Move to src dir usage (#99)

src/crewai/agents/output_parser.py

@@ -0,0 +1,75 @@
+import re
+from typing import Union
+
+from langchain.agents.output_parsers import ReActSingleInputOutputParser
+from langchain_core.agents import AgentAction, AgentFinish
+
+from .cache import CacheHandler, CacheHit
+from .exceptions import TaskRepeatedUsageException
+from .tools_handler import ToolsHandler
+
+FINAL_ANSWER_ACTION = "Final Answer:"
+FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE = (
+    "Parsing LLM output produced both a final answer and a parse-able action:"
+)
+
+
+class CrewAgentOutputParser(ReActSingleInputOutputParser):
+    """Parses ReAct-style LLM calls that have a single tool input.
+
+    Expects output to be in one of two formats.
+
+    If the output signals that an action should be taken,
+    should be in the below format. This will result in an AgentAction
+    being returned.
+
+    ```
+    Thought: agent thought here
+    Action: search
+    Action Input: what is the temperature in SF?
+    ```
+
+    If the output signals that a final answer should be given,
+    should be in the below format. This will result in an AgentFinish
+    being returned.
+
+    ```
+    Thought: agent thought here
+    Final Answer: The temperature is 100 degrees
+    ```
+
+    It also prevents tools from being reused in a roll.
+    """
+
+    class Config:
+        arbitrary_types_allowed = True
+
+    tools_handler: ToolsHandler
+    cache: CacheHandler
+
+    def parse(self, text: str) -> Union[AgentAction, AgentFinish, CacheHit]:
+        FINAL_ANSWER_ACTION in text
+        regex = (
+            r"Action\s*\d*\s*:[\s]*(.*?)[\s]*Action\s*\d*\s*Input\s*\d*\s*:[\s]*(.*)"
+        )
+        if action_match := re.search(regex, text, re.DOTALL):
+            action = action_match.group(1).strip()
+            action_input = action_match.group(2)
+            tool_input = action_input.strip(" ")
+            tool_input = tool_input.strip('"')
+
+            if last_tool_usage := self.tools_handler.last_used_tool:
+                usage = {
+                    "tool": action,
+                    "input": tool_input,
+                }
+                if usage == last_tool_usage:
+                    raise TaskRepeatedUsageException(
+                        tool=action, tool_input=tool_input, text=text
+                    )
+
+            if result := self.cache.read(action, tool_input):
+                action = AgentAction(action, tool_input, text)
+                return CacheHit(action=action, cache=self.cache)
+
+        return super().parse(text)