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from langchain.schema.output_parser import StrOutputParser |
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from chains.utils import getToolPromptTemplate |
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from model import llm4 |
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from langchain.tools import tool |
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step2ToolName = "Problem Deconstruction" |
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step2ToolContext = """\ |
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For addressing scalable problems, utilizing the [Problem Deconstruction Canvas] can help in delving into various \ |
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aspects of the issue such as the superficial problem, underlying causes, affected groups, and the resulting impact. \ |
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Here are some tips: |
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1. Distinguishing Superficial Problems from Underlying Causes: |
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- Help participants differentiate between the superficial phenomena and the underlying causes of the problem. \ |
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Superficial problems are observable direct manifestations leading to some impact, for example, pedestrian pathways being occupied by vehicles, \ |
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higher resume rejection rates for disabled individuals during job applications, \ |
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or visually impaired users finding it difficult to use a particular brand's app for online orders. |
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- Underlying causes focus on the root causes behind superficial problems. \ |
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Identifying these often requires proposing hypotheses and logical multi-level deduction and research. \ |
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For instance, the occupation of pedestrian pathways might be linked to lack of public education on traffic regulations, \ |
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insufficient regulatory enforcement, or poor road design in specific areas. |
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2. Refining Broad Problems: |
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- If the problem posed is relatively broad, such as when its solution spans multiple industries or fields \ |
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and the team lacks the necessary expertise and resources to address it, consider further segmenting the problem. \ |
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For example, the issue of gender discrimination could be narrowed down to a specific domain like the workplace, \ |
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transforming it into "gender discrimination in the workplace." |
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3. Monitoring Changes: |
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- Pay attention to changes over time in the underlying causes. Have they evolved? Will they continue to change in the future? |
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These guiding suggestions aim to facilitate a more structured and insightful exploration of the problem, \ |
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enabling teams to delve deeper into understanding the issues at hand and how they might be addressed in a scalable and impactful manner.\ |
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""" |
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step2ToolSuggestion = """\ |
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1. This step unfolds around the [Problem Deconstruction Canvas], with each participant selecting a problem from the first step to refine and structure. |
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2. It's suggested to spend 5 minutes before starting to clarify with everyone the definition of each dimension on the canvas. |
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3. Each participant should choose a specific color of sticky note, and use the four \ |
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dimensions divided on the canvas to provide a structured description of the selected problem within 10 minutes.\ |
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""" |
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prompt = getToolPromptTemplate(step2ToolName, step2ToolContext, step2ToolSuggestion) |
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step2Chain = prompt | llm4 | StrOutputParser() |
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@tool("Problem Deconstruction") |
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def step2Tool(context: str) -> str: |
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"""Useful for find some detail advise or examples when you process on "Problem Deconstruction" step. Need to input current problem context about Problem Deconstruction.""" |
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return step2Chain.invoke({"current_situation": context}) |
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