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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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step1ToolName = "Problem Storming" |
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step1ToolContext = """\ |
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Follow your intuition and experience, and on the [Problem Storming Board], jot down all the social issues \ |
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this organization needs to solve or is pending to solve that are swirling in your mind. \ |
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What is a common issue you have observed? Record it in any way you like, for example: |
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- One or several keywords |
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- A small story with a clear beginning, development, and conclusion |
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- A clear problem statement |
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- A doodle drawn casually |
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- A photograph |
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The source of the problem could be: |
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- A real story from a beneficiary |
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- An impressive personal experience |
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- A news report that caught your attention |
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- A sudden flash of insight |
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During the problem conceptualization process, you can ask yourself the following questions to better diverge on the problem: |
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- Is there a significant gap between the goal you want to achieve and today's reality? |
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- Are you stating a problem, or a solution to the problem? |
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- Are you focusing on defining the problem itself, rather than how to solve it? |
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- If you have identified many problems, can they be merged? |
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- Is the problem you identified a common root cause or a manifestation of a category of problems? |
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- Is the problem you want to solve unique? Are there others doing the same thing as you? |
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- What is the difference between the problem you want to solve and the problems others want to solve? |
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- These reflective questions can help ensure that you have a well-defined, distinct, and actionable problem to work on, \ |
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which is crucial for devising effective solutions. |
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These reflective questions can help ensure that you have a well-defined, distinct, and actionable problem to work on, \ |
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which is crucial for devising effective solutions.\ |
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""" |
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step1ToolSuggestion = """\ |
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1. Problem storming revolves around the [Problem Storming Board], where participants fill in questions on sticky notes according to the description provided. |
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2. The electronic version of the [Problem Storming Board] can be found in the attachment; if it's an offline workshop, \ |
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a physical whiteboard can also be used to create it manually. |
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3. The facilitator can directly reiterate the wording on the left to explain to workshop participants how to fill it out. |
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4. A series of questions are listed at the bottom left of the problem storming board for participants to refer to for better conceptualizing of questions. \ |
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When participants encounter confusion while filling out the [Problem Storming Board], \ |
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the facilitator can proactively throw out these questions to guide participants' thinking.\ |
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""" |
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prompt = getToolPromptTemplate(step1ToolName, step1ToolContext, step1ToolSuggestion) |
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step1Chain = prompt | llm4 | StrOutputParser() |
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@tool("Problem Storming") |
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def step1Tool(context: str) -> str: |
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"""Useful for find some detail advise or examples when you process on "Problem Storming" step. Need to input current problem context about Problem Storming.""" |
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return step1Chain.invoke({"current_situation": context}) |
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