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참고/instructor-main/docs/prompting/decomposition/decomp.md

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+---
+description: "DECOMP involves using a LLM to break down a complicated task into sub tasks that it has been provided with"
+---
+
+Decomposed Prompting<sup><a href="https://arxiv.org/pdf/2210.02406">1</a></sup> leverages a Language Model (LLM) to deconstruct a complex task into a series of manageable sub-tasks. Each sub-task is then processed by specific functions, enabling the LLM to handle intricate problems more effectively and systematically.
+
+In the code snippet below, we define a series of data models and functions to implement this approach.
+
+The `derive_action_plan` function generates an action plan using the LLM, which is then executed step-by-step. Each action can be
+
+1. InitialInput: Which represents the chunk of the original prompt we need to process
+2. Split : An operation to split strings using a given separator
+3. StrPos: An operation to help extract a string given an index
+4. Merge: An operation to join a list of strings together using a given character
+
+We can implement this using `instructor` as seen below.
+
+```python hl_lines="57-58"
+import instructor
+from pydantic import BaseModel, Field
+from typing import Union
+client = instructor.from_provider("openai/gpt-5-nano")
+
+
+class Split(BaseModel):
+    split_char: str = Field(
+        description="""This is the character to split
+        the string with"""
+    )
+
+    def split_chars(self, s: str, c: str):
+        return s.split(c)
+
+
+class StrPos(BaseModel):
+    index: int = Field(
+        description="""This is the index of the character
+        we wish to return"""
+    )
+
+    def get_char(self, s: list[str], i: int):
+        return [c[i] for c in s]
+
+
+class Merge(BaseModel):
+    merge_char: str = Field(
+        description="""This is the character to merge the
+        inputs we plan to pass to this function with"""
+    )
+
+    def merge_string(self, s: list[str]):
+        return self.merge_char.join(s)
+
+
+class Action(BaseModel):
+    id: int = Field(
+        description="""Unique Incremental id to identify
+        this action with"""
+    )
+    action: Union[Split, StrPos, Merge]
+
+
+class ActionPlan(BaseModel):
+    initial_data: str
+    plan: list[Action]
+
+
+def derive_action_plan(task_description: str) -> ActionPlan:
+    return client.create(
+        messages=[
+            {
+                "role": "system",
+                "content": """Generate an action plan to help you complete
+                the task outlined by the user""",
+            },
+            {"role": "user", "content": task_description},
+        ],
+        response_model=ActionPlan,
+        max_retries=3,
+        model="gpt-4o",
+    )
+
+
+if __name__ == "__main__":
+    task = """Concatenate the second letter of every word in Jack
+    Ryan together"""
+    plan = derive_action_plan(task)
+    print(plan.model_dump_json(indent=2))
+    """
+    {
+      "initial_data": "Jack Ryan",
+      "plan": [
+        {
+          "id": 1,
+          "action": {
+            "split_char": " "
+          }
+        },
+        {
+          "id": 2,
+          "action": {
+            "index": 1
+          }
+        },
+        {
+          "id": 3,
+          "action": {
+            "merge_char": ""
+          }
+        }
+      ]
+    }
+    """
+
+    curr = plan.initial_data
+    cache = {}
+
+    for action in plan.plan:
+        if isinstance(action.action, Split) and isinstance(curr, str):
+            curr = action.action.split_chars(curr, action.action.split_char)
+        elif isinstance(action.action, StrPos) and isinstance(curr, list):
+            curr = action.action.get_char(curr, action.action.index)
+        elif isinstance(action.action, Merge) and isinstance(curr, list):
+            curr = action.action.merge_string(curr)
+        else:
+            raise ValueError("Unsupported Operation")
+
+        print(action, curr)
+        #> id=1 action=Split(split_char=' ') ['Jack', 'Ryan']
+        #> id=2 action=StrPos(index=1) ['a', 'y']
+        #> id=3 action=Merge(merge_char='') ay
+
+    print(curr)
+    #> ay
+```
+
+### References
+
+<sup id="ref-1">1</sup>: [Decomposed Prompting: A Modular Approach for Solving Complex Tasks](https://arxiv.org/pdf/2210.02406)