{"role": "user", "content": prompt, "avatar": ":material/person:"} |
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) |
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with st.chat_message(name="ai", avatar=":material/smart_toy:"): |
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result = rag.run( |
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{"prompt_builder": {"query": prompt}, "text_embedder": {"text": prompt}}, |
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) |
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result = result["llm"]["replies"][0] |
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result = result.split("Question:")[0] |
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st.write(result) |
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st.session_state.messages.append( |
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{"role": "ai", "content": result, "avatar": ":material/smart_toy:"} |
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) |
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