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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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step4ToolName = "Problem Reconstruction" |
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step4ToolContext = """\ |
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In the format provided, the final scalable problem definition would be structured as follows: |
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"For [a specific demographic], in the context of [a particular scenario], due to [a certain reason], \ |
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the specific problem they face is what we need to find answers to in this toolkit. \ |
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How we can [in a particular manner], achieve [a desired impact] is what we will continue to research." |
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Here is an example using the format: |
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"For [urban adolescents], in the context of [high unemployment rates], \ |
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due to [a lack of job readiness and skills], \ |
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the specific problem they face—[difficulty in securing first-time employment]—is what \ |
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we need to find answers to in this toolkit. How we can [through vocational training and apprenticeship programs], \ |
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achieve [an increase in youth employment rates] is what we will continue to research."\ |
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""" |
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step4ToolSuggestion = """\ |
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1. The facilitator needs to provide a new workspace for everyone (a whiteboard or a blank canvas). |
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2. Each participant, using a sticky note and the format provided on the right, \ |
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should combine the supplementary information and feedback obtained from the \ |
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previous step to reconstruct the description of the problem they have posed. |
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3. Different participants who identified similar problems in the previous step can either \ |
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reconstruct the issue separately according to their own understanding or work in \ |
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pairs to complete the reconstruction of the same problem. |
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""" |
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prompt = getToolPromptTemplate(step4ToolName, step4ToolContext, step4ToolSuggestion) |
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step4Chain = prompt | llm4 | StrOutputParser() |
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@tool("Problem Reconstruction") |
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def step4Tool(context: str) -> str: |
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"""Useful for find some detail advise or example when you process on "Problem Reconstruction" step. Need to input current problem context about Problem Reconstruction.""" |
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return step4Chain.invoke({"current_situation": context}) |
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