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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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step3ToolName = "Problem Sharing" |
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step3ToolContext = """\ |
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After structuring the problem descriptions in the previous step, the team will share the following content within the group: |
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- The problem/story described on the [Problem Storming Board]. |
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- The refined problem description on the [Problem Deconstruction Canvas] based on the aforementioned problem/story. |
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- The rationale behind posing such a question and the logical considerations involved. |
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- Seek additional information inputs from the team, relevant stakeholders, or even the target users, considering their perspectives. |
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During the sharing process, the facilitator should guide the group to merge similar problems according to the four dimensions of problem refinement. \ |
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This collaborative approach ensures that the team consolidates their understanding of the problems at hand, \ |
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enriches the context with diverse perspectives, and streamlines the focus for more targeted problem-solving efforts.\ |
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""" |
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step3ToolSuggestion = """\ |
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1. Centering on the [Problem Deconstruction Canvas]: \ |
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Each participant takes a problem identified from the initial problem-storming phase and uses the canvas to dissect and organize it further. |
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2. Clarification of Dimensions: Before diving into the task, \ |
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it's recommended to take 5 minutes to ensure that all participants have a clear understanding of each dimension on the canvas. \ |
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This step is crucial for maintaining consistency in how problems are deconstructed and ensuring that all team members are on the same page.\ |
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""" |
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prompt = getToolPromptTemplate(step3ToolName, step3ToolContext, step3ToolSuggestion) |
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step3Chain = prompt | llm4 | StrOutputParser() |
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@tool("Problem Sharing") |
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def step3Tool(context: str) -> str: |
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"""Useful for find some detail advise or examples when you process on "Problem Sharing" step. Need to input current problem context about Problem Sharing.""" |
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return step3Chain.invoke({"current_situation": context}) |
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