PatchWork GenerateREADME

patchwork/common/multiturn_strategy/planning_strategy.py

@@ -0,0 +1,240 @@
+import asyncio
+from functools import partial
+from typing import Any, Optional, Union
+
+from pydantic import BaseModel
+from pydantic_ai import Agent
+from pydantic_ai.agent import AgentRunResult
+
+from patchwork.common.client.llm.protocol import LlmClient
+from patchwork.common.client.llm.utils import example_json_to_base_model
+from patchwork.common.tools import Tool
+
+
+class StepCompletedResult(BaseModel):
+    is_step_completed: bool
+
+
+class PlanCompletedResult(BaseModel):
+    is_plan_completed: bool
+
+
+class ExecutionResult(BaseModel):
+    json_data: str
+    message: str
+    is_completed: bool
+
+
+class _Plan:
+    def __init__(self, initial_plan: Optional[list[str]] = None):
+        self.__plan = initial_plan or []
+        self.__cursor = 0
+
+    def advance(self) -> bool:
+        self.__cursor += 1
+        return self.__cursor < len(self.__plan)
+
+    def is_empty(self) -> bool:
+        return len(self.__plan) == 0
+
+    def register_steps(self, agent: Agent):
+        agent.tool_plain(self.get_current_plan)
+        agent.tool_plain(self.get_current_step)
+        agent.tool_plain(self.get_current_step_index)
+        agent.tool_plain(self.add_step)
+        agent.tool_plain(self.delete_step)
+
+    def get_current_plan(self) -> str:
+        return "\n".join([f"{i}. {step}" for i, step in enumerate(self.__plan)])
+
+    def get_current_step(self) -> str:
+        if len(self.__plan) == 0:
+            return "There is currently no plan"
+
+        return self.__plan[self.__cursor]
+
+    def get_current_step_index(self) -> int:
+        return self.__cursor
+
+    def add_step(self, index: int, step: str) -> str:
+        if index < 0:
+            return "index cannot be a negative number"
+
+        if index >= len(self.__plan):
+            insertion_func = self.__plan.append
+        else:
+            insertion_func = partial(self.__plan.insert, index)
+
+        insertion_func(step)
+        return "Added step\nCurrent plan:\n" + self.get_current_plan()
+
+    def delete_step(self, step: str) -> str:
+        try:
+            i = self.__plan.index(step)
+            self.__plan.pop(i)
+            return self.get_current_plan()
+        except ValueError:
+            return "Step not found in plan\nCurrent plan:\n" + self.get_current_plan()
+
+
+class PlanningStrategy:
+    def __init__(
+        self,
+        llm_client: LlmClient,
+        planner_system_prompt: str,
+        executor_system_prompt: str,
+        executor_tool_set: dict[str, Tool],
+        example_json: Union[str, dict[str, Any]] = '{"output":"output text"}',
+    ):
+        """
+        Use this like this::
+
+            class DatabaseAgent(Step, input_class=DatabaseAgentInputs, output_class=DatabaseAgentOutputs):
+                 def __init__(self, inputs):
+                     super().__init__(inputs)
+
+                     llm_client = AioLlmClient.create_aio_client(inputs)
+
+                     data = inputs.get("prompt_value", {})
+                     self.task = mustache_render(inputs["task"], data)
+
+                     db_dialect = inputs["db_dialect"]
+                     self.planner = PlanningStrategy(
+                         llm_client,
+                         planner_system_prompt=f'''\\
+            You are a {db_dialect} database query planning assistant. You are tasked to plan the steps to assist with the provided task.
+            You will not execute the steps in the plan. The user will do that instead.
+            The first step of the plan should be as follows:
+            1. Tell me all tables currently available.
+                        After the list of table names is provided, get the DDL of the tables that is relevant.
+                        Your steps should be clear and concise like the following example:
+            1. Tell me the column descriptions of the table `orders`.
+            2. Execute the SQL Query: `SELECT * FROM orders`
+                        After every step, you will be asked to edit the plan so feel free to plan 1 step at a time.
+            ''',
+                        executor_system_prompt=f'''\\
+            You are a {db_dialect} database query execution assistant. You will be provided instructions on what to do.
+            ''',
+                    )
+
+                def run(self) -> dict:
+                    planner_response = self.planner.run(self.task, 10)
+                    return {**planner_response, **self.planner.usage()}
+
+        """
+        self.planner = Agent(
+            llm_client,
+            name="Planner",
+            system_prompt=planner_system_prompt,
+            model_settings=dict(
+                parallel_tool_calls=False,
+                model="gemini-2.0-flash",
+            ),
+        )
+
+        self.plan = _Plan()
+        self.plan.register_steps(self.planner)
+
+        self.executor = Agent(
+            llm_client,
+            name="Executor",
+            system_prompt=executor_system_prompt,
+            result_type=ExecutionResult,
+            tools=[tool.to_pydantic_ai_function_tool() for tool in executor_tool_set.values()],
+            model_settings=dict(
+                parallel_tool_calls=False,
+                model="gemini-2.0-flash",
+            ),
+        )
+
+        self.__summariser = Agent(
+            llm_client,
+            result_retries=5,
+            system_prompt="""\
+Please summarise the conversation given and provide the result in the structure that is asked of you.
+""",
+            result_type=example_json_to_base_model(example_json),
+            model_settings=dict(
+                parallel_tool_calls=False,
+                model="gemini-2.0-flash",
+            ),
+        )
+
+        self.reset()
+
+    def reset(self):
+        self.__request_tokens = 0
+        self.__response_tokens = 0
+
+    def usage(self):
+        return {
+            "request_tokens": self.__request_tokens,
+            "response_tokens": self.__response_tokens,
+        }
+
+    def __agent_run(self, agent: Agent, prompt: str, **kwargs) -> AgentRunResult[Any]:
+        loop = asyncio.new_event_loop()
+        planner_response = loop.run_until_complete(agent.run(prompt, **kwargs))
+        loop.close()
+        self.__request_tokens += planner_response.usage().request_tokens
+        self.__response_tokens += planner_response.usage().response_tokens
+
+        return planner_response
+
+    def run(self, task: str, conversation_limit: int = 10) -> dict:
+
+        planner_response = self.__agent_run(self.planner, f"Produce the initial plan for {task}")
+        planner_history = planner_response.all_messages()
+        if self.plan.is_empty():
+            planner_response = self.__agent_run(
+                self.planner, f"Please use the tools provided to setup the plan", message_history=planner_history
+            )
+            planner_history = planner_response.all_messages()
+
+        for i in range(conversation_limit):
+            step = self.plan.get_current_step()
+            executor_prompt = f"Please execute the following task: {step}"
+            response = self.__agent_run(self.executor, executor_prompt)
+
+            plan_str = self.plan.get_current_plan()
+            step_index = self.plan.get_current_step_index()
+            planner_prompt = f"""\
+The current plan is:
+{plan_str}
+
+We are current at {step_index}.
+If the current step is not completed, edit the current step.
+
+The execution result for the step {step_index} is: 
+{response.data}
+
+"""
+            planner_response = self.__agent_run(
+                self.planner,
+                planner_prompt,
+                message_history=planner_history,
+                result_type=StepCompletedResult,
+            )
+            planner_history = planner_response.all_messages()
+            if not planner_response.data.is_step_completed:
+                continue
+
+            if self.plan.advance():
+                continue
+
+            planner_response = self.__agent_run(
+                self.planner,
+                "Is the task completed? If the task is not completed please add more steps using the tools provided.",
+                message_history=planner_history,
+                result_type=PlanCompletedResult,
+            )
+            if planner_response.data.is_plan_completed:
+                break
+
+        final_result = self.__agent_run(
+            self.__summariser,
+            "From the actions taken by the assistant. Please give me the result.",
+            message_history=planner_history,
+        )
+
+        return final_result.data.dict()

patchwork/common/tools/db_query_tool.py

@@ -0,0 +1,38 @@
+from typing_extensions import Any, Union
+
+from patchwork.common.tools import Tool
+from patchwork.steps import CallSQL
+
+
+class DatabaseQueryTool(Tool, tool_name="db_query_tool"):
+    def __init__(self, inputs: dict[str, Any]):
+        super().__init__()
+        self.db_settings = inputs.copy()
+        self.db_dialect = inputs.get("db_dialect", "SQL")
+
+    @property
+    def json_schema(self) -> dict:
+        return {
+            "name": "db_query_tool",
+            "description": f"""\
+Run SQL Query on current {self.db_dialect} database.
+""",
+            "input_schema": {
+                "type": "object",
+                "properties": {
+                    "query": {
+                        "type": "string",
+                        "description": "Database query to run.",
+                    }
+                },
+                "required": ["query"],
+            },
+        }
+
+    def execute(self, query: str) -> Union[list[dict[str, Any]], str]:
+        db_settings = self.db_settings.copy()
+        db_settings["db_query"] = query
+        try:
+            return CallSQL(db_settings).run().get("results", [])
+        except Exception as e:
+            return str(e)

patchwork/steps/AgenticLLM/typed.py

@@ -11,10 +11,14 @@ class AgenticLLMInputs(TypedDict, total=False):
     user_prompt: str
     max_llm_calls: Annotated[int, StepTypeConfig(is_config=True)]
     openai_api_key: Annotated[
-        str, StepTypeConfig(is_config=True, or_op=["patched_api_key", "google_api_key", "client_is_gcp", "anthropic_api_key"])
+        str,
+        StepTypeConfig(
+            is_config=True, or_op=["patched_api_key", "google_api_key", "client_is_gcp", "anthropic_api_key"]
+        ),
     ]
     anthropic_api_key: Annotated[
-        str, StepTypeConfig(is_config=True, or_op=["patched_api_key", "google_api_key", "client_is_gcp", "openai_api_key"])
+        str,
+        StepTypeConfig(is_config=True, or_op=["patched_api_key", "google_api_key", "client_is_gcp", "openai_api_key"]),
     ]
     patched_api_key: Annotated[
         str,
@@ -31,10 +35,16 @@ class AgenticLLMInputs(TypedDict, total=False):
         ),
     ]
     google_api_key: Annotated[
-        str, StepTypeConfig(is_config=True, or_op=["patched_api_key", "openai_api_key", "anthropic_api_key", "client_is_gcp"])
+        str,
+        StepTypeConfig(
+            is_config=True, or_op=["patched_api_key", "openai_api_key", "anthropic_api_key", "client_is_gcp"]
+        ),
     ]
     client_is_gcp: Annotated[
-        str, StepTypeConfig(is_config=True, or_op=["patched_api_key", "openai_api_key", "anthropic_api_key", "google_api_key"])
+        str,
+        StepTypeConfig(
+            is_config=True, or_op=["patched_api_key", "openai_api_key", "anthropic_api_key", "google_api_key"]
+        ),
     ]
 
 

patchwork/steps/CallLLM/typed.py

@@ -13,10 +13,14 @@ class CallLLMInputs(TypedDict, total=False):
     model_args: Annotated[str, StepTypeConfig(is_config=True)]
     client_args: Annotated[str, StepTypeConfig(is_config=True)]
     openai_api_key: Annotated[
-        str, StepTypeConfig(is_config=True, or_op=["patched_api_key", "google_api_key", "client_is_gcp", "anthropic_api_key"])
+        str,
+        StepTypeConfig(
+            is_config=True, or_op=["patched_api_key", "google_api_key", "client_is_gcp", "anthropic_api_key"]
+        ),
     ]
     anthropic_api_key: Annotated[
-        str, StepTypeConfig(is_config=True, or_op=["patched_api_key", "google_api_key", "client_is_gcp", "openai_api_key"])
+        str,
+        StepTypeConfig(is_config=True, or_op=["patched_api_key", "google_api_key", "client_is_gcp", "openai_api_key"]),
     ]
     patched_api_key: Annotated[
         str,
@@ -33,10 +37,16 @@ class CallLLMInputs(TypedDict, total=False):
         ),
     ]
     google_api_key: Annotated[
-        str, StepTypeConfig(is_config=True, or_op=["patched_api_key", "openai_api_key", "anthropic_api_key", "client_is_gcp"])
+        str,
+        StepTypeConfig(
+            is_config=True, or_op=["patched_api_key", "openai_api_key", "anthropic_api_key", "client_is_gcp"]
+        ),
     ]
     client_is_gcp: Annotated[
-        str, StepTypeConfig(is_config=True, or_op=["patched_api_key", "openai_api_key", "anthropic_api_key", "google_api_key"])
+        str,
+        StepTypeConfig(
+            is_config=True, or_op=["patched_api_key", "openai_api_key", "anthropic_api_key", "google_api_key"]
+        ),
     ]
     file: Annotated[str, StepTypeConfig(is_path=True)]